Genome-wide association studies (GWAS) are routinely conducted for both quantitative and binary (disease) traits. We present two analytical tools for use in the experimental design of GWAS. Firstly, we present power calculations quantifying power in a unified framework for a range of scenarios. In this context we consider the utility of quantitative scores (e.g. endophenotypes) that may be avai...

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to ...

Summary Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases. Ava...

Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a com...

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...

The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype...

We identified the genetic variants for eye color by Genome-Wide Association Study (GWAS) in a Dutch Caucasian family-based population sample and examined the genetic correlation between hair and eye color using data from unrelated participants from the Netherlands Twin Register. With the Genome-wide Complex Trait Analysis software package, we found strong genetic correlations between various co...

In order to understand cold adaptability and explore additional genetic resources for the cold tolerance improvement of rice, we investigated the genetic variation of 529 rice accessions under natural chilling and cold shock stress conditions at the seedling stage using genome-wide association studies; a total of 132 loci were identified. Among them, 12 loci were common for both chilling and co...

Objective We previously conducted a genome-wide association study for AIS susceptibility in Japanese and identified a SNP, rs11190870 near the LBX1 gene on chromosome 10q24.1 that showed significant association. The association is replicated in other populations of different ethnicities. However, it is controversial whether rs11190870 is also associated with severity of the scoliosis curve in A...

Genetic Correlation Between Schizophrenia and Epilepsy Neuropathological, clinical, and epidemiological data suggest that schizophrenia and epilepsy are associated.1 Reported estimates of the prevalence of schizophrenia among people with epilepsy vary, depending on phenotypic definition, but may be around 7%.2 One hypothesis to account for the relationship is that the 2 diseases share a common ...