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In Plant biotechnology journal

Plant genomes demonstrate significant presence/absence variation (PAV) within a species, however the factors that lead to this variation have not been studied systematically in Brassica across diploids and polyploids. Here, we developed pangenomes of polyploid Brassica napus and its two diploid progenitor genomes B. rapa and B. oleracea to infer how PAV may differ between diploids and polyploids. Modelling of gene loss suggests that loss propensity is primarily associated with transposable elements in the diploids while in B. napus, gene loss propensity is associated with homoeologous recombination. We use these results to gain insights into the different causes of gene loss, both in diploids and following polyploidisation, and pave the way for the application of machine learning methods to understanding the underlying biological and physical causes of gene presence/absence.

Bayer Philipp E, Scheben Armin, Golicz Agnieszka A, Yuan Yuxuan, Faure Sebastien, Lee HueyTyng, Chawla Harmeet Singh, Anderson Robyn, Bancroft Ian, Raman Harsh, Lim Yong Pyo, Robbens Steven, Jiang Lixi, Liu Shengyi, Barker Michael S, Schranz M Eric, Wang Xiaowu, King Graham J, Pires J Chris, Chalhoub Boulos, Snowdon Rod J, Batley Jacqueline, Edwards David

2021-Jul-26

\nBrassica, XGBoost, gene loss propensity, machine learning, pangenome, transposable elements