Machine Learning Reveals Spatiotemporal Genome Evolution in Asian Rice Domestication

Abstract
Domestication is anthropogenic evolution that fulfills mankind's critical food demand. As such, elucidating the molecular mechanisms behind this process promotes the development of future new crops. With the aim of understanding the whole domestication process of Asian rice and by employing the ^{indica} and $^{japonica}) as an Asian rice domestication model, we scrutinized genomic introgressions between them as traces of domestication. Here we show the genome-wide introgressive region (IR) map of Asian rice, by utilizing 4,587 accession genotypes with a stable outgroup species, particularly at the finest resolution through a machine learning-aided method. The IR map revealed that 14.2% of the rice genome consists of IRs, including both wide IRs (recent) and narrow IRs (ancient). This introgressive landscape with their time calibration indicates that introgression events happened in multiple genomic regions over multiple periods. From the correspondence between our wide IRs and so-called Selective Sweep Regions, we provide a definitive answer to a long-standing controversy in plant science: Asian rice phylogeny appears to depend on which regions and time frames are examined.

Citation
Ohyanagi, H., Goto, K., Negrão, S., Wing, R. A., Tester, M. A., McNally, K. L., … Gojobori, T. (2019). Machine Learning Reveals Spatiotemporal Genome Evolution in Asian Rice Domestication. doi:10.1101/829168

Acknowledgements
The research reported in this publication was supported through funding from King Abdullah University of Science and Technology (KAUST), under award numbers BAS/1/1059-01-01 (to T.G.), BAS/1/1606-01-01 (to V.B.B.), FCC/1/1976-03-01 (to T.G.) and FCC/1/1976-2001 (to T.G.).

Publisher
Cold Spring Harbor Laboratory

DOI
10.1101/829168

Additional Links
http://biorxiv.org/lookup/doi/10.1101/829168

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