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Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees

Genetic Analysis Workshop 18 (GAW18) focused on identification of genes and functional variants that influence complex phenotypes in human sequence data. Data for the workshop were donated by the T2D-GENES Consortium and included whole genome sequences for odd-numbered autosomes in 464 key individua...

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Autores principales: Almasy, Laura, Dyer, Thomas D, Peralta, Juan M, Jun, Goo, Wood, Andrew R, Fuchsberger, Christian, Almeida, Marcio A, Kent, Jack W, Fowler, Sharon, Blackwell, Tom W, Puppala, Sobha, Kumar, Satish, Curran, Joanne E, Lehman, Donna, Abecasis, Goncalo, 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/PMC4145406/
https://www.ncbi.nlm.nih.gov/pubmed/25519314
http://dx.doi.org/10.1186/1753-6561-8-S1-S2
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author Almasy, Laura
Dyer, Thomas D
Peralta, Juan M
Jun, Goo
Wood, Andrew R
Fuchsberger, Christian
Almeida, Marcio A
Kent, Jack W
Fowler, Sharon
Blackwell, Tom W
Puppala, Sobha
Kumar, Satish
Curran, Joanne E
Lehman, Donna
Abecasis, Goncalo
Duggirala, Ravindranath
Blangero, John
author_facet Almasy, Laura
Dyer, Thomas D
Peralta, Juan M
Jun, Goo
Wood, Andrew R
Fuchsberger, Christian
Almeida, Marcio A
Kent, Jack W
Fowler, Sharon
Blackwell, Tom W
Puppala, Sobha
Kumar, Satish
Curran, Joanne E
Lehman, Donna
Abecasis, Goncalo
Duggirala, Ravindranath
Blangero, John
author_sort Almasy, Laura
collection PubMed
description Genetic Analysis Workshop 18 (GAW18) focused on identification of genes and functional variants that influence complex phenotypes in human sequence data. Data for the workshop were donated by the T2D-GENES Consortium and included whole genome sequences for odd-numbered autosomes in 464 key individuals selected from 20 Mexican American families, a dense set of single-nucleotide polymorphisms in 959 individuals in these families, and longitudinal data on systolic and diastolic blood pressure measured at 1-4 examinations over a period of 20 years. Simulated phenotypes were generated based on the real sequence data and pedigree structures. In the design of the simulation model, gene expression measures from the San Antonio Family Heart Study (not distributed as part of the GAW18 data) were used to identify genes whose mRNA levels were correlated with blood pressure. Observed variants within these genes were designated as functional in the GAW18 simulation if they were nonsynonymous and predicted to have deleterious effects on protein function or if they were noncoding and associated with mRNA levels. Two simulated longitudinal phenotypes were modeled to have the same trait distributions as the real systolic and diastolic blood pressure data, with effects of age, sex, and medication use, including a genotype-medication interaction. For each phenotype, more than 1000 sequence variants in more than 200 genes present on the odd-numbered autosomes individually explained less than 0.01-2.78% of phenotypic variance. Cumulatively, variants in the most influential gene explained 7.79% of trait variance. An additional simulated phenotype, Q1, was designed to be correlated among family members but to not be associated with any sequence variants. Two hundred replicates of the phenotypes were simulated, with each including data for 849 individuals.
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spelling pubmed-41454062014-09-02 Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees Almasy, Laura Dyer, Thomas D Peralta, Juan M Jun, Goo Wood, Andrew R Fuchsberger, Christian Almeida, Marcio A Kent, Jack W Fowler, Sharon Blackwell, Tom W Puppala, Sobha Kumar, Satish Curran, Joanne E Lehman, Donna Abecasis, Goncalo Duggirala, Ravindranath Blangero, John BMC Proc Proceedings Genetic Analysis Workshop 18 (GAW18) focused on identification of genes and functional variants that influence complex phenotypes in human sequence data. Data for the workshop were donated by the T2D-GENES Consortium and included whole genome sequences for odd-numbered autosomes in 464 key individuals selected from 20 Mexican American families, a dense set of single-nucleotide polymorphisms in 959 individuals in these families, and longitudinal data on systolic and diastolic blood pressure measured at 1-4 examinations over a period of 20 years. Simulated phenotypes were generated based on the real sequence data and pedigree structures. In the design of the simulation model, gene expression measures from the San Antonio Family Heart Study (not distributed as part of the GAW18 data) were used to identify genes whose mRNA levels were correlated with blood pressure. Observed variants within these genes were designated as functional in the GAW18 simulation if they were nonsynonymous and predicted to have deleterious effects on protein function or if they were noncoding and associated with mRNA levels. Two simulated longitudinal phenotypes were modeled to have the same trait distributions as the real systolic and diastolic blood pressure data, with effects of age, sex, and medication use, including a genotype-medication interaction. For each phenotype, more than 1000 sequence variants in more than 200 genes present on the odd-numbered autosomes individually explained less than 0.01-2.78% of phenotypic variance. Cumulatively, variants in the most influential gene explained 7.79% of trait variance. An additional simulated phenotype, Q1, was designed to be correlated among family members but to not be associated with any sequence variants. Two hundred replicates of the phenotypes were simulated, with each including data for 849 individuals. BioMed Central 2014-06-17 /pmc/articles/PMC4145406/ /pubmed/25519314 http://dx.doi.org/10.1186/1753-6561-8-S1-S2 Text en Copyright © 2014 Almasy 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
Almasy, Laura
Dyer, Thomas D
Peralta, Juan M
Jun, Goo
Wood, Andrew R
Fuchsberger, Christian
Almeida, Marcio A
Kent, Jack W
Fowler, Sharon
Blackwell, Tom W
Puppala, Sobha
Kumar, Satish
Curran, Joanne E
Lehman, Donna
Abecasis, Goncalo
Duggirala, Ravindranath
Blangero, John
Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title_full Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title_fullStr Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title_full_unstemmed Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title_short Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
title_sort data for genetic analysis workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4145406/
https://www.ncbi.nlm.nih.gov/pubmed/25519314
http://dx.doi.org/10.1186/1753-6561-8-S1-S2
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