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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]
Linkage disequilibrium in asexual populations can be defined in a similar way in terms of population allele frequencies. Furthermore, it is also possible to define linkage disequilibrium among three or more alleles, however these higher-order associations are not commonly used in practice. [1]
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).
In 2018, several genome-wide association studies are reaching a total sample size of over 1 million participants, including 1.1 million in a genome-wide study of educational attainment [39] follow by another in 2022 with 3 million individuals [40] and a study of insomnia containing 1.3 million individuals. [41]
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 independent event.
There are two distinctive mapping approaches used in the field of genome mapping: genetic maps (also known as linkage maps) [7] and physical maps. [3] While both maps are a collection of genetic markers and gene loci, [8] genetic maps' distances are based on the genetic linkage information, while physical maps use actual physical distances usually measured in number of base pairs.
It comprises three phases; the complete data obtained in Phase I were published on 27 October 2005. [1] The analysis of the Phase II dataset was published in October 2007. [ 2 ] The Phase III dataset was released in spring 2009 and the publication presenting the final results published in September 2010.
The two graphics illustrate sampling distributions of polygenic scores and the predictive ability of stratified sampling on polygenic risk score with increasing age. + The left panel shows how risk—(the standardized PRS on the x-axis)—can separate 'cases' (i.e., individuals with a certain disease, (red)) from the 'controls' (individuals without the disease, (blue)).