Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

Chia-Yi Cheng, Ying Li, Kranthi Varala, Jessica Bubert, Ji Huang, Grace J. Kim, Justin Halim, Jennifer Arp, Hung-Jui S. Shih, Grace Levinson, Seo Hyun Park, Ha Young Cho, Stephen P. Moose and Gloria M. Coruzzi ()
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Chia-Yi Cheng: New York University
Ying Li: Purdue University
Kranthi Varala: Purdue University
Jessica Bubert: University of Illinois at Urbana-Champaign
Ji Huang: New York University
Grace J. Kim: New York University
Justin Halim: New York University
Jennifer Arp: University of Illinois at Urbana-Champaign
Hung-Jui S. Shih: New York University
Grace Levinson: New York University
Seo Hyun Park: New York University
Ha Young Cho: New York University
Stephen P. Moose: University of Illinois at Urbana-Champaign
Gloria M. Coruzzi: New York University

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: Abstract Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

Date: 2021
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DOI: 10.1038/s41467-021-25893-w

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