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Open Access Methodology article

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

Luciano Da Costa E Silva1, Shengchu Wang1 and Zhao-Bang Zeng2*

Author affiliations

1 Department of Statistics & Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA

2 Department of Genetics, North Carolina State University, Raleigh, NC 27695-7566, USA

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Citation and License

BMC Genetics 2012, 13:67  doi:10.1186/1471-2156-13-67

Published: 1 August 2012

Abstract

Background

Although 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.

Results

Throughout 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.

Conclusions

The 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.

Keywords:
Genetic architecture; Genotypic variance-covariance; Pleiotropy; Power; QTL by environment interaction; Score statistics; Statistical genetics