A robust association test with multiple genetic variants and covariates

Lee Jen-Yu (), Shen Pao-Sheng and Cheng Kuang-Fu
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Lee Jen-Yu: Department of Statistics, Feng Chia University, Taichung, Taiwan, ROC
Shen Pao-Sheng: Department of Statistics, Tunghai University, Taichung, Taiwan, ROC
Cheng Kuang-Fu: Biostatistics Center, Taipei Medical University, Taipei, Taiwan, ROC

Statistical Applications in Genetics and Molecular Biology, 2022, vol. 21, issue 1, 14

Abstract: Due to the advancement of genome sequencing techniques, a great stride has been made in exome sequencing such that the association study between disease and genetic variants has become feasible. Some powerful and well-known association tests have been proposed to test the association between a group of genes and the disease of interest. However, some challenges still remain, in particular, many factors can affect the performance of testing power, e.g., the sample size, the number of causal and non-causal variants, and direction of the effect of causal variants. Recently, a powerful test, called T REM , is derived based on a random effects model. T REM has the advantages of being less sensitive to the inclusion of non-causal rare variants or low effect common variants or the presence of missing genotypes. However, the testing power of T REM can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of T REM , we propose a novel test, called T ROB , which keeps the advantages of T REM and is more robust than T REM in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that T ROB has a stable type I error rate and outperforms T REM when the proportion of risk variants decreases to a certain level and its advantage over T REM increases as the proportion decreases. Furthermore, T ROB outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.

Keywords: association test; bootstrap; effect direction; missing genotype; robustness (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/sagmb-2021-0029

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