Volume 49 Issue 12, December 2017

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.

A new study reports genome-wide variation in 163 vervet monkeys from across their taxonomic and geographic ranges. The analysis suggests a complex history of admixture and identifies signals of repeated evolutionary selection, some of which may be linked to response to simian immunodeficiency virus.

A new analysis of cancer genomes identifies a decrease in the mutation burden of exons, but not introns, as compared to expectation. This difference can be explained by preferential recruitment of the DNA mismatch repair machinery to a protein modification that marks exons.

The CRISPR–Cas9 system enables global screens of gene function with high sensitivity and specificity, but off-target effects have been reported for CRISPR guide RNAs targeting genes that are amplified at high copy number. A new study describes a computational approach to correct for this copy number effect, increasing the specificity of CRIPSR screens to identify essential genes.

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.

This analysis of cancer sequencing data identifies a reduced somatic mutation rate in exons and shows that this phenomenon is due to higher mismatch-repair activity in exons as compared to introns. These findings have implications for the understanding of mutational and DNA repair processes and for studying the evolution of both tumors and species.

Analysis of a large cohort of EGFR-mutant lung cancer cell-free DNA samples along with longitudinal samples from a patient with EGFR-mutant lung cancer identifies pathways that inhibit EGFR-inhibitor response. Co-occurring genetic alterations influence clinical outcomes and underscore the need for combination therapies.

Analysis of whole-genome sequencing data from 163 vervet monkeys from Africa and the Caribbean shows high diversity among taxa and identifies signatures of selection. Selection signals affect viral processes, and genes that show response to SIV in vervets but not macaques have elevated selection scores.

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.

A meta-analysis of exome-wide association studies for blood lipid levels in East Asian populations identifies a novel coding variant. Exome array data from the Global Lipids Genetics Consortium were integrated and led to the discovery of novel and population-specific variants associated with cholesterol and triglycerides.

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.

Analysis of a large bread-wheat genomic data set through a quantitative genetic framework designed to study the genetic basis of heterosis shows that hybrids outperform midparents in grain yield by 10%. Genome-wide prediction and association mapping indicate that epistasis plays a significant role in heterosis of grain yield in wheat.

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.

This large-scale genome-wide association analysis of subjects with asthma, hay fever or eczema provides insights into the shared genetic basis of these allergic diseases. The findings suggest that these diseases partly co-occur because they share many genetic risk variants that dysregulate the expression of immune-related genes.

Exome-wide genetic analysis on >300,000 individuals identifies associations with plasma lipid traits. Loci significantly associated with cholesterol and triglycerides are examined together to determine the effects of alleles on type 2 diabetes and coronary artery disease risk.

Roger Milne and colleagues conduct a genome-wide association study for estrogen receptor (ER)-negative breast cancer combined with BRCA1 mutation carriers in a large cohort. They identify ten new risk variants and find high genetic correlation between breast cancer risk for BRCA1 mutation carriers and risk of ER-negative breast cancer in the general population.

CERES is a new computational method to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for copy number effects and variable sgRNA activity. Applying CERES to new genome-scale CRISPR–Cas9 essentiality screen data from 342 cancer cell lines and other published data sets shows that CERES decreases false-positive results and provides consistent estimates of sgRNA activity.

This study presents a probabilistic framework for inferring negative and positive selection in human cancers that addresses the problem of mutation rate variation. Applying the model to sequencing data from 17 cancer types identifies new significantly mutated genes and detects significant signals of negative selection in many cancer types.

Covariates for multiphenotype studies (CMS), a new approach for testing for associations from large-scale datasets, leverages genetic and environmental factors shared between correlated variables measured on the same samples. Applying CMS to real and simulated data demonstrates a large increase in power equivalent to that gained by doubling the sample size.