Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants
across many genomes to find those statistically associated with a specific trait or disease …

Recent technological advances have expanded the breadth of available omic data, from
whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic …

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture
effects of inherited variation (direct effects), demography (population stratification …

Estimates from Mendelian randomization studies of unrelated individuals can be biased due
to uncontrolled confounding from familial effects. Here we describe methods for within-family …

Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide
association studies (GWAS) and research in population genetics. However, the steady …

Several recent papers have reported strong signals of selection on European polygenic
height scores. These analyses used height effect estimates from the GIANT consortium and …

Background DNA methylation levels change with age. Recent studies have identified
biomarkers of chronological age based on DNA methylation levels. It is not yet known …

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait
associations (MTAs) using whole genome-wide variants for complex quantitative traits in …

Mixed linear model (MLM) methods have proven useful in controlling for population structure
and relatedness within genome-wide association studies. However, MLM-based methods …

Whole-genome association studies (WGAS) bring new computational, as well as analytic,
challenges to researchers. Many existing genetic-analysis tools are not designed to handle …