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

Abstract

BackgroundThe 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.MethodsGAW19 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.

Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

“…Results on sensitivity to phase 1 sampling variation suggest that the extreme and combined allocations are less sensitive to sampling variation, mainly because of the enrichment of the strata of interest that leads to sampling of most (or all) of the subjects in those categories (Appendix C supplementary material). Generating values for the multivariate model used for SBP were provided by GAW19 data simulators (Blangero et al, 2016). Sampling fractions are in percentages.…”

Genetic association analysis based on a joint model of gene expression and blood pressure

“…The family data set of the GAW 19 contains information on 157 unrelated individuals from the San Antonio family studies pedigrees, including systolic BP (SBP) and diastolic BP (DBP) measurements, information regarding current use of antihypertensive medication, and non-genetic covariates (sex, age, and current tobacco smoking status) at one or more examination time points [4]. GE measurements in lymphocytes at the first time point are available for 20,634 transcripts, which were already quality-checked, filtered for detectable expression, and quantile-normalized.…”

Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations

“…For an application of the proposed approach and to illustrate how its result can lead to different conclusions compared to traditional approaches, we performed a genetic association analysis of the GAW19 data (Blangero et al, 2016). The data contains whole genome-sequence data, gene expression in lymphocytes measured with microarrays, blood pressure phenotypes, as well as nongenetic covariates from the T2D-GENES Consortium.…”

“…In addition, we compare CIEE in the simulation study with the traditional multiple regression methods (MR and RR), the SEM method (Rosseel, ), and the sequential G‐estimation methods (Lipman et al., ; Vansteelandt et al., ). Finally, in an application to the Genetic Analysis Workshop 19 (GAW19) dataset (Blangero et al., ), we estimate and test direct effects of single nucleotide polymorphisms (SNPs) on blood pressure accounting for intermediate gene expression phenotypes and available covariates using CIEE and MR, and discuss the different results obtained. An R package with the implementation of CIEE is publicly available from https://cran.r-project.org/web/packages/CIEE/.…”