Genome-wide association studies (GWAS) identify genetic variants that distinguish a control
population from a population with a specific trait. Two challenges in GWAS are:(1) …

Genome-wide association studies (GWAS) have identified thousands of genetic variants
associated with human complex traits. However, the genes or functional DNA elements …

The accurate mapping of causal variants in genome-wide association studies requires the
consideration of both, confounding factors (for example, population structure) and nonlinear …

Large-scale association studies hold substantial promise for unraveling the genetic basis of
common human diseases. A well-known problem with such studies is the presence of …

Population stratification continues to bias the results of genome-wide association studies
(GWAS). When these results are used to construct polygenic scores, even subtle biases can …

Identifying genetic correlations between complex traits and diseases can provide useful
etiological insights and help prioritize likely causal relationships. The major challenges …

Despite the success of genome‐wide association studies, much of the genetic contribution
to complex human traits is still unexplained. One potential source of genetic variation that …

The availability of genome-wide genetic data on hundreds of thousands of people has led to
an equally rapid growth in methodologies available to analyse these data. While the …

Replication has become the gold standard for assessing statistical results from genome-
wide association studies. Unfortunately this replication requirement may cause real genetic …

We performed a scan for genetic variants associated with multiple phenotypes by comparing
large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 …