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This is why all modern GWAS use a very low p-value threshold. In addition to easily correctible problems such as these, some more subtle but important issues have surfaced. A high-profile GWA study that investigated individuals with very long life spans to identify SNPs associated with longevity is an example of this. [72]
Association mapping has been most widely applied to the study of human disease, specifically in the form of a genome-wide association study (GWAS). A genome-wide association study is performed by scanning an entire genome for SNPs associated with a particular trait of interest, or in the case of human disease, with a particular disease of interest.
The Wellcome Trust Case Control Consortium (abbreviated WTCCC) is a collaboration between fifty research groups in the United Kingdom in the field of human genetics. ...
In genome-wide association studies, genome-wide significance (abbreviated GWS) is a specific threshold for determining the statistical significance of a reported association between a given single-nucleotide polymorphism (SNP) and a given trait.
GWAS Central is a core component of the GEN2PHEN project and intends to provide an operational model, plus an open-source software package, so others can create similar databases across the world. These will be hosted by institutes, consortia, and even individual laboratories; providing those groups a toolkit for publicising and publishing ...
In the field of genetic sequencing, genotyping by sequencing, also called GBS, is a method to discover single nucleotide polymorphisms (SNP) in order to perform genotyping studies, such as genome-wide association studies (). [1]
In that case the signal produced from GWAS is an indirect (synthetic) association between one or more rare causal variants in linkage disequilibrium. It is important to recognize that this phenomenon is possible when selecting a group for tag SNPs.
[2] [3] [4] It is a complementary approach to the genome-wide association study, or GWAS, methodology. [5] A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk.