Simple Algorithms to Calculate Asymptotic Null Distributions of Robust Tests in Case-Control Genetic Association Studies in R. The case-control study is an important design for testing association between genetic markers and a disease. The Cochran-Armitage trend test (CATT) is one of the most commonly used statistics for the analysis of case-control genetic association studies. The asymptotically optimal CATT can be used when the underlying genetic model (mode of inheritance) is known. However, for most complex diseases, the underlying genetic models are unknown. Thus, tests robust to genetic model misspecification are preferable to the model-dependant CATT. Two robust tests, MAX3 and the genetic model selection (GMS), were recently proposed. Their asymptotic null distributions are often obtained by Monte-Carlo simulations, because they either have not been fully studied or involve multiple integrations. In this article, we study how components of each robust statistic are correlated, and find a linear dependence among the components. Using this new finding, we propose simple algorithms to calculate asymptotic null distributions for MAX3 and GMS, which greatly reduce the computing intensity. Furthermore, we have developed the R package Rassoc implementing the proposed algorithms to calculate the empirical and asymptotic p values for MAX3 and GMS as well as other commonly used tests in case-control association studies. For illustration, Rassoc is applied to the analysis of case-control data of 17 most significant SNPs reported in four genome-wide association studies.

References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Carmen Pardo, M.; Lu, Ying; Franco-Pereira, Alba M.: Extensions of empirical likelihood and chi-squared-based tests for ordered alternatives (2022)
  2. Lin Wang, Wei Zhang, Qizhai Li: AssocTests: An R Package for Genetic Association Studies (2020) not zbMATH
  3. Zang, Yong; Fung, Wing Kam; Cao, Sha; Ng, Hon Keung Tony; Zhang, Chi: Robust tests for gene-environment interaction in case-control and case-only designs (2019)
  4. Xiong, Wenjun; Su, You; Ding, Juan: Empirical likelihood-based robust tests for genetic association analysis with quantitative traits (2017)
  5. Kozlitina, Julia; Schucany, William R.: A robust distribution-free test for genetic association studies of quantitative traits (2015)
  6. Zheng, Gang; Zhang, Wei; Li, Qizhai; Xu, Jinfeng; Yuan, Ao; Gastwirth, Joseph L.: Analysis of genetic association studies incorporating prior information of genetic models (2015)
  7. Geller, Nancy L.; Wu, Colin O.: In memoriam: Gang Zheng (May 6, 1965 -- January 9, 2014) (2014)
  8. Langaas, Mette; Bakke, √ėyvind: Robust methods to detect disease-genotype association in genetic association studies: calculate (p)-values using exact conditional enumeration instead of simulated permutations or asymptotic approximations (2014)
  9. Qu, Long: Combining dependent (F)-tests for robust association of quantitative traits under genetic model uncertainty (2014)
  10. Chen, Zhongxue: Association tests through combining (p)-values for case control genome-wide association studies (2013)
  11. Wang, Linglu; Li, Qizhai; Li, Zhaohai; Zheng, Gang: Bayes factor based on a maximum statistic for case-control genetic association studies (2012)
  12. Yong Zang; Wing Fung; Gang Zheng: Simple Algorithms to Calculate Asymptotic Null Distributions of Robust Tests in Case-Control Genetic Association Studies in R (2010) not zbMATH
  13. Zheng, Gang; Joo, Jungnam; Zaykin, Dmitri; Wu, Colin; Geller, Nancy: Robust tests in genome-wide scans under incomplete linkage disequilibrium (2009)