The Genotype-Tissue Expression project

A long-planned effort to examine gene expression and gene regulation in all the major tissues in the human body across many people comes to fruition.

A collection of papers catalogues the associations between genetic variation and gene expression in healthy tissues – the largest analysis of this kind so far. See Article p.204 & Letters p.239, p.244 & p.249

Project obtains tissues from recently deceased individuals to look for the origins of disease.

Genetic variants have been associated with myriad molecular phenotypes that provide new insight into the range of mechanisms underlying genetic traits and diseases. Identifying any particular genetic variant's cascade of effects, from molecule to individual, requires assaying multiple layers of molecular complexity. We introduce the Enhancing GTEx (eGTEx) project that extends the GTEx project to combine gene expression with additional intermediate molecular measurements on the same tissues to provide a resource for studying how genetic differences cascade through molecular phenotypes to impact human health.

Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues.

The authors show that rare genetic variants contribute to large gene expression changes across diverse human tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Multiple transcriptome approaches, including single-cell sequencing, demonstrate that escape from X chromosome inactivation is widespread and occasionally variable between cells, chromosomes, and tissues, resulting in sex-biased expression of at least 60 genes and potentially contributing to sex-specific differences in health and disease.

Using the GTEx data and others, a comprehensive analysis of adenosine-to-inosine RNA editing in mammals is presented; targets of the various ADAR enzymes are identified, as are several potential regulators of editing, such as AIMP2.

Ira Hall, Donald Conrad, the GTEx consortium and colleagues identify 23,602 high-confidence structural variants (SVs) and 24,884 cis expression quantitative trait loci (eQTLs) across 13 human tissues. They estimate that SVs are the causal variant at 3.5–6.8% of eQTLs and identify 789 SVs predicted to directly alter gene expression, most of which are noncoding variants in regulatory elements.

This study presents a new approach to estimate the tissues contributing to the genetic causality for complex traits and diseases. The method assesses tissue sharing of eQTLs among 44 tissues and then uses these tissue-sharing estimates to infer the tissues where trait-associated variants likely exert their function.

CaVEMaN is a new method that uses whole-genome sequencing and RNA-sequencing data to implicate likely causal variants affecting gene expression. The set of high-confidence causal variants found in multiple tissues is enriched for variants associated with complex traits.

RNA Capture Long Seq (CLS) is a new method for transcript annotation that combines targeted RNA capture with long-read sequencing. CLS reannotates GENCODE lncRNAs and increases the number of validated splice junctions and transcript models for targeted loci.

Nelson Freimer and colleagues analyze gene expression data from multiple tissue samples combined with genotype data from vervet monkeys to catalog expression quantitative trait loci (eQTLs). They generate a transcriptome resource analogous to the GTEx project and perform comparative and eQTL enrichment analyses for various traits.

Analysis of GTEx, cancer and autism data sets shows that cis-regulatory variation can modify the penetrance of coding variants. Deleterious coding variants on regulatory haplotypes resulting in high expression are enriched in disease cohorts and selected against in general populations.

Integration of expression quantitative trait locus (eQTL) data from the Genotype-Tissue Expression project with genome-wide association study data shows that eQTLs are enriched for trait associations in disease-relevant tissues.

RNA levels in post-mortem tissue can differ greatly from those before death. Studying the effect of post-mortem interval on the transcriptome in 36 human tissues, Ferreira et al. find that the response to death is largely tissue-specific and develop a model to predict time since death based on RNA data.

Hae Kyung Im and colleagues report a method for predicting gene expression perturbations from genotype data after training on reference transcriptome data sets. Association of predicted gene expression with disease traits identifies known and new candidate disease genes.

Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

Multivariate adaptive shrinkage (mash) is a method for estimating and testing multiple effects in multiple conditions. When applied to GTEx data, mash can be used to analyze sharing of eQTL effects by examining variation in effect sizes.

The EAGLE algorithm and software identifies replicable gene-by-environment interactions based on associations between environment and allele-specific expression.

DroNc-seq enables low-cost, high-throughput single-nucleus RNA-seq of tissues that are archived or difficult to dissociate, such as post-mortem human brain.

Genome interpretation and analysis of allelic activity requires appropriate haplotype phasing. Here the authors present phASER, a fast and accurate method for variant phrasing from RNA-seq and genome sequencing data.

A new set of functional annotations based on fine-mapped molecular quantitative trait loci from GTEx and BLUEPRINT consortium data are enriched for disease heritability across 41 diseases and complex traits.

A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.