Leland Barnard

TL;DR: The release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants, illustrating the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes and demonstrating the value of genome sequencing in large population-based studies.

TL;DR: RegenerIE as mentioned in this paper is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.

TL;DR: A novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency, is presented.

TL;DR: In this paper, the authors report results from a genome-wide association studies (GWAS) meta-analysis of heart failure comprising 47,309 cases and 930,014 controls.

TL;DR: The first tranche of large-scale exome sequence data for 49,960 study participants is described, revealing approximately 4 million coding variants and 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants.