Zachary Zappala

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 mathematical expression is derived to compute PrediXcan results using summary data, and the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes are investigated.

TL;DR: It is demonstrated that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

TL;DR: 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.