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Composite interval mapping to identify quantitative trait loci for point-mass mixture phenotypes


Increasingly researchers are conducting quantitative trait locus (QTL) mapping in metabolomics and proteomics studies. These data often are distributed as a point-mass mixture, consisting of a spike at zero in combination with continuous non-negative measurements. Composite interval mapping (CIM) is a common method used to map QTL that has been developed only for normally distributed or binary data. Here we propose a two-part CIM method for identifying QTLs when the phenotype is distributed as a point-mass mixture. We compare our new method with existing normal and binary CIM methods through an analysis of metabolomics data from Arabidopsis thaliana. We then conduct a simulation study to further understand the power and error rate of our two-part CIM method relative to normal and binary CIM methods. Our results show that the two-part CIM has greater power and a lower false positive rate than the other methods when a continuous phenotype is measured with many zero observations.

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*Corresponding author: One Shields Avenue, Department of Statistics, University of California, Davis, CA 95616, USA. Tel: +1 (916) 248 1963. Fax: +1 (530) 752 7099. e-mail:
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Genetics Research
  • ISSN: 0016-6723
  • EISSN: 1469-5073
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