Data for Genetic Analysis Workshop 18: human whole genome sequence, blood pressure, and simulated phenotypes in extended pedigrees

Abstract

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.

Comparison of haplotype-based statistical tests for disease association with rare and common variants

“…SKAT and SKAT-O perform much better in these data compared with the earlier simulations because the GAW18 data simulation model was based on effects of individual SNPs and no haplotype effect (e.g. multiple causal variants acting in cis) was modeled as such [25].…”

“…While in the second type of data, the causal variants are SNPs and for this we consider GAW18 simulated data, which has no haplotype effects. In GAW data, 1458 functional variants interact with each other and other factors in a complex manner to affect systolic and diastolic blood pressure levels [25,31]. Thus, these data allow us to evaluate the approaches in scenarios where the causal variants are individual SNPs rather than haplotypes.…”

“…There are 200 replicates, each consisting of the same genotypes as in the real data. The phenotypes of systolic and diastolic blood pressures were generated [25,31]. We used a binary hypertension status derived from these phenotypes and medication status to define case/control status as in [26].…”

“…However, we do not intend to provide an in-depth comparison of haplotyping versus collapsing approaches, which has been reported elsewhere [11]. We use our own simulated data as well as Genetic Analysis Workshop (GAW) 18 simulated data [25,26]. We further apply the methods to two real data sets-hypertension data from GAW18 and Dallas Heart Study (DHS) case-control data [27,28].…”

A Family-Based Rare Haplotype Association Method for Quantitative Traits

“…Specifically, we consider a mixed-effects model for quantitative trait with random polygenic effects to capture the relationships between family members [28]. We carry out several simulation studies, including the analysis of the Genetic Analysis Workshop (GAW) 18 simulated data [29], to study the properties of famQBL and to compare with the haplotype-based test of FBAT [19]. Finally, we analyze the Framingham Heart Study (FHS) data from GAW16 [30] and find an rHTV association with diastolic blood pressure (DBP).…”