Abstract
An interval mapping procedure based on the random model approach was applied to investigate its appropriateness and robustness for QTL mapping in populations with prevailing half-sib family structures. Under a random model, QTL location and variance components were estimated using maximum likelihood techniques. The estimation of parameters was based on the sib-pair approach. The proportion of genes identical-by-descent (IBD) at the QTL was estimated from the IBD at two flanking marker loci. Estimates for QTL parameters (location and variance components) and power were obtained using simulated data, and varying the number of families, heritability of the trait, proportion of QTL variance, number of marker alleles and number of alleles at QTL. The most important factors influencing the estimates of QTL parameters and power were heritability of the trait and the proportion of genetic variance due to QTL. The number of QTL alleles neither influenced the estimates of QTL parameters nor the power of QTL detection. With a higher heritability, confounding between QTL and the polygenic component was observed. Given a sufficient number of families and informative polyallelic markers, the random model approach can detect a QTL that explains at least 15 % of the genetic variance with high power and provides accurate estimates of the QTL position. For fine QTL mapping and proper estimation of QTL variance, more sophisticated
Highlights
The development of linkage maps with large numbers of molecular markers has stimulated the search for methods to map genes involved in quantitative traits
When the QTL explained 50 % of the genetic variance, the estimates were close to the true QTL position when the heritability of the trait was 0.30
When the genetic variance is completely due to the QTL, the accuracy of the QTL position estimates, given as a width of the 95 % empirical confidence interval, was strongly influenced by the heritability of the trait and the number of families
Summary
The development of linkage maps with large numbers of molecular markers has stimulated the search for methods to map genes involved in quantitative traits. The search for QTL has been most successful in plants and laboratory animals for which data are available for backcross and F2 generation from inbred lines. With such data, the parental genotypes, the linkage phases of the loci, and the number of alleles at the putative QTL are known precisely. With an unknown number of QTL alleles it is impossible to determine the exact number of genotypes, i.e. the number of normal distributions that build up the overall distribution of genotypic values In such situations, the detection of linkage relationships between a putative QTL and the marker loci can only be based on robust model-free (non-parametric) and computationally rapid linkage methods, such as the random model approach
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