A gene frequency model for QTL mapping using Bayesian inference

To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.Molecular information is currently being used for mapping quantitative trait loci (QTL) and for genetic evaluation. This information usually consists of molecular genotypes at polymorphic loci. These loci can be broadly classified into two types: I) those that have a direct effect on the trait, and II) those that do not have a direct effect on the trait but are linked to a trait locus (markers). Loci of type II can be further classified into two types: IIa) loci that are in linkage disequilibrium with the trait locus across the population (LD markers), and IIb) loci that are in linkage equilibrium with the trait locus (LE markers) [1]. In outbred populations, until recently, marker analyses were primarily based on LE markers [2-6]. LE markers do not provide information to model the mean at linked QTL, but they do provide information to model covariances at the linked QTL. These covariances can be written in terms of the conditional IBD probabilities at