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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.
Afiliação
  • Almasy L; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Dyer TD; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Peralta JM; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Jun G; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, 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, Michigan, 48109, USA.
  • Almeida MA; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Kent JW; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Fowler S; Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, Texas, 78229, USA.
  • Blackwell TW; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
  • Puppala S; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Kumar S; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Curran JE; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Lehman D; Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, Texas, 78229, USA.
  • Abecasis G; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA.
  • Duggirala R; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
  • Blangero J; Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, 78227, USA.
BMC Proc ; 8(Suppl 1): S2, 2014.
Article em En | MEDLINE | ID: mdl-25519314
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BMC Proc Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BMC Proc Ano de publicação: 2014 Tipo de documento: Article