Skip to main content
    • Aa
    • Aa

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.

Corresponding author
*Corresponding author: One Shields Avenue, Department of Statistics, University of California, Davis, CA95616, USA. Tel: +1 (916) 248 1963. Fax: +1 (530) 752 7099. e-mail:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

K. W. Broman , H. Wu , S. Sen & G. A. Churchill (2003). R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889890.

W. Deng , H. Chen & Z. Li (2006). A logistic regression mixture model for interval mapping of genetic trait loci affecting binary phenotypes. Genetics 172, 13491358.

G. Diao , D. Y. Lin & F. Zou (2004). Mapping quantitative trait loci with censored observations. Genetics 168, 16891698.

J. P. Fine , F. Zou & B. S. Yandell (2004). Nonparametric estimation of the effects of quantitative trait loci. Biostatistics 5, 501513.

C. A. Hackett & J. I. Weller (1995). Genetic mapping of quantitative trait loci for traits with ordinal distributions. Biometrics 51, 12521263.

C. Jin , J. P. Fine & B. S. Yandell (2007). A unified semiparametric framework for quantitative trait loci analyses, with application to spike phenotypes. Journal of the American Statistical Association 102, 5667.

O. Loudet , E. S. Chaillou , C. Camilleri , D. Bouchez & F. Daniel-Vedele (2002). Bay-0×Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theoretical and Applied Genetics 104, 11721184.

O. Martinez & R. N. Curnow (1992). Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics 85, 480488.

L. H. Moulton & N. A. Halsey (1995). A mixture model with detection limits for regression analyses of antibody response to vaccine. Biometrics 51, 15701578.

L. H. Moulton , F. C. Curriero & P. F. Barroso (2002). Mixture models for quantitative HIV RNA data. Statistical Methods in Medical Research 11, 317325.

H. C. Rowe , B. G. Hansen , B. A. Halkier & D. J. Kliebenstein (2008). Biochemical networks and epistasis shape in the Arabidopsis thaliana metabolome. Plant Cell 20, 11991216.

F. Zou , J. P. Fine & B. S. Yandell (2002). On empirical likelihood for a semiparametric mixture model. Biometrika 89, 6175.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Genetics Research
  • ISSN: 0016-6723
  • EISSN: 1469-5073
  • URL: /core/journals/genetics-research
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
Type Description Title
Supplementary Materials

Taylor Supplementary Material
Supplementary table S2

 Excel (24 KB)
24 KB
Supplementary Materials

Taylor supplementary material
Supplementary table S1

 Excel (39 KB)
39 KB