Selective genotyping to detect quantitative trait loci affecting multiple traits: interval mapping analysis

Segregating quantitative trait loci can be detected via linkage to genetic markers. By selectively genotyping individuals with extreme phenotypes for the quantitative trait, the power per individual genotyped is increased at the expense of the power per individual phenotyped, but linear-model estima tes of the quantitative-locus effect will be biased. The properties of sing le- and multiple-trait maximum-likelihood estimates of quantitative-loci pa rameters derived from selectively genotyped samples were investigated using Monte-Carlo simulations of backcross populations. All individuals with tra it records were included in the analyses. All quantitative-locus parameters and the residual correlation were unbiasedly estimated by multiple-trait m aximum-likelihood methodology. With single-trait maximum-likelihood, unbias ed estimates for quantitative-locus effect and location, and the residual v ariance, were obtained for the trait under selection, but biased estimates were derived for a correlated trait that was analyzed separately. When an e ffect of the QTL was simulated only on the trait under selection, a "ghost" effect was also found for the correlated trait. Furthermore, if an effect was simulated only for the correlated trait, then the statistical power was less than that obtained with a random sample of equal size. With multiple- trait analyses, the power of quantitative-trait locus detection was always greater with selective genotyping.