Polygenic risk scores have shown great promise in predicting complex disease risk and will
become more accurate as training sample sizes increase. The standard approach for …

Large-scale genome-wide association results are typically obtained from a fixed-effects
meta-analysis of GWAS summary statistics from multiple studies spanning different regions …

Compared to univariate analysis of genome-wide association (GWA) studies, machine
learning–based models have been shown to provide improved means of learning such …

Genome-wide association studies (GWAS) have rapidly spread across the globe over the
last few years becoming the de facto approach to identify candidate regions associated with …

Epigenetics is the study of heritable change other than those encoded in DNA sequence.
Cytosine methylation of DNA at CpG dinucleotides is the most well-studied epigenetic …

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic
component of gene expression variation. A major challenge in the analysis of such studies …

We show that epigenome-and transcriptome-wide association studies (EWAS and TWAS)
are prone to significant inflation and bias of test statistics, an unrecognized phenomenon …

Background The prediction of the genetic disease risk of an individual is a powerful public
health tool. While predicting risk has been successful in diseases which follow simple …

Logistic regression is often used instead of Cox regression to analyse genome-wide
association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease …

Motivation: The advent of high-throughput genomics has produced studies with large
numbers of predictors (eg genome-wide association, microarray studies). Machine learning …