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Open Access Methodology article

Regression-based approach for testing the association between multi-region haplotype configuration and complex trait

Yanling Hu1, Sinnwell Jason2, Qishan Wang1, Yuchun Pan1*, Xiangzhe Zhang1, Hongbo Zhao1, Changlong Li3 and Libin Sun4

Author Affiliations

1 School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, PR China

2 Mayo Clinic, Division of Biomedical Statistics and Informatics, Rochester, Minnesota, USA

3 Zhejiang Provincial Laboratory of Experimental Animals & Non-clinical Studies, Hangzhou, PR China

4 Shanghai Institute of Veterinary Hygiene, Shanghai, PR China

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BMC Genetics 2009, 10:56  doi:10.1186/1471-2156-10-56

Published: 17 September 2009

Abstract

Background

It is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable.

Results

In this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the minP approach. The P value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association.

Conclusion

Our simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.