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Robustness of Bayesian Multilocus Association Models to Cryptic Relatedness
Author(s) -
Kärkkäinen Hanni P.,
Sillanpää Mikko J.
Publication year - 2012
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2012.00729.x
Subject(s) - population stratification , biology , genetic association , population , bayesian probability , trait , robustness (evolution) , genetics , context (archaeology) , confounding , evolutionary biology , statistics , computer science , genotype , mathematics , demography , single nucleotide polymorphism , paleontology , sociology , gene , programming language
Summary Population‐based association analyses are more powerful than within‐family analyses in identifying genetic loci associated with a phenotype of interest. However, if the population or sample structure is omitted from the model, population stratification and cryptic relatedness may lead to false positive and negative signals caused by relatedness between individuals, rather than association due to close linkage of the marker and the trait loci. Therefore it is important to correct or account for these confounders in population‐based association analyses. However, there is cumulative evidence that when fitting a multilocus association model, the genetic relationships between the individuals can be captured by the markers themselves, bringing about a possibility to use the models without an additional correction for the population or sample structure. In this work we have further investigated this possibility in the Bayesian multilocus association model context using the extended Bayesian LASSO and the indicator‐based variable selection. In particular, we have studied whether these multilocus models benefit from an insertion of an additional polygenic term representing the genetic variation not captured by the markers and taking account of the residual dependencies between the individuals. We have found that although the models may benefit from the insertion of the polygenic component, omitting the component does not damage the model performance severely.