Biology subjects

The GTEx Consortium,

Publications and source records attributed to The GTEx Consortium,.

4 recordsLinked to original sources

bioRxiv2019

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Large-scale genomic and transcriptomic initiatives offer unprecedented ability to study the biology of complex traits and identify target genes for precision prevention or therapy. Translation to clinical contexts, however, has been slow and challenging due to lack of biological context for identified variant-level associations. Moreover, many translational researchers lack the computational or analytic infrastructures required to fully use these resources. We integrate genome-wide association study (GWAS) summary statistics from multiple publicly available sources and data from Genotype-Tissue Expression (GTEx) v8 using PrediXcan and provide a user-friendly platform for translational researchers based on state-of-the-art algorithms. We develop a novel Bayesian colocalization method, fastENLOC, to prioritize the most likely causal gene-trait associations. Our resource, PhenomeXcan, synthesizes 8.87 million variants from GWAS on 4,091 traits with transcriptome regulation data from 49 tissues in GTEx v8 into an innovative, gene-based resource including 22,255 genes. Across the entire genome/phenome space, we find 65,603 significant associations (Bonferroni-corrected p-value of 5.5 x 10-10), where 19,579 (29.8 percent) were colocalized (locus regional colocalization probability > 0.1). We successfully replicate associations from PheWAS Catalog (AUC=0.61) and OMIM (AUC=0.64). We provide examples of (a) finding novel and underreported genome-to-phenome associations, (b) exploring complex gene-trait clusters within PhenomeXcan, (c) studying phenome-to-phenome relationships between common and rare diseases via further integration of PhenomeXcan with ClinVar, and (d) evaluating potential therapeutic targets. PhenomeXcan (phenomexcan.org) broadens access to complex genomic and transcriptomic data and empowers translational researchers. One-Sentence SummaryPhenomeXcan is a gene-based resource of gene-trait associations with biological context that supports translational research.

genetics↗

bioRxiv2019

Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

Transcriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

genetics↗

bioRxiv2019

CCmed: cross-condition mediation analysis for identifying robust trans-eQTLs and assessing their effects on human traits

Trans-eQTLs collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are individually weak. Many trans-effects are mediated by cis-gene expression and some of those effects are shared across tissue types/conditions. To detect robust cis-mediated trans-associations at the gene-level and for specific single nucleotide polymorphisms (SNPs), we proposed two Cross-Condition Mediation methods - CCmedgene and CCmedGWAS, respectively. We analyzed data from 13 brain tissue types from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-eQTLs of a cis-gene having associations with a trans-gene, many of which show evidence of replication in other datasets. By applying CCmedGWAS, we identified trans-genes associated with known schizophrenia susceptibility loci. We further conducted validation analyses assessing the schizophrenia-risk-associations of the identified trans-genes, by harnessing GWAS summary statistics from the Psychiatric Genomics Consortium and multitissue eQTL statistics from GTEx.

genetics↗

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗