title: Composite interval mapping to identify quantitative trait loci for point-mass mixture phenotypes.
abstract: 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.
authors.affiliation: Biostatistics Graduate Group, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA. sltaylor@ucdavis.edu
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Taylor
authors.forename: Katherine S
authors.initials: KS
authors.lastname: Pollard
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001931:Brain Mapping; D002874:Chromosome Mapping / Q000379:methods*; D055432:Metabolomics; D010641:Phenotype; D040641:Quantitative Trait Loci