Regression-based approach for testing the association between multi-region haplotype configuration and complex trait
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p>...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2009-09-01
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| Series: | BMC Genetics |
| Online Access: | http://www.biomedcentral.com/1471-2156/10/56 |
| _version_ | 1818246306106179584 |
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| author | Zhao Hongbo Zhang Xiangzhe Pan Yuchun Wang Qishan Jason Sinnwell Hu Yanling Li Changlong Sun Libin |
| author_facet | Zhao Hongbo Zhang Xiangzhe Pan Yuchun Wang Qishan Jason Sinnwell Hu Yanling Li Changlong Sun Libin |
| author_sort | Zhao Hongbo |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>minP </it>approach. The <it>P </it>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.</p> <p>Conclusion</p> <p>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.</p> |
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| format | Article |
| id | doaj.art-0e2c0a96e0394fe3b90e8bbe3e8a25e6 |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-12-12T14:46:43Z |
| publishDate | 2009-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-0e2c0a96e0394fe3b90e8bbe3e8a25e62022-12-22T00:21:05ZengBMCBMC Genetics1471-21562009-09-011015610.1186/1471-2156-10-56Regression-based approach for testing the association between multi-region haplotype configuration and complex traitZhao HongboZhang XiangzhePan YuchunWang QishanJason SinnwellHu YanlingLi ChanglongSun Libin<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>minP </it>approach. The <it>P </it>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.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1471-2156/10/56 |
| spellingShingle | Zhao Hongbo Zhang Xiangzhe Pan Yuchun Wang Qishan Jason Sinnwell Hu Yanling Li Changlong Sun Libin Regression-based approach for testing the association between multi-region haplotype configuration and complex trait BMC Genetics |
| title | Regression-based approach for testing the association between multi-region haplotype configuration and complex trait |
| title_full | Regression-based approach for testing the association between multi-region haplotype configuration and complex trait |
| title_fullStr | Regression-based approach for testing the association between multi-region haplotype configuration and complex trait |
| title_full_unstemmed | Regression-based approach for testing the association between multi-region haplotype configuration and complex trait |
| title_short | Regression-based approach for testing the association between multi-region haplotype configuration and complex trait |
| title_sort | regression based approach for testing the association between multi region haplotype configuration and complex trait |
| url | http://www.biomedcentral.com/1471-2156/10/56 |
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