Abstract
BackgroundAlthough many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software.ResultsThroughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits.ConclusionsThe MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.
Highlights
Many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis
Model size The number of QTL in the multiple trait multiple interval mapping (MTMIM) model of scenario scenario is: (SI) was much closer to the simulated parameter when compared to scenario SII, for any genome-wide significance level
While FDR is expected to increase with increments in genome-wide significance level, our results show that for a fixed logarithm of odds ratio (LOD)-d level FDR changed little with increments in genome-wide significance levels, in both multiple interval mapping (MIM) and MTMIM models
Summary
Many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. We propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. The availability of genome-wide molecular markers provides the means for us to locate and map those quantitative trait loci (QTL) in a systematic way. Since the publication of interval mapping method for QTL genome-wide scan [1], many statistical methods have been proposed and developed to map multiple QTL with or without epistasis in single trait in a variety of populations [2], e.g. single trait QTL mapping methods have been applied in many studies to estimate the genetic basis and architecture of complex traits, these methods did not utilize the information of genetic and environmental correlations between traits, and are not ideal for data analysis. Multiple trait analysis can allow the estimation of genetic variance-covariance matrix between traits and its decomposition in terms of individual QTL ([11,12] pages 109-110)
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