Author
Listed:
- Chia-Yi Cheng
(New York University
National Taiwan University)
- Ying Li
(Purdue University
Purdue University)
- Kranthi Varala
(Purdue University
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)
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.
Suggested Citation
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 , 2021.
"Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships,"
Nature Communications, Nature, vol. 12(1), pages 1-15, December.
Handle:
RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25893-w
DOI: 10.1038/s41467-021-25893-w
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