Pathway analysis for family data using nested random-effects models
Recently we proposed a novel two-step approach to test for pathway effects in disease progression. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to certain genes. By using random effects, our approach acknowledges the correlations with...
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| Format: | Online |
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BioMed Central
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287857/ |
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pubmed-3287857 |
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pubmed-32878572012-02-28 Pathway analysis for family data using nested random-effects models Houwing-Duistermaat, Jeanine J Uh, Hae-Won Tsonaka, Roula Proceedings Recently we proposed a novel two-step approach to test for pathway effects in disease progression. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to certain genes. By using random effects, our approach acknowledges the correlations within and between genes when testing for pathway effects. Gene-gene and gene-environment interactions can be included in the model. The method can be implemented with standard software, and the distribution of the test statistics under the null hypothesis can be approximated by using standard chi-square distributions. Hence no extensive permutations are needed for computations of the p-value. In this paper we adapt and apply the method to family data, and we study its performance for sequence data from Genetic Analysis Workshop 17. For the set of unrelated subjects, the performance of the new test was disappointing. We found a power of 6% for the binary outcome and of 18% for the quantitative trait Q1. For family data the new approach appears to perform well, especially for the quantitative outcome. We found a power of 39% for the binary outcome and a power of 89% for the quantitative trait Q1. BioMed Central 2011-11-29 /pmc/articles/PMC3287857/ /pubmed/22373228 http://dx.doi.org/10.1186/1753-6561-5-S9-S22 Text en Copyright ©2011 Houwing-Duistermaat et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Houwing-Duistermaat, Jeanine J Uh, Hae-Won Tsonaka, Roula |
| spellingShingle |
Houwing-Duistermaat, Jeanine J Uh, Hae-Won Tsonaka, Roula Pathway analysis for family data using nested random-effects models |
| author_facet |
Houwing-Duistermaat, Jeanine J Uh, Hae-Won Tsonaka, Roula |
| author_sort |
Houwing-Duistermaat, Jeanine J |
| title |
Pathway analysis for family data using nested random-effects models |
| title_short |
Pathway analysis for family data using nested random-effects models |
| title_full |
Pathway analysis for family data using nested random-effects models |
| title_fullStr |
Pathway analysis for family data using nested random-effects models |
| title_full_unstemmed |
Pathway analysis for family data using nested random-effects models |
| title_sort |
pathway analysis for family data using nested random-effects models |
| description |
Recently we proposed a novel two-step approach to test for pathway effects in disease progression. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to certain genes. By using random effects, our approach acknowledges the correlations within and between genes when testing for pathway effects. Gene-gene and gene-environment interactions can be included in the model. The method can be implemented with standard software, and the distribution of the test statistics under the null hypothesis can be approximated by using standard chi-square distributions. Hence no extensive permutations are needed for computations of the p-value. In this paper we adapt and apply the method to family data, and we study its performance for sequence data from Genetic Analysis Workshop 17. For the set of unrelated subjects, the performance of the new test was disappointing. We found a power of 6% for the binary outcome and of 18% for the quantitative trait Q1. For family data the new approach appears to perform well, especially for the quantitative outcome. We found a power of 39% for the binary outcome and a power of 89% for the quantitative trait Q1. |
| publisher |
BioMed Central |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287857/ |
| _version_ |
1611508747191975936 |