Objective: To analyze the association between low-frequency variants of ARID1A gene and primary liver cancer using latent category model. Methods: The low-frequency variants of ARID1A gene was combined according to different functional areas, and the combined variables were analyzed by using the latent class model to obtain the latent variables. Then the logistic regression was used to analyze the association between low-frequency variants of ARID1A gene and primary liver cancer. Results: The low-frequency variants of ARID1A gene were divided into three categories by the latent class model. The class 1 was mainly unmutated population, the proportion was 94.2% (2 454/2 603). The class 2 was mainly transcriptional regulatory domain mutation, take 4.8% (124/2 603). The class 3 was dominantly exon mutation, about 1.0% (27/2 603). Using class 1 as a reference, it was found that mutations in the transcriptional regulatory domain could reduce the risk of liver cancer (OR=0.601, 95% CI=0.364-0.992, P=0.046). Conclusion: The latent class model can identify low-frequency variants of gene associated with liver cancer and can be extended to more genetic association studies of low-frequency variants related to complex diseases.
目的： 应用潜在类别模型分析ARID1A基因低频变异与原发性肝癌的关系。 方法： 根据基因的不同功能区域将ARID1A基因低频变异进行合并，应用潜在类别模型分析合并后的ARID1A基因低频变异得到分类潜变量，采用logistic回归分析ARID1A基因低频变异与肝癌发生之间的关系。 结果： 潜在类别模型将ARID1A基因低频变异人群分为3类，类别1主要为基因未突变人群，占比94.2%(2 452/2 603)；类别2主要为基因转录调控功能区突变人群，占比4.8%(124/2 603)；类别3主要为基因外显子突变人群，占比1.0%(27/2 603)。以类别1人群作为参照，转录调控功能区基因突变可降低肝癌的发生风险(OR为0.601，95% CI为0.364～0.992, P＝0.046)。 结论： 潜在类别模型可识别肝癌相关基因低频变异，潜在类别模型可推广应用于更多复杂疾病相关低频变异的遗传关联研究。.
Keywords: ARID1A gene; Latent category model; Liver neoplasms; Low-frequency variants.