This article is part of the supplement: Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)
Comparison of linear mixed model analysis and genealogy-based haplotype clustering with a Bayesian approach for association mapping in a pedigreed population
- Equal contributors
1 Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830 Tjele, Denmark
2 Department of Animal Science, Ferdowsi University of Mashhad, 91775 Mashhad, Iran
BMC Proceedings 2012, 6(Suppl 2):S4 doi:10.1186/1753-6561-6-S2-S4Published: 21 May 2012
Despite many success stories of genome wide association studies (GWAS), challenges exist in QTL detection especially in datasets with many levels of relatedness. In this study we compared four methods of GWA on a dataset simulated for the 15th QTL-MAS workshop. The four methods were 1) Mixed model analysis (MMA), 2) Random haplotype model (RHM), 3) Genealogy-based mixed model (GENMIX), and 4) Bayesian variable selection (BVS). The data consisted of phenotypes of 2000 animals from 20 sire families and were genotyped with 9990 SNPs on five chromosomes.
Out of the eight simulated QTL, these four methods MMA, RHM, GENMIX and BVS identified 6, 6, 8 and 7 QTL respectively and 4 QTL were common across the methods. GENMIX had the highest power to detect QTL however it also produced 4 false positives. BVS was the second best method in terms of power, detecting all QTL except the one on chromosome 5 with epistatic interaction. Two spurious associations were obtained across methods. Though all the methods considered the full pedigree in the analyses, it was not sufficient to avoid all the spurious associations arising due to family structure.
Using several methods with divergent approaches for GWAS can be useful in gaining confidence on the QTL identified. In our comparison, GENMIX was found to be the best method in terms of power but it needs appropriate correction for multiple testing to avoid the false positives. This study shows that the issues of multiple testing and the relatedness among study samples need special attention in GWAS.