The largest known collection of human genetic variants is reported in Nature this week, with applications of the resource described in a series of papers in Nature, Nature Communications, and Nature Medicine. To build the Genome Aggregation Database, or gnomAD, a team led by Broad Institute scientists aggregated 125,748 exomes and 15,708 genomes from human sequencing studies. After accounting for sequencing and annotation errors, they identify 443,769 high-confidence predicted loss-of-function variants in the collection and go on to classify human protein-coding genes along a spectrum that represents tolerance to inactivation. They validate their classification method using known gene sets and data from model organisms and show how it can be used to investigate human gene function and discover disease-related genes. In accompanying papers in Nature, gnomAD is used to help interpret human loss-of-function variants in the context of drug discovery; to build a structural variation reference for medical and population genetics; and to create a transcript-level annotation metric for rare variant interpretation. In Nature Communications, gnomAD is leveraged in a study of the loss-of-function impact of 5' untranslated region variants and in a study exploring the mutational mechanisms of multi-nucleotide variants. In Nature Medicine, the database is used to analyze loss-of-function variants in the gene LRRK2, which is associated with increased risk for Parkinson's disease. GenomeWeb has more on the gnomAD papers here.
A new method for Mendelian randomization analysis that accounts for problematic correlated and uncorrelated horizontal pleiotropic effects is described by University of Chicago scientists in Nature Genetics this week. Called Causal Analysis Using Summary Effect estimates, or CAUSE, the approach can avoid more false positives than other methods, its developers write. When applied to recent genome-wide association studies, CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships.