Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

The Application of Bayesian Methods in Cancer Prognosis and Prediction.

With the development of high-throughput biological techniques, high-dimensional omics data have emerged. These molecular data provide a solid foundation for precision medicine and prognostic prediction of cancer. Bayesian methods contribute to constructing prognostic models with complex relationships in omics and improving performance by introducing different prior distribution, which is suitable for modelling the high-dimensional data involved. Using different omics, several Bayesian hierarchical approaches have been proposed for variable selection and model construction. In particular, the Bayesian methods of multi-omics integration have also been consistently proposed in recent years. Compared with single-omics, multi-omics integration modelling will contribute to improving predictive performance, gaining insights into the underlying mechanisms of tumour occurrence and development, and the discovery of more reliable biomarkers. In this work, we present a review of current proposed Bayesian approaches in prognostic prediction modelling in cancer.

Analysis of the genetic variants associated with recurrent thromboembolism in a patient with hemoglobin H disease following splenectomy: A case report.

Reports of recurrent thromboembolism in thalassemia, particularly in hemoglobin H (HbH) disease associated with congenital thrombophilic mutations, are scarce. However, several mutations were detected in a 22-year-old woman with HbH disease. The patient experienced the first thrombotic event at the age of 20 years and had four recurrent thromboses in a short time interval, despite receiving anticoagulant treatment. The present study reports a case with six nucleotide substitutions, including a missense 565C>T (Arg189Trp) mutation and two synonymous mutations, 66T>C (Pro22Pro) and 423G>T (Ser141Ser), identified in the protein C gene. The other three mutations, 947G>A (Arg316His), 981A>G (Val327Val), and 775C>A (rs13146272), were identified in the protein S, antithrombin and cytochrome P450, family 4, subfamily V, polypeptide 2 genes, respectively. These findings suggest that if thrombotic events repeatedly occur in a patient with thalassemia, not only the risk factors associated with a hypercoagulable state, but the acquired and congenital thrombophilia should be screened for.