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