Human genome-wide association studies (GWAS) have successfully identified thousands of susceptibility loci for common diseases with complex genetic etiologies. Although the susceptibility variants identified by GWAS usually have only modest effects on individual disease risk, they contribute to a substantial burden of trait variation in the overall population. GWAS also offer valuable clues to ...

Local interactions between neighbouring SNPs are hypothesized to be able to capture variants missing from genome-wide association studies (GWAS) via haplotype effects but have not been thoroughly explored. We have used a new high-throughput analysis tool to probe this underexplored area through full pair-wise genome scans and conventional GWAS in diastolic and systolic blood pressure and six me...

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this come...

As a result of technological advances, the genomic analysis of stroke has shifted from candidate gene association studies to genome-wide association studies (GWAS). Agnostic GWAS evaluate up to 90% of common genetic variation in a single experiment, creating an improved framework for identifying novel genetic leads for biochemical and cellular mechanisms underlying stroke. Given the ubiquity of...

Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the ...

To search the entire human genome for association is a novel and promising approach to unravelling the genetic basis of complex genetic diseases. In these genome-wide association studies (GWAs), several hundreds of thousands of single nucleotide polymorphisms (SNPs) are analyzed at the same time, posing substantial biostatistical and computational challenges. In this paper, we discuss a number ...

Context Genome-wide association studies (GWAS) and meta-analyses can be used to detect variants that affect quantitative traits. Multi-breed GWAS may lead increased power precision compared with within-breed GWAS. However, not all causal segregate in breeds, multiple breeds have different allele frequencies breeds. It is known how differences minor frequency (MAF) multi-breed meta-analyses.Aims...

Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past 10 years. Over 2,000 human GWAS reports now appear in the scientific journals. The technology is continuing to improve, and has recently become accessible to researchers studying a wide variety of animals, plants and model organisms. Here, we...

Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as disease, in group of individuals. Unfortunately, careless sharing GWAS statistics might give rise to privacy attacks. Several works attempted reconcile secure processing privacy-preserving releases GWASes. However, we highlight these approaches remain vulnerable if GWASes...

Genome-wide association study (GWAS) is nowadays widely used to identify genes involved in human complex disease. The standard GWAS analysis examines SNPs/genes independently and identifies only a number of the most significant SNPs. It ignores the combined effect of weaker SNPs/genes, which leads to difficulties to explore biological function and mechanism from a systems point of view. Althoug...