|
Activity Number:
|
70
- Statistical Advances for Multi-Omics Data of Complex Diseases
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, August 9, 2021 : 10:00 AM to 11:50 AM
|
|
Sponsor:
|
Section on Statistics in Genomics and Genetics
|
|
Abstract #316866
|
|
|
Title:
|
Integrating GWAS and multi-omics QTL summary statistics to elucidate disease genetic mechanisms via a hierarchical low-rank model
|
|
Author(s):
|
Lin Chen* and Yihao Lu
|
|
Companies:
|
University of Chicago and University of Chicago
|
|
Keywords:
|
GWAS;
expression quantitative-trait-locus (eQTL);
methylation QTL
|
|
Abstract:
|
In the post-GWAS era, evidences suggested that many of trait-associated SNPs affect complex traits/diseases through their effects on expression levels and other omics traits. Extensive evaluations of genetic effects on omics traits have revealed an abundance of quantitative trait loci for omics traits (omics QTLs). With the availability of rich resources on GWAS and omics QTL summary statistics from different omics data types and different tissue types, in this work we propose an integrative methods for jointly analyzing GWAS and multiple sets of omics QTL summary statistics accounting for the hierarchical structure underlying omics QTLs. We propose an integrative analysis method that model the hierarchical low-rank structure of the latent association status between SNPs and tissue types for various omics data types. The proposed method was motivated by and was applied to analyses of multi-tissue eQTL and methylation QTL statistics from the Genotype-Tissue Expression (V8) project.
|
Authors who are presenting talks have a * after their name.
