QTL 回归分析 Regression_analysis.pdf

Chapter 9 Methods for QTL analysis Chapter 9 Methods for QTL analysis Julius van der Werf Regression Methods 80 ANOVA analysis using single marker genotypes 80 ANOVA analysis using multiple marker genotypes. 80 Regression on QTL probability, conditional on marker haplotypes. 80 Haley-Knott regression 81 Regression of phenotype on marker type 81 Maximum Likelihood estimation: 82 Comparison of likelihood and regression procedures 85 Multiple regression on marker genotypes, 87 Inverval mapping with marker co-factors (composite interval mapping) 88 Precision of mapping and hypothesis testing 88 Permutation testing 89 Bootstrapping 89 Accounting for multiple testing 89 References 90 In this Chapter we will discuss in more detail regression analysis and Maximum likelihood methods for QTL mapping. Regression methods are generally much easier to use (standard software like SAS or ASREML can easily be used), and the method is much faster computationally. Maximum likelihood is computationally more demanding, and specific software is needed. For many designs, results are very similar to regression. This makes regression analysis attractive as it can be used in resampling methods. Resampling methods are use to determine test statistics for hypothesis testing. In this Chapter we will discuss bootstrapping and permutation tests. We will also discuss QTL mapping with multiple markers (more than 2) and methods to account for more than one QTL. Accounting for other QTL has been proposed by including cofactors, or by using composite interval mapping. There are two classes of methods that are not discussed in the chapter. Those are the mixed model methods and Monte Carlo Markov Chain methods. In both methods,