Abstract
Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Analysis Workshop 16 (GAW16) Problem 3 simulated data, we compared the statistical power to detect weak gene-gene interactions using a haplotype-based test in the UNPHASED software with genotypic mixed model (GMM) and additive mixed model (AMM) mixed linear regression model in SAS. We assumed a candidate-gene approach where a single-nucleotide polymorphism (SNP) in one gene is fixed and multiple SNPs are at the second gene. We analyzed the quantitative low-density lipoprotein trait (heritability 0.7%), modulated by simulated interaction of rs4648068 from 4q24 and another gene on 8p22, where we analyzed seven SNPs. We generally observed low power calculated per SNP (</= 37% at the 0.05 level), with the haplotype-based test being inferior. Over all tests, the haplotype-based test performed within chance, while GMM and AMM had low power (~10%). The haplotype-based and mixed models detected signals at different SNPs. The haplotype-based test detected a signal in 50 unique replicates; GMM and AMM featured both shared and distinct SNPs and replicates (65 replicates shared, 41 GMM, 27 AMM). Overall, the statistical signal for the weak gene-gene interaction appears sensitive to the sample structure of the replicates. We conclude that using more than one statistical approach may increase power to detect such signals in studies with limited number of loci such as replications. There were no results significant at the conservative 10-7 genome-wide level.
Highlights
With efforts to uncover more genetic variation for common polygenic disorders, there is accrued interest in the analysis of genetic interactions [1]
Using 200 replicates, we compared the power of the haplotypebased test in the computer program UNPHASED [3] to that of genotypic and additive mixed models (GMM and AMM) as implemented with a mixed linear regression model in SAS
At the 0.01 level, the power to detect the interaction calculated per single-nucleotide polymorphism (SNP) was greater with the mixed models
Summary
With efforts to uncover more genetic variation for common polygenic disorders, there is accrued interest in the analysis of genetic interactions [1]. It is very plausible to expect a plurality of statistical genetic interactions with each having a small effect, rather than a few strong interactions. We compare the statistical power of several methods to detect a weak (page number not for citation purposes). BMC Proceedings 2009, 3(Suppl 7):S77 http://www.biomedcentral.com/1753-6561/3/S7/S77 gene-gene interaction for a quantitative trait in a combined data set that includes both familial and unrelated samples, in a hypothetical candidate-gene design in which the single-nucleotide polymorphism (SNP) in one gene is fixed (for example, as replicated by marginal association analysis and/or for a known functional factor) and multiple SNPs are at the second interacting candidate gene. Using 200 replicates, we compared the power of the haplotypebased test in the computer program UNPHASED [3] to that of genotypic and additive mixed models (GMM and AMM) as implemented with a mixed linear regression model in SAS
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