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Pedigree-based random effect tests to screen gene pathways
The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Fami...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143680/ https://www.ncbi.nlm.nih.gov/pubmed/25519354 http://dx.doi.org/10.1186/1753-6561-8-S1-S100 |
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author | Almeida, Marcio Peralta, Juan M Farook, Vidya Puppala, Sobha Kent, John W Duggirala, Ravindranath Blangero, John |
author_facet | Almeida, Marcio Peralta, Juan M Farook, Vidya Puppala, Sobha Kent, John W Duggirala, Ravindranath Blangero, John |
author_sort | Almeida, Marcio |
collection | PubMed |
description | The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable, suggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers. |
format | Online Article Text |
id | pubmed-4143680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41436802014-09-02 Pedigree-based random effect tests to screen gene pathways Almeida, Marcio Peralta, Juan M Farook, Vidya Puppala, Sobha Kent, John W Duggirala, Ravindranath Blangero, John BMC Proc Proceedings The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable, suggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers. BioMed Central 2014-06-17 /pmc/articles/PMC4143680/ /pubmed/25519354 http://dx.doi.org/10.1186/1753-6561-8-S1-S100 Text en Copyright © 2014 Almeida 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. 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 Almeida, Marcio Peralta, Juan M Farook, Vidya Puppala, Sobha Kent, John W Duggirala, Ravindranath Blangero, John Pedigree-based random effect tests to screen gene pathways |
title | Pedigree-based random effect tests to screen gene pathways |
title_full | Pedigree-based random effect tests to screen gene pathways |
title_fullStr | Pedigree-based random effect tests to screen gene pathways |
title_full_unstemmed | Pedigree-based random effect tests to screen gene pathways |
title_short | Pedigree-based random effect tests to screen gene pathways |
title_sort | pedigree-based random effect tests to screen gene pathways |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143680/ https://www.ncbi.nlm.nih.gov/pubmed/25519354 http://dx.doi.org/10.1186/1753-6561-8-S1-S100 |
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