title: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits.
abstract: The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: T
authors.initials: T
authors.lastname: Qi
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: B
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: Z
authors.initials: Z
authors.lastname: Zhu
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: C
authors.initials: C
authors.lastname: Wei
authors.affiliation: Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China.
authors.forename: Y
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: S
authors.initials: S
authors.lastname: Zhu
authors.affiliation: Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.
authors.forename: H
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: X
authors.initials: X
authors.lastname: Lou
mesh_terms: D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D018556:Crops, Agricultural / Q000235:genetics*; D018745:Genome, Plant / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci / Q000235:genetics*; D015203:Reproducibility of Results; D012639:Seeds / Q000235:genetics*