Comparison of association mapping methods in a complex pedigreed population

Abstract

Association mapping methods were compared using a simulation with a complex pedigree structure. The pedigree was simulated while keeping the present Danish Holstein population pedigree in view. A total of 15 quantitative trait loci (QTL) with varying effect sizes (10%, 5% and 2% of total genetic variance) were simulated. We compared the single-marker test, haplotype-based analysis, mixed model approach, and Bayesian analysis. The methods were compared for power, precision of location estimates, and type I error rates. Results found the best performance in a Bayesian method that included genetic background effects and simultaneously fitted all single-nucleotide polymorphisms (SNPs) with a variable selection method. A mixed model analysis that fitted genetic background effects and tested one SNP at a time performed nearly as well as the Bayesian method. For the Bayesian method, it proved necessary to collect SNP signals in intervals, to avoid the scattering of a QTL signal over multiple neighboring SNPs. Methods not accounting for genetic background (full pedigree information) performed worse, and methods using haplotypes were considerably worse with a high false-positive rate, probably due to the presence of low-frequency haplotypes. It was necessary to account for full relationships among individuals to avoid excess false discovery. Although the methods were tested on a cattle pedigree, the results are applicable to any population with a complex pedigree structure.