{"title":"A robust association test with multiple genetic variants and covariates.","authors":"Jen-Yu Lee, Pao-Sheng Shen, Kuang-Fu Cheng","doi":"10.1515/sagmb-2021-0029","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>T</i><sub><i>REM</i></sub> , is derived based on a random effects model. <i>T</i><sub><i>REM</i></sub> 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 <i>T</i><sub><i>REM</i></sub> can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of <i>T</i><sub><i>REM</i></sub> , we propose a novel test, called <i>T</i><sub><i>ROB</i></sub> , which keeps the advantages of <i>T</i><sub><i>REM</i></sub> and is more robust than <i>T</i><sub><i>REM</i></sub> in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that <i>T</i><sub><i>ROB</i></sub> has a stable type I error rate and outperforms <i>T</i><sub><i>REM</i></sub> when the proportion of risk variants decreases to a certain level and its advantage over <i>T</i><sub><i>REM</i></sub> increases as the proportion decreases. Furthermore, <i>T</i><sub><i>ROB</i></sub> outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.</p>","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":" ","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Applications in Genetics and Molecular Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/sagmb-2021-0029","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
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 TREM , is derived based on a random effects model. TREM 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 TREM can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of TREM , we propose a novel test, called TROB , which keeps the advantages of TREM and is more robust than TREM in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that TROB has a stable type I error rate and outperforms TREM when the proportion of risk variants decreases to a certain level and its advantage over TREM increases as the proportion decreases. Furthermore, TROB outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.
期刊介绍:
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.