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In genetics, association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link phenotypes (observable characteristics) to genotypes (the genetic constitution of organisms), uncovering genetic associations.
Once linkage disequilibrium has been calculated for a dataset, a visualization method is often chosen to display the linkage disequilibrium to make it more easily understandable. The most common method is to use a heatmap, where colors are used to indicate the loci with positive linkage disequilibrium, and linkage equilibrium. This example ...
Hence, GWAS is a non-candidate-driven approach, in contrast to gene-specific candidate-driven studies. GWA studies identify SNPs and other variants in DNA associated with a disease, but they cannot on their own specify which genes are causal. [1] [2] [3] The first successful GWAS published in 2002 studied myocardial infarction. [4]
In statistical genetics, linkage disequilibrium score regression (LDSR [1] or LDSC [2]) is a technique that aims to quantify the separate contributions of polygenic effects and various confounding factors, such as population stratification, based on summary statistics from genome-wide association studies (GWASs).
The most commonly used approach, block-based method, exploits the principle of linkage disequilibrium observed within haplotype blocks. [12] Several algorithms have been devised to partition chromosomal regions into haplotype blocks which are based on haplotype diversity , LD , four-gamete test and information complexity and tag SNPs are ...
A genome-wide association study, or GWAS, is a genetic tool that uses single nucleotide polymorphisms, or SNPs, to identify if a trait or disease is linked to a specific genetic variant. By observing if frequencies of a specific variant are more commonly associated, or higher than expected, with the given trait; an association is developed ...
Linkage disequilibrium (LD) calculation; Identity by descent (IBD) and identity by state (IBS) matrix calculation; population stratification, such as a Principal component analysis; association analysis such as genome-wide association study for both basic case/control studies and quantitative traits; tests for epistasis
These factors include priorities in SNPs, relative risk of functional change in genes, and linkage disequilibrium among SNPs. [ 13 ] In addition, the availability of genetic information through online databases enables researchers to mine existing data and web-based resources for new candidate gene targets. [ 14 ]