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HOME > J Prev Med Public Health > Volume 37(4); 2004 > Article
Original Article Statistical Algorithm in Genetic Linkage Based on Haplotypes.
Jinheum Kim, Dae Ryong Kang, Yun Kyung Lee, Sun Mi Shin, Il Suh, Chung Mo Nam
Journal of Preventive Medicine and Public Health 2004;37(4):366-372
DOI: https://doi.org/
Published online: November 30, 2004
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1Department of Applied Statistics, University of Suwon, Korea.
2Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Korea. cmnam@yumc.yonsei.ac.kr
3Graduate School of Public Health, Yonsei University, Korea.

OBJECTIVES
This study was conducted to propose a new transmission/disequilibrium test (TDT) to test the linkage between genetic markers and diseasesusceptibility genes based on haplotypes. Simulation studies were performed to compare the proposed method with that of Zhao et al. in terms of type I error probability and powers. METHODS: We estimated the haplotype frequencies using the expectation-maximization (EM) algorithm with parents' genotypes taken from a trio dataset, and then constructed a two-way contingency table containing estimated frequencies to all possible pairs of parents' haplotypes. We proposed a score test based on differences between column marginals and their corresponding row marginals. The test also involved a covariance structure of marginal differences and their variances. In simulation, we considered a coalescent model with three genetic markers of biallele to investigate the performance of the proposed test under six different configurations. RESULTS: The haplotype-based TDT statistics, our test and Zhao et al.'s test satisfied a type I error probability, but the TDT test based on single locus showed a conservative trend. As expected, the tests based on haplotypes also had better powers than those based on single locus. Our test and that of Zhao et al. were comparable in powers. CONCLUSION: We proposed a TDT statistic based on haplotypes and showed through simulations that our test was more powerful than the single locus-based test. We will extend our method to multiplex data with affected and/or unaffected sibling (s) or simplex data having only one parent's genotype.

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