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. 2006 Dec;79(6):1002-16.
doi: 10.1086/509704. Epub 2006 Oct 20.

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions

Nilanjan Chatterjee  1 , Zeynep Kalaylioglu, Roxana Moslehi, Ulrike Peters, Sholom Wacholder
Affiliations

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions

Nilanjan Chatterjee et al. Am J Hum Genet. 2006 Dec.

Abstract

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

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Figures

Figure  1.
Figure  1.
A conceptual framework for modeling gene-gene interactions in indirect-association studies.
Figure  2.
Figure  2.
Empirical power, at α=0.01, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the purely epistatic model (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  3.
Figure  3.
Empirical power, at α=0.01, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the purely multiplicative model, with φ1=φ2 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  4.
Figure  4.
Empirical power, at α=0.01, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the additive model, with φ2 chosen so that MRR2=2.0 when f2=0.12 and MRR2=5.0 when f2=0.04 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  5.
Figure  5.
Empirical power, at α=0.01, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the crossover model, with φ1=0.90 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  6.
Figure  6.
Empirical power, at α=0.0001, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the purely epistatic model (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  7.
Figure  7.
Empirical power, at α=0.0001, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the purely multiplicative model, with φ1=φ2 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  8.
Figure  8.
Empirical power, at α=0.0001, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of causal SNPs in G1 and G2 follows the additive model, with φ2 chosen so that MRR2=2.0 when f2=0.12 and MRR2=5.0 when f2=0.04 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
Figure  9.
Figure  9.
Empirical power, at α=0.0001, to detect the association of the disease with candidate gene G1 as a function of the MRR of the underlying causal SNP, S*1. The joint effect of casual SNPs in G1 and G2 follows the crossover model, with φ1=0.90 (see table 2). f1 and f2 denote minor-allele frequencies for causal SNPs in G1 and G2, respectively, and R2geno denotes the value of multiple R2 between the causal and marker loci within a gene.
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References

Web Resources

    1. NCI Advanced Technology Center, http://cgf.nci.nih.gov/
    1. Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/ (for NAT2, GPX3, GPX4, and colorectal cancer)

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