Mathematics

Prof. Arunabha Majumdar
University of California, Los Angeles

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points during their development. For example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or through the control of fat storage by dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) to prioritize the tissue of interest underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize tissues of interest for the trait in the population, our approach probabilistically quantifies the tissue-specific genetic contribution to the trait for a given individual. We implement a variant of finite mixture of regression models based on a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Through simulations using the UK Biobank genotype data, we show that our approach can predict the relevant tissue of interest accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) in the UK Biobank to identify individuals who have their genetic contribution manifested through their brain versus adipose tissue. Notably, we find that the individuals with a particular tissue of interest have specific phenotypic features beyond BMI that distinguish them from random individuals in the data, demonstrating the role of tissue-specific genetic contribution for these traits.