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

John Blangero 1, Tanya M Teslovich 2, Xueling Sim 2, Marcio A Almeida 1, Goo Jun 3, Thomas D Dyer 1, Matthew Johnson 1, Juan M Peralta 1, Alisa Manning 4, Andrew R Wood 5, Christian Fuchsberger 2, Jack W Kent Jr 6, David A Aguilar 7, Jennifer E Below 8, Vidya S Farook 1, Rector Arya 1, Sharon Fowler 9, Tom W Blackwell 2, Sobha Puppala 6, Satish Kumar 1, David C Glahn 10, Eric K Moses 11, Joanne E Curran 1, Farook Thameem 12, Christopher P Jenkinson 1, Ralph A DeFronzo 13, Donna M Lehman 9, Craig Hanis 8, Goncalo Abecasis 2, Michael Boehnke 2, Harald Göring 1, Ravindranath Duggirala 1, Laura Almasy 14; T2D-GENES Consortium

Affiliations

18 October 2016

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doi: 10.1186/s12919-016-0008-y

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.

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