Both polygenicity (many small genetic effects) and confounding biases, such as cryptic
relatedness and population stratification, can yield an inflated distribution of test statistics in …

Population structure, including population stratification and cryptic relatedness, can cause
spurious associations in genome-wide association studies (GWAS). Usually, the scaled …

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic
effects: the causal effects on an individual's phenotype of the alleles that they carry …

To infer that a single‐nucleotide polymorphism (SNP) either affects a phenotype or is
linkage disequilibrium with a causal site, we must have some assurance that any SNP …

Aggregate results from genome-wide association studies (GWAS),,, such as genotype
frequencies for cases and controls, were until recently often made available on public …

When testing genotype–phenotype associations using linear regression, departure of the
trait distribution from normality can impact both Type I error rate control and statistical power …

The potential of genome‐wide association analysis can only be realized when they have
power to detect signals despite the detrimental effect of multiple testing on power. We …

We have derived a versatile gene-based test for genome-wide association studies (GWAS).
Our approach, called VEGAS (versatile gene-based association study), is applicable to all …

Publicly available genome-wide association studies (GWAS) summary statistics exhibit
uneven quality, which can impact the validity of follow-up analyses. First, we present an …

The variance component tests used in genome-wide association studies (GWAS) including
large sample sizes become computationally exhaustive when the number of genetic …