Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's gene...

AIMS Genome-wide association studies (GWAS) have identified several risk variants for prostate cancer (pCa) mainly in Europeans, which need to be further verified in other racial groups. We selected six previously identified variants as candidates and to define the association with PCa in Northern Han Chinese. METHODS 749 subjects from Beijing and Tianjin in Northern China were included. Six ...

There are errors in the ninth, tenth and eleventh sentences of the Abstract. The correct sentences are: Individual genotyping of KLF7 rs2284932 revealed that the frequency of the minor C allele was significantly increased (P = 0.00062, Pc = 0.003, OR = 2.98, 95%CI = 1.58–5.65) in group A. The minor T allele of rs4473559 in FRMD4 demonstrated a significant association in the A group (P = 0.00015...

BACKGROUND Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. METHODS We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. ...

Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulat...

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Genome-wide association studies (GWAS) have identified thousands of robust and replicable genetic associations for complex disease. However, the identification of the causal variants that underlie these associations has been more difficult. This problem of fine-mapping association signals predates GWAS, but the last few years have seen a surge of studies aimed at pinpointing causal variants usi...

Genome-Wide Association studies (GWAS) have brought a revolutionary change or paradigm shift in detecting novel variants for complex disorders and shifting the burden of finding the biological relevance of these newly discovered variants on biochemists and physiologists, hence it is a movement from forward to reverse genetics. Here we discuss the role of such studies with GWAS designs from anth...

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 ...

Psoriasis is a complex disease of skin with a prevalence of about 2%. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for psoriasis to date, including data from eight different Caucasian cohorts, with a combined effective sample size >39,000 individuals. We identified 16 additional psoriasis susceptibility loci achieving genome-wide significance, increasing the ...