Mapping Quantitative Trait Loci in F₂ Incorporating Phenotypes of F₃ Progeny

Corresponding author: Shizhong Xu, 3126 Batchelor Hall, University of California, Riverside, California 92521-0124., xu{at}genetics.ucr.edu (E-mail)

Communicating editor: J. B. WALSH

In plants and laboratory animals, QTL mapping is commonly performed using F₂ or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F₂ individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F₃ progeny derived from selfing F₂ plants in place of the F₂ phenotype itself. All F₃ progeny derived from the same F₂ plant belong to the same F_2:3 family, denoted by F_2:3. If the size of each F_2:3 family (the number of F₃ progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F₂ plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F₂ marker genotypes and F₃ average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F_2:3 strategy. We also propose to combine both the F₂ phenotypes and the F_2:3 average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F_2:3 families of heterozygous F₂ plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F₂ design, even if only a single F₃ progeny is collected from each F_2:3 family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.

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