Population‐Based Association and Gene by Environment Interactions in Genetic Analysis Workshop 18

Abstract

In the past decade, genome-wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions. We summarize the contributions to Genetic Analysis Workshop 18 (GAW18) that concern this search using methods for population-based association analysis. Many of the members of our GAW18 working group made use of data types that have only recently become available through the use of next-generation sequencing technologies, with many focusing on the investigation of rare variants instead of or in combination with common variants. Some contributors used a haplotype-based approach, which to date has been used relatively infrequently but may become more important for analyzing rare variant association data. Others analyzed gene-gene or gene-environment interactions, where novel statistical approaches were needed to make the best use of the available information without requiring an excessive computational burden. GAW18 provided participants with the chance to make use of state-of-the-art data, statistical techniques, and technology. We report here some of the experiences and conclusions that were reached by workshop participants who analyzed the GAW18 data as a population-based association study.

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

“…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].…”

Modeling gene-environment interactions in longitudinal family studies: a comparison of methods and their application to the association between the IGF pathway and childhood obesity

“…The family-wise permutation test for p -value estimation represents one such approach. This permutation procedure accounts for familial correlation by restricting the random shuffling of outcome-predictor pairing to each family, but without distinguishing the specific pedigree relations within the family [34]. However, model simplification by averaging over time or assuming equal familial correlation could result in a loss of power as all the information in the data is not being utilized.…”

“…Nonparametric approaches have been developed to relax this assumption. For example, the partition-based score I (PBI) test evaluates hypothesized interactions between categorical factors by comparing data sets partitioned using different combinations of predictor variables [34, 35]. The significance of interactions is assessed by comparing the outcome explanatory powers of different partitioning variable combinations, and so the test does not assume specific interaction relationships between the factors [34, 35].…”