title: Estimating allelic number and identity in state of QTLs in interconnected families.
abstract: When multiple related families derived from inbred lines are jointly analysed to detect quantitative trait loci (QTLs), the analysis should estimate allelic effects as accurately as possible and estimate the probability that different parents carry alleles that are identical in state. Analyses exist that assume that all parents carry unique alleles or that all parents but one carry the same allele. In practice, many configurations are possible that group different parents according to their identity-in-state condition at a putative QTL allele. Here, we propose a variable model Bayesian analysis that selects among possible identity-in-state configurations and jointly estimates the allelic effects of identical-in-state parents. We contrast this analysis with a fixed model analysis that estimates unique allelic effects for all parents. We analyse two simulated mating designs: an experimental design in which three inbred parents were crossed to generate two families of 150 doubled haploid lines; and a breeding design in which 20 inbred parents were crossed to generate 60 families of 20 doubled haploid lines, with each parent contributing to six families. In all cases where some parents were simulated to carry alleles of identical effect (that is, they were identical in state), the variable analysis estimated allelic effects with lower mean-squared error than the fixed analysis. The variable analysis showed that, unless each family contains many individuals (more than 100), there is insufficient information in DNA-marker and phenotypic data to determine with high probability the QTL allelic number.
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA 50011-1010, USA. jjannink@iastate.edu
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Jannink
authors.forename: Xiao-Lin
authors.initials: XL
authors.lastname: Wu
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D008390:Markov Chains; D009010:Monte Carlo Method; D010944:Plants / Q000235:genetics; D040641:Quantitative Trait Loci; D012044:Regression Analysis