title: A new simple method for improving QTL mapping under selective genotyping.
abstract: The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/.
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China.
authors.forename: Hsin-I
authors.initials: HI
authors.lastname: Lee
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China.
authors.forename: Hsiang-An
authors.initials: HA
authors.lastname: Ho
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China Division of Biometry, Institute of Agronomy, National Taiwan University, Taipei 10617, Taiwan, Republic of China chkao@stat.sinica.edu.tw.
authors.forename: Chen-Hung
authors.initials: CH
authors.lastname: Kao
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D004843:Epistasis, Genetic; D005828:Genetics, Population; D060005:Genotyping Techniques; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci
keywords: QTL mapping; epistasis; multiple interval mapping; selective genotyping; truncated model