Novel methods for increasing efficiency of quantitative trait locus mapping

Abstract

The aim of quantitative trait locus (QTL) mapping is to identify association between DNA marker genotype and trait phenotype in experimental populations. Many QTL mapping methods have been developed to improve QTL detecting power and estimation of QTL location and effect. Recently, shrinkage Bayesian and penalized maximum-likelihood estimation approaches have been shown to give increased power and resolution for estimating QTL main or epistatic effect. Here I describe a new method, shrinkage interval mapping, that combines the advantages of these two methods while avoiding the computing load associated with them. Studies based on simulated and real data show that shrinkage interval mapping provides higher resolution for differentiating closely linked QTLs and higher power for identifying QTLs of small effect than conventional interval-mapping methods, with no greater computing time. A second new method developed in the course of this research toward increasing QTL mapping efficiency is the extension of multi-trait QTL mapping to accommodate incomplete phenotypic data. I describe an EM-based algorithm for exploiting all the phenotypic and genotypic information contained in the data. This method supports conventional hypothesis tests for QTL main effect, pleiotropy, and QTL-by-environment interaction. Simulations confirm improved QTL detection power and precision of QTL location and effect estimation in comparison with casewise deletion or imputation methods.