Understanding the genetics underlying mastitis using a multi-pronged approach

Abstract

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.