Bayesian Modeling for Identifying Selection in B cell Maturation

This thesis focuses on modeling the selection effects on B cell antibody mutations to identify amino acids under strong selection. Site-wise selection coefficients are parameterized by the fitnesses of amino acids. First, we conduct simulation studies to evaluate the accuracy of the Monte Carlo p-value approach for identifying selection for specific amino acid/location combinations. Then, we adopt Bayesian methods to infer location-specific fitness parameters for each amino acid. In particular, we propose the use of a spike-and-slab prior and implement Markov chain Monte Carlo (MCMC) algorithms for posterior sampling. Further simulation studies are conducted to evaluate the performance of the proposed Bayesian methods in inferring fitness parameters and identifying strong selection. The results demonstrate the reliable inference and detection performance of the proposed Bayesian methods. Finally, an example using real antibody sequences is provided. This work can help identify important early mutations in B cell antibodies, which is crucial for developing an effective HIV vaccine.