Orthogonal approaches for surveying genetic variation and its consequences
| dc.contributor.author | Schlamp, Maria Florencia | |
| dc.contributor.chair | Clark, Andrew | |
| dc.contributor.committeeMember | Wolfner, Mariana Federica | |
| dc.contributor.committeeMember | Messer, Philipp | |
| dc.date.accessioned | 2019-10-15T16:48:29Z | |
| dc.date.available | 2019-10-15T16:48:29Z | |
| dc.date.issued | 2019-08-30 | |
| dc.description | Supplemental file(s) description: Supplemental Figures and Supplemental Material, Supplemental Tables | |
| dc.description.abstract | Our morphological traits, responses to stimuli, and the composition of our microbiomes are all phenotypic adaptations influenced by the genetic variation that defines us. Understanding this multimodal network of relationships requires the analysis of a multitude of orthogonal biological systems. Tailoring our approach to the individual biological outputs and systems allows us to reach a deeper understanding of the evolution, regulation, and interactions among biological processes. When available, we can use genomic data from large populations to establish links between genetic variation and phenotypic adaptation. For instance, positive selection can be inferred from variation computationally and statistically via evidence of selective sweeps. In Chapter 2, I evaluate eight selection scans to detect selective sweeps in domestic dogs, a population with well-documented selection pressures imposed by human preferences for specific morphologies and other traits. Pathogen-driven selective pressures modulate adaptation in the immune response, because hosts must keep up in the host-pathogen arms race. The high energetic cost of mounting an immune response reduces resource availability to other physiological processes. To explore these trade-offs, in Chapter 3 I profile the transcription dynamics of the Drosophila melanogaster innate immune response in a dense time course and I apply a broad range of statistical methods, including temporal clustering, gene set expression analysis, and Granger causality to construct putative gene interactions networks. The interaction of hosts with mutualistic symbionts can drive genetic adaptation in hosts through mutually-beneficial processes. In humans, the gut microbiome provides a wealth of symbiotic interactions. To address whether this mutualistic relationship drives host adaptation, in Chapter 4 I study the influence of host genetics on microbiome composition by performing high-resolution QTL mapping to identify genetic variation in Diversity Outbred mice significantly associated with specific bacterial abundances. This thesis presents three orthogonal approaches for surveying genetic variation and its consequences, using a combination of data collected through three sequencing methods: population genomic data using genotyping, global transcriptome dynamics using RNA-sequencing, and microbiome composition using 16S rRNA gene sequencing. | |
| dc.identifier.doi | https://doi.org/10.7298/h9t7-tn77 | |
| dc.identifier.other | Schlamp_cornellgrad_0058F_11584 | |
| dc.identifier.other | http://dissertations.umi.com/cornellgrad:11584 | |
| dc.identifier.other | bibid: 11050565 | |
| dc.identifier.uri | https://hdl.handle.net/1813/67582 | |
| dc.language.iso | en_US | |
| dc.subject | RNA-Seq | |
| dc.subject | Biology | |
| dc.subject | Innate immunity | |
| dc.subject | Genetics | |
| dc.subject | Selection scans | |
| dc.subject | Timecourse | |
| dc.subject | Drosophila melanogaster | |
| dc.subject | Microbiome | |
| dc.subject | Bioinformatics | |
| dc.title | Orthogonal approaches for surveying genetic variation and its consequences | |
| dc.type | dissertation or thesis | |
| dcterms.license | https://hdl.handle.net/1813/59810 | |
| thesis.degree.discipline | Genetics, Genomics and Development | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph.D., Genetics, Genomics and Development |
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