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http://dx.doi.org/10.5351/KJAS.2010.23.5.845

Comparison of Trend Tests for Genetic Association with Sibship Data  

Oh, Young-Sin (Department of Biostatistics, Graduate School, The Catholic University of Korea)
Kim, Han-Sang (Department of Biostatistics, Graduate School, The Catholic University of Korea)
Son, Hae-Hiang (Department of Biostatistics, Graduate School, The Catholic University of Korea)
Publication Information
The Korean Journal of Applied Statistics / v.23, no.5, 2010 , pp. 845-855 More about this Journal
Abstract
Extensively used case-control designs in medical studies can also be powerful and efficient for family association studies as long as an analysis method is developed for the evaluation of association between candidate genes and disease. Traditional Cochran-Armitage trend test is devised for independent subjects data, and to apply this trend test to the biologically related siblings one has to take into account the covariance among related family members in order to maintain the correct type I error rate. We propose a more powerful trend test by introducing weights that reflect the number of affected siblings in families for the evaluation of the association of genetic markers related to the disease. An application of our method to a sample family data, in addition to a small-scale simulation, is presented to compare the weighted and unweighted trend tests.
Keywords
Cochran-Armitage trend test; case-control designs; genetic association; sibship data;
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  • Reference
1 Armitage, P. (1955). Tests for linear trends in proportions and frequencies, Biometrics, 11, 375-386.   DOI   ScienceOn
2 Breslow, N. E. (1996). Statistics in epidemiology: The case-control study, Journal of the American Statistical Association, 91, 14-28.   DOI
3 Cochran, W. G. (1954). Some methods for strengthening the common chi-squared test, Biometrics, 10, 417-451.   DOI   ScienceOn
4 Fingerlin, T. E., Boehnke, M. and Abecasis, G. R. (2004). Increasing the power and efficiency of diseasemarker case-control association studies through use of allele-sharing information, American Journal of Human Genetics, 74, 432-443.   DOI   ScienceOn
5 Gauderman, W. J., Witte, J. S. and Thomas, D. C. (1999). Family-based association studies, Journal of the National Cancer Institute Monographs, 26, 31-37.
6 Kerber, R. A., Amos, C. I., Yeap, B. Y., Finkelstein, D. M. and Thomas, D. C. (2008). Design considerations in a sib-pair study of linkage for susceptibility loci in cancer, BMC Medical Genetics, 9, 64.   DOI   ScienceOn
7 Li, C. C. and Sacks, L. (1954). The derivation of joint distribution and correlation between relatives by the use of stochastic matrices, Biometrics, 10, 347-360.   DOI   ScienceOn
8 Li, M., Boehnke, M. and Abecasis, G. R. (2006). Efficient study designs for test of genetic association using sibship data and unrelated cases and controls, American Journal of Human Genetics, 78, 778-792.   DOI   ScienceOn
9 Monks, S. A., Kaplan, N. L. and Weir, B. S. (1998). A comparative study of sibship tests of linkage and/or association, American Journal of Human Genetics, 63, 1507-1516.   DOI   ScienceOn
10 Moore, R. M., Pinel, T., Zhao, J. H., March, R. and Jawaid, A. (2005). Selecting cases from nuclear families for case-control association analysis, BMC Genetics, 6(Suppl I), S105.   DOI
11 Risch, N. (2000). Searching for genetic determinants in the new millennium, Nature, 405, 847-856.   DOI   ScienceOn
12 Risch, N. and Merikangas, K. (1996). The future of genetic studies of complex human disease, Science, 273, 1516-1517.   DOI   ScienceOn
13 Risch, N. and Teng, J. (1998). The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. I. DNA pooling, Genome Research, 8, 1273-1288.   DOI
14 Slager, S. L. and Schaid, D. J. (2001). Evaluation of candidate genes in case-control studies: A statistical method to account for related subjects, American Journal of Human Genetics, 68, 1457-1462.   DOI   ScienceOn
15 Yates, F. (1948). The analysis of contingency tables with groupings based on quantitative characters, Biometrika, 35, 176-181.   DOI