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Rare-Variant Approaches to Complex Traits Across Population Biobanks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Rare-Variant Approaches to Complex Traits Across Population Biobanks./
Author:	Venkataraman, Guhan Ram.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	109 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Medical records. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003847
ISBN:	9798209788720

Rare-Variant Approaches to Complex Traits Across Population Biobanks.
Venkataraman, Guhan Ram.

Rare-Variant Approaches to Complex Traits Across Population Biobanks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 109 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Complex diseases are a significant global burden, accounting for 70% of deaths in the U.S. annually. For example, 70,000 new cases of inflammatory bowel disease are diagnosed every year. Many such diseases have underlying genetic etiologies responsible for their pathology. Understanding their genetic basis could lead to more timely diagnosis and improved prognosis. Furthermore, human genetics presents an opportunity to identify new therapeutic targets. However, while much of the diseasecausative common variation is well-documented, our understanding of rare, disease-contributory variation is sparse, largely due to the lack of power (limited sample size) and ascertainment to detect such variation with accuracy. While the above goal of understanding rare variation is not achievable with small cohort (n ≤ 100,000) studies, population-level biobanks (n ~ 500,000) o↵er the ability to study this type of genetic variation. These datasets present a unique opportunity for rapid discovery of robustly-validated disease-causative variants because they possess large sample sizes, better population-level annotations, and next-generation sequencing technologies like whole-exome and -genome sequencing.This dissertation contains six Chapters. In Chapter 1, I introduce key terms and methodologies and then outline my contributions to the human genetics space that are a) key and b) ancillary to the dissertation. In the subsequent Chapters (2, 3, 4, and 5), I detail the main tenets of my research, which involve the application and development of methods for the study of rare variation in the genome.The HLA region in chromosome 6 of the genome, specifically, is a hyper-polymorphic source of rare variation and of much interest because of its involvement in autoimmune disorders and cancers. Chapter 2 explores the human leukocyte antigen (HLA) region in the UK Biobank, cataloging smalland large-e↵ect rare variations that explain additional heritability of complex diseases. In addition to single-allele association testing, we also perform the Bayesian Model Averaging technique for model selection, explore non-additive associations, and investigate the e↵ect of HLA homozygosity on phenotype [184].In Chapter 3, I introduce Multiple Rare-variants and Phenotypes (MRP), a novel, flexible, Bayesian framework for rare-variant signal aggregation across variants, studies, and phenotypes. I generate gene-based results across exome data for more than 2,000 traits in the UK Biobank, v compare these findings to the existing literature, and identify novel gene-phenotype associations. In addition, I explore the use of MRP in the multi-phenotype setting by grouping related sets of biomarkers; in this joint phenotype setting, we find several genes for which power gains were substantial. This work was submitted to the American Journal of Human Genetics in 2021 [182].As a result of performing gene-based tests, the interpretability of the e↵ect profile of individual variants on the single or multivariate phenotype is not easily characterized. Chapter 4 details a corollary method to MRP, the Multiple Rare-variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their e↵ects on a multivariate phenotype. I apply this method, as in MRP, across all single traits in the UK Biobank as well as lipid-related and renal-related multivariate phenotypes. This work was submitted to PLOS Genetics in 2021 [181].While the previous Chapters focus on applying methods across population biobanks, I also leverage targeted studies to perform detailed analysis of single phenotypes. In Chapter 5, I, along with collaborators from the International Inflammatory Bowel Disease Genetics Consortium, identify rare coding variants newly associated with Crohn's disease using a mixed-model approach with a software called SAIGE. We submitted this work to Nature Genetics in 2021 [158].I conclude in Chapter 6 with a summary of the value in studying rare variation in the genome, the takeaways from my research, and the areas in which future research should go.

ISBN: 9798209788720Subjects--Topical Terms:

927772
Medical records.

Rare-Variant Approaches to Complex Traits Across Population Biobanks.
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520 $a Complex diseases are a significant global burden, accounting for 70% of deaths in the U.S. annually. For example, 70,000 new cases of inflammatory bowel disease are diagnosed every year. Many such diseases have underlying genetic etiologies responsible for their pathology. Understanding their genetic basis could lead to more timely diagnosis and improved prognosis. Furthermore, human genetics presents an opportunity to identify new therapeutic targets. However, while much of the diseasecausative common variation is well-documented, our understanding of rare, disease-contributory variation is sparse, largely due to the lack of power (limited sample size) and ascertainment to detect such variation with accuracy. While the above goal of understanding rare variation is not achievable with small cohort (n ≤ 100,000) studies, population-level biobanks (n ~ 500,000) o↵er the ability to study this type of genetic variation. These datasets present a unique opportunity for rapid discovery of robustly-validated disease-causative variants because they possess large sample sizes, better population-level annotations, and next-generation sequencing technologies like whole-exome and -genome sequencing.This dissertation contains six Chapters. In Chapter 1, I introduce key terms and methodologies and then outline my contributions to the human genetics space that are a) key and b) ancillary to the dissertation. In the subsequent Chapters (2, 3, 4, and 5), I detail the main tenets of my research, which involve the application and development of methods for the study of rare variation in the genome.The HLA region in chromosome 6 of the genome, specifically, is a hyper-polymorphic source of rare variation and of much interest because of its involvement in autoimmune disorders and cancers. Chapter 2 explores the human leukocyte antigen (HLA) region in the UK Biobank, cataloging smalland large-e↵ect rare variations that explain additional heritability of complex diseases. In addition to single-allele association testing, we also perform the Bayesian Model Averaging technique for model selection, explore non-additive associations, and investigate the e↵ect of HLA homozygosity on phenotype [184].In Chapter 3, I introduce Multiple Rare-variants and Phenotypes (MRP), a novel, flexible, Bayesian framework for rare-variant signal aggregation across variants, studies, and phenotypes. I generate gene-based results across exome data for more than 2,000 traits in the UK Biobank, v compare these findings to the existing literature, and identify novel gene-phenotype associations. In addition, I explore the use of MRP in the multi-phenotype setting by grouping related sets of biomarkers; in this joint phenotype setting, we find several genes for which power gains were substantial. This work was submitted to the American Journal of Human Genetics in 2021 [182].As a result of performing gene-based tests, the interpretability of the e↵ect profile of individual variants on the single or multivariate phenotype is not easily characterized. Chapter 4 details a corollary method to MRP, the Multiple Rare-variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their e↵ects on a multivariate phenotype. I apply this method, as in MRP, across all single traits in the UK Biobank as well as lipid-related and renal-related multivariate phenotypes. This work was submitted to PLOS Genetics in 2021 [181].While the previous Chapters focus on applying methods across population biobanks, I also leverage targeted studies to perform detailed analysis of single phenotypes. In Chapter 5, I, along with collaborators from the International Inflammatory Bowel Disease Genetics Consortium, identify rare coding variants newly associated with Crohn's disease using a mixed-model approach with a software called SAIGE. We submitted this work to Nature Genetics in 2021 [158].I conclude in Chapter 6 with a summary of the value in studying rare variation in the genome, the takeaways from my research, and the areas in which future research should go.
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