Probabilistic reconstruction of genealogies for polyploid plant species

TL;DR

This paper introduces a probabilistic method for reconstructing genealogies in polyploid plant populations, addressing allelic uncertainty and enabling the inference of breeding history and missing links using likelihood-based and graph-theoretic approaches.

Contribution

It presents a novel probabilistic model and algorithm for reconstructing polyploid genealogies, handling allelic multiplicity and missing links with a likelihood-based framework.

Findings

Successfully applied to simulated data showing accurate genealogy reconstruction.

Demonstrated effectiveness on real rose bush population data.

Provided insights into breeding selection and lineage connections.

Abstract

A probabilistic reconstruction of genealogies in a polyploid population (from 2x to 4x) is investigated, by considering genetic data analyzed as the probability of allele presence in a given genotype. Based on the likelihood of all possible crossbreeding patterns, our model enables us to infer and to quantify the whole potential genealogies in the population. We explain in particular how to deal with the uncertain allelic multiplicity that may occur with polyploids. Then we build an \textit{ad hoc} penalized likelihood to compare genealogies and to decide whether a particular individual brings sufficient information to be included in the taken genealogy. This decision criterion enables us in a next part to suggest a greedy algorithm in order to explore missing links and to rebuild some connections in the genealogies, retrospectively. As a by-product, we also give a way to infer the individuals that may have been favored by breeders over the years. In the last part we highlight the results given by our model and our algorithm, firstly on a simulated population and then on a real population of rose bushes. Most of the methodology relies on the maximum likelihood principle and on graph theory.