Genome-wide association (GWA) studies have typically focused on the analysis of single
markers, which often lacks the power to uncover the relatively small effect sizes conferred by …

Genome-wide data sets are increasingly being used to identify biological pathways and
networks underlying complex diseases. In particular, analyzing genomic data through sets …

Both linear mixed models (LMMs) and sparse regression models are widely used in
genetics applications, including, recently, polygenic modeling in genome-wide association …

While GWAS identify many disease-associated SNPs, using them to decipher disease
mechanisms is hindered by the difficulty in mapping SNPs to genes. Most SNPs are in non …

Regression models are a form of supervised learning methods that are important for
machine learning, statistics, and general data science. Despite the fact that classical …

Recent studies have demonstrated that gene set analysis, which tests disease association
with genetic variants in a group of functionally related genes, is a promising approach for …

Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic
component, originally mapped to only the human leukocyte antigen (HLA) region. In the last …

The last decade of human genetic research witnessed the completion of hundreds of
genome-wide association studies (GWASs). However, the genetic variants discovered …

To maximize the potential of genome‐wide association studies, many researchers are
performing secondary analyses to identify sets of genes jointly associated with the trait of …

In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still
the most applied approach. However, the identified SNPs have small effects and provide …