Genetics, Vol. 168, 2339-2348, December 2004, Copyright © 2004
doi:10.1534/genetics.103.024653

Association Test Algorithm Between a Qualitative Phenotype and a Haplotype or Haplotype Set Using Simultaneous Estimation of Haplotype Frequencies, Diplotype Configurations and Diplotype-Based Penetrances

Toshikazu Ito, Eisuke Inoue and Naoyuki Kamatani¹

Division of Genomic Medicine, Department of Applied Biomedical Engineering and Science and Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan and Algorithm Team, Japan Biological Information Research Center (JBIRC), Japan Biological Informatics Consortium (JBIC), Tokyo 135-0064, Japan

¹ Corresponding author: Division of Genomic Medicine, Department of Applied Biomedical Engineering and Science and Institute of Rheumatology, Tokyo Women's Medical University, 10-22 Kawada-cho, Shinjuku-ku, Shinjuku, Tokyo 162-0054, Japan.
E-mail: kamatani{at}ior.twmu.ac.jp

Analysis of the association between haplotypes and phenotypes is becoming increasingly important. We have devised an expectation-maximization (EM)-based algorithm to test the association between a phenotype and a haplotype or a haplotype set and to estimate diplotype-based penetrance using individual genotype and phenotype data from cohort studies and clinical trials. The algorithm estimates, in addition to haplotype frequencies, penetrances for subjects with a given haplotype and those without it (dominant mode). Relative risk can thus also be estimated. In the dominant mode, the maximum likelihood under the assumption of no association between the phenotype and presence of the haplotype (L_0max) and the maximum likelihood under the assumption of association (L_max) were calculated. The statistic –2 log(L_0max/L_max) was used to test the association. The present algorithm along with the analyses in recessive and genotype modes was implemented in the computer program PENHAPLO. Results of analysis of simulated data indicated that the test had considerable power under certain conditions. Analyses of two real data sets from cohort studies, one concerning the MTHFR gene and the other the NAT2 gene, revealed significant associations between the presence of haplotypes and occurrence of side effects. Our algorithm may be especially useful for analyzing data concerning the association between genetic information and individual responses to drugs.

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