title: An improved method for quantitative trait loci detection and identification of within-line segregation in F2 intercross designs.
abstract: We present a new flexible, simple, and powerful genome-scan method (flexible intercross analysis, FIA) for detecting quantitative trait loci (QTL) in experimental line crosses. The method is based on a pure random-effects model that simultaneously models between- and within-line QTL variation for single as well as epistatic QTL. It utilizes the score statistic and thereby facilitates computationally efficient significance testing based on empirical significance thresholds obtained by means of permutations. The properties of the method are explored using simulations and analyses of experimental data. The simulations showed that the power of FIA was as good as, or better than, Haley-Knott regression and that FIA was rather insensitive to the level of allelic fixation in the founders, especially for pedigrees with few founders. A chromosome scan was conducted for a meat quality trait in an F(2) intercross in pigs where a mutation in the halothane (Ryanodine receptor, RYR1) gene with a large effect on meat quality was known to segregate in one founder line. FIA obtained significant support for the halothane-associated QTL and identified the base generation allele with the mutated allele. A genome scan was also performed in a previously analyzed chicken F(2) intercross. In the chicken intercross analysis, four previously detected QTL were confirmed at a 5% genomewide significance level, and FIA gave strong evidence (P < 0.01) for two of these QTL to be segregating within the founder lines. FIA was also extended to account for epistasis and using simulations we show that the method provides good estimates of epistatic QTL variance even for segregating QTL. Extensions of FIA and its applications on other intercross populations including backcrosses, advanced intercross lines, and heterogeneous stocks are also discussed.
authors.affiliation: Linnaeus Centre for Bioinformatics, Uppsala University, 75124 Uppsala, Sweden. lars.ronnegard@lcb.uu.se
authors.forename: Lars
authors.initials: L
authors.lastname: Rönnegård
authors.forename: Francois
authors.initials: F
authors.lastname: Besnier
authors.forename: Orjan
authors.initials: O
authors.lastname: Carlborg
mesh_terms: D000483:Alleles; D000818:Animals; D001835:Body Weight; D002645:Chickens / Q000235:genetics*; D020090:Chromosome Segregation / Q000235:genetics*; D033481:Chromosomes, Mammalian / Q000235:genetics; D003198:Computer Simulation; D003433:Crosses, Genetic; D004843:Epistasis, Genetic; D005260:Female; D005821:Genetic Techniques; D016678:Genome; D016013:Likelihood Functions; D008297:Male; D040641:Quantitative Trait Loci / Q000235:genetics*; D012044:Regression Analysis; D013552:Swine / Q000235:genetics*