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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...

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Autores principales: Almeida, Marcio, Peralta, Juan M, Farook, Vidya, Puppala, Sobha, Kent, John W, Duggirala, Ravindranath, Blangero, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
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.
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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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