A Powerful and Robust Test in Genetic Association Studies

There are several well-known single SNP tests presented in the literature for detecting gene-disease association signals. Having in place an efficient and robust testing process across all genetic models would allow a more comprehensive approach to analysis. Although some studies have shown that it is possible to construct such a test when the variants are common and the genetic model satisfies certain conditions, the model conditions are too restrictive and in general difficult to verify. In this paper, we propose a powerful and robust test without assuming any model restrictions. Our test is based on the selected 2 × 2 tables derived from the usual 2 × 3 table. By signals from these tables, we show through simulations across a wide range of allele frequencies and genetic models that this approach may produce a test which is almost uniformly most powerful in the analysis of low- and high-frequency variants. Two cancer studies are used to demonstrate applications of the proposed test.

[1]  Joseph L. Gastwirth,et al.  Trend Tests for Case-Control Studies of Genetic Markers: Power, Sample Size and Robustness , 2002, Human Heredity.

[2]  Xihong Lin,et al.  Rare-variant association testing for sequencing data with the sequence kernel association test. , 2011, American journal of human genetics.

[3]  Xavier Estivill,et al.  Maximizing association statistics over genetic models , 2008, Genetic epidemiology.

[4]  Dan-Yu Lin,et al.  A general framework for detecting disease associations with rare variants in sequencing studies. , 2011, American journal of human genetics.

[5]  Xihong Lin,et al.  Optimal tests for rare variant effects in sequencing association studies. , 2012, Biostatistics.

[6]  Hon Keung Tony Ng,et al.  A Robust Method for Testing Association in Genome-Wide Association Studies , 2011, Human Heredity.

[7]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[8]  K F Cheng.  A maximum likelihood method for studying gene-environment interactions under conditional independence of genotype and exposure. , 2006, Statistics in medicine.

[9]  D. Balding.  A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.

[10]  Shan Wang-Gohrke,et al.  ESR1/SYNE1 Polymorphism and Invasive Epithelial Ovarian Cancer Risk: An Ovarian Cancer Association Consortium Study , 2010, Cancer Epidemiology, Biomarkers & Prevention.

[11]  V. Bansal,et al.  Statistical analysis strategies for association studies involving rare variants , 2010, Nature Reviews Genetics.

[12]  P. Sasieni.  From genotypes to genes: doubling the sample size. , 1997, Biometrics.

[13]  Joseph L. Gastwirth,et al.  The Use of Maximin Efficiency Robust Tests in Combining Contingency Tables and Survival Analysis , 1985 .

[14]  S. Leal,et al.  Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. , 2008, American journal of human genetics.

[15]  I. Thompson,et al.  Single and Multivariate Associations of MSR1, ELAC2, and RNASEL with Prostate Cancer in an Ethnic Diverse Cohort of Men , 2010, Cancer Epidemiology, Biomarkers & Prevention.

[16]  Wei Pan,et al.  Relationship between genomic distance‐based regression and kernel machine regression for multi‐marker association testing , 2011, Genetic epidemiology.

[17]  Zheng-Zheng Tang,et al.  A general framework for detecting disease associations with rare variants in sequencing studies. , 2011 .

[18]  Joseph L. Gastwirth,et al.  Choice of scores in trend tests for case-control studies of candidate-gene associations , 2003 .

[19]  M. Boehnke,et al.  So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. , 2007, American journal of human genetics.

[20]  J. Hirschhorn,et al.  Genetic model testing and statistical power in population‐based association studies of quantitative traits , 2007, Genetic epidemiology.

[21]  Gang Zheng,et al.  A Robust Genome‐Wide Scan Statistic of the Wellcome Trust Case–Control Consortium , 2009, Biometrics.

[22]  Qizhai Li,et al.  Efficient Approximation of P‐value of the Maximum of Correlated Tests, with Applications to Genome‐Wide Association Studies , 2008, Annals of human genetics.

[23]  Eleftheria Zeggini,et al.  Rare variant association analysis methods for complex traits. , 2010, Annual review of genetics.

[24]  Kai Wang,et al.  A constrained-likelihood approach to marker-trait association studies. , 2005, American journal of human genetics.