UNDERSTANDING THE GENETICS UNDERLYING MASTITIS
USING A MULTI-PRONGED APPROACH
A Dissertation
Presented to the Faculty of the Graduate School
of Cornell University
In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
by
Asha Marie Miles
December 2019
© 2019 Asha Marie Miles
UNDERSTANDING THE GENETICS UNDERLYING MASTITIS
USING A MULTI-PRONGED APPROACH
Asha Marie Miles, Ph. D.
Cornell University 2019
This dissertation addresses deficiencies in the existing genetic characterization of
mastitis due to granddaughter study designs and selection strategies based primarily on
lactation average somatic cell score (SCS). Composite milk samples were collected across 6
sampling periods representing key lactation stages: 0-1 day in milk (DIM), 3- 5 DIM, 10-14
DIM, 50-60 DIM, 90-110 DIM, and 210-230 DIM. Cows were scored for front and rear teat
length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear
udder height, and rear udder width. Independent multivariable logistic regression models were
used to generate odds ratios for elevated SCC (≥ 200,000 cells/ml) and farm-diagnosed
clinical mastitis. Within our study cohort, loose fore udder attachment, flat teat ends, low rear
udder height, and wide rear teats were associated with increased odds of mastitis. Principal
component analysis was performed on these traits to create a single new phenotype describing
mastitis susceptibility based on these high-risk phenotypes. Cows (N = 471) were genotyped
on the Illumina BovineHD 777K SNP chip and considering all 14 traits of interest, a total of
56 genome-wide associations (GWA) were performed and 28 significantly associated
quantitative trait loci (QTL) were identified. Special focus was given to the aforementioned
mastitis risk traits, and candidate gene investigation revealed both immune function and cell
proliferation related genes in the areas surrounding associated QTL, suggesting that selecting
for mastitis resistant cows based on these traits would be an effective method for increasing
mastitis resiliency in a herd. Tracking the progression of SCS during the study period
identified extreme populations of cows that remained “chronic” (SCS ≥ 4) or “healthy” (SCS
< 4). Fixation indices were calculated and 2 SNPs identified that demonstrated moderate
allelic differentiation of “healthy” from both “chronic” and “average” cows (FST ≥ 0.4).
GWAs were performed for SCS at each sampled stage in lactation, area under the SCS curve,
and median SCS, and each approach significantly associated unique QTL spanning the
genome. This suggests that alternative methods to lactation average SCS must be employed to
more efficiently select for mastitis-resistant cows.
BIOGRAPHICAL SKETCH
Asha Miles was born in Ithaca, NY, just before her family moved to California’s Central
Valley. Growing up in a little town in rural San Joaquin County, her passion for agriculture
sprouted when she joined the 4-H program in high school. She graduated high school in 2008
during which she spent three years in a Biotechnology Regional Occupation Program. She
attended University of California at Davis and earned her Bachelor of Science in Biotechnology
in 2012, where she completed an undergraduate honors research thesis in which she created a
bioassay for endocrine disrupters in the glucocorticoid signaling pathway. Shortly thereafter,
she completed a Master of Science in Animal Biology in 2015, examining the differential
expression of antimicrobial peptides in the porcine small intestine after E. coli challenge and
treatment with human lysozyme transgenic goat milk. Returning to her Ithaca roots, she
enrolled in Cornell’s Animal Science doctoral program in 2015, investigating the genetics
underlying mastitis in dairy cattle. Upon completion of her Ph.D., she will begin a postdoctoral
scholar position at Pennsylvania State University exploring the role of the microbiome in food
animal health and disease.
As a biotechnologist, she believes the technological augmentation of agriculture is a
critical tool. As an academic, she believes progressive change always begins with education.
As a humanitarian, she believes that technology and education can only have meaning if
brought to people who need them in an accessible manner. For these reasons, she is dedicated
to agricultural research and the global dissemination of information to multiple facets of society,
including scientists, regulators, students, and farmers.
v
To all the teachers, learners, and inspirers I’ve met along the way.
vi
ACKNOWLEDGEMENTS
This work would not have been possible without the support of my Special
Committee, the work of many graduate and undergraduate students associated with Cornell
Animal Science, the patience of the Cornell Statistical Consulting Unit, research funding by
the NIFA Animal Health Project #NYC-127898, personal funding by Investigative Biology in
the College of Arts and Sciences, the participation of dairy farmers, and of course, their cows.
vii
TABLE OF CONTENTS
BIOGRAPHICAL SKETCH …………………………………………………………………v
DEDICATION ……………………………………………………………………………... vi
ACKNOWLEDGEMENTS ……………………………………………………………….. vii
CHAPTER 1: INTRODUCTION
1.A: Origins …………………………………………………………………………. 1
1.B: A critical issue for the dairy industry ………………………………………….. 2
1.C: Genetic evaluation of complex traits …………………………………………... 5
1.D: Mastitis and genomic selection ………………………………………………... 9
1.E: Current understanding and gaps in knowledge ……………………………….. 11
References …………………………………………………………………………. 14
CHAPTER 2: UDDER AND TEAT CONFORMATIONAL RISK FACTORS FOR
ELEVATED SOMATIC CELL COUNT AND CLINICAL MASTITIS IN NEW
YORK HOLSTEINS
Title Page …………………………………………………………………………. 23
Abstract …………………………………………………………………………… 24
Introduction ……………………………………………………………………….. 26
Materials and Methods ……………………………………………………………. 27
Results …………………………………………………………………………….. 33
Discussion ………………………………………………………………………… 40
Acknowledgements ……………………………………………………………….. 45
References ………………………………………………………………………… 46
CHAPTER 3: GENOME WIDE ASSOCIATIONS FOR UDDER AND TEAT
CONFORMATIONAL RISK FACTORS FOR MASTITIS IN HOLSTEIN COWS
Title Page ………………………………………………………………………….. 50
Abstract ……………………………………………………………………………. 51
Introduction ………………………………………………………………………... 53
Materials and Methods …………………………………………………………….. 55
Results ……………………………………………………………………………... 57
Discussion …………………………………………………………………………. 64
Conclusions ………………………………………………………………………... 72
Declarations ………………………………………………………………………... 73
viii
References …………………………………………………………………………. 75
CHAPTER 4: TIME- AND POPULATION-DEPENDENT GENETIC PATTERNS
UNDERLIE BOVINE MILK SOMATIC CELL COUNT
Title Page ………………………………………………………………………….. 90
Abstract ……………………………………………………………………………. 91
Introduction ………………………………………………………………………... 93
Materials and Methods …………………………………………………………….. 94
Results ……………………………………………………………………………... 99
Discussion ………………………………………………………………..………. 105
Conclusions ………………………………………………………………………. 114
Acknowledgements ..……………………………………………………………... 115
References ..………………………………………………………………………. 116
CHAPTER 5: IMPLICATIONS FOR FUTURE RESEARCH AND CONCLUDING
REMARKS
Concluding Remarks.……………………………………………………………... 139
References ..………………………………………………………………………. 144
ix
LIST OF FIGURES
Figure 1.1. The “Iceberg Principle”………………………………………………………….. 4
Figure 2.1. The characterization of teat ends………………………………………………..32
Figure 3.1. Creation of a new “mastitis risk” trait…………………………………………..59
Figure 3.2. Manhattan plot for fore udder attachment………………………………………61
Figure 3.3. Manhattan plot for udder depth…………………………………………………62
Figure 3.4. Manhattan plot for udder height………………………………………………...63
Figure 3.5. Manhattan plot for rear teat end shape………………………………………….65
Figure 3.6. Manhattan plot for rear teat width………………………………………………66
Supplementary Figure 3.1. Quantile-Quantile plots………………………………………...86
Figure 4.1. Overview of SCS-based population stratification………………………………97
Figure 4.2. FST by marker comparison of populations……………………………………..101
Figure 4.3. FST by marker and linkage disequilibrium on BTA 4………………………….102
Figure 4.4. Manhattan plots for SCS by lactation stage……………………………………106
Supplementary Figure 4.1. Quantile-Quantile plots………………………………………..129
Figure 5.1. Gene ontology across all approaches…………………………………………..141
x
LIST OF TABLES
Table 2.1. Explanation of six selected sampling periods………………………………....…29
Table 2.2. A description of the cow demographics………………………………………….34
Table 2.3. Udder and teat trait frequencies……………………………………………… ….36
Table 2.4. Contrast odds ratios for elevated SCC model……………………………………38
Table 2.5. Contrast odds ratios for clinical mastitis model………………………………….39
Table 2.6. The Goodman-Kruskal Gamma statistic of association………………………….41
Table 2.7. Univariate analyses of variables of interest……………………………………... 42
Table 3.1. Udder and teat genome-wide association models………………………………. 58
Supplementary Table 3.1. Significantly associated QTL and candidate genes…………….. 87
Table 4.1. Descriptive statistics of each SCS phenotype………………………………….. 100
Table 4.2. Differentiating markers and their allele frequencies……………………………103
Table 4.3. SCS genome-wide association models………………………………………… 104
Supplementary Table 4.1. Differentiated QTL and candidate genes……………………… 130
Supplementary Table 4.2. Significantly associated QTL and candidate genes…………… 132
xi
CHAPTER 1: INTRODUCTION
1.A: Origins
The term “mastitis” originates from the Greek mastos, meaning “breast”, and -itis, a
suffix meaning “inflammation”. Inflammation etymology can be traced to the Latin
inflammationem, which may be translated as “a kindling, a setting on fire”. Consequently,
inflammation can be clinically described as heat, pain, redness, and swelling; symptoms which
reflect the influence of inflammatory mediators on the infected tissue. Heat, redness, and
swelling are accounted for by the increased permeability of blood vessels, subsequent leakage
of fluid and proteins, and pain explained by the migration and action of immune response cells.
We define these effects as “clinical mastitis”, producing visible abnormalities in the milk and
mammary gland. Mastitis can also present subclinically, where inflammatory effects are only
detectable with diagnostic tests for microorganisms and/or the influx of inflammatory response
cells (somatic cells) in the milk. This host response to threats on biological homeostasis is a
protective function intended destroy the pathogen and return the mammary gland to normal
function. The innate immune response predates metazoans, and adaptive immunity first
appeared in jawed fish prior to the evolution of mammals; it is therefore likely that mastitis has
been a prevalent issue since mammals first appeared in the late Triassic period, an estimated
200 million years ago (Novacek, 1997; Flajnik and Kasahara, 2010; Muthamilarasan and
Prasad, 2013).
Archaeozoological studies exploring mitochondrial DNA variation suggest that cattle
were first domesticated approximately 10,500 years ago, and historical evidence suggests that
humans began milking cows around 3000 years after that (Itan et al., 2009; Bollongino et al.,
1
2012). We know today that mastitis is propagated by both environmental and contagious
pathogens, the latter of which is often spread during milking. It is likely that concerns with the
management and control of mastitis began in Central Europe with early humans and the first
dairy farmers. However, our ability to understand mastitis was limited until the advent and
improvement of the light microscope in the 17th century, and consequently the earliest scientific
report on bovine mastitis came in 1917, when Breed and Brew presented their microscopic
method of grading dairies based on the quantity of bacteria in milk (Breed and Brew, 1917;
Wollman et al., 2015). Since then, research efforts have evolved from an initial focus on
understanding the etiology of intramammary infections to the application of preventative
measures including investigating genetic predisposition to mastitis and the possibility of
breeding cows resistant to infection.
1.B: A critical issue for the dairy industry
Mastitis is internationally regarded as an economic problem with animal welfare and
public health implications. While the mortality rate for clinical mastitis is very low, farms with
mastitis problems suffer substantial economic loss due to factors such as milk production
decrease, diagnostic tests, drug treatments, discarded milk, veterinary services, treatment labor,
product quality decline, as well as culling (Seegers et al., 2003). Considering all of these factors,
a recent study estimated the average cost of a case of clinical mastitis to be $444, suggesting
that annual losses in the United States alone amount to nearly 2 billion dollars (Rollin et al.,
2015; iGEM, 2016). Secondary to lameness, mastitis is the leading antagonist of animal health
and welfare, and improving cow recovery and reducing infection severity is considered to be
2
the fastest way to enhance cow welfare with regards to intramammary infection (Hillerton and
Berry, 2005). Furthermore, bacterial presence in milk poses a public health problem, as mastitis
pathogens have been known to cause human disease and improper or lack of pasteurization
techniques can result in the contamination of food products (Oliver et al., 2005). Additionally,
the often low cure rates and potential antibiotic residues in milk lead to fears of increasing
antimicrobial resistance and a call for methods of mastitis control other than antibiotic treatment
(Gomes and Henriques, 2016).
A major obstacle for mastitis control lies in the problem of disease detection. A nation-
wide survey estimated clinical mastitis to have 25% prevalence, but with no mandate for
participation in monthly milk testing there is no way to accurately measure the occurrence of
subclinical mastitis in United States dairy herds (National Animal Health and Monitoring
System, 2016). This is known as the “Iceberg Principle”, where only a small proportion of
information is visible while the bulk of the information about a phenomenon is unavailable or
undetectable (Figure 1.1). In the case of mastitis, the problem extends below the subclinical
mastitis tier to the entire population of milking cows, all of whom are susceptible to
intramammary infection. In response to this high, population-wide risk for developing mastitis,
stringent protocols for mastitis management and control have been implemented. Approaches
have included reducing exposure to pathogens by prioritizing improvements to cow housing,
milking hygiene, and minimizing teat lesions (Dodd, 1983; Bartlett et al., 1992). Additional
emphasis has been given to milking machine effects and examining resistance of the teat canal
itself to bacterial invasion, considering factors like over-milking, milk flow, vacuum level, liner
slips, and pulsation (Thompson et al., 1978; Natzke et al., 1982; Baxter et al., 1992; Lacy-
Hulbert and Hillerton, 1995; Wieland et al., 2018). Research focus has also been given to the
3
Figure 1.1. The “Iceberg Principle”. Only a small percentage of mastitis cases are clinically
detectable. Beneath those cases a much larger number of cows have subclinical mastitis, and
beneath that there is an even larger population of susceptible cows who are at risk of developing
mastitis.
4
potential dispersal of bacteria in the udder by considering factors like hand-stripping to remove
foremilk and frequency of udder evacuations (Mein et al., 2004; Shoshani et al., 2017). Other
external factors such as cow age, season, and climate also have been examined for their
influence on mastitis occurrence (Fox et al., 1995; Bates and Dohoo, 2016; Zhang et al., 2016b).
While numerous studies have shown regulation of these factors to reduce the incidence of
mastitis on farm, they do not address the effect of the individual cow on mastitis susceptibility.
Mastitis is well-established as a multi-faceted issue with an estimated 30% of mastitis problems
attributable each to farm and milking management, 20% to the milking machine, and 20% to
the cow herself (Mein et al., 2004). In 1994, milk somatic cell count (SCC) was added to
national genomic evaluation systems specifically to address the high prevalence of mastitis in
United States dairy herds (Schutz, 1994).
1.C: Genetic evaluation of complex traits
Mastitis is an example of a complex trait, where the phenotype can be attributed to
multiple genes, their interactions with each other, and the environment. The genetics underlying
complex traits such as disease susceptibility are investigated using a quantitative genetics
approach because, as compared to simple Mendelian inheritance, more advanced statistical
methods are required to investigate traits which are both polygenic and influenced by
environmental factors. Such traits have a complicated genotype to phenotype relationship,
where a single genotype may produce a range of phenotypes based on the genotype by
environment interaction. Furthermore, the wide phenotypic ranges for each genotype may
overlap, presenting a challenge in distinguishing whether individuals differ due to genetic or
environmental factors. This phenomenon is amplified by the polygenic nature of the trait,
5
resulting in a multitude of highly varied phenotypes and creating an obstacle to discovering the
genetic regulation of the trait. Chromosomal regions responsible for a small proportion of the
genetic variation in a complex trait are called quantitative trait loci (QTL) and their
identification and mapping are key to understanding the genetics underlying complex traits.
The use of genetics in bovine production originated with blood-typing for parentage
verification to ensure correct pedigree records, but advances in molecular methods allowed the
commercial utilization of marker-based parentage testing by the turn of the 21st century
(Stormont, 1967; Dekkers, 2004). However, genetic progress attributable to marker-based
selection was limited due to the difficulty in generating high quality data sets, as well as the
realization that most economically important dairy cattle traits are controlled by many genes
with small effects (Cole et al., 2009). This was remedied following the successful sequencing
of the human genome and the initiation of the Bovine Genome Project in 2003, which together
with additional low-depth sequencing of multiple breeds provided enough information for
single nucleotide polymorphism (SNP) assay development (Wiggans et al., 2017).
Unfortunately, the 15,036 SNPs on the first genotyping panel released in 2005 were poorly
distributed across the genome and not able to adequately detect linkage disequilibrium (LD),
making it inappropriate for use in QTL mapping and genomic selection (Khatkar et al., 2007;
Hayes et al., 2013). In December 2007, the Illumina BovineSNP50 BeadChip offering 54,001
SNPs was released, followed three years later by the high-density Bovine HD chip which
boasted 777,962 SNPs (Matukumalli et al., 2009; Illumina, 2010). Genotype imputation of data
from low density SNP chips has been used to maximize genome coverage while minimizing
costs, and is based on the principle that any two individuals may share genetic information
derived from a distant common ancestor. Under this assumption, low density genotypes may
6
be compared to a large reference population with no missing data (e.g., whole genome
sequences), and their complete genotypes inferred or “imputed” based on the haplotypes (sets
of markers inherited together) observed in the reference population (Das et al., 2018). While
genotype imputation can dramatically increase genome coverage, it introduces bias towards
population averages and limits researcher ability to detect rare variants which do not occur
frequently in the reference population (Marchini and Howie, 2010; Calus et al., 2014; Marete
et al., 2018a).
Prior to the availability of high density genomic data complex traits were primarily
analyzed via QTL mapping. This approach is based on the concept of linkage disequilibrium,
which contrary to Mendel’s principle of independent assortment, suggests that alleles at
different loci are linked and may be inherited together. This method assumes markers that are
consistently associated with the inheritance of a particular trait must be linked with a QTL
which contributes to that trait. Note that QTL mapping is traditionally performed within
families or in controlled crosses of contrasting parents for the trait of interest, where only one
recombination event is recorded in the population, and the F1 generation is assessed for
correlation between the inheritance of a particular allele and a quantitative phenotype (Broman,
2001). This presents both a computing and financial problem when applied to large dairy cattle
populations, considering high genotyping costs, attempts to increase resolution with advanced
intercrosses, and the long generation intervals required for genotyping progeny: enter the
genome-wide association study.
Unlike traditional linkage mapping, which assesses whether a disease and an allele show
correlated transmission within a pedigree, genome-wide association studies (GWAS) associate
markers and traits within a freely interbreeding population and do not consider familial
7
inheritance patterns (Lander and Schork, 1994). These GWAS were originally conducted as
case-control studies comparing allelic frequencies in affected and unaffected populations;
appropriately selecting a control group is a major challenge in contrast to linkage methods,
which do not require one. While GWAS are high resolution and less labor intensive, they are
sensitive to underlying population structure which can cause spurious associations (Li, 1969;
Platt et al., 2010). This is a particular problem in dairy cattle, in which desirable traits have been
selected for over centuries using both phenotypic and genotypic data, meaning that positive
associations must be cautiously interpreted. Observed positive associations may occur because
the allele in question is actually causative or if that allele is in LD with the actual cause, but
disturbingly, can also occur as an artifact of population admixture. In other words, if
independent alleles and traits occur in high frequency within a certain population they may be
falsely associated with one another. This problem is best ameliorated by conducting GWAS
only in large, homogenous populations and by selecting control groups which are perfectly
matched for ancestry (Lander and Schork, 1994). Because that is impossible in practice,
numerous methods have been implemented to account for bias introduced by underlying
population structure, including selection of internal controls by haplotype relative risk
estimation and the inclusion of identity-by-state matrices in GWAS models (Falk and
Rubinstein, 1987; Kang et al., 2010).
Other challenges with the genetic analysis of complex traits lie in the determination of
statistical significance. A GWAS represents a massive multiple testing problem, where
depending on the genotype density researchers may be running millions of regressions for
genetic variants against a single trait. To mitigate association inflation with false positives, a
Bonferroni multiple testing correction may be applied, or the less stringent False Discovery
8
Rate calculation may be performed, where the researcher sets a significance level accounting
for a certain proportion of falsely associated QTL (Benjamini and Hochberg, 1995; Weller et
al., 1998). Despite measures to minimize false positives and correct population structure, the
only evidence that positive associations are “real” is repeatability in independent populations,
thus GWAS validation is crucial. This presents a challenge in dairy cattle where the overuse of
favorite sires have contributed to high inbreeding and replicated population structure in multiple
data sets, suggesting that the most convincing validation is across breeds (Forutan et al., 2018).
To minimize these confounding issues, quality control measures should also be applied to all
genetic data, including the filtering of highly related animals, poorly represented SNPs in the
study population, the removal of rare variants which GWAS do not have the power to detect,
and evaluation of Hardy-Weinberg equilibrium as a potential indicator of non-random
influences on the population. Significantly associated markers are most compelling when linked
with biologically relevant genes, which should be further validated through targeted sequencing
and functional analyses at the transcriptional and translational level.
1.D: Mastitis and genomic selection
Reports on mastitis heritability, or the degree of variation in mastitis that can be
explained by additive genetic differences, were made as early as 1944 and have ranged from
0.1 up to 0.88 (Gaunt et al., 1980; Shook and Schutz, 1994; Nash et al., 2000; Vallimont et al.,
2009; Ruegg, 2017). The lack of consensus for mastitis heritability estimates is due not only to
high variation in management systems and environments, but the lack of standardized mastitis
diagnosis and reporting. Consequently, genetic selection for mastitis resistance stagnated until
the widespread adoption of Dairy Herd Improvement testing and milk SCC measures, which
9
though founded in 1905, still has only 46% nation-wide participation today (Voelker, 1981;
DairyOne, 2019). Nevertheless, significant effort has been made to determine genetic markers
associated with mastitis susceptibility or resistance.
Current genetic evaluation for mastitis is based on indicator traits, where measures like
somatic cell score (SCS) and udder composite traits are used as a proxy due to the time and
financial complications of measuring clinical mastitis directly. However, the predictions for
these traits have only minimally reduced mastitis incidence in U.S. dairy herds, likely because
of the incomplete correlation of mastitis with these proxy traits, possible inaccuracy in farm
diagnosis and recording, as well as the inability to standardize across farms (Wenz and Giebel,
2012; Vukasinovic et al., 2017). Furthermore, due to high genotyping costs these genetic
predictions have been historically made through indirect associations of daughter production
traits with bull genotypes. Daughter yield deviations (DYD) are averages of daughter
performance adjusted for the effects of their dam, and because they are not regressed on their
sire’s breeding values, are considered the most accurate and unbiased measure of daughter
performance and typically used in national genomic evaluations (Szyda et al., 2008). These
evaluations include a genomic prediction estimated for SNP effects on the traits of interest,
information from traditional evaluations (based solely on performance and pedigree data), and
a “subset evaluation” to determine what proportion of the traditional data was accounted for by
genomics (VanRaden et al., 2009). Genomic evaluation accuracy is measured via reliabilities,
a measure of the standard error of genomic breeding values, which are routinely evaluated to
maximize genomic improvement efficiency (Liu et al., 2017).
Selection indices are based on these genomic evaluations, and calculated by the
weighted regression of genomic prediction values for traits of interest. The most common
10
selection index used by United States producers in culling and breeding decisions is Net Merit,
in which the only considerations of mastitis are the inclusion of SCS with a reliability of merely
75%, and an udder composite score with reliability of 80% (Wiggans et al., 2017). While 75%
is much higher than the reliability for SCS using traditional evaluations and no incorporation
of genomic information (35.3%), it is considerably lower than reliabilities for directly evaluated
phenotypes like protein, fat, and milk yield (all 90%) (VanRaden and Cole, 2014). Diminished
reliabilities in genomic evaluation for SCS relate back to the complex trait problem. Numerous
human medical studies have struggled to identify the genetics underlying disease when armed
only with a generalized clinical phenotype, a problem remedied by restricting case definitions
to specific etiologies and comparing extremes in the distribution of continuous traits (Lander
and Schork, 1994). Somatic cell score as a generalized proxy for mastitis is an inefficient
phenotype, given the varied pathogenesis of mastitis and the non-pathogenic reasons for
inflammation in the mammary gland, including injury, lactogenesis, and changes in metabolic
state across a cow’s lactation (Bionaz et al., 2007; Graugnard et al., 2012; Akbar et al., 2015).
1.E: Current understanding and gaps in knowledge
Progress on the investigation of quantitative traits in cattle is documented in the Cattle
Quantitative Trait Locus Database (Cattle QTLdb) to facilitate meta-analyses and validation
of association data (Hu et al., 2013). As of the most recent release in August 2019, a total of
2,045 QTL have been associated with the mastitis phenotypes of SCC, SCS, and clinical
mastitis diagnosis. These QTL span all 29 autosomes and the X chromosome, and were
generated via a combination of traditional linkage analysis, QTL mapping, and genome-wide
association studies (CattleQTLdb, 2019). The complex, polygenic nature of mastitis is
11
reinforced by the significant association of many different regions across the genome, but also
brings into question whether these genomic loci can be refined with more selective
phenotyping.
Though udder and teat morphology are widely accepted to influence mastitis
susceptibility, especially given their relation to complete milk evacuation and the effect of the
milking machine, there is little consensus in the literature regarding their heritabilities and direct
association to mastitis risk (Seykora and McDaniel, 1985). Furthermore, udder composite
scores included in Net Merit estimations do not currently include teat characteristics, and teat
width and end shape have never been used for genomic evaluation (Wiggans et al., 2017). Most
extant research on the genetics of udder and teat type traits have relied on traditional evaluation
methods and the indirect association of bull genotypes with the phenotypes of their daughters
(Rupp and Boichard, 1999; Nash et al., 2000; Chrystal et al., 2001; DeGroot et al., 2002).
Similarly, there is a long history of genetic evaluation of SCC, but these studies have relied on
genotype imputation which may introduce error and bias allele frequencies towards the
population average, as well as indirect genotype-phenotype associations, de-regressed breeding
values, and pedigree-based methods to estimate disease allele inheritance (Zhang et al., 1998;
Cole et al., 2011a; Biffani et al., 2017; Fang et al., 2017). Furthermore, there is a need to refine
the use of SCS as a mastitis indicator by removing phenotype bias introduced by non-
pathogenic causes of inflammation, such as lactogenesis and uterine involution during early
lactation (Bionaz et al., 2007; Graugnard et al., 2012).
This dissertation is aimed towards addressing these gaps in mastitis genetic
investigation. Our objectives were to first assemble thorough cow phenotypes in a prospective
cohort study that allows researcher control over exposure and disease assessment and
12
consideration of multiple indicator traits for mastitis. Secondly, our objective was to assemble
high density genotypes on the same cows to determine a direct genotype to phenotype
relationship. The major findings of this project are detailed in the following chapters.
13
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CHAPTER 2: UDDER AND TEAT CONFORMATIONAL RISK FACTORS FOR
ELEVATED SOMATIC CELL COUNT AND CLINICAL MASTITIS IN NEW YORK
HOLSTEINS
Asha M. Milesa, Jessica A. A. McArtb, Francisco A. Leal Yepesa, Cassandra R. Stambuka, Paul
D. Virklerbc, and Heather J. Husona1
aDepartment of Animal Science, and
bDepartment of Population Medicine and Diagnostic Sciences, and
cQuality Milk Production Services, Animal Health Diagnostic Center, Cornell University,
Ithaca, NY 14853
1Corresponding author: Heather J. Huson, 201 Morrison Hall, 507 Tower Road, Ithaca, NY
14853. Phone: (607) 255-2289. E-mail: hjh3@cornell.edu
Preventative Veterinary Medicine 163 (2019) 7-13
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ABSTRACT
Our primary objective was to identify udder and teat conformational risk factors associated with
the occurrence of elevated somatic cell count (SCC) and clinical mastitis using a prospective cohort
study design with careful assessment of exposure and disease outcomes. Mastitis prevalence was
evaluated by parity across 6 sampling periods representing key physiological transitions during
lactation: 0 to 1 day in milk (DIM), 3 to 5 DIM, 10 to 14 DIM, 50 to 60 DIM, 90 to 110 DIM, and 210
to 230 DIM. Cows were scored for front and rear teat length, width, end shape, and placement, fore
udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Two independent
multivariable logistic regression models were used to generate odds ratios (OR) for elevated SCC (≥
200,000 cells/ml) and farm-diagnosed clinical mastitis. We identified that loose fore udder attachment
(reference level: strong fore udder attachment, OR = 2.1, 95% confidence interval (CI) = 1.2 to 3.8)
and flat teat end shape (reference level: round teat end shape, OR = 1.4, 95% CI = 1.1 to 1.9) increased
the odds of an elevated SCC event, whereas a negative California Mastitis Test score at 0 to 1 DIM
decreased the odds of an elevated SCC event (OR = 0.6, 95% CI = 0.4 to 0.8). Loose fore udder
attachment (reference level: strong fore udder attachment, OR = 3.7, 95% CI = 1.3 to 10.7), flat teat
end shape (reference level: round teat end shape, OR = 1.5, 95% CI = 1.0 to 2.4), low rear udder height
(reference level: intermediate rear udder height, OR = 2.8, 95% CI = 0.3 – 6.2), and increasing rear teat
width (OR = 2.2, 95% CI = 1.2 – 4.4) heightened the odds of developing clinical mastitis. We identified
that within our study cohort, loose fore udder attachment and flat teat ends had an important association
with increased odds of both an elevated SCC event and clinical mastitis diagnosis. The identification
of these udder and teat conformational risk factors for mastitis can provide farmers an effective and
inexpensive tool to manage mastitis.
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Key words: mastitis, udder conformation, teat conformation, logistic regression
26
INTRODUCTION
Mastitis is one of the most prevalent diseases among United States dairy herds, and
consequently each year the dairy industry suffers substantial economic loss (Fetrow, 2015;
Rollin et al., 2015). Mastitis prevalence can be attributed to problems with herd and farm
management, milking management, the milking machine, and the cow itself (Mein et al., 2004).
Many herd-level risk factors for mastitis such as season, climate, and management have already
been identified (Fox et al., 1995; Cook et al., 2002; Bates and Dohoo, 2016; Zhang et al.,
2016b), but identifying udder and teat conformational risk factors can improve our strategies
for on-farm mastitis management by identifying high-risk animals.
Anecdotal commentary on relationships among parity, udder size, and mastitis is not
uncommon, and some work has been done to illustrate the relationships among teat dimensions
and mastitis (Zwertvaegher et al., 2013; Guarin and Ruegg, 2016). However, there is little
consensus in the literature regarding the relationship of elevated somatic cell count (SCC) and
clinical mastitis to various udder and teat traits, or their respective heritabilities (Seykora and
McDaniel, 1985). For example, some researchers report no association between SCC and teat
end shape (Chrystal et al., 2001; Guarin et al., 2017), contrary to previous research indicating
that less pointed teat ends may be associated with fewer cases of mastitis (Hodgson et al., 1980).
These disparities might be explained by retrospective study designs which do not always allow
researcher control over exposure and disease assessment, mitigation of selection and
information bias, and the ability to establish a time sequence. Indeed, most studies assessed
disease as lactation averages of somatic cell score instead of considering test day data
individually across a lactation period (Rogers et al., 1998; Rupp and Boichard, 1999; Koeck et
al., 2012). Many of these extant studies examining udder and teat morphology emphasize
27
genetic correlations, and the external validity of these studies is under further question given
the use of genotyped bulls and the indirect phenotyping of their daughters.
Therefore, our aim was to conduct a prospective cohort study with careful and consistent
assessment of exposure and disease to elucidate the relationships among udder and teat
conformation and the occurrence of elevated SCC and clinical mastitis at any time during
lactation. This work was devised as an intensive genetics study to improve genomic selection
for mastitis-resistant cows by examining multiple mastitis related traits using direct genotyping
of cows to address the inherent limitations of characterizing mastitis genetics using sire
evaluations. Here the epidemiological analysis of our phenotypic data is presented.
MATERIALS AND METHODS
Study Design
We conducted a prospective cohort study on 2 commercial Holstein herds in New York
State from June 2015 to July 2016. The enrollment period lasted from June to December 2015,
with follow-up on all cows completed by July 2016. Farm 1 milked roughly 1,000 cows twice
daily in a 14 by 14 parallel parlor, and cows were housed in a free-stall barn bedded with sand
and sawdust. Farm 2 milked roughly 3,800 cows thrice daily in a 100-stall rotary parlor, and
cows were housed in free-stall barns bedded with anaerobically digested and separated manure
solids with added quicklime. Both farms maintained extensive data records during our study
period describing parity, pedigree, mating, reproduction, health traits, and productive yield. In
addition, each farm participated in monthly DHI testing (including test day milk, protein, and
fat yields, as well as individual cow SCC). Both farms worked with Cornell University Quality
Milk Production Services (QMPS) in maintaining ongoing mastitis control programs and
28
followed QMPS milking protocol recommendations. Any Holstein cow whose milk was
collected within 24 hours of freshening was a candidate for inclusion in our study and remained
in the study cohort for as long as the cow was part of the herd. Three quarter cows at time of
calving were excluded. A convenience sample of 523 cows was taken equally across both farms.
Composite milk samples representing equal volumes from all 4 quarters were collected from
each cow for SCC analysis, and body condition, udder, and teat conformation were scored prior
to milking (Table 2.1). This study was approved by the Institutional Animal Care and Use
Committee under authorization reference number 2014-0121 on 2/20/2015.
Sample Size Justification
A sample size calculation was conducted with desired α = 0.05 and power of 80% using
an open-source calculator (OpenEpi, http://www.openepi.com/SampleSize/SSCohort.htm)
based on our primary conformational exposure of interest, flat teat end shape. We estimated an
unequal ratio of unexposed (round teat ends) to exposed (flat teat ends) cows of 1.5:1, with only
5% of unexposed cows experiencing mastitis and 25% of exposed cows experiencing mastitis
based on a recent nation-wide survey (National Animal Health and Monitoring System, 2016).
This yielded a minimum sample size of 479 cows with the Fleiss method of sample size
calculation with continuity correction. We inflated the enrollment requirement an additional
10% to account for loss to follow up.
Data Collection
Two trained researchers diagnosed high or low inflammatory cell presence in the
composite colostrum sample collected on 0 or 1 DIM using a California Mastitis Test (CMT).
Cows were given a positive score if the CMT solution reacted to create distinct thickening of
the sample, or a negative score if the sample remained liquid (Schalm and Noorlander, 1957).
29
30
Table 2.1. Explanation of six selected sampling periods wherein the described data was collected on 523 Holstein cows from
two New York dairies. Linear descriptive traits include scoring for udder depth, width, height, cleft, fore udder attachment, and
front and rear teat placement. Additional teat traits include scoring front and rear teat end shape and measuring front and rear
teat width and length.
Body Linear
Milk Additional
Condition Descriptive
Sample Teat Traitsb
Score Traitsa
0 to 1 DIM
X X
Parturition; colostrum
3 to 5 DIM
X X
Baseline milk
10 to 14 DIM
Peak mastitis incidence; negative X X X X
energy balance
50 to 60 DIM
Neutral/positive energy balance; end X X
of voluntary breeding period
90 to 110 DIM
X X X
Peak production; early pregnancy
210 to 230 DIM
X X
Mid-late lactation; mid-pregnancy
aassessed by 4 trained researchers (Elanco Animal Health 2009)
bassessed by 1 trained researchers (Holstein Association USA, Inc. 2014)
Specific assumptions regarding colostrum SCC were not made as there is evidence that
established guidelines relating CMT scores to milk SCC are not applicable to colostrum
(Maunsell et al., 1998). Composite milk samples collected at 3 to 5, 10 to 14, 50 to 60, 90 to
110, and 210 to 230 DIM were outsourced to a milk laboratory, recognized and approved by
the Association of Official Analytical Chemists Research Institute and FDA as an independent
reference laboratory, for SCC by flow cytometry (FossomaticTM, Dairy One, Ithaca, NY). This
same laboratory was used for DHI milk testing for both farms. All milk samples were collected
in the parlor during milking with a minimum volume of 20 mL. Cases of elevated SCC were
defined as SCC ≥ 200,000, as a composite milk sample from all 4 quarters with 200,000 cells/ml
has been well-established as a threshold at which an intramammary infection is likely present
(McDermott et al., 1982; Dohoo and Leslie, 1991; Schepers et al., 1997). Cases of clinical
mastitis were farm-diagnosed and did not require veterinary diagnosis. High SCC prevalence
was calculated as new and existing cases over the total population at risk. Cows were given a
body condition score (BCS) at each time point on a 5-point scoring system with 0.25 increments
by 1 of 4 trained researchers (Ferguson et al., 1994; Elanco Animal Health, 2009). In addition,
to identify cow-level risk factors, each cow was scored based on her udder and teat
conformation by 1 trained researcher. Teats were assessed at 10 to 14 DIM and again at 90 to
110 DIM to evaluate consistency in researcher scoring. Teats were only evaluated on the right
side of each cow to minimize interference in the milking routine. A difference in scores was
observed in <3% of cows, and in those cows the second set of scores were used in analysis. At
the termination of the study period, individual cow data (including test day SCC, parity, and
health/treatment records) were collected from herd management software (DairyComp 305,
31
Valley Agricultural Software, Tulare, CA). Data on culled cows were included in analyses up
until the point of culling.
Definitions
Udder conformation and teat placement traits were scored with 3 categorical levels
adapted from the Holstein Association’s scoring for linear descriptive traits (Holstein
Association USA, 2014). Fore udder attachment was evaluated as extremely loose, of
intermediate strength, or extremely strong. The udder cleft was scored as weak, intermediate,
or extremely strong. The udder depth was determined by measuring the udder floor as extremely
deep well below the hocks, of intermediate depth, or extremely high above the hocks. Rear
udder height was assessed as extremely low, of intermediate height, or extremely high. Rear
udder width was evaluated as narrow, of intermediate width, or extremely wide. Front and rear
teat placement were scored as extremely wide placement on outside of quarter and pointing
laterally, centrally placed on quarter and pointing straight down, or base of teats on extreme
inside of quarter and pointing medially. In addition to these linear descriptive traits, front and
rear right teat end shape were scored as flat, round, or pointed (Figure 2.1). Right teat width
and length were also measured using a translucent measuring ruler with a scale unit of 1 cm.
Statistical Analysis
All statistical analyses were performed using R version 3.2.5 (R Core Team, 2016). An
independent multiple logistic regression model was fit for each outcome, elevated SCC and
clinical mastitis. Clinical mastitis outcomes were binary (0 = no event, 1 = at least one event
during lactation) and varying days at risk accounted for by including lactation length as a
covariate in the model. Elevated SCC outcomes were binary (0 = low, 1 = elevated), and
modeled using repeated measure logistic regression including our sample SCC data and farm
32
33
Figure 2.1. The characterization of teat ends as A) flat, B) round, or C) pointed. Arrows indicate hyperkeratosis at the teat
ends.
test day data, with cow as a random effect. Varying days at risk were accounted for by
including lactation length as a covariate in the model.
All variables of interest were tested for association with elevated SCC or clinical
mastitis using univariate analysis, and any variables with P < 0.2 were offered to the
multivariable model. Because of the small proportion of observed pointed teat ends and the
difficulty in distinguishing them from round teat ends in the presence of hyperkeratosis (Figure
1), pointed teat ends were grouped with round teat ends. The Goodman-Kruskal gamma statistic
(γ) was used to measure the strength of association among ordinal variables and Pearson’s
product moment correlation (ρ) was used to measure the correlation among continuous
variables (Goodman and Kruskal, 1954). Backward stepwise elimination was used to refine the
model, all biologically plausible interactions were considered, variables were retained in the
final model if P < 0.05, and final models evaluated for goodness of fit using a Likelihood Ratio
Test and by only considering models with less residual deviance than degrees of freedom. Any
variables which caused more than 10% change in the estimates of one or more predictors were
retained as confounders. Odds ratios were calculated to determine the odds of an elevated SCC
event or developing clinical mastitis based on presence of the various risk factors measured for
each cow. Both odds ratios and 95% confidence intervals (CI) were generated using the least
squares means package (Lenth, 2016).
RESULTS
Enrollment demographics throughout the study period are described in Table 2.2.
34
Table 2.2. A description of the cow demographics (n = 523) across the sampling period
Parity (n)
1 2 3+ Total
Farm 1 85 78 92 255
Farm 2 68 100 100 268
Total Enrolled 153 178 192 523
Cows with complete linear descriptive traits 152 175 185 512
Cows with complete additional teat traits 152 176 181 509
Remaining at 3 to 5 DIM 153 177 190 520
Remaining at 10 to 14 DIM 152 177 185 514
Remaining at 50 to 60 DIM 152 171 173 496
Remaining at 90 to 110 DIM 149 167 165 481
Remaining at 210 to 230 DIM 140 157 137 434
35
Among the entire study cohort, the most common phenotypes were centrally placed
teats pointing straight down, round teat-end shapes, loose fore udder attachment, intermediately
defined udder cleft, intermediate udder depth, intermediate rear udder height, and intermediate
rear udder width (Table 2.3). Front right teats had a median length of 5.0 cm with first and third
quartiles 4.5 and 6.0 cm, respectively. Front right teats had a median width of 2.5 cm with first
and third quartiles 2.5 and 3.0 cm, respectively. We observed a median rear right teat length of
4.0 cm with first and third quartiles 3.5 and 4.5 cm, respectively. Rear right teats had a median
width of 2.5 cm with first and third quartiles 2.0 and 2.5 cm, respectively. From these statistics,
udders tend to be deeper, wider, and have looser fore udder attachment with increasing parities,
while there is no obvious age-related trend in teat conformation. At time of parturition, 3 to 5
DIM, and 210 to 230 DIM, the median BCS was 3.0 with first and third quartiles 3 and 3.25,
respectively. At 10 to 14 DIM the median, first, and third quartiles were 3.0. At 50 to 60 and
90 to 110 DIM, the median BCS was 3.0 with first and third quartiles 2.75 and 3.0, respectively.
While cows followed a normal body condition curve across lactation, there was not enough
variation for BCS to be informative and it was not significantly associated with high SCC or
clinical mastitis. From Table 2.3, the majority of farm-culled cows were in their third or higher
parity (59.5%). Forty-six percent of the farm-culled cows were removed from the herd for
mastitis or chronically high SCC reasons.
At time of parturition, we observed a 24% prevalence of positive CMT scores. Positive
CMT prevalence was 42% within first parity animals, 15% within second parity animals, and
18% for cows in their third or higher parity. Cumulative incidences of 44.2% and 28.9% were
observed for elevated SCC events and clinical mastitis, respectively, in close agreement with
national reports (National Animal Health and Monitoring System, 2016).
36
Table 2.3. Udder and teat trait frequencies (%) measured for 523 Holstein cows, stratified by parity and
animals culled by the farm prior to the end of the study period
Parity
1 2 3+ Farm-culled Total
n = 152 n = 175a/176b n = 185a/181b n = 74 n = 512a/509b
Front teat placementa
Lateral 0.7 2.8 7.0 2.7 3.7
Central 92.7 76.6 77.3 74.3 81.5
Medial 6.6 20.6 15.7 23.0 14.8
Front teat shapeb
Flat 39.5 39.4 40.9 59.4 40.0
Round 55.2 48.6 49.2 33.8 50.9
Pointed 5.3 12.0 9.9 6.8 9.1
Fore udder attachmenta
Loose 24.4 56.0 76.2 62.2 53.7
Intermediate 55.9 38.3 22.2 32.4 37.8
Strong 19.7 5.7 1.6 5.4 8.5
Rear teat placementa
Lateral 20.4 1.7 6.5 2.7 3.4
Central 78.3 43.4 42.2 51.3 53.5
Medial 1.3 54.9 51.3 46.0 43.1
Rear teat shapeb
Flat 38.8 43.5 45.3 63.5 42.9
Round 57.9 47.4 44.8 28.4 49.6
Pointed 3.3 9.1 9.9 8.1 7.5
Udder clefta
Weak 20.4 5.1 9.2 20.3 11.2
Intermediate 66.4 65.8 61.1 54.1 64.4
Strong 13.2 29.1 29.7 25.6 24.4
Udder deptha
Deep 0.7 13.7 57.3 36.5 25.2
Intermediate 28.9 58.9 40.5 47.3 43.5
High 70.4 27.4 2.2 16.2 31.3
Rear udder heighta
Low 0.7 4.0 20.0 13.5 8.3
Intermediate 62.5 73.7 66.5 71.6 68.1
High 36.8 22.3 13.5 14.9 23.6
Rear udder widtha
Narrow 27.0 6.3 5.9 10.8 12.4
Intermediate 72.3 86.8 75.7 77.0 78.3
Wide 0.7 6.9 18.4 12.2 9.3
Parity = 1 − − − 16.2 29.9
Parity = 2 − − − 24.3 34.4
Parity = 3+ − − − 59.5 35.7
aHolstein Association linear descriptive traits and associated sample sizes
bAdditional teat conformational traits and associated sample sizes, measured only on right side
37
Final logistic regression models evaluating the odds of a case of elevated SCC or clinical
mastitis are presented in Tables 2.4 and 2.5, respectively. Cows with loose fore udder
attachment had 1.9 times greater odds of an elevated SCC event in an individual sample
compared to cows with fore udder attachment of intermediate strength (P < 0.01), and 2.1 times
the odds compared to cows with strong fore udder attachment (P = 0.03). Cows with flat right
rear teat ends had 1.4 times greater odds of a high SCC event in an individual sample compared
to those with round teat ends (P = 0.01). Cows in their first parity had 1.6 times greater odds of
an elevated SCC event in a sample compared to those in their second parity (P = 0.04), and
cows in their second parity had half the odds of an elevated SCC event in an individual sample
than those in their third or higher parity (P < 0.01). There was no significant difference in odds
between first parity cows and those in their third or higher parity, and compared to both, second
parity cows had lesser odds of a high SCC event in a sample during their lactation. Cows with
a negative CMT score just after parturition had 0.6 times lesser odds of a high SCC event in an
individual sample during the rest of their lactation compared to cows with a positive CMT score
(P < 0.01).
In agreement with the elevated SCC model, cows with loose fore udder attachment had
2.2 times greater odds of a clinical mastitis event as those with attachments of intermediate
strength (P < 0.01) and 3.7 times greater odds as those with strong attachments (P = 0.04).
Cows with flat right teat ends had 1.5 times the odds of a clinical mastitis event as those with
round rear teat ends (P = 0.07). Cows with low rear udder height had 2.8 times the odds of
developing clinical mastitis than those with intermediate rear udder height (P = 0.03). A cow’s
odds of clinical mastitis increased 2.2 times for every 1 cm increase in right rear teat width (P
= 0.02).
38
Table 2.4. Contrast odds ratios (OR), confidence intervals (CI), and P-values reported for the
final elevated SCC logistic regression model, reference levels are listed second in contrast
column
Variable Contrast OR 95% CI P-value
Farm 1-2 0.4 0.3 – 0.5 <.01
Fore Udder Attachment loose-intermeda 1.9 1.4 – 2.6 <.01
loose-strong 2.1 1.2 – 3.8 0.03
intermeda-strong 1.1 0.7 – 2.0 0.89
Teat End Shape flat-round 1.4 1.1 – 1.9 0.01
Parity 1-2 1.6 1.1 – 2.3 0.04
1-3+ 0.9 0.6 – 1.3 0.76
2-3+ 0.5 0.4 – 0.8 <.01
CMT low-high 0.6 0.4 – 0.8 <.01
aintermediate
39
Table 2.5. Contrast odds ratios (OR), confidence intervals (CI), and P-values for the final
clinical mastitis logistic regression model, reference levels are listed second in contrast
column
Variable Contrast OR 95% CI P-value
Farm 1-2 0.1 0.1 – 0.2 <.01
Fore Udder Attachment loose-intermeda 2.2 1.3 – 3.6 <.01
loose-strong 3.7 1.3 – 10.7 0.04
intermeda-strong 1.7 0.6 – 5.0 0.61
Teat End Shape flat-round 1.5 1.0 – 2.4 0.07
Rear Udder Height low-intermeda 2.8 1.3 – 6.2 0.03
low-high 2.5 1.0 – 6.3 0.14
intermeda-high 0.9 0.5 – 1.7 0.92
Rear Teat Width (cm) NA 2.2 1.2 – 4.4 0.02
aintermediate
40
Variables of interest, including parity, CMT score, and the previously described udder
and teat traits, were measured for strength and direction of association using a Goodman-
Kruskal gamma statistic (Table 2.6). Udder depth, rear udder height, udder width, and fore
udder attachment were strongly associated (γ ≥ 0.5) with parity. Rear udder height, udder width,
and fore udder attachment were strongly associated (γ ≥ 0.5) with udder depth. Rear udder
height and udder width were also strongly associated (γ ≥ 0.5) with fore udder attachment. Front
and rear right teat end shape demonstrated the highest degree of association (γ = 0.88). Front
and rear teat placement were strongly associated with each other (γ ≥ 0.72), and rear teat
placement was strongly associated with udder cleft (γ ≥ 0.6). Univariate inferential statistics
relating each variable of interest to elevated SCC or clinical mastitis, even if they were not
retained in thfinal models, are reported in Table 2.7.
DISCUSSION
Logistic regression models presented in this paper associated cow conformational
factors with elevated SCC in an individual sample during lactation and CM. Flat teat ends have
been associated with higher rate of milk flow as well as increased risk for milk leakage (Seykora
and McDaniel, 1985; Klaas et al., 2005; Guarin and Ruegg, 2016), which may inform the higher
odds of both elevated SCC and clinical mastitis in cows with flat right teat ends. It is likely that
flat front teat ends were also problematic in cases of elevated SCC and clinical mastitis, though
not retained in the final models due to high association with rear teat end shape. Farm-culled
animals had a higher proportion of cows with flat teats compared to the other strata, but
considering some of these animals were not culled for udder health reasons this does not
necessarily indicate flat teats increase risk of culling.
41
Table 2.6. The absolute value of the Goodman-Kruskal Gamma statistic of association among
categorical covariates considered in our models.
Front Teat Shape 0.05
Rear Teat Shape 0.12 0.88
Udder Depth 0.85 0.07 0.14
Rear Udder Height 0.50 0.01 0.04 0.56
Udder Width 0.61 0.01 0.14 0.70 0.24
Front Teat Placement 0.05 0.05 0.06 0.15 0.29 0.08
Rear Teat Placement 0.31 0.03 0.01 0.13 0.01 0.05 0.72
Udder Cleft 0.28 0.04 0.07 0.06 0.03 0.05 0.36 0.60
Fore Udder Attachment 0.61 0.10 0.01 0.77 0.52 0.61 0.24 0.18 0.05
42
Parity
Front Teat
Shape
Rear Teat
Shape
Udder
Depth
Rear Udder
Height
Udder
Width
Front Teat
Placement
Rear Teat
Placement
Udder Cleft
Table 2.7. Univariate analysis of variables of interest against elevated SCC and clinical mastitis.
Elevated SCC Clinical Mastitis
Odds 95% CI P- Odds 95% Confidence P-
Ratio value Ratio Interval value
Parity
1 reference level
2 0.1 0.5 – 1.1 0.20 1.5 1.3 – 1.8 <.01
3+ 1.8 1.2 – 2.6 <.01 2.3 2.0 – 2.7 <.01
CMT
high reference level
low 0.4 0.2 – 0.5 <.01 0.8 0.5 – 1.3 0.41
Front Teat Length (cm) 1.0 0.8 – 1.1 0.73 1.1 1.0 – 1.2 <.01
Front Teat Width (cm) 1.2 0.9 – 1.5 0.12 2.3 2.0 – 2.6 <.01
Rear Teat Length (cm) 1.0 0.8 – 1.3 0.75 1.2 1.1 – 1.3 <.01
Rear Teat Width (cm) 1.9 1.2 – 2.9 <.01 2.5 2.2 – 2.9 <.01
Front Teat Shape
flat reference level
round 0.8 0.6 – 1.0 0.07 0.7 0.6 – 0.8 <.01
Rear Teat Shape
Flat reference level
Round 0.6 0.5 – 0.9 <.01 0.6 0.5 – 0.6 <.01
Udder Depth
low reference level
intermediate 0.4 0.3 – 0.6 <.01 0.3 0.3 – 0.4 <.01
high 0.4 0.2 – 0.5 <.01 0.2 0.2 – 0.3 <.01
Rear Udder Height
low reference level
intermediate 0.4 0.2 – 0.7 <.01 0.3 0.3 – 0.4 <.01
high 0.2 0.1 – 0.4 <.01 0.2 0.1 – 0.2 <.01
Front Teat Placement
lateral reference level
intermediate 0.6 0.3 – 1.4 0.22 0.6 0.5 – 0.8 <.01
medial 0.6 0.3 – 1.6 0.33 0.5 0.3 – 0.6 <.01
Rear Teat Placement
lateral reference level
intermediate 1.0 0.4 – 2.3 0.94 0.8 0.6 – 1.0 0.06
medial 1.2 0.5 – 2.9 0.65 0.8 0.6 – 1.1 0.20
Udder Width
narrow reference level
intermediate 1.4 0.9 – 2.3 0.17 2.2 1.8 – 2.6 <.01
wide 3.1 1.7 – 6.3 <.01 4.8 3.8 – 6.1 <.01
Fore Udder Attachment
loose reference level
intermediate 0.5 0.3 – 0.7 <.01 0.4 0.3 – 0.4 <.01
medial 0.4 0.2 – 0.7 <.01 0.2 0.2 – 0.3 <.01
43
The strong Goodman-Kruskal gamma associations (Table 2.6) make biological sense,
suggesting that if udders are generally large in one dimension they will tend to also be large in
other dimensions (e.g., if udders are wide, they are also deep). It also follows that the deepness
of the udder cleft may inform the positioning and angle of rear teats. Furthermore, as cows age
they tend to have lower, more pendulous udders. These associations are noteworthy as
numerous previous studies suggested cows with higher, less pendulous udders have increased
mastitis resistance (Seykora and McDaniel, 1985).
While univariate inferential statistics are less comprehensive than a multivariable
model, they allow characterization of the relationship of each variable of interest to elevated
SCC or clinical mastitis even if they were not retained in the final models (Table 2.7). Though
not retained in the final elevated SCC model, udder depth, rear udder height, and udder width
were significantly associated with repeated high SCC events. It is likely they were not retained
in the final models due to their high association with fore udder attachment (γ = 0.77, 0.52, and
0.61, respectively) though individually they likely contribute to increased odds of elevated
SCC. Indeed, past studies have identified similar correlations between deeper udder floors and
lower rear udder height with high somatic cell score (Young et al., 1960). Farm was a significant
covariate in these models, reaffirming that farm size and management style plays a significant
role in mastitis prevalence and risk. The results presented in this study are most reflective of
farms with similar size, climate, and management practices.
Most variables of interest appear independently related to odds of clinical mastitis.
Similarly to the elevated SCC model, udder depth and width were likely not retained in the
model due to their strong correlation with fore udder attachment (Table 2.6). Front and rear
right teat length have a strong Pearson’s correlation coefficient (ρ = 0.69), and rear right teat
44
width has a moderately strong correlation with rear right teat length (ρ = 0.42), which likely
explains why neither teat length trait was retained in the final clinical mastitis model. There is
little consensus in the literature regarding the relationship of teat size to mastitis, with some
studies reporting a positive correlation between teat diameter and mastitis incidence (Hickman,
1964) and others reporting no significant mastitis association or heritability (Seykora and
McDaniel, 1985). In addition, these univariate analyses suggest that wide right front teats were
related to increased odds of clinical mastitis, and laterally pointing front teats may have
influenced odds of clinical mastitis. The association between front teat placement and rear udder
height (γ = 0.29), and a similar correlation between front and rear teat width (ρ = .38), may
explain their individual association with clinical mastitis but statistical insignificance in the
multivariate model. It is also possible that the small proportion of cows with laterally pointing
teats reduced the power such that a statistically significant association of teat placement with
clinical mastitis could not be made in the multivariable models.
CONCLUSIONS
Our study associated loose fore udder attachment and flat teat ends with increased odds
of both elevated SCC and clinical mastitis diagnosis. Parity and CMT scores were related to a
cow’s odds of experiencing a high SCC event in an individual sample during the course of her
lactation, and rear udder height and rear teat width were related to odds of developing clinical
mastitis. There is little consensus in the literature regarding the impact of udder and teat
conformation on mastitis, and this prospective cohort study with precise measurement of
exposure and disease outcomes aided in the clarification of those relationships. A notable aspect
of this study was the use of repeated measure logistic regression to associate type traits with
45
elevated SCC events – as opposed to previous studies which examined lactation averages,
markedly reducing the udder health information considered. In addition, monitoring these traits
can be used as a low-cost management tool to recognize animals that may have increased risk
of elevated SCC and clinical mastitis. In particular, if farmers are concerned with elevated SCC
or high prevalence of clinical mastitis in their herd, utilizing the CMT at time of calving and
noting fore udder attachment as well as teat end shape may indicate high risk animals within
their herd. This could allow farmers to increase monitoring frequency of these high risk animals,
mitigating the time and financial burden of adjusting management practices for the entire herd.
In addition, the most marked risk factors of loose fore udder attachment and flat teat ends may
be effective criteria to include in culling protocols or inform mating strategies by selecting bulls
with strong evaluations for udder and teat morphology without sacrificing milk yield.
ACKNOWLEDGEMENTS
This work was made possible through funding by the NIFA Animal Health Project
#NYC-127898, the invaluable participation of commercial dairy farms, and the work of many
Cornell University graduate and undergraduate students associated with the Huson lab. Special
thanks to Stephen Parry of the Cornell Statistical Consulting Unit. Preliminary results were
presented as an abstract at the ADSA Annual Meeting in Pittsburgh, PA on June 25-28, 2017
as well as the 57th Annual National Mastitis Council Meeting in Tucson, AZ on January 30-
February 2, 2018.
46
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50
CHAPTER 3: GENOME WIDE ASSOCIATIONS FOR UDDER AND TEAT
CONFORMATIONAL RISK FACTORS FOR MASTITIS IN HOLSTEIN COWS
Asha M. Milesa, Christian J. Posbergha, and Heather J. Husona1
aDepartment of Animal Science, Cornell University, Ithaca, NY 14853
1Corresponding author: Heather J. Huson, 201 Morrison Hall, 507 Tower Road, Ithaca, NY
14853. Phone: (607) 255-2289. E-mail: hjh3@cornell.edu
In Review: BMC Genomics, October 2019
51
ABSTRACT
The objective of our study was to conduct high-density genome-wide association studies
of dairy cow udder and teat conformation with direct phenotyping. We identified and compared
quantitative trait loci (QTL) for a novel composite mastitis risk trait and considered
environmental impact of milking by comparing primiparous cows only. Cows (N = 471) were
genotyped on the Illumina BovineHD 777K beadchip and scored for front and rear teat length,
width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder
height, and rear udder width. Principal component analysis was performed on fore udder
attachment, rear teat end shape, rear teat width, and rear udder height, to create a single new
phenotype describing mastitis susceptibility based on these high-risk traits. Over all 14 traits of
interest, a total of 56 genome-wide associations were performed and 28 significantly associated
(Bonferroni multiple testing correction < 0.05) QTL were identified. The linkage disequilibrium
(LD) block surrounding the associated QTL or a 1 Mb window in the absence of LD was
interrogated for candidate genes, resulting in the identification of genes with functions related
to both cell proliferation and immune signaling, including ZNF683, DHX9, CUX1, TNNT1, and
SPRY1. We assessed a primiparous only subset of cows (n = 144) to account for the possibility
that the genetic variance component of the phenotype is greater for cows who have had less
exposure to the environment, and observed different associated QTL and inheritance patterns
for udder depth in primiparous cows compared to the total cohort. Special focus was given to
the aforementioned mastitis risk traits, and candidate gene investigation revealed both immune
function and cell proliferation related genes in the areas surrounding significantly associated
52
QTL, suggesting that selecting for mastitis resistant cows based on these traits would be an
effective method for increasing mastitis resiliency in a herd.
Key words: genome wide association, mastitis, principal component analysis, udder
conformation, teat conformation
53
INTRODUCTION
Mastitis, a condition characterized by inflamed mammary tissue and udder gland, is the
costliest disease facing U.S. dairy producers, accounting for an estimated $2 billion in annual
losses and 11% of total milk lost according to a recent market analysis (iGEM, 2016). While
mastitis has been historically considered a management problem, genetic correlations among
milk yield, mastitis susceptibility, and udder morphology encouraged selection for udder and
teat type traits as early as the 1950s (O’Bleness et al., 1960). In 2009, udder composite values
were incorporated into official national genomic evaluation systems to account for the influence
of cow conformation on health traits (Wiggans et al., 2011; Interbull, 2013). However, the lack
of standardized reporting in the United States has led to a deficit in health-related phenotypes,
hindering genetic improvement in U.S. dairy herds, and there is little consensus in the literature
regarding mastitis and udder and teat trait relationships or their respective heritabilities
(Seykora and McDaniel, 1985; Parker Gaddis et al., 2014). Most extant research has relied on
pedigree information to calculate relationship matrices for estimation of heritability, genetic
correlation, and variance, given the strong correlations between health, production, and
conformation traits (Seykora and McDaniel, 1986; Rupp and Boichard, 1999; Chrystal et al.,
2001; DeGroot et al., 2002). Focus has been also given to sire transmitting abilities, and while
these studies comprehensively evaluate udder morphology, teat length, and teat placement,
other teat characteristics such as width and shape are neglected (Nash et al., 2000; DeGroot et
al., 2002).
As genotyping technologies have become increasingly cost-effective more molecular-
based studies are emerging. A large study utilized publicly available whole genome sequences
to impute medium and high-density single nucleotide polymorphism (SNP) data up to over 20
54
million sequence variants to identify quantitative trait loci (QTL) for a number of udder and
teat type traits in Fleckvieh cattle (Pausch et al., 2016). While Pausch et al. had extremely high
density genetic data, they relied on bull genotypes and the indirect phenotyping of their
daughters, raising external validity concerns. Currently, only one study has directly associated
udder traits with cow genotypes, relating beef cow teat length, teat diameter, and a composite
“udder support” score to genomic data from the low-density Bovine Illumina50k chip (Tolleson
et al., 2017). Large-scale studies using genotype imputation from low density SNP arrays may
maximize genome coverage and make large sample sizes more practical, but they introduce
bias towards population averages and limit researcher ability to detect rare variants (Marete et
al., 2018a).
A high-density genome wide association (GWA) study of dairy cow udder and teat
conformation with direct phenotyping and no reliance on genotype imputation has yet to be
conducted. Thus, we previously associated udder and teat conformational traits with mastitis in
a prospective cohort study, where flat teat ends and loose fore udder attachment significantly
increased odds of both elevated SCC and clinical mastitis, and low rear udder height and wider
rear teats increased odds of clinical mastitis diagnosis alone (Miles et al., 2019). We posited
that these risk factors may be effective criteria to include in culling protocols or inform mating
strategies by selecting bulls with strong evaluations for udder and teat morphology without
sacrificing milk yield. The purpose of this current study was to use GWA approaches to identify
SNP markers associated with udder and teat type traits, with special focus on these four risk
traits for incorporation into genomic selection marker panels for mastitis-resistant cows.
55
MATERIALS AND METHODS
Phenotyping. We conducted a prospective cohort study of 523 Holstein cows on 2 commercial
herds in New York State involving direct udder and teat phenotype determination as previously
described (Miles et al., 2019). Udder and teat traits included fore udder attachment, udder cleft,
udder depth, rear udder height, rear udder width, as well as front and rear teat placement, end
shape, length, and width. Front and rear teat length and width were scored quantitatively, while
the remainder were scored on 3 categorical levels according to the U.S. Holstein Association’s
linear descriptive traits (Holstein Association USA, 2014). Phenotypes were first considered
quantitatively to assess continuous variation in the trait, but also on a case-control basis
comparing extremes in morphology (Nazari-Ghadikolaei et al., 2018).
Genotyping and Quality Control. A whole blood sample from each cow was taken via the
coccygeal vessel, collected in 10 mL K2EDTA anticoagulant vacutainers, and stored at 4ºC or
-20ºC until DNA extraction. Genomic DNA was extracted from whole blood according to the
Gentra Puregene Blood Kit protocol (Gentra Systems, Inc. Minneapolis, MN, USA) using
laboratory-made buffers. In sum, 471 cows were submitted to GeneSeek (Neogen Genomics,
Lincoln, NE) for SNP genotyping on the Illumina BovineHD 777K beadchip (Illumina, Inc.,
San Diego, CA). Quality control filtering was applied to all genotypes via Golden Helix SNP
& Variation Suite software v8.3.4 (Golden Helix, Bozeman, MT). Genotypes were retained if
SNP and individual call rate ≥ 0.9, minor allele frequency ≥ 0.05, and allele number ≤ 2.
Identity-by-descent (IBD) estimates were calculated for all pairs of individuals based on
available genotype data and individuals with IBD estimate ≥ 0.9 were removed.
Principal Component Analysis. Principal Component Analysis (PCA) of phenotype data was
performed using RStudio version 3.2.5 (R Core Team, 2016) to identify components that may
56
describe multiple udder and teat traits. Four PCAs were performed: teat traits only (front and
rear teat placement, end shape, length, and width), udder traits only (fore udder attachment,
udder cleft, udder depth, rear udder height, and rear udder width), teat and udder traits together,
and “risk” traits only (rear teat end shape, rear teat width, fore udder attachment, and rear udder
height).
Genome-Wide Association. Efficient Mixed Model Linear analysis (EMMAX) models were
employed in Golden Helix SVS allowing the inclusion of the IBD matrix to correct for any
population structure among this cohort of cows (Kang et al., 2010). Additive, dominant, and
recessive inheritance models were considered along with the variables of farm, parity, batch,
rear teat length, udder depth, and rear udder width as potential covariates given their potential
correlation and confounding effects on traits of interest. To address the possibility that the
genetic variance component of the phenotype is greater for cows who have had less exposure
to the environment, a primiparous-only subset of the larger cohort of cows was also evaluated
(n = 144). P-values were adjusted for multiple testing using a Bonferroni correction and False
Discovery Rate (FDR).
Model Selection and Candidate Gene Investigation. Quantile–quantile (QQ) plots of the log10
(Expected P-values) and log10 (Observed P-values) and a genomic inflation factor lambda
(median observed P-value divided by the median expected P-value) were used to assess
goodness of fit. A stringent multiple testing correction was applied and only regions with SNPs
passing Bonferroni correction (adjusted P-value < 0.05) were interrogated for candidate genes.
Any gene in linkage disequilibrium (LD) with an associated marker, or in a 500 kb upstream
or downstream range of said marker if LD was not present, was identified using the NCBI
RefSeq Database (O'Leary et al., 2016). Gene functions were investigated using the GeneCards
57
database (Stelzer et al., 2016). All genome coordinates given use the most recent ARS UCD
1.2 bovine genome assembly.
RESULTS
Principal Component Analysis. Principal component (PC) analysis was used to create novel
composite traits for udder conformation, and mastitis risk factors. Udder traits-only PC1
accounted for 41% of the variance in phenotype and described over-all udder size, loading
towards deeper, lower, and wider rear udders, as well as looser fore udder attachment. Risk
traits-only PC1 accounted for 35% of phenotypic variance, loading in the direction of the low
risk phenotypes of thinner rear teats, tighter fore udders, and higher rear udders (Figure 3.1a).
No other PCs were informative or considered for GWA.
Genome-wide association. After all quality assurance measures were applied, 458 cows with
581,663 SNPs remained for analysis. Phenotypes were first considered quantitatively to assess
continuous variation in the trait, then on a case-control basis comparing extremes in
morphology. Primiparous cows (n = 144) were only evaluated linearly to preserve the sample
size and due to low representation of many udder and teat morphologies in first parity cows
(Miles et al., 2019). Of the 56 GWA studies performed, 13 had significantly associated SNPs,
minimally passing a multiple testing correction threshold of false discovery rate (FDR) < 0.05
and no evidence of genomic inflation (Table 3.1). Quantile-Quantile plots are presented in
Supplementary Figure 3.1. All QTL positions and candidate genes residing within an LD block
or 1 Mb surrounding associated SNPs are reported in Supplementary Table 3.1. No significant
associations (FDR < 0.05) with udder PC1, front teat end shape, rear teat placement, front teat
placement, or udder cleft were identified in our total cohort of cows (N = 458).
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Table 3.1. Models selected. Summary of final models including sample size (N), model
type, inheritance patterns, quality control measures, trait heritabilities, and total number of
significantly associated SNPs.
Trait N Model Inheritance Pseudo- Pseudo- FDR2 Bonferroni2
Type Lambda1 heritability
Front Teat 458 Linear Recessive 1.00 0.33 5 2
Length
Front Teat 458 Case- Recessive 1.01 0.06 4 4
Width control3
Fore Udder 288 Case- Additive 1.00 0.45 4 3
Attachment control4
Risk PC1 458 Linear Dominant 1.01 0.07 2 1
Rear Teat 458 Case- Additive 1.00 0.42 1 1
Length control5
Rear Teat 227 Case- Recessive 1.02 0.50 1 1
End Shape control6
Rear Teat 458 Case- Recessive 1.02 0.03 3 1
Width control3
Rear Teat 458 Linear Recessive 0.98 0.18 50 23
Width
Udder Depth 265 Case- Dominant 0.99 0.99 1 1
control7
Udder 458 Case- Recessive 1.00 0.02 129 32
Height control8
Udder Width 458 Case- Dominant 1.00 0.30 1 1
control9
Front Teat 144 Linear Recessive 1.02 0.38 10 7
Placement10
Udder 144 Linear Recessive 1.05 0.62 10 0
Depth10
1genomic inflation factor for model quality control, Quantile-Quantile plots in Supplementary Figure 1
2number of SNP associations passing either Bonferroni or False Discovery Rate (FDR) multiple testing
corrections at P < 0.05
3narrow versus wide teats, split by median measurement
4loose versus tight fore udders, excluding all cows with intermediate fore udder attachment
5short versus long rear teats, split by median measurement
6flat versus pointed rear teat ends, excluding all cows with round rear teat end shape
7deep versus high udder depth, excluding all cows with intermediate udder depth
8low versus all other udder heights (intermediate and high rear udders combined)
9wide versus all other udder widths (intermediate and narrow udders combined)
10primiparous cow subset
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In the primiparous cow subset (n = 144), significant associations (FDR < 0.05) were
identified for front teat placement and udder depth only.
Risk PC1. A linear GWAS with dominant inheritance and covariates of farm, parity, rear teat
length, udder depth, and udder width significantly associated 1 SNP on Bos taurus autosome
(BTA) 15 (Bonferroni < 0.05; Figure 3.1b).
Udder traits. An additive mixed model with no covariates comparing loose versus tight fore
udder attachment significantly associated 1 SNP on BTA 2 and 2 SNPs on the X chromosome
passing Bonferroni correction and 1 additional SNP on BTA 2 passing FDR (Figure 3.2).
Significant associations were made for udder depth in both the total cohort and primiparous
subset populations of cows. In a case-control comparison of deep versus high udders among all
cows, a dominant mixed model with parity and udder width as fixed effects significantly
associated 1 SNP on BTA 5 passing both FDR and Bonferroni correction (Figure 3.3a). In a
linear scoring of udder depth among the subset of primiparous cows only, a recessive mixed
model significantly associated 10 SNPs on BTA 17 (FDR < 0.05; Figure 3.3b). In a case-control
comparison of low udder heights to all others (intermediate and high udders combined), a
recessive mixed model with covariates udder depth, udder width, and fore udder attachment
identified 129 SNPs which passed FDR on 20 chromosomes spanning the genome, and 32 SNPs
passing Bonferroni correction on BTAs 6, 14, 15, 18, and 22 (Figure 3.4). In a case-control
comparison of wide versus all other widths (intermediate and narrow combined), a dominant
mixed model with no covariates significantly associated 1 SNP passing both FDR and
Bonferroni correction on BTA 15.
Teat Traits. In a quantitative representation of front teat length, a recessive mixed model with
farm, parity, front teat width, and rear teat length and width covariates significantly associated
61
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Figure 3.2. Manhattan plot showing –log10 P-values by chromosome in a case-control GWA for fore udder attachment,
comparing loose fore udders to tight fore udders (n = 288). The black line represents the Bonferroni multiple testing
correction threshold.
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5 SNPs passing FDR on BTAs 9, 10, and 12, and 2 SNPs passing Bonferroni correction on
BTA 10. The most appropriate model for a case-control comparison of wide versus narrow
front teats was a recessive mixed model with farm, parity, and front teat length included as fixed
effects, yielding 4 SNPs passing Bonferroni correction on BTA 23. A case-control comparison
of short versus long rear teats significantly associated (Bonferroni < 0.05) 1 SNP on BTA 2 in
an additive mixed model with no covariates. In a case-control comparison of flat versus pointed
rear teat ends, a recessive mixed model significantly associated (Bonferroni < 0.05) 1 SNP on
BTA 26 (Figure 3.5). In an examination of rear teat width, two recessive mixed models with no
covariates yielded significantly associated SNPs. In the first model, a case-control comparison
of narrow versus wide rear teats yielded 3 SNPs passing FDR on BTAs 18, 25, and 28, and 1
SNP passing Bonferroni correction on BTA 25 (Figure 3.6a). Rear teat width was represented
quantitatively in the second model and 50 SNPs were significantly associated (FDR < 0.05) on
BTAs 1, 4, 5, 10, 11, 15, 16, 18, 19, 25, 26, 27, and the X chromosome. A total of 23 SNPs
passed Bonferroni correction on BTAs 10, 11, 16, 18, 19, and 25 (Figure 3.6b). Examining
primiparous cows only, a linear representation of front teat placement with a recessive mixed
model yielded 10 SNPs passing FDR on BTA 9.
DISCUSSION
We performed genome-wide association studies to associate QTL with udder and teat
conformation traits, with special focus on previously identified mastitis-risk traits of fore udder
attachment, rear teat end shape, rear teat width, and udder height (Miles et al., 2019). These
studies provide insight into the genetic regulation of teat and udder confirmation and mastitis
susceptibility as well as novel traits and specific markers that may be used in genetic selection.
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A comprehensive list of candidate genes may be found in Supplementary Table 3.1; here we
discuss those most biologically relevant to mastitis risk associated traits.
Our goal in performing PCA on these risk traits was to identify a single measure which
may describe mastitis risk based on udder and teat conformation. Risk PC1 described this risk
(excepting rear teat end shape), and GWA for this new composite measure significantly
associated novel QTL (15:7287030-7311314) not identified by individual assessment of those
risk traits (Figure 3.1). Candidate genes identified in this region were related to both cell
division (Centrosomal Protein 126 (CEP126)) and immune cell progenitor differentiation
(Angiopoietin Like 5 (ANGPTL5)), suggesting that Risk PC1 does indeed reflect both mastitis
and udder and teat morphology (Drake et al., 2011; Bonavita et al., 2014).
In this study cohort, we previously associated loose fore udder attachment with high
odds of elevated milk somatic cell count and clinical mastitis diagnosis, making fore udder
attachment among these cows a relevant criterion on which to base culling and management
decisions for mastitis control (Miles et al., 2019). A case-control GWA for extremes in fore
udder attachment (loose versus tight) identified a number of genes related to both immune
function and cell proliferation near associated markers at 2:126359098-126364670 (Figure 3.2).
Wiskott-Aldrich Syndrome Protein Family 2 (WASF2) is a cytoplasmic protein implicated in
cell migration, phagocytosis, and immune synapse formation (Thrasher and Burns, 2010).
Similarly, Nuclear Distribution C (NUDC) is critical to cytokinesis, Stratifin (SFN) may
regulate cell cycle progression, and Keratinocyte Differentiation Factor 1 (KDF1) serves as an
essential regulator of epidermis formation (Ghahary et al., 2004; Lee et al., 2013; Zhang et al.,
2016a). In contrast, this same genomic region houses genes related to immune function,
including Ficolin 3 (FCN3) which is an essential component of the lectin complement pathway,
68
Ribosomal Protein S6 Kinase A1 (RPS6KA1) which has been implicated in activated Toll Like
Receptor 4 signaling, High Mobility Group Nucleosomal Binding Domain 2 (HMGN2) which
is known to have antimicrobial activity against bacteria, viruses, and fungi, as well as Zinc
Finger 683 (ZNF683), a tissue-resident T-cell transcription regulator (Moller et al., 1994; Vieira
Braga et al., 2015; Plovsing et al., 2016; Tian et al., 2018). The variety in function among
candidate genes associated with fore udder attachment, relating to both the physical trait and
immune function, reinforce the role of this udder type trait as a major risk factor for mastitis.
Similarly, we previously associated rear teat end shape with increased odds of both
elevated somatic cell count and clinical mastitis in this cohort of cows, and in this study
investigated the potential genetic regulation of this trait (Miles et al., 2019). Upon examining
rear teat end shape (Figure 3.5), a similar candidate gene pattern emerged with gene functions
related to both cell division (explaining teat morphology variation) and immune response
regulation (reinforcing teat end shape as an appropriate indicator of mastitis risk). Significantly
associated SNP BovineHD2600014687 positioned at 26:50630351 resides within Kinase Non-
Catalytic C-Lobe Domain Containing 1 (KNDC1), which has been implicated in the regulation
of cellular senescence and cell cycle progression (Zhang et al., 2014). In addition, Adhesion G
Protein-Coupled Receptor A1 (ADGRA1) was identified within a 1 Mb window of this SNP
and belongs to a family of receptors known to regulate immune signaling (Knapp and Wolfrum,
2016). A pattern of candidate gene function emerged like that of fore udder attachment,
reinforcing that it may be appropriate to select for mastitis-resilient cows based on udder and
teat conformational traits.
In this cohort of cows, we previously associated increasing rear teat width with
increased odds of clinical mastitis (Miles et al., 2019). We first assessed this trait quantitatively
69
to account for continuous variation in rear teat width, and a linear GWA for rear teat width also
revealed candidate genes related to both cell division and immune function at a number of
different QTL spanning the genome (Figure 3.6). Near a significantly associated SNP at BTA
11:104129366, we identified Caspase Recruitment Domain-Containing Protein 9 (CARD9), a
key modulator of immune response related to TLR and NOD2 signaling pathways as well as
NF-kB activation (Bertin et al., 2000; Lamas et al., 2018). Furthermore, genes related to cell
differentiation and survival were identified including Notch Receptor 1 (NOTCH1), a highly
conserved protein with an extracellular domain containing many epidermal growth factor
repeats and whose signaling is heavily involved in cell fate specification, and Epidermal
Growth Factor- Like 7 (EGFL7) which is involved in Notch binding (Siebel and Lendahl,
2017). In addition, a Myomaker Myoblast Fusion Factor (MYMK) resides in this region and has
been associated with muscle hypertrophy, making it a strong candidate for impacting variation
in teat morphology (Si et al., 2019). Genes were also investigated in various regions on BTA
16, including Centrosomal Protein 350 (CEP350) which plays a critical role in microtubule
binding and spindle integrity during cell replication (Yan et al., 2006). This region is also home
to Laminin Subunit Gamma 1 and 2 (LAMC1/2), which are thought to regulate cell organization
into tissues, potentially contributing to variation in teat width (Nakad et al., 2017). In regards
to immune function, nearby genes were investigated including Major Histocompatibility
Complex Class I-Related (MR1) which is critical to adaptive immune response, Ribonuclease
L (RNASEL) is involved in interferon regulation, and DExH-Box Helicase 9 (DHX9), which has
been found to control TLR-stimulated immune responses (Squire et al., 1994; Yamaguchi et
al., 1998; Dempsey et al., 2018). A significantly associated QTL at BTA 19:29058547-
29063744 is located within the Growth Arrest Specific 7 (GAS7) gene, which while previously
70
understood to influence neuron differentiation, was recently found to be abundantly expressed
in murine alveolar macrophages, though its exact roles in immune responses are still unknown
(Xu et al., 2017). Furthermore, HIC ZBTB Transcriptional Repressor (HIC1) and Tyrosine 3-
Monooxygenase Activation Protein (YWHAE) have both been associated with the regulation of
cell proliferation, and in the case of HIC1 its function as a transcriptional regulator has been
specifically tied to immune homeostasis (Leal et al., 2016; Burrows et al., 2017). A significantly
associated QTL on BTA 25:40126743-40190566 resides within the Sidekick Cell Adhesion
Molecule 1 (SDK1) gene, an adhesion molecule isoform in the immunoglobulin superfamily
primarily known for synapse formation in the retina, though Sidekicks in general are expressed
in many different tissue types (Yamagata and Sanes, 2008). A significantly associated SNP at
25:35208040 was interrogated for candidate genes in the surrounding area, which included Cut
Like Homeobox 1 (CUX1), known for its role in morphogenesis as well as regulation of antigen
presenting cells (Kuhnemuth et al., 2015; Xu et al., 2018). Myosin Light Chain 10 (MYL10),
implicated in immune cell transmigration, and Tripartite Motif Containing 56 (TRIM56), an
ubiquitin-ligase with a role in antiviral innate immunity, were also found in this region (Oltz et
al., 1992; Chen et al., 2018). A case-control GWA study was also performed to identify genes
which may potentially drive extremes in morphology, and significantly associated a single SNP
at BTA 25:38568564. Candidate genes near this SNP included Polypeptide N-
Acetylgalactosaminyltransferase 2 (GALNT2) which is believed to be involved in O-linked
glycosylation of the immunoglobulin A1 hinge region and PiggyBac Transposable Element
Derived 5 (PGBD5), a transposase suspected to mediate genomic rearrangements (Iwasaki et
al., 2003; Henssen et al., 2015). The large number of QTL identified spanning the genome
suggests rear teat width may be highly polygenic.
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We previously associated low rear udder height with increased odds of clinical mastitis
(Miles et al., 2019), and a case-control comparison of low udder height versus all other udder
height types also identified several significantly associated QTL spanning the genome (Figure
4). A significantly associated SNP at 14:27024015 lay within the Aspartate Beta-Hydroxylase
(ASPH) gene, known for hydroxylating epidermal growth factors and contributing to
dysmorphic features (Patel et al., 2014). Furthermore, a significantly associated QTL on BTA
18 was near Troponin T1, Slow Skeletal Type (TNNT1), a sarcomere regulatory complex
associated with muscle weakness, which could feasibly contribute to weak rear udder
attachments and consequently lower udder height (Fox et al., 2018). In addition, NLR Family
Pyrin Domain Containing 2 (NLRP2), a member of the NLR family known to be regulators of
immune responses, was in this same region (Jones et al., 2016). While we considered low rear
udder height as a risk factor for clinical mastitis, the majority of candidate genes identified in
this area seem more related to the physical trait itself rather than the indirect trait of “mastitis-
risk”, making rear udder height a less compelling trait by which to base breeding and culling
decisions for mastitis control.
We hypothesized that the genotype by environment interaction may be greater in
multiparous cows who have had greater mastitis exposure and mechanical manipulation of the
udder and teats via milking, and thus evaluated a primiparous-only subset of cows (n = 144) for
each trait. Only linear GWAs assessing continuous variation in the trait resulted in significant
associations; the lack of success with the case-control approach is likely explained by few risk
phenotypes (< 20%) observed in primiparous cows. In these linear GWA, QTL were only
significantly associated with two traits (front teat placement and udder depth) likely due to low
power from a smaller sample size. For udder depth, different inheritance patterns and QTL were
72
identified for each population (Figure 3.3), which is likely explained by farm culling resulting
in different genotypic frequencies among primiparous and multiparous cows. The total cohort
GWA associated a single SNP at BTA 5:113268242 located within Transcription Factor 20
(TCF20) which is primarily associated with human neurodevelopmental disorders (Schafgen et
al., 2016). The primiparous only subset significantly associated one QTL on BTA 17. Antisense
genes Nudix Hydrolase 6 (NUDT6) and Fibroblast Growth Factor 2 (FGF2) were in linkage
with this QTL, and are hypothesized to regulate cell proliferation (Asa et al., 2001). Also in
linkage with this QTL lay Sprouty RTK Signaling Antagonist 1 (SPRY1), which has been shown
to influence mammary epithelial morphogenesis during post-natal development by negatively
regulating epidermal growth factor signaling in the murine mammary gland (Koledova et al.,
2016). We hypothesize that this QTL was masked in our analysis of the total cohort due to the
differing genotypic frequencies in multiparous versus primiparous populations, suggesting to
truly elucidate the genetic mechanism underlying these morphological characteristics
primiparous populations with minimal exposure to selective pressures must be evaluated.
CONCLUSIONS
This is the first study to use high density SNP chip data with direct phenotyping and no
reliance on imputation to investigate the genetic mechanisms regulating bovine udder and teat
conformation. Potential biases due to indirect phenotyping and genotype imputation are
mitigated by assessing high density genotypes, researcher-controlled phenotypes, and disease
risk all within the same population of cows. For many traits, we found significantly associated
QTL spanning the genome, suggesting that udder and teat morphologies are complex traits with
many causal variants with small effect sizes. A noteworthy aspect of this study was the
73
examination of a primiparous only subset of cows, which demonstrated different inheritance
patterns and associated QTL than the total multiparous cohort. In particular, the objective of
this study was to identify genetic mechanisms which may underlie our previously identified
mastitis risk factors of loose fore udder attachment, flat rear teat ends, wide rear teats, and low
udder height in this cohort of cows. In the case of these risk factors, candidate genes surrounding
significantly associated QTL were identified with functions relating to both the immune
response and cell proliferation and tissue morphology, suggesting these traits are indeed
representative of mastitis susceptibility.
DECLARATIONS
Ethics approval and consent to participate: Farm owner signed consent was obtained prior to
sampling and this study was approved under Cornell University IAUC Protocol #2014-0121.
Consent for publication. Not applicable.
Availability of data and materials. The datasets used and/or analyzed during the current study
are available from the corresponding author on reasonable request.
Competing interests. The authors declare that they have no competing interests.
Funding. This work was made possible through funding by the NIFA Animal Health Project
#NYC-127898.
Authors’ contributions. AM performed on-farm data collection, DNA extraction, sample
preparation for genotyping, statistical and genetic analyses, interpretation, and manuscript
preparation. CP aided in on-farm data collection, DNA extraction, and interpretation of genetic
analyses. HH provided funding and study design. All authors read and approved the final
manuscript.
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Acknowledgements. We thanks commercial dairy farms for their invaluable participation, and
many Cornell University graduate and undergraduate students associated with the Huson lab
for their assistance with on farm sampling and lab work.
75
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Supplementary Figure 3.1.
Quantile-Quantile plots showing
the –log10(Expected P Values)
plotted against the –
log10(Observed P Values) for
each final model A) Front Teat
Length, B) Front Teat Width, C)
Fore Udder Attachment, D) Risk
PC1, E) Rear Teat Length, F)
Rear Teat End Shape, G) Rear
Teat Width (case-control), H)
Rear Teat Width (linear), I)
Udder Depth, J) Udder Height,
K) Udder Width, L) Front Teat
Placement (primiparous cows
only), M) Udder Depth
(primiparous cows only. The
black line represents X = Y.
87
Supplementary Table 3.1. Significantly associated QTL (Bonferroni < 0.05) and potential
candidate genes for all models
Trait Chromosome1 Position (bp)1 Candidate Genes2
Front Teat 50107685-
10 FOXB1, LOC112448464
Length 50117321
PLA2G7, ANKRD66, TRD6,
Front Teat Width 23 19946762
SLC25A27
20566460-
23 TNFRSF21, CD2AP, ADGRF2
20576922
WASF2, GPR3, CD164L2, FCN3,
MAP3K6, SYTL1, TMEM222,
WDTC1, SLC9A1, FAM46B, TRNP1,
Fore Udder 126354670- KDF1, NUDC, NR0B2, GPN2, SFN,
2
Attachment 126359098 ZDHHC18, PIGV, LOC112443408,
ARID1A, RPS6KA1, HMGN2,
LIN28A, ZNF683, CRYBG2, UBXN11,
CEP85
PHEX, LOC112445297, SMS,
121461599- LOC112445297, MBTPS2, SMPX,
X
121477381 KLHL34, TRNAC-GCA,
LOC112445299, CNKSR2
7287030-
Risk PC1 15 CEP126, ANGPTL5, TRPC6
7311314
Rear Teat SCG2, AP1S3, WDFY1, MRPL44,
2 112245780
Length SERPINE2, FAM124B, CUL3
KNDC1, SYCE1, TCERG1L, CYP2E1,
LOC528422, LOC112444506,
ADGRA1, LOC100850437,
Rear Teat End
26 50630351 LOC101906198, LOC112444503,
Shape
CFAP46, LOC112444502,
LOC112444507, NKX6-2, INPP5A,
LOC112444505
Rear Teat
3 18 18781078 BRD7, ADCY7 Width
25 38568564 LOC618554, LOC101906717
LOC112444763, GALNT2, PGBD5,
28 2835028 LOC112444731, LOC100337323,
LOC112444759, LOC614741
Rear Teat CTDSPL2, EIF3J, SPG11,
10
Width4 LOC112448593, PATL2
LOC112448855, LHX3, QSOX2,
LOC787891, GPSM1,
11 104129366 LOC101902280, DNLZ, CARD9,
SNAPC4, ENTR1, PMPCA, INPP5E,
SEC16A, NOTCH1, LOC112448856,
88
EGFL7, MIR126, LOC101902839,
LOC101902895, FAM69B,
LOC112448928, LOC107132967,
LOC100848307, LOC112448857,
ABO, LOC112448956, SURF6,
LOC11244890, MED22, RPL7A,
LOC100139115, LOC100112448907,
LOC100112448908,
LOC100112448904,
LOC100112448903,
LOC100112448905, SURF2, SURF4,
STKLD1, LOC107132968, REXO4,
ADAMTS13, CACFD1, SLC2A6,
LOC112448858, TRNAC-GCA,
MYMK, ADAMTSL2, FAM163B,
DBH, SARDH, VAV2
CEP350, QSOX1, LOC112441858,
LHX4, ACBD6, MIR669, XPR1,
61802991-
16 TRNAC-ACA, LOC107133256,
62196774
KIAA1614, STX6, MR1, IER5,
CACNA1E, LOC104974498, ZNF648,
LOC101905162, GLUL, TEDDM1,
RGSL1, RNASEL, RGS16, RGS8,
16 63823597 LOC101905664, NPL, DHX9,
SHCBP1L, LAMC1, LAMC2,
NMNAT2
LOC112442233, CBLN1,
18 17655467
C18H16orf78, ZNF423, TRNAG-CCC
18 20537778 LOC516179, TOX3
MIR2899, LOC112442482, TRNAG-
18 42468232 CCC, LOC617301, ZNF507,
DPY19L3
VPS53, MIR2336, RFLNB,
C19H17orf97, TRNAG-UCC,
RPH3AL, LOC104975006, DOC2B,
LOC112442619, YWHAE, TRNAE-
UUC, CRK, MYO1C, INPP5K,
19 22640468 PITPNA, SLC43A2, SCARF1, RILP,
PRPF8, TLCD2, MIR22, WDR81,
SMYD4, SERPINF1, RPA1,
RTN4RL1, LOC112442621, DPH1,
OVCA1, MIR132, MIR212, HIC1,
SMG6, LOC112442776
23978522- RAP1GAP2, OR1D5, LOC101906737,
19
23997890 LOC618593, OR1G1, LOC540082,
89
LOC532238, LOC522582,
LOC112442765, LOC520835,
LOC59525, LOC59526
29058547-
19 GLP2R, RCVRN, GAS7
29063744
CUX1, TRNAW-CCA, MIR2388,
LOC112444316, LOC112444338,
MYL10, COL26A1, LOC104970468,
IFT22, FIS1, PLOD3, LOC618076,
25 35208040
NAT16, VGF, AP1S1,
LOC101902751, SERPINE1, TRIM56,
LOC101902689, LOC101909082,
LOC107131854
40126743-
25 SDK1
40190566
Udder Depth 5 113268242 TCF20, LOC104976976
102964124-
Udder Height 6 LOC100298890
102982437
14 27024015 CLVS1, ASPH
15545765-
15 AMOTL1, LOC112441606
15782913
TNNT1, PPP1R12C, LOC112442386,
LOC112442387, EPS8L1, RDH13,
62273143-
18 LOC100848752, GP6, NLRP2,
62481417
LOC100336589, LOC100852077,
LOC112442414
22 46733454 CACNA2D3, LOC112443534
PRDM11, LOC101906676, SYT13,
LOC107133190, LOC112441655,
CHST1, LOC104974324,
Udder Width 15 75722222
LOC107133191, SLC35C1, CRY2,
MAPK8IP1, C15H11orf94, PEX16,
LARGE2, PHF21A, CREB3L1
Front Teat 58002055-
9 LOC101902249, LOC112448054
Placement5 58079933
5,6 34476230- SPRY1, SPATA5, LOC112442097, Udder Depth 17
34552407 NUDT6, FGF2
1Positions based on ARS-UCD 1.2
2Genes in LD with significantly associated SNP, or +/- 500 kb of associated SNP in case of no LD,
bolded text indicates associated SNP is within the gene
3case-control GWA of narrow versus wide rear teats divided at the median value
4linear GWA of quantitative rear teat width scores
5primiparous only subset of cows (N = 144)
6not passing Bonferroni correction, QTL significantly associated at FDR < 0.05
90
CHAPTER 4: TIME- AND POPULATION-DEPENDENT GENETIC PATTERNS
UNDERLIE BOVINE MILK SOMATIC CELL COUNT
Asha M. Milesa and Heather J. Husona1
aDepartment of Animal Science, Cornell University, Ithaca, NY 14853
1Corresponding author: Heather J. Huson, 201 Morrison Hall, 507 Tower Road, Ithaca, NY
14853. Phone: (607) 255-2289. E-mail: hjh3@cornell.edu
In Prep
91
ABSTRACT
The objective of this study was to determine whether genetic regulation of bovine milk
somatic cell count (SCC) varied throughout the course of an individual lactation and to
identify quantitative trait loci (QTL) which may differentiate populations of chronically
mastitic and robustly healthy cows. Milk SCC has long been used as a proxy for clinical
mastitis diagnosis in management and genetic improvement strategies to control the disease.
Cows (N = 471) were genotyped on the Illumina BovineHD 777K beadchip and composite
milk samples collected for SCC at 0-1 DIM, 3-5 DIM, 10-14 DIM, 90-110 DIM, and 210-230
DIM, each time point representing key physiological transitions for the cow. Median lactation
somatic cell score (SCS) and area under the SCS curve were calculated from farm test data. A
total of 8 genome-wide associations were performed and 167 single nucleotide
polymorphisms (SNPs) spanning the genome were significantly associated (False Discovery
Rate < 0.05). Of these associated regions, 27 out of 48 associated QTL were novel for clinical
mastitis or SCC. The linkage disequilibrium (LD) block surrounding the associated QTL or a
1 Mb window in the absence of LD was interrogated for candidate genes, and many of those
identified were related to multiple arms of the immune system including toll-like receptor
signaling, macrophage activation, B cell maturation, T cell recruitment, and the complement
pathway. These genes included EXOC4, BAMBI, ITSN2, IL34, FCN3, and CD8a/b. In
addition, we identified populations of robustly healthy (SCS ≤ 4 from 10-14 DIM until study
end), chronically mastitic (SCS > 4 from 10-14 DIM until study end), and average cows with
fluctuating SCS, and calculated fixation indices to identify regions of the genome
differentiating these three populations. A total of 12 SNPs were identified showing moderate
allelic differentiation (FST ≥ 0.4) between Chronic, Healthy, and Average populations of
92
cows. Candidate genes in the region surrounding differentiated QTL were related to cell
signaling and immune response, such as JAKMIP1 and MADCAM1. The wide range of
significantly associated QTL spanning the genome and diversity of gene functions reinforces
that mastitis is a complex trait and suggests that selection based on lactation stage-specific
SCS rather than a generalized score may lead to greater success in breeding mastitis-resistant
cows.
Key words: genome wide association, mastitis, somatic cell score, single nucleotide
polymorphisms
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INTRODUCTION
Mastitis, a condition characterized by inflamed mammary tissue and the costliest
disease facing U.S. dairy producers, can be attributed to problems with herd and farm
management, milking management, the milking machine, and the cow itself (Mein et al., 2004;
Rollin et al., 2015). In 1994, milk somatic cell count (SCC), a common proxy for mastitis
diagnosis, was added to national bovine genetic evaluations specifically to address the impact
of elevated SCC on cow production and health. Though the genetic evaluation of dairy cattle
has long been used to improve production and health traits, improvements by linkage based
marker assisted selection have been hindered by the small proportion of genetic variation
explained by each marker in regards to complex traits, as well as the high costs associated with
marker validation (Soller, 1994; Andersson, 2001; Misztal, 2006). In addition, the use of bull
genotypes and indirect phenotyping of their daughters, the lack of standardized reporting in the
United States, and the use of population and lactation average data in trait definition, potentially
introduce bias to mastitis phenotypes and their genetic associations (Interbull Code of Practice,
2018). As genotyping technologies become increasingly affordable, more studies have emerged
utilizing single nucleotide polymorphism (SNP) chip, genotype imputation, RNAseq, and
whole genome sequence data, though these studies do not directly phenotype study animals and
primarily rely on the less time-intensive method of de-regressing breeding values for trait
definition (Biffani et al., 2017; Fang et al., 2017; Cai et al., 2018). To date, no genome wide
association (GWA) study has been conducted utilizing high density cow genotypes and direct
longitudinal SCC phenotyping on the same animals.
While SCC is a well-established proxy for mastitis diagnosis, lactation somatic cell
score (SCS) averages and de-regressed breeding values are generalized phenotypes which may
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not detect the specific mechanisms underlying the recruitment of somatic cells to the udder.
Inflammatory states post-partum have been well documented, and numerous studies have
established that inflammatory responses are elevated following parturition given the stresses of
lactogenesis, uterine involution, and changes in metabolic state, even in the absence of disease
(Bionaz et al., 2007; Graugnard et al., 2012; Akbar et al., 2015). Elevated milk SCC due to
postpartum inflammation may create an upward skew in lactation average data, resulting in an
inaccurate characterization of mastitis susceptibility. The objective of this study was to
longitudinally characterize SCC in a cohort of cows and use high density genotype data to
examine the genetics driving elevated SCC across different stages of lactation, and to assess the
genetic differentiation among populations of chronically elevated and robustly healthy cows.
This study was designed specifically to address the limitations of using average lactation
somatic cell score for genomic selection of mastitis resistant dairy cattle, given that there are
non-pathological reasons for udder inflammation and that SCC is not exclusively an indicator
of disease.
MATERIALS AND METHODS
Phenotyping. From June 2015 to July 2016, a convenience sample of 523 dairy cows from two
farms in upstate New York were enrolled in a prospective cohort study involving six composite
milk sample collections representing key physiological time points in lactation as previously
described (Miles et al., 2019). The physiological conditions sampled for were parturition (0-1
days in milk (DIM)), baseline milk after clearance of colostrum (3-5 DIM), peak mastitis
incidence and negative energy balance (10-14 DIM), return to neutral/positive energy balance
(50-60 DIM), peak production and early pregnancy (90-110 DIM), and mid to late lactation
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(210-230 DIM). A California Mastitis Test (CMT) was used to assign a binary score to
colostrum samples (0-1 DIM); positive if the CMT solution reacted to create distinct thickening
of the sample, or a negative score if the sample remained liquid (Schalm and Noorlander, 1957)
. All remaining milk samples were outsourced to a milk laboratory for SCC by flow cytometry
as previously described (FossomaticTM, Dairy One, Ithaca, NY (Miles et al., 2019). Both farms
participated in monthly Dairy Herd Improvement testing and milk test data from the study
period was pulled from herd management software (DairyComp 305, Valley Agricultural
Software, Tulare, CA). Farm milk test data was generated by the same laboratory that processed
the researcher-collected samples. All SCC data were logarithmically transformed to a linear
𝑆𝐶𝐶
𝑙𝑛( 5 )
somatic cell score by the following equation: 𝑆𝐶𝑆 = 10 + 3 in R Studio version 3.3.2 (R
𝑙𝑛(2)
Core Team, 2016). Median SCS and area under the SCS curve (AUC) were calculated from the
farm milk test data. Farm owner signed consent was obtained prior to sampling and this study
was approved by Cornell University’s Institutional Animal Care and Use Committee under
authorization reference number 2014-0121.
Genotyping and Quality Control. Genomic DNA was extracted from whole blood taken from
the coccygeal vessel, collected in 10 mL K2EDTA anticoagulant vacutainers, and stored at 4ºC
or -20ºC until DNA extraction. Extractions were performed according to the Gentra Puregene
Blood Kit protocol (Gentra Systems, Inc. Minneapolis, MN, USA) using laboratory-made
buffers. A total of 471 cows were submitted for SNP genotyping on the Illumina BovineHD
777K beadchip (Illumina, Inc., San Diego, CA) by GeneSeek (Neogen Genomics, Lincoln,
NE). Quality assurance measures filtered out samples with SNP and individual call rate < 0.9,
minor allele frequency < 0.05, and allele number > 2. The relatedness of all pairs of individuals
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was assessed via Identity-by-descent (IBD) estimates from genotype data and individuals with
IBD estimate ≥ 0.9, demonstrating significant relatedness, were removed. After all quality
control filtering was applied, 458 cows with 581,663 SNPs remained for analysis.
Population Analysis. To identify distinct populations of cows, SCS at 10-14 DIM was plotted
against SCS at 50-60 DIM (Figure 4.1a) and quadrants drawn at a threshold of SCS = 4 (SCC
= 200,000 cells/ml), a well-established threshold at which intramammary infection is likely
present (McDermott et al., 1982; Dohoo and Leslie, 1991; Schepers et al., 1997). Cows with
SCS ≤ 4 at both 10-14 DIM and 50-60 DIM were designated “healthy” (green squares), those
with SCS ≤ 4 10-14 DIM but SCS > 4 at 50-60 DIM were designated “new cases” (black
circles), those with SCS > 4 at 10-14 DIM but SCS ≤ 4 at 50-60 DIM were labeled “cures”
(blue stars), and cows with SCS > 4 at both time points were labeled “chronic” (red triangles).
The progression of these 4 groups of cows over their lactation was tracked by plotting SCS at
50-60 DIM against SCS at 90-110 DIM (Figure 4.1b), and SCS at 90-110 DIM against SCS at
210-230 DIM (Figure 4.1c). Populations were defined as cows that remained in the “Healthy”
quadrant the entire study period (solid green squares, n = 239), those with “Chronic” elevated
SCC throughout the study period (solid red triangles, n = 12), and “Average” cows whose SCC
fluctuated across quadrants during the study (n = 168). Wright’s F Statistic (FST) by marker was
calculated to measure the genetic divergence between the subpopulations of “Healthy” and
“Average”, “Healthy and “Chronic”, and “Average” and “Chronic” with 0 representing no
allelic divergence between populations and values up to 1 suggesting increasing population
differentiation (Weir and Cockerham, 1984). Any SNP with FST ≥ 0.4 was considered
moderately differentiated given the high prevalence of inbreeding within Holstein cattle, and
surrounding regions investigated for candidate genes as described below (Forutan et al., 2018).
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A
B
C
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Figure 4.1. Overview of somatic cell score (SCS) based population stratification. Linear SCS at A) 10 to 14 DIM plotted
against 50 to 60 DIM, B) 50 to 60 DIM plotted against 90 to 110 DIM, and C) 90 to 110 DIM plotted against 210 to 230
DIM. Cows with SCS < 4 at 10 to 14 DIM and 50 to 60 DIM are designated with a green square (□), cows with SCS < 4
at 10 to 14 DIM and > 4 at 50 to 60 DIM are designated with a black circle (o), cows with SCS > 4 at 10 to 14 DIM and
50 to 60 DIM are designated with a red triangle (∆), cows with SCS > 4 at 10 to 14 DIM but < 4 at 50 to 60 DIM are
designated with a blue star (*). Solid green squares (■) represent cows that had SCS < 4 at all 4 time points; solid red
triangles (▲) represent cows that had SCS > 4 at all 4 time points.
Genome-Wide Association. Efficient Mixed Model Linear analysis (EMMAX) models were
used to allow the inclusion of the IBD matrix to correct for any population structure in Golden
Helix SNP & Variation Suite software (Kang et al., 2010). Additive, dominant, and recessive
inheritance
models were considered along with the variables of farm, parity, and milk yield given their
potential confounding effects on traits of interest. Lactation length was included as a covariate
in all genome wide associations (GWA) for total lactation traits (median SCS, AUC) to address
the influence of varying time at risk on longitudinal SCS measures. P-values were adjusted for
multiple testing using a Bonferroni correction and False Discovery Rate (FDR) threshold of
0.05.
Model Selection and Candidate Gene Investigation. To identify QTL associated with elevated
SCC and potential subclinical mastitis at time of calving, a case-control GWA using the CMT
score at 0-1 DIM was performed. To determine the influence of lactation stage on elevated SCC
events, 5 GWA were performed for the quantitative trait of SCS at 3-5 DIM, 10-14 DIM, 50-
60 DIM, 90-110 DIM, and 210-230 DIM. To establish a point of comparison for these lactation
stage analyses, a GWA for the traditional phenotype of average lactation SCS was performed.
To account for variation in SCS not represented by a median score, a GWA was performed for
SCS AUC. A genomic inflation factor lambda (median observed P-value divided by the median
expected P-value) and Quantile–Quantile (QQ) plots of the log10 (Expected P-values) against
the log10 (Observed P-values) were used to select the most appropriate models for each trait.
Regions with SNPs passing FDR were interrogated for candidate genes, and any gene in linkage
disequilibrium (LD) with an associated marker (r2 ≥ 0.8), or in a 500 kb upstream or
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downstream range of said marker if LD was not present, was identified using the NCBI RefSeq
Database (O'Leary et al., 2016). All genome coordinates given use the most recent bovine
genome assembly ARS UCD 1.2.
RESULTS
After all quality assurance measures were applied, 454 cows with 581,663 SNPs remained for
analysis. Descriptive statistics for SCS phenotypes are reported in Table 4.1. A comprehensive
list of differentiated or significantly associated markers, their positions, and surrounding
candidate genes can be found in Supplementary Tables 4.1 and 4.2, respectively.
Population Analyses. FST by marker comparing the different populations is shown in Figure
4.2. There was no apparent allelic differentiation between Average and Healthy populations
(Figure 4.2a). There were 12 SNPs indicating divergence (FST ≥ .4) between Average and
Chronic populations on Bos taurus autosomes (BTA) 4, 6, and 7 (Figure 4.2b). In a comparison
of Healthy and Chronic populations, 3 SNPs were differentiated (FST ≥ .4) on BTA 4 and 5
(Figure 4.2c). All 9 moderately differentiated SNPs on BTA 4 resided in an LD block including
two SNPs located at BTA 4:18655887 and 18669066 which were identified in both the Average
and Healthy cow comparisons to the Chronic cows (Figure 4.3). For each SNP, chromosome,
position, major and minor alleles, and minor allele frequencies (MAF) stratified by Healthy,
Average, and Chronic populations are summarized in Table 4.2.
Genome-Wide Associations. Final models selected are summarized in Table 4.3 with respective
Manhattan plots shown in Figure 4.4. A dominant inheritance model with farm and parity
covariates significantly associated 9 SNPs (FDR < 0.05) with CMT score at time of calving on
BTAs 4 and 13 (Figure 4.4a). Recessive inheritance models with no covariates identified QTL
associated with SCS at different stages in lactation spanning the genome. A total of 23 SNPs
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Table 4.1. Descriptive statistics of each SCS phenotype. Mean, median, variance, interquartile range (IQR),
and sample size (n) across all cows are reported.
n Mean Median Variance IQR
Area Under Lactation SCS Curve 451 577.2 512.4 179557 574.6
Lactation Median SCS 454 2.2 1.8 2.8 2
SCS at 3-5 DIM 454 4.1 3.7 3.5 2.1
SCS at 10-14 DIM 451 2.9 2.5 4.9 2.4
SCS at 50-60 DIM 448 1.9 1.4 6.1 2.6
SCS at 90-110 DIM 438 1.9 1.3 5.6 2.5
SCS at 210-230 DIM 418 2.3 1.9 4.5 2.8
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A
B
C
Figure 4.2. FST by marker comparing a) Average and Healthy, b) Average and Chronic, and c) Healthy and Chronic
populations. SNPs with moderate allelic differentiation (FST ≥ 4) are indicated in purple.
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A
B
C
Figure 4.3. FST by marker on BTA 4 comparing a) Average and Chronic, b) Healthy and Chronic populations, and c) linkage
disequilibrium in this region. SNPs with moderate allelic differentiation (FST ≥ 4) are indicated in purple.
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Table 4.2. Marker based FST analysis identifies markers differentiating Holstein cow populations based on their SCS. Chromosome,
position, FST values, major and minor alleles, as well as minor allele frequency by population are given. Bolded rows indicate SNPs
differentiating Chronic cows from both Healthy and Average populations.
Minor Allele Frequency
Major Minor
Marker BTA Position FST Healthy
3 Average4 Chronic5
Allele Allele
BovineHD0400005562 4 18655237 0.471 G A 0.10 0.07 0.45
BovineHD0400005563 4 18655887 0.471/ 0.402 A G 0.10 0.07 0.45
BovineHD0400005564 4 18662663 0.471 G A 0.10 0.07 0.45
BovineHD0400005565 4 18669066 0.471/0.402 C A 0.09 0.07 0.45
BovineHD0400005570 4 18685097 0.471 G A 0.10 0.07 0.45
BovineHD0400005575 4 18698840 0.601 G A 0.11 0.06 0.50
BovineHD0400005577 4 18705074 0.441 A G 0.10 0.08 0.45
BovineHD0400005578 4 18707492 0.441 A G 0.10 0.08 0.45
BovineHD0400005579 4 18718084 0.471 G A 0.10 0.07 0.45
BovineHD0700012914 7 43160366 0.421 G A 0.06 0.04 0.32
ARS-BFGL-NGS-87868 7 43877784 0.421 G A 0.06 0.04 0.32
BovineHD0600029283 6 103041074 0.421 G A 0.16 0.13 0.55
BovineHD0500004192 5 13984552 0.422 G A 0.03 0.06 0.27
1moderately differentiated SNPs (FST > 0.4) between Average and Chronic populations
2moderately differentiated SNPs (FST > 0.4) between Healthy and Chronic populations
3SCS ≤ 4 from 10-14 DIM until study end
4Cows with fluctuating SCS during study period
5SCS > 4 from 10-14 DIM until study end
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Table 4.3. Genome-wide association models selected. Summary of final models including sample size (N), model type,
inheritance patterns, quality control measures, trait heritabilities, and total number of significantly associated SNPs.
Pseudo- Pseudo-
SCS Trait N Model Type Inheritance FDR2 Bonferroni2
lambda1 heritability
CMT 454 Case-control Dominant 1.00 0.40 9 0
3-5 DIM 454 Linear Recessive 0.98 0.12 0 0
10-14 DIM 451 Linear Recessive 1.00 0.54 23 10
50-60 DIM 448 Linear Recessive 1.00 0.31 8 0
90-110 DIM 438 Linear Recessive 0.99 0.43 0 0
210-230 DIM 418 Linear Recessive 0.99 0.35 106 24
Median SCS 454 Linear Recessive 1.01 0.49 25 0
AUC 454 Linear Recessive 0.99 0.55 0 0
1genomic inflation factor for model quality control, Quantile-Quantile plots in Supplementary Figure 1
2number of SNP associations passing either Bonferroni or False Discovery Rate (FDR) multiple testing corrections at P < 0.05
were significantly associated with SCS at 10-14 DIM on BTAs 3, 5, 6, 11, and 29 (Figure 4.4c);
8 SNPs were significantly associated with SCS at 50-60 DIM on BTAs 1, 3, 4, and 26 (Figure
4.4d); and 106 SNPs were significantly associated with SCS at 210-230 DIM on ten
chromosomes spanning the genome (Figure 4.4f). There were no significant associations with
3-5 DIM or 90-110 DIM (Figure 4.4b and e, respectively). Recessive inheritance models with
covariates of parity, farm, and lactation length significantly associated 25 SNPs with median
SCS on 12 chromosomes across the genome (Figure 4.4g); no SNPs were significantly
associated with AUC.
DISCUSSION
Population Analyses. In a comparison of Healthy and Average populations, the low FST by
marker values suggest there is not great genetic difference between cows with consistently low
SCC and those who experience fluctuations in SCC as their lactation progresses. However, FST
by marker analysis reveals genomic regions differentiated Chronic cows from both Average
and Healthy cows, suggesting that cows which chronically experience elevated SCC are
genetically different from cows with more consistently low milk SCC. Of particular interest is
the QTL identified on BTA 4, which indicated differentiation of Chronic from all other cows
(Figure 4.3). The breaks in this LD block suggest this region may be a recombination site; only
2 of the 9 markers differentiating Chronic and Average also differentiated Healthy cows, but
LD suggests they are inherited together in the study population. An overlapping QTL has been
previously mapped for milk SCS in a large Holstein study using microsatellite data and a grand-
daughter design (Zhang et al., 1998), reinforcing the relevance of these SNP markers for use in
selecting for mastitis resistant cows. The SNP at BTA 6:103041074 has not been previously
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A
B
C
D
E
F
G
Figure 4.4. Manhattan plots showing associated SNPs at a) 0-1 DIM, b) 3-5 DIM. c) 10-14 DIM, d) 50-
60 DIM, e) 90-110 DIM, f) 210-230 DIM, and g) Median SCS for entire lactation. Significantly associated
SNPs (FDR < 0.05) are indicated in blue. Bonferroni multiple correction threshold indicated by the black
line.
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associated with mastitis-related traits, but falls directly within the protein-coding gene Janus
Kinase and Microtubule Interacting Protein 1 (JAKMIP1), which is suspected to play a role in
the microtubule-dependent transport of the GABA-B receptor and is commonly expressed in
lymphoid tissues (Costa et al., 2007). The QTL identified on BTA 7 differentiating Average
and Chronic cows overlaps with QTL previously mapped for milk somatic cell score in German
Holsteins (Kuhn et al., 2003). Other QTL in this region have been mapped for milking speed
and milk yield, both of which have been associated with mastitis risk (Boichard et al., 2003;
Schrooten et al., 2004; Japertiene et al., 2007; Marete et al., 2018b). Within this QTL, a SNP at
BTA 7:43160366 lies directly within Mucosal Vascular Addressin Cell Adhesion Moledule 1
(MADCAM1), which encodes an endothelial cell adhesion molecule involved in directing
leukocytes towards inflammation sites (Vavricka et al., 2018). A second SNP at
BTA7:43877784 is within Adenomatous Polyposis Coli Protein 2 (APC2), a regulator of the
WNT2 signaling pathway which has been associated with the pathogenesis of several human
cancers (Katoh, 2003). We propose that these differentiated markers are candidates for
inclusion in selection indices for mastitis-resistant cows, given prior association to mastitis-
related traits through QTL mapping and the immune-related functions of the genes within which
these SNPs reside. Selection strategies based on this genomic region should be made with
caution, given that it has been mapped for the traits of milk yield and milking speed, and further
investigation is required to ensure selection for mastitis resistance is not antagonistic to milk
yield, and vice versa.
Genome-wide Associations. To assess the genetics underlying postpartum elevated SCC, a
case-control GWA for CMT score at 0-1 DIM (negative n = 398, positive n = 125) was
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performed, and SNPs significantly associated on BTA 4 and 13, the former of which has not
been previously mapped for any health traits (Figure 4.4a). The associated marker at BTA
4:97507560 lies directly within Exocyst Complex Component 4 (EXOC4), a protein complex
which has been implicated in mechanisms of host defense due to its role in long-range cell
signaling and phagosome maturation (Martin-Urdiroz et al., 2016). A significantly associated
SNP located at BTA 13:35930715 overlaps with a previously mapped QTL for SCS and milk
yield in Danish Holsteins, which suggests this QTL may have pleiotropic effects related to milk
production traits (Lund et al., 2008). Nearby candidate genes include Lysozyme Like 1 (LYZL1),
an antimicrobial protein, BMP And Activin Membrane Bound Inhibitor (BAMBI), which has
been implicated in macrophage and T cell regulation (Sun et al., 2019). We previously
associated positive CMT scores at time of calving with increased odds of experiencing an
elevated SCC event, suggesting that not only may CMT score at time of calving be a useful
measure for selective management of high risk cows, but that these associated markers are
strong candidates for inclusion in health trait indices for genomic selection (Miles et al., 2019).
However, the potential pleiotropy of the associated QTL on BTA 13 makes it difficult to use in
genomic selection due to the potentially inverse relationships of SCS and milk yield.
The GWA for milk SCS at 10-14 DIM, representing peak mastitis incidence,
significantly associated SNPs at both novel and previously mapped QTL for mastitis traits
(Figure 4.4c). A significantly associated SNP at BTA 3:89806231 overlaps with a QTL
previously associated with SCS in a GWA study using imputed SNP chip data in French
Holsteins (Marete et al., 2018b). The SNPs significantly associated with SCS at 10-14 DIM at
BTA 5:955228 and 6:111916870 have had no prior association with milk SCS, whereas the
associated SNP at BTA 6:30947112 overlaps with QTL previously associated with both clinical
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mastitis and SCS (Klungland et al., 2001; Bennewitz et al., 2004; Michenet et al., 2016). Two
large QTL were significantly associated on BTA 11, and while neither have been previously
mapped for SCS or clinical mastitis, both overlap with previously mapped QTL for milk yield,
which has a well-established relationship to mastitis, in a study of Danish Holsteins (Kucerova
et al., 2006). The QTL at BTA29: 21528226-21543306 significantly associated with SCS at 10-
14 DIM has not been associated with mastitis before, though does overlap with a QTL
previously mapped for milk yield (Kucerova et al., 2006). While not within any genes, the SNPs
in this associated block neighbor the Small VCP Interacting Protein (SVIP), which is an
inhibitor of the Endoplasmic Reticulum-Associated Degradation (ERAD) pathway, and thus
suspected to protect the cell from excessive degradation (Wang et al., 2011). The identification
of novel QTL for SCS during peak mastitis incidence (10-14 DIM) suggests that some genetic
drivers of mastitis are not detected with de-regressed breeding values or lactation averages.
Perhaps only QLT not previously mapped for milk yield should be used in selection for mastitis
resistance, given the multiple reports of positive genetic correlation between clinical mastitis
and milk yield, and more precise SCS phenotyping is required to make this distinction (Pösö
and Mäntysaari, 1996; Heringstad et al., 1999; Hansen et al., 2002).
To assess the genetics underlying milk SCC at the return to neutral/positive energy
balance and the end of the voluntary breeding period, we performed GWA for SCS at 50-60
DIM and significantly associated a marker block at BTA 1: 65045803-65045168 which has
not been previously mapped for mastitis-related traits (Figure 4.4d). The SNPs in this block
were within the G Protein-Coupled Receptor 156 (GPR156) gene, a cell-surface receptor
involved in GABA signaling, known to influence many pathways including immune response
(Liu et al., 2019). Significantly associated QTL on BTA 3: 118976220-119034557 have not
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been previously mapped for mastitis related traits, but were associated with udder height and
fore udder attachment, traits we previously associated with increased odds of elevated SCC
(Cole et al., 2011; Miles et al., 2019). A significantly associated SNP at BTA 4: 19709677
overlaps with QTL previously mapped for SCS in multiple studies, and one at BTA 26:
30956828 was previously mapped for both SCS and clinical mastitis (Zhang et al., 1998;
Rupp and Boichard, 1999; Kuhn et al., 2003; Longeri et al., 2006; Lund et al., 2008). Nearby
candidate genes include MAX Interactor 1 (MXI1), a dimerization protein implicated in
hematopoietic stem cell differentiation and implicated in the inflammatory response (Yoo et
al., 2007). We posit that examining SCS at this time point removes bias introduced in the
phenotype by commonly observed non-pathogenic causes of inflammation in early lactation,
such as lactogenesis, uterine involution, and negative energy balance. Furthermore, the
confidence of the associations on BTAs 4 and 26 are strongly validated given their mapping
in other independent studies.
A GWA for SCS at 210-230 DIM, representing mid to late lactation and mid pregnancy,
significantly associated SNPs on BTA 4 at 22557323 and 118912645 (Figure 4.4f). The former
resides within Diacylglycerol Kinase Beta (DGKB), a regulator associated with stimulation of
peripheral blood hematopoietic stem cells (Mishima et al., 2017). Both of these associated SNPs
overlap with previously mapped QTL for both somatic cell count and score (Zhang et al., 1998;
Kuhn et al., 2003; Longeri et al., 2006; Ibeagha-Awemu et al., 2016). A region located on BTA
5:10674537-10679054 significantly associated with SCS at 210-230 DIM overlaps with
previously mapped QTL for both SCS and clinical mastitis (Lund et al., 2008; Pimentel et al.,
2011). We identified a novel QTL on BTA 5:113340241-113358226, in which significantly
associated SNPs lie inside the NFAT Activating Protein with ITAM Motif 1 (NFAM1) gene, a
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promoter for cytokine-encoding genes and also believed to regulate B cell development and
signaling (Ohtsuka et al., 2004). Also significantly associated with SCS at 210-230 DIM was a
QTL on BTA 6:4761931-4920678 which overlaps with QTL previously mapped for SCC
(Klungland et al., 2001). In addition, a significantly associated SNP in this region lies within
PR Domain Zinc Finger Protein 5 (PRDM5), a transcription factor associated with cell
proliferation and the modulation of pro-inflammatory cytokines (Wu et al., 2017). A
significantly associated SNP on BTA 7:95230312 was in near proximity to Endoplasmic
Reticulum Aminopeptidase 1 (ERAP1), polymorphisms of which have been shown to affect
antigen presentation by MHC class 1 molecules and underlie the pathogenesis of various
inflammatory diseases (Lopez de Castro et al., 2016). A large associated QTL on BTA
7:95730312-101060884 has been previously mapped for SCS, and contains the Solute Carrier
Organic Anion Transporter Family Member 4C1 and 6A1 (SLCO4C1 and SLCO6A1) genes,
which have been implicated in the modulation of inflammatory responses (Toyohara et al.,
2009; Cole et al., 2011b). Two significantly associated SNPs on BTA 11: 66762518 and
68023914 fall within a previously mapped QTL for SCC (Schnabel et al., 2005). In addition,
two novel QTL on BTA 11 were significantly associated and nearby candidate genes included
Lysocardiolipin Acetyltransferase 1 (LCLAT1) which has been suggested to control
hematopoietic cell differentiation, Yippee Like 5 (YPEL5) which has been shown to negatively
regulate innate immune responses, FOS Like 2 (FOSL2) whose signaling pathway is involved
in many branches of the immune system, as well as the BRISC and BRCA1 A Complex Member
2 (BABAM2), an adapter of the BRISC complex which has been shown to down-regulate the
immune response to bacterial lipopolysaccharides and is suspected to regulate interferon and
tumor necrosis factor alpha signaling (Li et al., 2004; Xiong et al., 2008; Ciofani et al., 2012;
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Zheng et al., 2013; Jeidane et al., 2016). Further candidate genes were identified in this region
on BTA 11, including Intersectin 2 (ITSN2) which is implicated in T cell antigen receptor
endocytosis (Burbage et al., 2018). A significantly associated QTL on BTA 18 was not
previously mapped for mastitis-related traits, but we identified nearby candidate genes
including Interleukin 34 (IL34), a cytokine involved in macrophage viability and differentiation
and whose signaling pathways affect many mechanisms of the immune system, Fucose Kinase
(FCSK), a catalyzing enzyme for L-fucose utilization, which has been implicated in antigen
recognition and immune cell-cell interactions, and Golgi Glycoprotein 1 (GLG1), which is part
of a critical ligand-receptor interaction for early response to lipopolysaccharides in bovine
(Yuan et al., 2008; Li et al., 2009; Higgins et al., 2014; Lin et al., 2019). An associated QTL on
BTA 21 was previously mapped for clinical mastitis, and nearby candidate genes include
Endothelin 1 (ET1), a cell receptor involved in the regulation of inflammation (Schulman et al.,
2004; Elisa et al., 2015). Significantly associated SNPs on BTAs 22 and 23 have previously
been mapped for SCC (Schulman et al., 2004; Maltecca et al., 2011). A significantly associated
QTL on BTA 23:14982097-15014257 has not been previously mapped for mastitis traits, but
candidate genes in LD with associated SNPs include Triggering Receptor Expressed On
Myeloid Cells Like 1 through 3 (TREM1, TREM2, TREM3), a well-established positive
regulator of inflammation, and Transcription Factor EB (TFEB), which has been implicated as
a negative regulator of endothelial inflammation and T cell recruitment (Lu et al., 2017; Liu et
al., 2018). By 210-230 DIM the confounding physiological factors of parturition, lactogenesis,
negative energy balance, and inflammation during early pregnancy have been removed and
milk SCS may be a more accurate reflection of mastitis. The use of generalized milk SCC and
lack of pathogenesis-specific phenotyping may explain the extremely polygenic nature of this
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trait and diverse arms of the immune system implicated in these analyses. Milk SCC with
differential or microbial profiling would be required to refine the phenotype at this lactation
stage and may result in more concentrated signals to specific genomic regions.
A GWA was performed for the traditional phenotype of lactation median SCS to provide
a point of comparison for our lactation stage-specific analyses, and significantly associated
SNPs on 11 chromosomes spanning the genome (Figure 4.4g). Novel QTL were associated on
BTAs 2 and 4; nearby candidate genes included Sphingomyelin Phosphodiesterase Acid Like
3B (SMPDL3B), which has been shown to negatively regulate Toll Like Receptor signaling and
to influence macrophage membrane fluidity, Thymocyte Selection Associated Family Member
2 (THEMIS2), which has been implicated in positive regulation of B cells and is believed to
regulate macrophage TLR signaling, and Ficolin 3 (FCN3), a driver of the lectin complement
pathway, as well as Synaptotagmin Like 1 (SYTL1), a mediator of neutrophil chemotaxis and
granule exocytosis (Garred et al., 2009; Peirce et al., 2010; Heinz et al., 2015; Cheng et al.,
2017; Ramadass et al., 2019). Similarly, mutations in Non-Homologous End Joining Factor 1
(NHEJ1) have been implicated in immunodeficiency disorders (Al-Marhoobi et al., 2019).
Significantly associated SNPs on BTA 6 in a gene-scarce region were previously mapped for
both SCS and clinical mastitis (Bennewitz et al., 2004; Sodeland et al., 2011). A significantly
associated SNP at BTA 11:48421277 overlaps with a QTL previously mapped for SCS, and
was positioned near T Cell Surface Glycoprotein CD8 Beta and Alpha Chain (CD8a and b),
which is heavily involved in antigen presentation (Zhang et al., 1998; Parel and Chizzolini,
2004). A significantly associated QTL on BTA 15 was previously mapped for SCS, and nearby
candidate genes include RAB39a Member RAS Oncogene Family (RAB39A) which has been
implicated in macrophage activation in response to lipopolysaccharides as well as ATM
114
Serine/Threonine Kinase (ATM) which is involved in B cell antigen receptor expression
(Boichard et al., 2003; Rossi and Gaidano, 2012; Seto et al., 2013). Both associated SNPs on
BTA 19 have been previously mapped for SCS in multiple studies, and candidate genes include
Interferon Induced Protein 35 (IFI35) a proinflammatory damage-associated molecular pattern
released by activated macrophages in response to lipopolysaccharides, Transmembrane Protein
106A (TMEM106A) which has been shown to upregulate and polarize macrophages thereby
inducing proinflammatory cytokine release, and CD300 Molecule Like Family Member G
(CD300LG), widely expressed on hematopoietic cells and involved in lymphocyte
transmigration in inflamed tissues (Bennewitz et al., 2004; Tal-Stein et al., 2010; Cole et al.,
2011b; Umemoto et al., 2013; Dai et al., 2015; Xiahou et al., 2017). A novel QTL was
associated on BTA 20:26074612-26079633, and associated SNPs are located inside Integrin
Subunit Alpha 2 (ITGA2), which is suspected to mediate inflammation by regulating immune
cell adhesion (Adorno-Cruz and Liu, 2019). Two SNPs on BTA 28 were significantly
associated, one novel (BTA 28:19095996) and one near a previously mapped QTL for SCS in
beef cattle (Imumorin et al., 2011). There were no commonalities among the median SCS and
lactation stage GWA, suggesting that there are indeed different genetic mechanisms driving
milk SCC over the course of a lactation.
CONCLUSIONS
A total of 48 QTL were identified via GWA for SCS, and of those 27 were novel,
having never been mapped for SCC or clinical mastitis previously. Numerous candidate genes
were identified relating to immunity, and their function varied across multiple branches of the
immune system including stimulation of inflammatory cytokines, macrophage activation, B
115
cell maturation, T cell recruitment, and the lectin pathway of the complement system.
Redundancy is built into the immune system, and the identification of candidate genes
belonging to different branches suggest that extremely mastitis susceptible cows may
experience failures in multiple immune response mechanisms. We found that extremely
susceptible cows (Chronic) are moderately differentiated from others (Healthy and Average)
and that this allelic differentiation can be localized to BTAs 4, 6, and 7. We posit that
variation in SCS across a lactation must be considered in genomic selection strategies given
the non-pathological causes of udder inflammation and the differing etiologies of mastitis.
The wide range of significantly associated QTL and diversity of candidate gene functions
suggest that selection based on phenotypes specific to lactation stage and pathogenesis rather
than generalized SCS may lead to greater success in breeding mastitis-resistant cows.
ACKNOWLEDGEMENTS
We thank commercial dairy farms for their invaluable participation, and many Cornell
University graduate and undergraduate students associated with the Huson lab for their
assistance with on farm sampling and laboratory work. Special thanks to Allison Herrick for
her assistance in data compilation and Stephen Parry of the Cornell University Statistical
Consulting Unit.
116
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129
A E
B F
C G
D
Supplementary Figure 4.1. Quantile-Quantile plots showing the –
log10(Expected P Values) plotted against the –log10(Observed P
Values) for each final model A) CMT at 0-1 DIM, B) SCS 3-5 DIM,
C) SCS 10-14 DIM, D) SCS 50-60 DIM, E) SCS 90-110 DIM, F)
SCS 210-230 DIM, G) Median Lactation SCS. The black line
represents X = Y.
130
Supplementary Table 4.1. Differentiated QTL (FST > 0.4) and potential candidate genes for
all comparisons
Population Chromosome1 Position1 Candidate Genes
Comparison
Average v. Chronic 4 18655237- LOC112446490, NDUFA4, PHF14
18718084
6 103041074 LOC781835, LOC523503, PPP2R2C,
WFS1, LOC100298890, JAKMIP1,
C6H4orf50, TRNAA-UGC, CRMP1,
EVC, EVC2
7 43160366- PGBD2, LOC104972393,
43877784 LOC789031, LOC789041,
LOC614592, LOC785149,
LOC100300085, LOC112447517,
LOC522560, LOC787611, PLPP2,
MIER2, THEG, C2CD4C, SHC2,
ODF3L2, MADCAM1, TPGS1,
LOC112447550, CDC34, GZMM,
BSG, HCN2, POLRMT, TRNAE-
UUC, FGF22, RNF126, FSTL3,
PRSS57, PALM, MISP, PTBP1,
LOC112447411, AZU1,
LOC112447659, PRTN2, ELANE,
CFD, MED16, R3HDM4, KISS1R,
ARID3A, WDR18, GRIN3B,
TMEM259, CNN2, ABCA7,
ARHGAP45, POLR2E, GPX4,
SBNO2, STK11, LOC112447411,
AZU1, LOC112447659, PRTN2,
ELANE, CFD, MED16, R3HDM4,
KISS1R, ARID3A, WDR18, GRIN3B,
TMEM259, CNN2, ABCA7,
ARHGAP45, POLR2E, GPX4,
SBNO2, STK11, CBARP, MIDN,
CIRBP, C7H19orf24, EFNA2,
MUM1, TRNAN-GUU, NDUFS7,
GAMT, DAZAP1, RPS15, APC2,
C7H19orf25, PCSK4, REEP6,
ADAMTSL5, PLK5, MEX3D,
LOC112447412, MBD3, UQCR11,
TCF3, ONECUT3, ATP8B3, REXO1,
LOC112447414, KLF16, ABHD17A,
SCAMP4, CSNK1G2, MIR6120,
BTBD2, SOWAHA, SHROOM1
131
Healthy v. Chronic 4 18655887- LOC112446490, NDUFA4, PHF14
18669066
5 13984552 none
1coordinates given using ARS UCD 1.2 bovine genome assembly
132
Supplementary Table 4.2. Significantly associated QTL (False Discovery Rate < 0.05) and
potential candidate genes for all genome-wide association models
Model Chromosome1 Position1 Candidate Genes
CMT2 0-1 DIM 4 97507560 TRNAS-GGA, EXOC4, MIR2423,
LOC11244642, LRGUK,
LOC101902920, SLC35B4
4 98076783 EXOC4, MIR2423, LOC11244642,
LRGUK, LOC101902920,
SLC35B4, TRNAC-ACA, AKR1B1,
LOC112446422, AKR1B10,
BPGM, CALD1
4 98245735 EXOC4, MIR2423, LOC11244642,
LRGUK, LOC101902920,
SLC35B4, TRNAC-ACA, AKR1B1,
LOC112446422, AKR1B10,
BPGM, CALD1, ABGL3,
LOC104972216
4 100313115 MTPN, TRNAQ-CUG, TRNAR-
GCG, MIR490, TRNAC-GCA,
CHRM2
4 100645568- CHRM2, MIR490, TRNAC-GCA
100688167
13 35930715 LYZL1, LOC112449396, BAMBI,
WAC, MPP7
SCS3 10-14 DIM 3 89806231 FYB2, LOC112446010, PRKAA2,
PLPP3, TRNAW-CCA
5 955228 LOC112446639, MIR2284Y-6,
TRNAC-GCA, TSPAN8, LGR5,
LOC112446921, ZFC3H1, THAP2,
TMEM19, RAB21, TBC1D15
6 30947112 ATOH1, GRID2, TRNAE-UUC,
LOC112447051
11 41419726 TRNAC-ACA, LOC107132926
11 42662341- LOC112448775
42673346
11 54859316- LOC112448941, CTNNA2,
55029984 LRRTM1
29 21528226- LOC112444900, CCDC179, SVIP
21556868
SCS3 50-60 DIM 1 65037783- MAATS1, NR1I2, GSK3B,
65045803 MIR6529B, MIR6529A,
LOC112447305, GPR156,
LOC101906506, LRRC58, FSTL1,
LOC112447306, NDUFB4, HGD,
RABL3, GTF2E1, LOC112448312
133
3 118976220- HDAC4, LOC112446043,
119034557 LOC101907486, LOC112446044,
NDUFA10, LOC618817,
LOC515090, LOC112445917,
LOC112445897, LOC112445898,
LOC112445899, LOC613418,
LOC782152, LOC782114,
LOC523582, LOC789457,
LOC112445900, LOC112445901,
LOC782190, LOC618660,
LOC782255, LOC615009,
LOC615000, LOC530175,
CSF2RA, LOC100336476,
LOC112446045, IL3RA,
LOC107131293, LOC112446046,
P2RY8
4 19709677 PHF14, THSD7A
26 30956828 XPNPEP1, ADD3,
LOC107131885, MXI1, SMNDC1,
LOC101905817, MIR2394,
DUSP5, LOC614416, SMC3,
RBM20, PDCD4, MIR6524
SCS3 210-230 DIM 4 22557323 ETV1, DGKB, TRNAC-GCA
4 118912645 UBE3C, LOC107131328,
LOC112446505, DNAJB6,
LOC104972306, PTPRN2,
MIR153-2, LOC112446464,
LOC112446465, NCAPG2
5 10674537- PTPRQ, LOC112446841, MYF6,
10679054 MYF5, LIN7A, ACSS3, PPFIA2
5 113340241- SREBF2, LOC112446779,
113358226 MIR33A, SHISA8, TNFRSF13C,
CENPM, LOC535121, SEPT3,
WBP2NL, NAGA, PHETA2,
SMDT1, NDUFA6, LOC785804,
MGC127055, CYP2014, TCF20,
MIR2442, LOC104976976,
LOC104972595, NFAM1,
LOC112446780, SERHL2,
TRNAG-CCC, RRP7A, POLDIP3,
CYB5R3, LOC104972597,
A4GALT, ARFGAP3, PACSIN2,
LOC101904078, TTLL1
6 4761931- NDNF, PRDM5, LOC112447177
4920678
134
7 95730312 ELL2, LOC107132664, PCSK1,
LOC104968993, CAST, ERAP1
7 95230312- TRNAE-UUC, SLCO4C1,
101060884 LOC104969005, SLCO6A1
11 66762518 C1D, LOC112448806, WDR92,
PNO1, PPP3R1, LOC112448807,
LOC112448808, CNRIP1, PLEK,
FBXO48, APLF, PROKR1,
LOC112448809, ARHGAP25,
BMP10, LOC509961
11 68023914 ANTXR1, GFPT1, NFU1, AAK1,
ANXA4, GMCL1, SNRNP27,
MXD1, ASPRV1, LOC101905499,
PCBP1, LOC107132938,
C11H2orf42, TIA1
11 69655025- CAPN13, LCLAT1,
71134648 LOC112448810, LOC112448811,
LBH, YPEL5, ALK, CLIP4,
C11H2orf71, TOGARAM2,
TRNAS-GGA, WDR43,
LOC112448919, LOC112448921,
TRMT61B, SPDYA, PPP1CB,
PLB1, FOSL2, LOC112448813,
BABAM2
11 75235530- ITSN2, FAM228A, FAM228B,
75240895 PFN4, TP53I3, LOC104973431,
SF3B6, FKBP1B, WDCP,
MFSD2B, UBXN2A,
LOC112448815, ATAD2B,
LOC112448816, KLHL29,
LOC112448817, LOC112448968,
LOC112448894
18 1700498- VSTM2B, LOC101902469,
1901923 TRNAG-GCC, VAC14,
LOC112442247, MTSS1L, IL34,
LOC112442474, SF3B3, MIR2324,
LOC112442441, LOC112442442,
COG4, FUK, ST3GAL2,
LOC112442248, DDX19A, AARS,
DDX19B, TRNAG-GCC,
CLEC18C, PDPR,
LOC101902851, GLG1, RFWD3,
MLKL, FA2H, WDR59, ZNRF1,
LOC112442249
135
18 15929792 NETO2, LOC112442279, ITFG1,
LOC112442458, PHKB
21 19070279- NTRK3, MRPL46, MRPS11, DET1
19072265
22 57501014 SYN2, LOC112443501, RBSN,
MRPS25, LOC101906319, NR2C2,
LOC101909196, FGD5,
C22H3orf20, CCDC174, GRIP2,
MIR2888-2, SLC6A6,
LOC101907298, LOC104975607
23 8617685 GRM4, HMGA1, LOC112443858,
NUDT3, LOC107131710, RPS10,
PACSIN1, SPDEF, C23H6orf106,
LOC112443899, SNRPC,
LOC112443807, UHRF1BP1,
TAF11, ANKS1A, TCP11
23 14982097- LRFN2, LOC112443815, TRNAI-
15014257 UAU, UNC5CL, APOBEC2,
OARD1, NFYA, LOC531747,
TREML1, TREM2, TREML2,
LOC112443770, LOC101902276,
LOC112443820, FOXP4,
LOC112443870, MDF1, TFEB
26 8041679 PRKG1, LOC112444473,
LOC112444474, A1CF
Median Lactation 2 107578011 NHEJ1, SLC23A3, CNPPD1,
SCS3 RETREG2, ABCB6, ZFAND2B,
ATG9A, ANKZF1, GLB1L, STK16,
TUBA4A, DNAJB2, PTPRN,
MIR153-1, RESP18, DNAEP, DES,
SPEG, GMPPA, ASIC4,
TMEM198, CHPF, OBSL1, INHA,
STK11IP, SLC4A3,
LOC112442385, LOC112443121
2 125909343 SMPDL3B, RPA2, THEMIS2,
PP1R8, LOC112443661, STX12,
FAM76A, IFI6, FGR, AHDC1,
WASF2, LOC112443702, GPR3,
CD164L2, FCN3, MAP3K6,
SYTL1, TMEM222, WDTC1,
TRNAE-UUC, SLC9A1, FAM46B,
TRNP1, KDF1, NUDC, NR0B2,
GPATCH3, GPN2
4 36570974- SEMA3A, LOC112446369,
36575407 LOC104972001, SEMA3E
136
4 62754393 NPSR1, LOC112446387,
LOC112446334, BMPER,
MIR1814C
6 47757210 none
6 52740136 TRNAC-GCA, LOC112447188
7 79412115 LOC112447478, LOC101905951,
TENM2
11 48421277 FABP1, SMYD1, KRCC1,
LOC104973374, CD8B, CD8A,
RMND5A, LOC112448876,
RNF103, CHMP3, KDM3A,
REEP1, LOC112448917, MRPL35,
IMMT, LOC112448917,
LOC112448932, PTCD3, POLR1A,
LOC112448788
15 17419841- LOC112441599, ALKBH8,
17422756 LOC112441676, ELMOD1, SLN,
LOC112441685, SLC35F2,
RAB39A, LOC112441607, CUL5,
ACAT1, NPAT, ATM
19 43386209 LOC112442876, SAO,
LOC100138645, G6PC,
LOC104975088, AARSD1,
RUNDC1, RPL27, IFI35, VAT1,
RND2, BRCA1, NBR1,
TMEM106A, LOC100335845,
LOC112442858, LOC104975091,
LOC112442859, LOC112442844,
LOC112442853, LOC112442846,
LOC112442860, LOC112442849,
LOC112442848, LOC112442845,
LOC112442857, LOC112442856,
LOC112442861, LOC112442850,
LOC112442851, LOC112442855,
LOC112442843, LOC112442854,
LOC112442847, LOC112442852,
ARL4D, DHX8, ETV4, MEOX1,
TRNAE-UUC, SOST, DUSP3,
CFAP97D1, MPP3, CD300LG,
MPP2, LOC788952, PPY, PYY,
PYY2, TMEM101, NAGS, LSM12
19 6334759 TMEM100, LOC112442789,
LOC513767, LOC781298,
LOC112442790, LOC112442592,
137
PCTP, LOC112442593,
LOC616574, LOC528282
20 26074612- FST, MOCS2, ITGA2, ITGA1,
26079633 PELO, LOC 112443001
25 18921923 ACSM3, ERI2, REXO5, MIR2384,
DCUN1D3, LYRM1, TRNAR-CCU,
DNAH3, TMEM159, ZP2,
ANKS4B, CRYM, LOC524391,
LOC786628
27 44648197- ZNF385D, LOC112444599,
44693405 TRNAW-CCA, LOC112444623,
TRNAG-GCC
28 16086213 ANK3, LOC101902425, TRNAG-
UUC, LOC112444794, CDK1
28 19095996 LOC112444734, LOC101905431,
ADO, EGR2, TRNAG-CCC,
NRBF2, JMJD1C, MIR1296
1coordinates given using ARS UCD 1.2 bovine genome assembly
2California Mastitis Test
3somatic cell score
138
CHAPTER 5: IMPLICATIONS FOR FUTURE RESEARCH AND CONCLUDING
REMARKS
The objective of this dissertation work was to use a multi-pronged approach to
understanding the genetics underlying mastitis resistance and susceptibility. This was achieved
by identifying cow conformation risk factors and performing genome-wise association (GWA)
studies to identify markers significantly associated with high risk udder and teat type traits.
Additionally, cows were longitudinally profiled for milk somatic cell count (SCC) at specific
stages in lactation and these measures used to categorize them into Healthy, Average, and
Chronic populations. Fixation indices (FST) by marker were calculated to determine allelic
differentiation among these populations, and GWA studies were performed for milk somatic
cell score (SCS) at each sampled stage in lactation. No significantly associated single nucleotide
polymorphisms (SNPs) were common across udder and teat and SCS GWA studies; 2 SNPs on
Bos taurus autosome (BTA) 4 were recurring in FST analysis.
In subsequent investigation of regions surrounding associated SNPs, 23 candidate genes
common to both SCC and udder and teat approaches were identified, of which FCN3 and SYTL1
have the most biologically plausible relevance to mastitis. Ficolin 3 (FCN3) is the primary
activator of the lectin pathway, binding to carbohydrates on pathogen surfaces and activating
MASP proteases to initiate the complement cascade (Hummelshoj et al., 2008). Primary FCN3
deficiency has been tentatively linked to increased susceptibility to infection in humans, has
known antimicrobial activity, and a modulatory role in host response to bacterial
lipopolysaccharides, which suggests FCN3 deficiencies may be particularly problematic in
cases of gram-negative bacterial infection (Fukutomi et al., 1996; Tsujimura et al., 2002;
139
Michalski et al., 2015a; Michalski et al., 2015b). Synaptotagmin Like 1 (SYTL1) is a known
effector of Rab27a, a protein differentially expressed in melanocytes, natural killer (NK) cells,
and cytotoxic T cells, and in which mutations have been demonstrated to disrupt granule
exocytosis (Gupta et al., 2019). In addition, SYTL1 has been shown to activate the
CXCL12/CXCR4 axis, which regulates neutrophil chemotaxis and progenitor cell homing to
the bone marrow or thymus, strongly implicating this gene in the promotion of inflammation
and adaptive immune response (Neumuller et al., 2009; Döring et al., 2014; Ramadass et al.,
2019). Both FCN3 and SYTL1 are strong candidates for explaining mastitis susceptibility, but
further validation is required to elucidate their roles in udder inflammation. Targeted
sequencing of these genes in our study cohort should be performed to determine whether
mutations are present in their DNA sequence and if these mutations elicit functional changes in
the gene. Their influence would be further supported by future expression analyses at the
message and protein level.
A wide range of genes related to the immune system were identified, from the innate
responses of neutrophil chemotaxis, complement pathway activation, and phagocytic cell
recruitment, to the adaptive response of lymphocyte maturation and homing. It is possible this
diverse function is reflective of the redundancy built into the immune system, and that all of
these pathways are in action during cases of mastitis. However, it must be noted that while this
dissertation was aimed to address deficits in current mastitis phenotyping, all traits considered
in this work are proxies for mastitis, and not direct measures of the disease. The reliance on
indicator traits for time and financial benefit, rather than more intensive phenotyping methods,
sacrifices the resolution of genetic studies and their ability to detect causal variants. This work
should be built upon by incorporating mastitis pathogen diagnosis into the approach. The
140
importance of pathogen diagnosis and targeted treatment has long been recognized, and on-
farm culture systems have long been used despite varied sensitivity and specificity reports
(McCarron et al., 2009; Ganda et al., 2016; Ferreira et al., 2018). While not yet practical on
farm, the incorporation of microbial sequencing techniques would allow the most precise
phenotyping of mastitis, and dissecting mastitis by specific etiology may clarify the genetic
mechanisms at play.
A gene ontology analysis performed via the Protein ANalysis THrough Evolutionary
Relationships (PANTHER) classification system revealed that of the 990 candidate genes
identified in this dissertation, only a handful were “core genes”, or obviously relevant protein-
coding genes relating to functions like immune response (Figure 5.1) (Thomas et al., 2003; Mi
et al., 2013). The small proportion of directly relevant genes may be explained by the recently
proposed “omnigenic model”, which posits that the genetic architecture of complex traits is
produced by a massive regulatory network of genes, each with very small effect (Boyle et al.,
2017). These core genes have a minimal impact compared to the “peripheral” genes which have
non-disease specific roles in regulating disease risk and exist in a much greater number. The
classical theory behind genetic association studies assumes that identifying core genes will
improve our understanding of the biology underlying disease; this is certainly true for
monogenic diseases, but may not hold for complex traits like mastitis. Part of this problem lies
in the incomplete annotation of genes, core or otherwise. A good example of this problem is
the limited functional annotation of individual synaptic genes – which does not reflect what is
known about the larger picture of synaptic biology (Lips et al., 2012; Wray et al., 2018). A
major tenet of the omnigenic model is that of universal pleiotropy, a hypothesis which suggests
that nearly all genes expressed in a cell type may have weak effects on disease expression, and
141
Figure 5.1. Gene Ontology summary across all genome wide association and fixation index
approaches. The number of genes identified were classified in PANTHER by biological
process (the function of the protein in the context of a larger network which accomplishes a
process at an organismal level).
142
that together these peripheral genes account for the majority of the heritability of the trait (Boyle
et al., 2017). The implications of this model are that we will need clearer understanding of
cellular networks to understand the genetic drivers of mastitis and produce accurate genetic
estimations of disease risk.
With high-density genotyping and the direct, researcher-controlled phenotyping of the
same animals, this work reduces the bias in existing studies of mastitis genetics. By utilizing a
multi-pronged approach and characterizing milk SCC as well as udder and teat morphology
on the same cows, we provide a more complete phenotypic characterization of mastitis. We
believe that with precise phenotyping we may refine markers for mastitis susceptibility to
smaller, more targeted regions of the genome and minimize overlap with quantitative trait loci
pleotropic for milk production traits, as selection for mastitis resistant cows without
sacrificing milk yield has been a major obstacle. The long term goal of this project is to
assemble a comprehensive marker panel for mastitis resistance for incorporation into national
genomic evaluation systems, and the application of these findings to selection strategies will
improve rates of genetic gain and help to reduce mastitis incidence in United States dairy
herds.
143
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