Simultaneous Estimation of Haplotype Frequencies and Quantitative Trait Parameters: Applications to the Test of Association Between Phenotype and Diplotype Configuration

doi:10.1534/genetics.104.029751

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Simultaneous Estimation of Haplotype Frequencies and Quantitative Trait Parameters

Applications to the Test of Association Between Phenotype and Diplotype Configuration

Kyoko Shibata^*^,1, Toshikazu Ito^,, Yutaka Kitamura, Naoko Iwasaki, Hiroshi Tanaka^** and Naoyuki Kamatani^,^,2

^* Department of Bioinformatics, Graduate School of Tokyo Medical and Dental University, Tokyo 113-8510, Japan
Mitsubishi Research Institute, Tokyo 100-8141, Japan
Algorithm Team, Japan Biological Information Research Center (JBIRC), Japan Biological Informatics Consortium (JBIC), Tokyo 135-0064, Japan
Diabetes Center, Tokyo Women's Medical University, Tokyo 162-8666, Japan
^** Department of Computational Biology, School of Biomedical Science, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
Division of Genomic Medicine, Department of Applied Biomedical Engineering and Science and Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan

^² Corresponding author: Japan Biological Information Research Center (JBIRC), Japan Biological Informatics Consortium (JBIC), TIME 24 Bldg., 10F, 2-45 Aomi, Koto-ku, Tokyo 135-0064, Japan and Department of Applied Biomedical Engineering, Tokyo Women's Medical University, Institute of Rheumatology, 10-22 Kawada-cho, Shinjuku-ku, Shinjuku, Tokyo 162-0054, Japan.
E-mail: kamatani{at}ior.twmu.ac.jp

The analysis of the haplotype-phenotype relationship has becomemore and more important. We have developed an algorithm, usingindividual genotypes at linked loci as well as their quantitativephenotypes, to estimate the parameters of the distribution ofthe phenotypes for subjects with and without a particular haplotypeby an expectation-maximization (EM) algorithm. We assumed thatthe phenotype for a diplotype configuration follows a normaldistribution. The algorithm simultaneously calculates the maximumlikelihood (L_0max) under the null hypothesis (i.e., nonassociationbetween the haplotype and phenotype), and the maximum likelihood(L_max) under the alternative hypothesis (i.e., association betweenthe haplotype and phenotype). Then we tested the associationbetween the haplotype and the phenotype using a test statistic,–2 log(L_0max/L_max). The above algorithm along with someextensions for different modes of inheritance was implementedas a computer program, QTLHAPLO. Simulation studies using single-nucleotidepolymorphism (SNP) genotypes have clarified that the estimationwas very accurate when the linkage disequilibrium between linkedloci was rather high. Empirical power using the simulated datawas high enough. We applied QTLHAPLO for the analysis of thereal data of the genotypes at the calpain 10 gene obtained fromdiabetic and control subjects in various laboratories.

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