title: Correcting for bias in estimation of quantitative trait loci effects.
abstract: Estimates of quantitative trait loci (QTL) effects derived from complete genome scans are biased, if no assumptions are made about the distribution of QTL effects. Bias should be reduced if estimates are derived by maximum likelihood, with the QTL effects sampled from a known distribution. The parameters of the distributions of QTL effects for nine economic traits in dairy cattle were estimated from a daughter design analysis of the Israeli Holstein population including 490 marker-by-sire contrasts. A separate gamma distribution was derived for each trait. Estimates for both the alpha and beta parameters and their SE decreased as a function of heritability. The maximum likelihood estimates derived for the individual QTL effects using the gamma distributions for each trait were regressed relative to the least squares estimates, but the regression factor decreased as a function of the least squares estimate. On simulated data, the mean of least squares estimates for effects with nominal 1% significance was more than twice the simulated values, while the mean of the maximum likelihood estimates was slightly lower than the mean of the simulated values. The coefficient of determination for the maximum likelihood estimates was five-fold the corresponding value for the least squares estimates.
authors.affiliation: Institute of Animal Sciences, ARO, The Volcani Center, Bet Dagan 50250, Israel. weller@agri.huji.ac.il
authors.forename: Joel Ira
authors.initials: JI
authors.lastname: Weller
authors.forename: Meital
authors.initials: M
authors.lastname: Shlezinger
authors.forename: Micha
authors.initials: M
authors.lastname: Ron
mesh_terms: D000465:Algorithms; D000818:Animals; D015982:Bias; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D005260:Female; D007246:Infertility / Q000235:genetics; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*; D012044:Regression Analysis; D012680:Sensitivity and Specificity