A generalized kruskal-wallis test incorporating group uncertainty with application to genetic association studies

Biometrics

Volumn 69, Issue 2, 2013, Pages 427-435

  Acar, Elif F ^a,b   Sun, Lei ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Genome wide association studies; Imputation; K sample problem; Nonparametric test; Probabilistic data; Ranks

Indexed keywords

DEGREES OF FREEDOM (MECHANICS); GENES; SAMPLING; STATISTICAL TESTS;

GENERALISATION; GENOME-WIDE ASSOCIATION STUDIES; GROUP UNCERTAINTIES; IMPUTATION; K-SAMPLE PROBLEM; KRUSKAL-WALLIS TESTS; NONPARAMETRIC TESTS; PROBABILISTIC DATA; RANK; UNCERTAINTY;

PROBABILITY DISTRIBUTIONS;

DIABETES; GENOME; GENOTYPE; PROBABILITY; RANKING; SAMPLING; STATISTICAL DISTRIBUTION; TESTING METHOD; UNCERTAINTY ANALYSIS;

EID: 84879603122     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12006     Document Type: Article

References (23)

1. Aulchenko, Y. S., Struchalin, M. V., and van Duijn, C. M. (2010). Probabel package for genome-wide association analysis of imputed data. BMC Bioinformatics 11, 1-10.
2. Calvo, S. E., Tucker, E. J., Compton, A. G., Kirby, D. M., Crawford, G., Burtt, N. P., Rivas, M., Guiducci, C., Bruno, D. L., Goldberger, O. A., Redman, M. C., Wiltshire, E., Wilson, C. J., Altshuler, D., Gabriel, S. B., Daly, M. J., Thorburn, D. R., and Mootha, V. K. (2010). High-throughput, pooled sequencing identifies mutations in NUBPL and FOXRED1 in human complex I deficiency. Nature Genetics 42, 851-858.
3. Carvalho, B. S., Louis, T. A., and Irizarry, R. A. (2010). Quantifying uncertainty in genotype calls. Bioinformatics 26, 242-249.
4. Fraser, D. A. S. (1957). Nonparametric Methods in Statistics. New York: Wiley.
5. Hájek, J., Sidák, Z., and Sen, P. K. (1999). Theory of Rank Tests. San Diego: Academic Press.
6. Iman, R. L., Quade, D., and Alexander, D. A. (1975). Exact probability levels for the Kruskal-Wallis test. In Selected Tables in Mathematical Statistics, H. L. Harter and D. B. Owen (eds.), Volume 3, 329-384. Providence, RI: Institute of Mathematical Statistics.
7. Korn, J. M., Kuruvilla, F. G., McCarroll, S. A., Wysoker, A., Nemesh, J., Cawley, S., Hubbell, E., Veitch, J., Collins, P. J., Darvishi, K., Lee, C., Nizzari, M. M., Gabriel, S. B., Purcell, S., Daly, M. J., and Altshuler, D. (2008). Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nature Genetics 40, 1253-1260.
8. Kruskal, W. H. (1952). A nonparametric test for the several sample problem. Annals of Mathematical Statistics 23, 525-540.
9. Kruskal, W. H., and Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of American Statistical Association 47, 583-621.
10. Kutalik, Z., Johnson, T., Bochud, M., Mooser, V., Vollenweider, P., Waeber, G., Waterworth, D., Beckmann, J. S., and Bergmann, S. (2010). Methods for testing association between uncertain genotypes and quantitative traits. Biostatistics 12, 1-17.
11. Li, Y., Willer, C. J., Ding, J., Scheet, P., and Abecasis, G. R. (2006). Mach: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genetic Epidemiology 34, 816-834.
12. Li, Y., Willer, C. J., Sanna, S., and Abecasis, G. R. (2009). Genotype imputation. Annual Review of Genomics and Human Genetics 10, 387-406.
13. Lin, D. Y., Hu, Y., and Huang, B. E. (2008). Simple and efficient analysis of disease association with missing genotype data. American Journal of Human Genetics 83, 535-539.
14. Marchini, J., and Howie, B. (2010). Genotype imputation for genome-wide association studies. Nature Review Genetics 11, 499-511.
15. Marchini, J., Howie, B., Myers, S., McVean, G., and Donnelly, P. (2007). A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics 39, 906-913.
16. Nicolae, D. L. (2006). Testing untyped alleles (tuna)-applications to genome-wide association studies. Genetic Epidemiology 30, 718-727.
17. Paterson, A. D., Waggott, D., Boright, A. P., Hosseini, S. M., Shen, E., Sylvestre, M.-P., Wong, I., Bharaj, B., Cleary, P. A., Lachin, J. M., MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium)., Below, J. E., Nicolae, D., Cox, N. J., Canty, A. J., Sun, L., Bull, S. B., and Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. (2010). A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both a1c and glucose. Diabetes 59, 539-549.
18. Pei, Y.-F., Li, J., Zhang, L., Papasian, C. J., and Deng, H.-W. (2008). Analyses and comparison of accuracy of different genotype imputation methods. PLoS ONE 3, e3551.
19. Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M., and Poland, G. A. (2002). Score tests for association between traits and haplotypes when linkage phase is ambiguous. American Journal of Human Genetics 70, 425-434.
20. Wald, A., and Wolfowitz, J. (1944). Statistical tests based on permutations of the observations. Annals of Mathematical Statistics 15, 358-372.
21. Wei, Z., Wang, W., Hu, P., Lyon, G. J., and Hakonarson, H. (2011). Snver: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data. Nucleic Acids Research 39, e132.
22. Ye, C., Canty, A. J., Waggott, D., Sylvestre, M.-P., Shen, E., Hosseini, M., Boright, A. P., Sun, L., Bull, S. B., Paterson, A. D., and DCCT/EDIC Research Group (2010).A repeated measures genome wide association study of blood pressure in type 1 diabetes. Abstract # 203 presented at the Nineteenth Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology 34, 973.
23. Zheng, J., Li, Y., Abecasis, G. R., and Scheet, P. (2011). A comparison of approaches to account for uncertainty in analysis of imputed genotypes. Genetic Epidemiology 35, 102-110.