Genetic association studies usually apply the simple chi-square ( χ 2 )-test for testing association between a single-nucleotide polymorphism (SNP) and a particular phenotype, assuming the genotypes and phenotypes are independent. So, the conventional χ 2 -test does not consider the increased risk of an individual carrying the increasing number of disease responsible allele (a particular genotype). But, the association tests should be performed with the consideration of this disease risk according to the mode of inheritance (additive, dominant, recessive). Practical demonstration of the two possible methods for considering such order or trends in contingency tables of genetic association studies using SNP genotype data is the purpose of this paper. One method is by pooling the genotypes, and the other is scoring the individual genotypes, based on the disease risk according to the inheritance pattern. The results show that the p -values obtained from both the methods are similar for the dominant and recessive models. The other important features of the methods were also extracted using the SNP genotype data for different inheritance patterns.
Beck, T., Shorter, T. and Brookes, A.J. (2020) GWAS Central: A Comprehensive Resource for the Discovery and Comparison of Genotype and Phenotype Data from Genome-Wide Association Studies. Nucleic Acids Research, 48, D933-D940. https://doi.org/10.1093/nar/gkz895
Svishcheva, G.R., Belonogova, N.M., Zorkoltseva, I.V., Kirichenko, A.V. and Axenovich, T.I. (2019) Gene-Based Association Tests Using GWAS Summary Statistics. Bioinformatics, 35, 3701-3708. https://doi.org/10.1093/bioinformatics/btz172
Cao, X., Wang, X., Zhang, S., and Sha, Q. (2022) Gene-Based Association Tests Using GWAS Summary Statistics and Incorporating eQTL. Scientific Reports, 12, Article No. 3553. https://doi.org/10.1038/s41598-022-07465-0
Wang, Y., Li, Y., Hao, M., Liu, X., Zhang, M., Wang, J., Xiong, M., Shugart, Y.Y., and Jin, L. (2019) Robust Reference Powered Association Test of Genome-Wide Association Studies. Frontiers in Genetics, 10, Article No. 319. https://doi.org/10.3389/fgene.2019.00319
Uffelmann, E., Huang, Q.Q., Munung, N.S., de Vries, J., Okada, Y., Martin, A.R., Martin, H.C., Lappalainen, T. and Posthuma, D. (2021) Genome-Wide Association Studies. Nature Reviews Methods Primers, 1, Article No. 59. https://doi.org/10.1038/s43586-021-00056-9
Boua, P.R., Brandenburg, J.T., Choudhury, A., Sorgho, H., Nonterah, E.A., Agongo, G., Asiki, G., Micklesfield, L., Choma, S., Gómez-Olivé, F.X., Hazelhurst, S., Tinto, H., Crowther, N.J., Mathew, C.G., Ramsay, M., AWI-Gen Study and the H3Africa Consortium (2022) Genetic Associations with Carotid Intima-Media Thickness Link to Atherosclerosis with Sex-Specific Effects in Sub-Saharan Africans. Nature Communications, 13, Article No. 855. https://doi.org/10.1038/s41467-022-28276-x
Tangdén, T., Gustafsson, S., Rao, A.S. and Ingelsson, E. (2022) A Genome-Wide Association Study in a Large Community-Based Cohort Identifies Multiple Loci Associated with Susceptibility to Bacterial and Viral Infections. Scientific Reports, 12, Article No. 2582. https://doi.org/10.1038/s41598-022-05838-z
Loos, R.J.F. (2020) 15 Years of Genome-Wide Association Studies and No Signs of Slowing down. Nature Communications, 11, Article No. 5900. https://doi.org/10.1038/s41467-020-19653-5
Patron, J., Serra-Cayuela, A., Han, B., Li, C. and Wishart, D.S. (2019) Assessing the Performance of Genome-Wide Association Studies for Predicting Disease Risk. PLOS ONE, 14, Article ID: e0220215. https://doi.org/10.1371/journal.pone.0220215
Bush, W.S. and Moore, J.H. (2012) Chapter 11: Genome-Wide Association Studies. PLOS Computational Biology, 8, Article ID: e1002822. https://doi.org/10.1371/journal.pcbi.1002822
Setu, T.J. and Basak, T. (2021) An Introduction to Basic Statistical Models in Genetics. Open Journal of Statistics, 11, 1017-1025. https://doi.org/10.4236/ojs.2021.116060
Basak, T. and Roy, N. (2022) A Preliminary Outline of the Statistical Inference Process in Genetic Association Studies. Open Journal of Statistics, 12, 200-209. https://doi.org/10.4236/ojs.2022.122014
Plackett, R.L. (1983) Karl Pearson and the Chi-Squared Test. International Statistical Review, 51, 59-72. https://doi.org/10.2307/1402731 https://www.jstor.org/stable/1402731
Moore, J.H., Hahn, L.W., Ritchie, M.D., Thornton, T.A. and White, B.C. (2004) Routine Discovery of Complex Genetic Models using Genetic Algorithms. Applied Soft Computing, 4, 79-86. https://doi.org/10.1016/j.asoc.2003.08.003
Cooper, D.N., Krawczak, M., Polychronakos, C., Tyler-Smith, C. and Kehrer-Sawatzk, H. (2013) Where Genotype Is Not Predictive of Phenotype: Towards an Understanding of the Molecular Basis of Reduced Penetrance in Human Inherited Disease. Human Genetics, 132, 1077-1130. https://doi.org/10.1007/s00439-013-1331-2
Ford, D., Easton, D.F., Stratton, M., Narod, S., Goldgar, D., Devilee, P., Bishop, D.T., Weber, B., Lenoir, G., Chang-Claude, J., Sobol, H., Teare, M.D., Struewing, J., Arason, A., Scherneck, S., Peto, J., Rebbeck, T.R., Tonin, P., Neuhausen, S., Barkardottir, R., Eyfjord, J., Lynch, H., Ponder, B.A.J., Gayther, S.A., Birch, J.M., Lindblom, A., Stoppa-Lyonnet, D., Bignon, Y., Borg, A., Hamann, U., Haites, N., Scott, R.J., Maugard, C.M., Vasen, H., Seitz, S., Cannon-Albright, L.A., Schofield, A., Zelada-Hedman, M. and The Breast Cancer Linkage Consortium (1998) Genetic Heterogeneity and Penetrance Analysis of the BRCA1 and BRCA2 Genes in Breast Cancer Families. American Journal of Human Genetics, 62, 676-689. https://doi.org/10.1086/301749
Ziegler, A. and Konig, I.R. (2010) A Statistical Approach to Genetic Epidemiology: Concepts and Applications. 2nd Edition, Wiley-VCH, Weinheim. https://doi.org/10.1002/9783527633654
Gong, G., Hannon, N. and Whittemore, A.S. (2010) Estimating Gene Penetrance from Family Data. Genetic Epidemiology, 34, 373-381. https://doi.org/10.1002/gepi.20493
Lewis, C.M. (2002) Genetic Association Studies: Design, Analysis and Interpretation. Briefings in Bioinformatics, 3, 146-153. https://doi.org/10.1093/bib/3.2.146
Fang, Y., Wang, Y. and Sha, N. (2009) Armitage’s Trend Test for Genome-Wide Association Analysis: One-Sided or Two-Sided? BMC Proceedings, 3, Article No. Article No. S37. https://doi.org/10.1186/1753-6561-3-S7-S37
Armitage, P. (1955) Tests for Linear Trends in Proportions and Frequencies. Biometrics, 11, 375-386. https://doi.org/10.2307/3001775 https://www.jstor.org/stable/3001775
Cochran, W.G. (1954) Some Methods for Strengthening the Common χ2 Test. Biometrics, 10, 417-451. https://doi.org/10.2307/3001616 https://www.jstor.org/stable/3001616