[Application of latent class model in genetic association between ARID1A low-frequency variants and primary liver cancer].

ABSTRACT

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.