Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Abstract

During the last decade, with the advent of inexpensive microarray and RNA-seq technologies, there have been many expression quantitative trait loci (eQTL) studies for identifying genetic variants called eQTLs that regulate gene expression. Discovering eQTLs has been increasingly important as they may elucidate the functional consequence of non-coding variants identified from genome-wide association studies. Recently, several eQTL studies such as the Genotype-Tissue Expression (GTEx) consortium have made a great effort to obtain gene expression from multiple tissues. One advantage of these multi-tissue eQTL datasets is that they may allow one to identify more eQTLs by combining information across multiple tissues. Although a few methods have been proposed for multi-tissue eQTL studies, they are often computationally intensive and may not achieve optimal power because they do not consider a biological insight that a genetic variant regulates gene expression similarly in related tissues. In this paper, we propose an efficient meta-analysis approach for identifying eQTLs from large multi-tissue eQTL datasets. We name our method RECOV because it uses a random effects (RE) meta-analysis with an explicit covariance (COV) term to model the correlation of effect that eQTLs have across tissues. Our approach is faster than the previous approaches and properly controls the false-positive rate. We apply our approach to the real multi-tissue eQTL dataset from GTEx that contains 44 tissues, and show that our approach detects more eQTLs and eGenes than previous approaches.

A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk

“…Previously, we developed a gene‐level association analysis approach named PrediXcan which applies elastic net to develop gene expression genetic prediction models 38 . Owing to the fact that approaches such as PrediXcan do not take into account the similarity of genetic regulation for genes across different human tissues, analysis becomes challenging when the effective number of relevant tissue samples is small 39,40 . To overcome this potential limitation in our study, we also leveraged two other modeling strategies, modified UTMOST and JTI.…”

“…38 Owing to the fact that approaches such as PrediXcan do not take into account the similarity of genetic regulation for genes across different human tissues, analysis becomes challenging when the effective number of relevant tissue samples is small. 39,40 To overcome this potential limitation in our study, we also leveraged two other modeling strategies, modified UTMOST and JTI. UTMOST is a powerful method to jointly analyze data from multiple genetically correlated tissues which has obvious advantages compared to many other methods.…”

Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions

“…Also because of alternative splicing, similar sequences can produce proteins with different functionality (Saha et al, 2017). Coexpression data work best with BP and CC ontologies (Song et al, 2014, Mazandu andMulder, 2014), but genes are expressed nonuniformly across different tissues (Duong et al, 2017). Depending on the data source, experiments using coexpression data can give highly varying outcomes.…”