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Optimal Trend Tests for Genetic Association Studies of Heterogeneous Diseases.

ABSTRACT: The Cochran-Armitage trend test is a standard procedure in genetic association studies. It is a directed test with high power to detect genetic effects that follow the gene-dosage model. In this paper, the author proposes optimal trend tests for genetic association studies of heterogeneous diseases. Monte-Carlo simulations show that the power gain of the optimal trend tests over the conventional Cochran-Armitage trend test is striking when the genetic effects are heterogeneous. The easy-to-use R 3.1.2 software (R Foundation for Statistical Computing, Vienna, Austria) code is provided. The optimal trend tests are recommended for routine use.

SUBMITTER: Lee WC

PROVIDER: S-EPMC4899796 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Optimal Trend Tests for Genetic Association Studies of Heterogeneous Diseases.

Lee Wen-Chung WC

Scientific reports 20160609

The Cochran-Armitage trend test is a standard procedure in genetic association studies. It is a directed test with high power to detect genetic effects that follow the gene-dosage model. In this paper, the author proposes optimal trend tests for genetic association studies of heterogeneous diseases. Monte-Carlo simulations show that the power gain of the optimal trend tests over the conventional Cochran-Armitage trend test is striking when the genetic effects are heterogeneous. The easy-to-use R ...[more]

PMID: 27278756

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Project description:A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 ⋅ 10-09) as well as replicate the interaction (p = 6.97 ⋅ 10-07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Dataset Information

Optimal Trend Tests for Genetic Association Studies of Heterogeneous Diseases.

Publications

Optimal Trend Tests for Genetic Association Studies of Heterogeneous Diseases.

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