A Conditional Autoregressive Model for Detecting Natural Selection in Protein-Coding DNA Sequences

Abstract

Phylogenetics, the study of evolutionary relationships among groups of organisms, has played an important role in modern biological research, such as genomic comparison, detecting orthology and paralogy, estimating divergence times, reconstructing ancient proteins, identifying mutations likely to be associated with disease, determining the identity of new pathogens, and finding the residues that are important to natural selection. Given an alignment of protein-coding DNA sequences, most methods for detecting natural selection rely on estimating the codon-specific nonsynonymous/synonymous rate ratios (d _N ∕ d _S ). Here, we describe an approach to modeling variation in the d _N ∕ d _S by using a conditional autoregressive (CAR) model. The CAR model relaxes the assumption in most contemporary phylogenetic models, i.e., sites in molecular sequences evolve independently. By incorporating the information stored in the Protein Data Bank (PDB) file, the CAR model estimates the d _N ∕ d _S based on the protein three-dimensional structure. We implement the model in a fully Bayesian approach with all parameters of the model considered as random variables and make use of the NVIDIA’s parallel computing architecture (CUDA) to accelerate the calculation. Our result of analyzing an empirical abalone sperm lysine data is in accordance with the previous findings.