Ancestral Graph with Bias in Gene Conversion

TL;DR

This paper models the biased gene conversion process in DNA, developing a stochastic framework and ancestral graph approach to understand nucleotide bias and providing an algorithm for likelihood computation.

Contribution

It introduces a stochastic model of biased gene conversion dynamics and an ancestral graph method, along with an importance-sampling algorithm for likelihood estimation.

Findings

Bias towards G and C nucleotides in gene conversion

Development of a stochastic model for gene conversion bias

An ancestral graph approach for analyzing gene conversion data

Abstract

Gene conversion is a mechanism by which a double-strand break in a DNA molecule is repaired using a homologous DNA molecule as a template. As a result, one gene is 'copied and pasted' onto the other gene. It was recently reported that the direction of gene conversion appears to be biased towards G and C nucleotides. In this paper a stochastic model of the dynamics of the bias in gene conversion is developed for a finite population of members in a multigene family. The dual process is the biased voter model, which generates an ancestral random graph for a given sample. An importance-sampling algorithm for computing the likelihood of the sample is also given.