Shrinkage estimation method for mapping multiple quantitative trait loci.
In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.
Zhang
Yuan-Ming
YM
Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement/Chinese National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China. soyzhang@njau.edu.cn
D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis; D039301:Computers, Molecular; D004843:Epistasis, Genetic; D008957:Models, Genetic; D040641:Quantitative Trait Loci / Q000235:genetics*; D012044:Regression Analysis
D015415:Biomarkers