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/.T HE data in the QTL mapping study are usually composed of two parts, phenotypic trait values and marker genotypes, in the individuals, and the cost of producing data includes both phenotyping and genotyping costs. The cost ratio of the phenotyping to genotyping may vary significantly depending on the traits and species in studies. For a fixed budget and time frame in the study, both costs must be considered and properly allocated to make the study optimally cost effective. If the total cost is not of primary concern in QTL experiments, all individuals in the entire sample will be genotyped and phenotyped for QTL analysis.However, QTL experiments are usually conducted under a limited budget, and researchers may not be allowed to genotype and phenotype a large amount of individuals for QTL analysis. It is hence necessary to make a reasonable and effective allocation for the genotypi...

Issues in determining the threshold values of QTL mapping are often investigated for the backcross and F2 populations with relatively simple genome structures so far. The investigations of these issues in the progeny populations after F2 (advanced populations) with relatively more complicated genomes are generally inadequate. As these advanced populations have been well implemented in QTL mapping, it is important to address these issues for them in more details. Due to an increasing number of meiosis cycle, the genomes of the advanced populations can be very different from the backcross and F2 genomes. Therefore, special devices that consider the specific genome structures present in the advanced populations are required to resolve these issues. By considering the differences in genome structure between populations, we formulate more general score test statistics and gaussian processes to evaluate their threshold values. In general, we found that, given a significance level and a genome size, threshold values for QTL detection are higher in the denser marker maps and in the more advanced populations. Simulations were performed to validate our approach.