Metrics Of Genetic Relatedness In Applications Of Human Genomics
| dc.contributor.author | Hunter-Zinck, Haley | en_US |
| dc.contributor.chair | Clark, Andrew | en_US |
| dc.contributor.committeeMember | Keinan, Alon | en_US |
| dc.contributor.committeeMember | Mezey, Jason G. | en_US |
| dc.contributor.committeeMember | Siepel, Adam Charles | en_US |
| dc.date.accessioned | 2015-01-07T20:57:20Z | |
| dc.date.available | 2019-08-19T06:01:19Z | |
| dc.date.issued | 2014-08-18 | en_US |
| dc.description.abstract | Measuring genetic relatedness is fundamental to many applications of human genomics. Genetic relatedness can be defined in several different ways ranging from global, genome-wide estimations to confined, locus-specific measurements. Local relatedness is often represented as identity-by-descent (IBD), but IBD is an approximation of time to most recent common ancestor (TMRCA), and using TMRCA directly has the potential to be a more informative metric. Applications using metrics of genetic relatedness in human genomics include diverse topics as inbreeding and relatedness measurements, population structure, demographic history, effective population size estimates, haplotype phasing, genome-wide association studies, natural selection inference, and many others. In this dissertation, I will describe three projects using metrics of human relatedness in three different applications. I will first give a general overview of the definition and use of metrics of genetics relatedness and set the context in applications in the field of human genomics. I will then describe a project using IBD to look at population substructure among a sample of Qatari individuals. In the subsequent two chapters I will move on to using TMRCA to develop more general methods of natural selection inference and association mapping. The last two chapters provide evidence that TMRCA provides a more informative and unifying metric than IBD for two different applications in human genomics. IBD was a metric of choice because of necessity. However, with the production of high coverage whole-genome sequences and advancement of computational methodology, using TMRCA as a metric of genetic relatedness is now feasible, providing an avenue to further biological insights via this more informative metric. | en_US |
| dc.identifier.other | bibid: 8793333 | |
| dc.identifier.uri | https://hdl.handle.net/1813/38825 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Time to most recent common ancestor | en_US |
| dc.subject | Natural Selection | en_US |
| dc.subject | Genome-wide association studies | en_US |
| dc.title | Metrics Of Genetic Relatedness In Applications Of Human Genomics | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Computational Biology | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Computational Biology |
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