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A novel statistical method for rare-variant association studies in general pedigrees
Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133499/ https://www.ncbi.nlm.nih.gov/pubmed/27980635 http://dx.doi.org/10.1186/s12919-016-0029-6 |
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author | Zhu, Huanhuan Wang, Zhenchuan Wang, Xuexia Sha, Qiuying |
author_facet | Zhu, Huanhuan Wang, Zhenchuan Wang, Xuexia Sha, Qiuying |
author_sort | Zhu, Huanhuan |
collection | PubMed |
description | Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Therefore, it is important to develop family-based statistical methods that can account for ascertainment. In this paper, we develop a novel statistical method for rare-variant association studies in general pedigrees for quantitative traits. This method uses a retrospective view that treats the traits as fixed and the genotypes as random, which allows us to account for complex and undefined ascertainment of families. We then apply the newly developed method to the Genetic Analysis Workshop 19 data set and compare the power of the new method with two other methods for general pedigrees. The results show that the newly proposed method increases power in most of the cases we consider, more than the other two methods. |
format | Online Article Text |
id | pubmed-5133499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51334992016-12-15 A novel statistical method for rare-variant association studies in general pedigrees Zhu, Huanhuan Wang, Zhenchuan Wang, Xuexia Sha, Qiuying BMC Proc Proceedings Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Therefore, it is important to develop family-based statistical methods that can account for ascertainment. In this paper, we develop a novel statistical method for rare-variant association studies in general pedigrees for quantitative traits. This method uses a retrospective view that treats the traits as fixed and the genotypes as random, which allows us to account for complex and undefined ascertainment of families. We then apply the newly developed method to the Genetic Analysis Workshop 19 data set and compare the power of the new method with two other methods for general pedigrees. The results show that the newly proposed method increases power in most of the cases we consider, more than the other two methods. BioMed Central 2016-10-18 /pmc/articles/PMC5133499/ /pubmed/27980635 http://dx.doi.org/10.1186/s12919-016-0029-6 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Proceedings Zhu, Huanhuan Wang, Zhenchuan Wang, Xuexia Sha, Qiuying A novel statistical method for rare-variant association studies in general pedigrees |
title | A novel statistical method for rare-variant association studies in general pedigrees |
title_full | A novel statistical method for rare-variant association studies in general pedigrees |
title_fullStr | A novel statistical method for rare-variant association studies in general pedigrees |
title_full_unstemmed | A novel statistical method for rare-variant association studies in general pedigrees |
title_short | A novel statistical method for rare-variant association studies in general pedigrees |
title_sort | novel statistical method for rare-variant association studies in general pedigrees |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133499/ https://www.ncbi.nlm.nih.gov/pubmed/27980635 http://dx.doi.org/10.1186/s12919-016-0029-6 |
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