Identifying tissue specific transcriptomic effects on brain volume measures from GWAS summary data (Abstract)

Published in Organization for Human Brain Mapping, 2021

Citation: Hung Mai, Jingxuan Bao, Paul Thompson, Dokyoon Kim, Li Shen (2021). "Identifying tissue specific transcriptomic effects on brain volume measures from GWAS summary data." Organization for Human Brain Mapping 2021.

Introduction

Genome-wide association studies (GWAS) of brain imaging phenotypes have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms (SNPs) and structural and functional traits in the brain. However, it is unclear how these genetic variations influence the regional gene expression levels which may subsequently lead to phenotypic changes in the brain. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. The method can be used to integrate the GWAS summary statistics of an imaging trait with the PrediXcan models linking SNPs to gene expressions in a specific brain tissue, and to detect genes whose expression levels have mediating effects on the imaging trait. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics biobanks, UK Biobank (UKB) and Enhancing Neuroimaging Genetics through Meta Analysis (ENIGMA), to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV).

Abstract

Hung Mai, Jingxuan Bao, Paul Thompson, Dokyoon Kim, Li Shen (2021). "Identifying tissue specific transcriptomic effects on brain volume measures from GWAS summary data." Organization for Human Brain Mapping 2021.