Optimal sampling of a population to determine QTL location, variance, and allelic number.
In a population intended for breeding and selection, questions of interest relative to a specific segregating QTL are the variance it generates in the population, and the number and effects of its alleles. One approach to address these questions is to extract several inbreds from the population and use them to generate multiple mapping families. Given random sampling of parents, sampling strategy may be an important factor determining the power of the analysis and its accuracy in estimating QTL variance and allelic number. We describe appropriate multiple-family QTL mapping methodology and apply it to simulated data sets to determine optimal sampling strategies in terms of family number versus family size. Genomes were simulated with seven chromosomes, on which 107 markers and six QTL were distributed. The total heritability was 0.60. Two to ten alleles were segregating at each QTL. Sampling strategies ranged from sampling two inbreds and generating a single family of 600 progeny to sampling 40 inbreds and generating 40 families of 15 progeny each. Strategies involving only one to five families were subject to variation due to the sampling of inbred parents. For QTL where more than two alleles were segregating, these strategies did not sample QTL alleles representative of the original population. Conversely, strategies involving 30 or more parents were subject to variation due to sampling of QTL genotypes within the small families obtained. Given these constraints, greatest QTL detection power was obtained for strategies involving five to ten mapping families. The most accurate estimation of the variance generated by the QTL, however, was obtained with strategies involving 20 or more families. Finally, strategies with an intermediate number of families best estimated the number of QTL alleles. We conclude that no overall optimal sampling strategy exists but that the strategy adopted must depend on the objective.
Wu
Xiao-Lin
XL
Department of Agronomy, Iowa State University, Ames, IA 50011-1010, USA.
Jannink
Jean-Luc
JL
D000483:Alleles; D000704:Analysis of Variance; D001947:Breeding; D003198:Computer Simulation; D005819:Genetic Markers; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010944:Plants / Q000235:genetics*; D040641:Quantitative Trait Loci / Q000235:genetics*; D012494:Sampling Studies
D005819:Genetic Markers