Ruchi Munshi

TL;DR: A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.

TL;DR: Using an improved human mutation rate model, human protein-coding genes are classified along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

TL;DR: A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.

TL;DR: A reference atlas of SVs from deep whole-genome sequencing of 14,891 individuals across diverse global populations as a component of gnomAD is constructed, finding strong correlations between constraint against predicted loss-of-function (pLoF) SNVs and rare SVs that both disrupt and duplicate protein-coding genes.