Skip to main content Accessibility help
×
×
Home

A nonparametric method to test for associations between rare variants and multiple traits

  • YING ZHOU (a1) (a2), YANGYANG CHENG (a1), WENSHENG ZHU (a1) and QIAN ZHOU (a3)

Summary

More and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall's τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits, especially for multivariate ordinal traits.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      A nonparametric method to test for associations between rare variants and multiple traits
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      A nonparametric method to test for associations between rare variants and multiple traits
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      A nonparametric method to test for associations between rare variants and multiple traits
      Available formats
      ×

Copyright

Corresponding author

*Corresponding author: Dr Wensheng Zhu, School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun 130024, PR China. E-mail: wszhu@nenu.edu.cn

References

Hide All
Baker, N. L., Mörgelin, M., Peat, R., Goemans, N., North, K. N., Bateman, J. F. & Lamandé, S. R. (2005). Dominant collagen VI mutations are a common cause of Ullrich congenital muscular dystrophy. Human Molecular Genetics 14, 279293.
Bodmer, W. & Bonilla, C. (2008). Common and rare variants in multifactorial susceptibility to common diseases. Nature Genetics 40, 695701.
Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H. & Nadeau, J. H. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics 11, 446450.
Fang, S. R., Sha, Q. Y. & Zhang, S. L. (2012). Two adaptive weighting methods to test for rare variant associations in family-based designs. Genetic Epidemiology 36, 499507.
Jin, L. N., Zhu, W. S., Yu, Y. Q., Kou, C., Meng, X., Tao, Y. & Guo, J. (2014). Nonparametric tests of associations with disease based on U-Statistic. Annals of Human Genetics 78, 141153.
Lange, C., Silverman, E. K., Xu, X., Weiss, S. T. & Laird, N. M. (2003). A multivariate family-based association test using generalized estimating equations: FBAT-GEE. Biostatistics 4, 195206.
Lee, S., Wu, M. & Lin, X. (2012). Optimal tests for rare variant effects in sequencing association studies. Biostatistics 4, 762775.
Li, B. & Leal, S. M. (2008). Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. The American Journal of Human Genetics 83, 311321.
Madsen, B. E. & Browning, S. R. (2009). A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genetics 5, e1000384.
Maher, B. (2008). Personal genomes: the case of the missing heritability. Nature 456, 1821.
Maierhaba, M., Zhang, J. A., Yu, Z. Y., Wang, Y., Xiao, W. X., Quan, Y. & Dong, B. N. (2008). Association of the thyroglobulin gene polymorphism with autoimmune thyroid disease in Chinese population. Endocrine 33, 294299.
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., Cho, J. H., Guttmacher, A. E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C. N., Slatkin, M., Valle, D., Whittemore, A. S., Boehnke, M., Clark, A. G., Eichler, E. E., Gibson, G., Haines, J. L., Mackay, T. F., McCarroll, S. A. & Visscher, P. M. (2009). Finding the missing heritability of complex diseases. Nature 461, 747753.
Metzker, M. L. (2010). Sequencing technologies – the next generation. Nature Reviews Genetics 11, 3146.
Morgenthaler, S. & Thilly, W. G. (2007). A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutation Research 615, 2856.
Ng, S. B., Buckingham, K. J. & Lee, C. (2010). Exome sequencing identifies the cause of a Mendelian disorder. Nature Genetics 42, 3035.
Pan, W. (2009). Asymptotic tests of association with multiple SNP in linkage disequilibrium. Genetic Epidemiology 5, e1000384.
Robinson, M. R., Wray, N. R. & Visscher, P. M. (2014). Explaining additional genetic variation in complex traits. Trends in Genetics 30, 124132.
Wu, M., Lee, S., Cai, T., Li, Y, Boehnke, M. & Lin, X. (2011). Rare variant association testing for sequencing data using the sequence kernel association test (SKAT). The American Journal of Human Genetics 89, 8293.
Zhang, L., Pei, Y. F., Li, J., Papasian, C. J. & Deng, H. W. (2010 a). Efficient utilization of rare variants for detection of disease-related genomic regions. PLoS ONE 5, e14288.
Zhang, H. P., Liu, C. T. & Wang, X. Q. (2010 b). An association test for multiple traits based on the generalized Kendall's tau. Journal of the American Statistical Association 105, 473481.
Zhu, W. S. & Zhang, H. P. (2009). Why do we test multiple traits in genetic association studies? Journal of the Korean Statistical Society 38, 110.
Zhu, W. S. & Zhang, H. P. (2013). A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines. Frontiers of Mathematics in China 3, 731743.
Zhu, W. S., Jiang, Y., & Zhang, H. P. (2012). Nonparametric covariate-adjusted association tests based on the generalized Kendall's tau. Journal of the American Statistical Association 107, 111.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Genetics Research
  • ISSN: 0016-6723
  • EISSN: 1469-5073
  • URL: /core/journals/genetics-research
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed