title: Power of QTL detection by either fixed or random models in half-sib designs.
abstract: The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance (lambda= 0.1), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios (lambda) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log10 of type I error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping.
authors.affiliation: Center for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario N1G 2W1, Canada. dkolbehd@uoguelph.ca
authors.forename: Davood
authors.initials: D
authors.lastname: Kolbehdari
authors.forename: Gerald B
authors.initials: GB
authors.lastname: Jansen
authors.forename: Lawrence R
authors.initials: LR
authors.lastname: Schaeffer
authors.forename: Brian O
authors.initials: BO
authors.lastname: Allen
mesh_terms: D000483:Alleles; D000818:Animals; D005191:Family Characteristics; D005787:Gene Frequency; D005819:Genetic Markers; D005838:Genotype; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci; D011897:Random Allocation; D012044:Regression Analysis; D012107:Research Design
chemical_list: D005819:Genetic Markers