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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.
Afiliação
  • Blangero J; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Teslovich TM; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Sim X; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Almeida MA; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Jun G; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA ; Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA.
  • Dyer TD; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Johnson M; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Peralta JM; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Manning A; Department of Genetics, Massachusetts General Hospital, Boston, MA 02114 USA.
  • Wood AR; Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK.
  • Fuchsberger C; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Kent JW; Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX 78227 USA.
  • Aguilar DA; Cardiovascular Division, Baylor College of Medicine, Houston, TX 77030 USA.
  • Below JE; Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA.
  • Farook VS; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Arya R; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Fowler S; Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA.
  • Blackwell TW; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Puppala S; Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX 78227 USA.
  • Kumar S; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Glahn DC; Department of Psychiatry, Yale University, New Haven, CT 06106 USA.
  • Moses EK; Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia.
  • Curran JE; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Thameem F; Department of Biochemistry, Faculty of Medicine, Kuwait University, Safat, Kuwait City, 13110 Kuwait.
  • Jenkinson CP; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • DeFronzo RA; Texas Diabetes Institute, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA.
  • Lehman DM; Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA.
  • Hanis C; Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA.
  • Abecasis G; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Boehnke M; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA.
  • Göring H; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Duggirala R; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA.
  • Almasy L; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA ; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104 USA.
BMC Proc ; 10(Suppl 7): 71-77, 2016.
Article em En | MEDLINE | ID: mdl-27980614
ABSTRACT

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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BMC Proc Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BMC Proc Ano de publicação: 2016 Tipo de documento: Article