Laura J. Scott

TL;DR: APEX as mentioned in this paper is an efficient toolkit for xQTL mapping and meta-analysis that provides highly optimized linear mixed models to account for relatedness and shared variation across molecular traits; rapid factor analysis to infer latent technical and biological variables from molecular trait data; fast and accurate trait-level omnibus tests that incorporate prior functional weights to increase statistical power.

TL;DR: In isolated populations that have expanded rapidly after a population bottleneck, deleterious alleles that passed through the bottleneck may be maintained at much higher frequencies than in other populations, and enriched alleles underlie 30 novel associations with 20 disease-related quantitative traits.

TL;DR: A general framework for testing pleiotropic effects of rare variants based on multivariate kernel regression (Multi-SKAT), which can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.

TL;DR: This corrects the article DOI: 10.1038/sdata.2017.179 to S Data 2017, which indicates that S Data was first published in 2017, not S Data 2016, which was originally published in 2016.

TL;DR: GAUS as discussed by the authors is a method for gene set association analysis that requires only GWAS summary statistics, which can identify the subset of genes that have the maximal evidence of association and can best account for the gene set-phenotype association.