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

Genet Res (Camb). 2010 Feb;92(1):39-53. doi: 10.1017/S0016672310000042. Epub 2010 Mar 3.


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.

MeSH terms

  • Arabidopsis / genetics
  • Arabidopsis / metabolism
  • Brain Mapping
  • Chromosome Mapping / methods*
  • Metabolomics
  • Phenotype*
  • Quantitative Trait Loci*