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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. [1] [2]
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).
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 ...
Over time, a pair of markers or points on a chromosome in the population move from linkage disequilibrium to linkage equilibrium, as recombination events eventually occur between every possible point on the chromosome. [1] Two loci are said to be in linkage equilibrium (LE) if their inheritance is an
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]
A .map accompanies a .ped file and provides information about variants, while .bim and .fam files accompany .bed files as part of the binary dataset. Additionally, PLINK accepts inputs of VCF, BCF, Oxford, and 23andMe files, which are typically extracted into the binary .bed format prior to performing desired analyses. With certain formats such ...
Genetic correlations can be used in GWASes by using polygenic scores or genome-wide hits for one (often more easily measured) trait to increase the prior probability of variants for a second trait; for example, since intelligence and years of education are highly genetically correlated, a GWAS for education will inherently also be a GWAS for ...
By observing if frequencies of a specific variant are more commonly associated, or higher than expected, with the given trait; an association is developed between the trait and the variant. However, many of these associations can be developed throughout an individual due to linkage disequilibrium and the large size of the genome. Although GWAS ...