Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Abstract

<jats:title>Abstract</jats:title><jats:p>Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple 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 that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.</jats:p>

Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

“…For example, Majumdar et al showed that tissue-specific eQTLs can be employed to generate tissue polygenic risk scores for complex traits. 54 Similarly, other groups have shown enrichments of complex traits for biologically relevant tissues by using colocalization or mediation approaches on eQTL data. 4,53,55 However, as a result of the inability of existing methods to fully evaluate allelic heterogeneity and LD, the extent of tissue specificity of eQTLs has not been previously fully explored or harnessed.…”

“…Several investigators have shied away from recommending the use of eQTL information to prioritize target tissues in GWAS, 42,50 citing the tissue-sharing of cis-eQTLs in a large fraction of trait associations as one reason. 51 Recent work by us 52 and others 4,53,54 has demonstrated the existence of tissue-specific eQTLs and shown potential for leveraging those eQTLs to understand broad patterns of tissue enrichment for human complex traits. For example, Majumdar et al showed that tissue-specific eQTLs can be employed to generate tissue polygenic risk scores for complex traits.…”

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

“…Variations of canonical correlation analysis (CCA) have characterized relationships between depressive symptoms and neuroimaging measures ( 58 ), and hierarchical clustering on resting-state fMRI measures have identified groups of depressed patients and their differential network dysfunctions ( 59 ), and machine learning methods have been used to cluster longitudinal responses to antidepressants to identify stable treatment response classes ( 60 ). In the future, these may be integrated with multi-omics for example, transcriptome-wide association (TWAS) approaches have begun to identify depression subtypes driven by brain and adipose tissue-specific gene expression ( 61 ).…”