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Power and robustness of three whole genome association mapping approaches in selected populations
Author(s) -
Erbe M.,
Ytournel F.,
Pimentel E.C.G.,
Sharifi A.R.,
Simianer H.
Publication year - 2011
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/j.1439-0388.2010.00885.x
Subject(s) - linkage disequilibrium , multiple comparisons problem , spurious relationship , genetic association , bonferroni correction , association mapping , biology , genome wide association study , linkage (software) , selection (genetic algorithm) , statistics , robustness (evolution) , genetics , computer science , mathematics , artificial intelligence , haplotype , single nucleotide polymorphism , gene , genotype
Summary Selection is known to influence the linkage disequilibrium (LD) pattern in livestock populations. Spurious LD may lead to a higher number of false‐positive signals in whole genome association mapping experiments. We compared three approaches for whole genome association mapping in a simulation study: single marker regression (SMR), a two‐step approach, which analyses residuals corrected for family effects with an SMR (GRAMMAR), and a combined linkage and LD approach, which applies the quantitative transmission disequilibrium test to the Mendelian sampling term (MTDT). Three different scenarios were simulated: idealized random mating, limited number of parents and directional selection. The number of false‐positive associations increased when the number of parents was limited. Mapping accuracy was the worst in the scenario with directional selection for all approaches. As SMR produced a high number of false‐positive signals in small populations, results of whole genome scans in livestock analysed with SMR should be considered with caution. GRAMMAR was the most accurate approach, but also the least powerful one. The Bonferroni‐corrected significance threshold seemed to be too stringent for this approach. Results obtained with MTDT changed only slightly with selected populations. MTDT combined sufficient power with a manageable number of false‐positive associations in all scenarios.