Abstract
Complex diseases are usually associated with multiple correlated phenotypes, and the analysis of composite scores or disease status may not fully capture the complexity (or multidimensionality). Joint analysis of multiple disease-related phenotypes in genetic tests could potentially increase power to detect association of a disease with common SNPs (or genes). Gene-based tests are designed to identify genes containing multiple risk variants that individually are weakly associated with a univariate trait. We combined three multivariate association tests (O’Brien method, TATES, and MultiPhen) with two gene-based association tests (GATES and VEGAS) and compared performance (type I error and power) of six multivariate gene-based methods using simulated data. Data (n = 2000) for genetic sequence and correlated phenotypes were simulated by varying causal variant proportions and phenotype correlations for various scenarios. These simulations showed that two multivariate association tests (TATES and MultiPhen, but not O’Brien) paired with VEGAS have inflated type I error in all scenarios, while the three multivariate association tests paired with GATES have correct type I error. MultiPhen paired with GATES has higher power than competing methods if the correlations among phenotypes are low (r < 0.57). We applied these gene-based association methods to a GWAS dataset from the Alzheimer’s Disease Genetics Consortium containing three neuropathological traits related to Alzheimer disease (neuritic plaque, neurofibrillary tangles, and cerebral amyloid angiopathy) measured in 3500 autopsied brains. Gene-level significant evidence (P < 2.7 × 10−6) was identified in a region containing three contiguous genes (TRAPPC12, TRAPPC12-AS1, ADI1) using O’Brien and VEGAS. Gene-wide significant associations were not observed in univariate gene-based tests.
Highlights
Genome-wide association study (GWAS) is a primary tool to identify association of genetic variants with phenotypes [1, 2]
The empirical type I errors of multivariate association methods with the gene-based tests are shown in Table 2 (VEGAS) and Table 3 (GATES) at different α levels based on the proportion of independent single-nucleotide polymorphism (SNP) in a gene and factor loading (Λ = 0.15, 0.35, 0.55, and 0.75)
Applying Versatile gene-based test for Genome-wide Association Studies (VEGAS) to rest of the multivariate association methods has inflated type I errors at α = 0.0001 for that uses the Extended Simes procedure (TATES) and at all α levels for MultiPhen for all scenarios irrespective of factor loadings or independent SNP proportions
Summary
Genome-wide association study (GWAS) is a primary tool to identify association of genetic variants with phenotypes [1, 2]. One plausible reason for unexplained heritability is due to the genetic architecture of complex diseases, which are affected by many common variants with low penetrance (i.e., small effect) [5]. Gene-based analysis, which considers the aggregate effect of multiple genic variants in a single test, is an alternative approach to overcome the genetic heterogeneity problem [7, 8]. It is well understood that some variants may influence multiple traits associated with a single complex disease, but association of those variants may not be detected in a model with a broadly defined outcome [10]. Multiphenotype analysis, which simultaneously considers more than one phenotype pathologically or clinically related with the disease, may help identify additional disease-related genetic associations
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