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Quantitative Trait Variation and Adaptation in Contemporary Humans
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Quantitative Trait Variation and Adaptation in Contemporary Humans. |
| 개인저자 | Mostafavi, Hakhamanesh. |
| 단체저자명 | Columbia University. Biological Sciences. |
| 발행사항 | [S.l.]: Columbia University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 195 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085621540 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Przeworski, Molly. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Human genomic data sets are now reaching sample sizes on the order of hundreds of thousands and soon exceeding millions, providing unprecedented opportunities to understand human evolution. Most studies of human adaptation so far have focused on selection that has acted over the past million to few thousand years. However, powered by large data sets, it is now feasible to study allele frequency changes that occur within the short timescale of a few generations, directly observing selection acting in contemporary humans. I take this approach in the work presented in Chapter 1 of this thesis, where we performed a genome-wide scan to identify a set of genetic variants that influence age-specific mortality in present-day samples. Our findings include two variants in the APOE and CHRNA3 loci, as well as sets of variants contributing to a number of traits, including coronary artery disease and cholesterol levels, and intriguingly, to timing of puberty and child birth. New research directions have also opened up with the advent of large-scale genome-wide association studies (GWAS), which have begun to uncover genetic variants underlying a number of human traits, ranging from disease susceptibility to social and behavioral traits such as educational attainment and neuroticism. One such direction is the use of polygenic scores (PGS), which aggregate GWAS findings into one score as a measure of genetic propensity for traits, for phenotypic prediction. A major obstacle to this application is that the prediction accuracy of PGS drops in samples that have a different genetic ancestry than the GWAS sample. Our work, presented in Chapter 2, demonstrates that PGS prediction accuracy is also variable within genetic ancestries depending on factors such as age, sex, and socioeconomic status, as well as GWAS study design. These findings have important implications for the increasing use of these measures in diverse disciplines such as social sciences and human genetics. |
| 일반주제명 | Biology. Genetics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
