Population based change-point detection for the identification of homozygosity islands

TL;DR

This paper introduces a new population-level change-point detection method for identifying homozygosity islands in genomes, using a penalized likelihood approach with efficient algorithms, applicable to categorical and Gaussian data.

Contribution

It presents a novel population-based method for detecting homozygosity islands that does not require individual analysis, with proven convergence and broad applicability.

Findings

Algorithms converge almost surely under general assumptions

Applicable to categorical and Gaussian variables

Efficient dynamic programming and greedy algorithms

Abstract

In this paper, we propose a new method for offline change-point detection on some parameters of the distribution of a random vector. We introduce a penalized maximum likelihood approach that can be efficiently computed by a dynamic programming algorithm or approximated by a fast greedy binary splitting algorithm. We prove both algorithms converge almost surely to the set of change-points under very general assumptions on the distribution and independent sampling of the random vector. In particular, we show the assumptions leading to the consistency of the algorithms are satisfied by categorical and Gaussian random variables. This new approach is motivated by the problem of identifying homozygosity islands on the genome of individuals in a population. Our method directly tackles the issue of identification of the homozygosity islands at the population level, without the need of analyzing single individuals and then combining the results, as is made nowadays in state-of-the-art approaches.