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Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

BACKGROUND: The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on anal...

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Autores principales: Blangero, John, Teslovich, Tanya M., Sim, Xueling, Almeida, Marcio A., Jun, Goo, Dyer, Thomas D., Johnson, Matthew, Peralta, Juan M., Manning, Alisa, Wood, Andrew R., Fuchsberger, Christian, Kent, Jack W., Aguilar, David A., Below, Jennifer E., Farook, Vidya S., Arya, Rector, Fowler, Sharon, Blackwell, Tom W., Puppala, Sobha, Kumar, Satish, Glahn, David C., Moses, Eric K., Curran, Joanne E., Thameem, Farook, Jenkinson, Christopher P., DeFronzo, Ralph A., Lehman, Donna M., Hanis, Craig, Abecasis, Goncalo, Boehnke, Michael, Göring, Harald, Duggirala, Ravindranath, Almasy, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133484/
https://www.ncbi.nlm.nih.gov/pubmed/27980614
http://dx.doi.org/10.1186/s12919-016-0008-y
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author Blangero, John
Teslovich, Tanya M.
Sim, Xueling
Almeida, Marcio A.
Jun, Goo
Dyer, Thomas D.
Johnson, Matthew
Peralta, Juan M.
Manning, Alisa
Wood, Andrew R.
Fuchsberger, Christian
Kent, Jack W.
Aguilar, David A.
Below, Jennifer E.
Farook, Vidya S.
Arya, Rector
Fowler, Sharon
Blackwell, Tom W.
Puppala, Sobha
Kumar, Satish
Glahn, David C.
Moses, Eric K.
Curran, Joanne E.
Thameem, Farook
Jenkinson, Christopher P.
DeFronzo, Ralph A.
Lehman, Donna M.
Hanis, Craig
Abecasis, Goncalo
Boehnke, Michael
Göring, Harald
Duggirala, Ravindranath
Almasy, Laura
author_facet Blangero, John
Teslovich, Tanya M.
Sim, Xueling
Almeida, Marcio A.
Jun, Goo
Dyer, Thomas D.
Johnson, Matthew
Peralta, Juan M.
Manning, Alisa
Wood, Andrew R.
Fuchsberger, Christian
Kent, Jack W.
Aguilar, David A.
Below, Jennifer E.
Farook, Vidya S.
Arya, Rector
Fowler, Sharon
Blackwell, Tom W.
Puppala, Sobha
Kumar, Satish
Glahn, David C.
Moses, Eric K.
Curran, Joanne E.
Thameem, Farook
Jenkinson, Christopher P.
DeFronzo, Ralph A.
Lehman, Donna M.
Hanis, Craig
Abecasis, Goncalo
Boehnke, Michael
Göring, Harald
Duggirala, Ravindranath
Almasy, Laura
author_sort Blangero, John
collection PubMed
description BACKGROUND: The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data. METHODS: GAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.
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spelling pubmed-51334842016-12-15 Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19 Blangero, John Teslovich, Tanya M. Sim, Xueling Almeida, Marcio A. Jun, Goo Dyer, Thomas D. Johnson, Matthew Peralta, Juan M. Manning, Alisa Wood, Andrew R. Fuchsberger, Christian Kent, Jack W. Aguilar, David A. Below, Jennifer E. Farook, Vidya S. Arya, Rector Fowler, Sharon Blackwell, Tom W. Puppala, Sobha Kumar, Satish Glahn, David C. Moses, Eric K. Curran, Joanne E. Thameem, Farook Jenkinson, Christopher P. DeFronzo, Ralph A. Lehman, Donna M. Hanis, Craig Abecasis, Goncalo Boehnke, Michael Göring, Harald Duggirala, Ravindranath Almasy, Laura BMC Proc Proceedings BACKGROUND: The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data. METHODS: GAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence. BioMed Central 2016-10-18 /pmc/articles/PMC5133484/ /pubmed/27980614 http://dx.doi.org/10.1186/s12919-016-0008-y 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
Blangero, John
Teslovich, Tanya M.
Sim, Xueling
Almeida, Marcio A.
Jun, Goo
Dyer, Thomas D.
Johnson, Matthew
Peralta, Juan M.
Manning, Alisa
Wood, Andrew R.
Fuchsberger, Christian
Kent, Jack W.
Aguilar, David A.
Below, Jennifer E.
Farook, Vidya S.
Arya, Rector
Fowler, Sharon
Blackwell, Tom W.
Puppala, Sobha
Kumar, Satish
Glahn, David C.
Moses, Eric K.
Curran, Joanne E.
Thameem, Farook
Jenkinson, Christopher P.
DeFronzo, Ralph A.
Lehman, Donna M.
Hanis, Craig
Abecasis, Goncalo
Boehnke, Michael
Göring, Harald
Duggirala, Ravindranath
Almasy, Laura
Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title_full Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title_fullStr Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title_full_unstemmed Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title_short Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
title_sort omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133484/
https://www.ncbi.nlm.nih.gov/pubmed/27980614
http://dx.doi.org/10.1186/s12919-016-0008-y
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