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Here the authors conduct a multi-ancestry meta-analysis of telomere length, used diverse approaches to identify genes underlying association signals, and experimentally validated POP5 and KBTBD6 as regulators of telomere length in human cells.

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.

A new open-access RNA sequencing dataset, MAGE, of 731 individuals across geographically diverse human populations provides a valuable resource to study genetic diversity and evolution and expands the capacity to identify new genetic associations.

Jonathan Pritchard, Christopher Garcia and colleagues examine associations between different T cell receptor V genes and MHC alleles by eQTL mapping. They find that there are strong associations between MHC variation and T cell receptor gene usage and map these signals to specific MHC amino acids, many of which physically interact with germline-encoded amino acids on the T cell receptor.

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.

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.

Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.

Analyses of expression profiles from whole blood of 31,684 individuals identify cis-expression quantitative trait loci (eQTL) effects for 88% of genes and trans-eQTL effects for 37% of trait-associated variants.

Large-scale genetic association studies have identified many trait-associated variants that influence gene expression. Here, the authors present ARCHIE, a tool for identifying sets of genes whose regulation may be related to specific complex traits.

Here, the authors develop fastASSET, a method for efficient detection of variant-level pleiotropic association across many traits. Using this method, they characterize genome-wide pleiotropy and links to genomic features, identifying 21 trait-specific SNPs.

This Review explores the relationship between emerging infectious diseases and biodiversity loss, and how both are connected to global environmental changes in the Anthropocene.

Ordinary baryonic particles account for only one-sixth of the total matter in the Universe, the rest being the mysterious 'dark matter'. This paper presents high-fidelity maps of the large-scale distribution of dark matter, resolved in both angle and depth. The results are consistent with predictions of gravitationally induced structure formation.

Heart failure has a heterogeneous etiology and the genetic underpinnings are not well understood. Here, Arvanitis et al. perform GWAS meta-analysis including 10,976 heart failure cases and 437,573 controls, identify new loci near ABO and ACTN2 and show that deletion of a ACTN2 enhancer leads to reduced ACTN2 expression in differentiating cardiomyocytes.

An optimized pipeline for improved inference and analysis of structural variants (SVs) has been developed, which uses Iris for refining breakpoints and sequences, and Jasmine for comparing SV calls at population scale.

Here the authors develop DAESC, a statistical method for differential allele-specific expression analysis using single-cell RNA-seq data. Application of DAESC identifies dynamic regulatory effects along endoderm differentiation and differential effects between type 2 diabetes and healthy controls.

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

A genetic study identifies hundreds of loci associated with risk tolerance and risky behaviors, finds evidence of substantial shared genetic influences across these phenotypes, and implicates genes involved in neurotransmission.

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.

Francesco Cucca, Stephen Montgomery and colleagues identify regulatory variants that influence gene expression and splicing using whole-genome and transcriptome sequence data from 624 Sardinians. They find a high frequency of splicing and expression quantitative trait loci near genes involved in malarial resistance and multiple sclerosis.

This genome-wide study of age at first sexual intercourse and first birth identifies 371 signals driven by reproductive biology, externalising behaviour and environmental effects, with later onset associated with lower incidence of some diseases.

Many genetic variants identified in genome-wide association studies are associated with gene expression. Here, Porcu et al. propose a transcriptome-wide summary statistics-based Mendelian randomization approach (TWMR) that, applied to 43 human traits, uncovers hundreds of previously unreported gene–trait associations.

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.

To comprehend the genetic regulatory mechanisms underlying brain-related traits in humans, Qi et al. estimate the correlation of expression and DNA methylation QTL effects in cis between blood and brain and show that using blood eQTL/mQTL data of large sample size  can increase power in gene discovery for brain-related traits and diseases.

GWAS have so far identified 129 loci associated with type 2 diabetes (T2D). Here, the authors meta-analyse three large T2D GWA studies which uncovers 42 additional loci, further prioritize 33 functional genes using eQTL and mQTL data and propose regulatory mechanisms for three putative T2D genes.

The authors construct brain-wide coexpression networks to characterize regional versus global features, determine if disease susceptibility maps onto regional or brain-wide processes and assess how these networks capture genetic models of disease risk.

The Impact of Genomic Variation on Function Consortium is combining single-cell mapping, genomic perturbations and predictive modelling to investigate relationships between human genomic variation, genome function and phenotypes and will provide an open resource to the community.

A diagnostic tool based on blood RNA-seq is shown to identify causal genes and variants linked to clinical phenotypes in individuals with rare diseases for which whole-exome genetic sequencing was uninformative.

The authors transcriptionally profiled postmortem retinas from 453 age-related macular degeneration (AMD) cases and controls. Integration of AMD GWAS with eQTL analysis and TWAS identified several AMD-associated genes.

Molecular quantitative trait locus (molQTL) mapping associates genetic variation with molecular traits that can be measured as gene expression, splicing and chromatin accessibility. In this Primer, Aguet et al. discuss the study design and implementation of molQTL mapping in various applications, with a focus on technical developments for functional characterization.

A community of researchers report the lessons learned from applying 44 algorithms to predict drug sensitivity in cancer cell lines using genomic, epigenetic and proteomic datasets