Date of Award

2020

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Statistics (PhD)

Administrative Home Department

Department of Mathematical Sciences

Advisor 1

Shuanglin Zhang

Committee Member 1

Qiuying Sha

Committee Member 2

Kui Zhang

Committee Member 3

Jingfeng Jiang

Abstract

Polygenic risk scores (PRSs) is a method to summarize the additive trait variance captured by a set of SNPs and can increase the power of set-based analyses by leveraging public genome-wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We proposed a gene-level association test (GLAT) through polygenic risk scores (PRS). GLAT is a gene-based method adjusted for linkage disequilibrium (LD) between genetic variants in a region. The LD-adjusted WPRS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals' SNP allele counts with LD-adjusted dosages. Using a raw genotype dataset together with SNP effect sizes from a second independent dataset, GLAT can be used for set-based analysis.

LD-adjusted PRS has the advantages that it produces a risk score per person per set using all available SNPs, and aims to increase power by leveraging the effect sizes from the discovery set in a self-contained test of association in the test dataset. We applied GLAT to UK Biobank data set to test the association with the COPD phenotype and found the new significant genes associated with COPD that have not been discovered before.

Available for download on Saturday, June 26, 2021

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