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 “Now that we can more accurately predict which
corn hybrids are better at using nitrogen fertilizer in the
field, we can rapidly improve this trait. Increasing nitrogen
use efficiency in corn and other crops offers three key benefits
by lowering farmer costs, reducing environmental pollution, and
mitigating greenhouse gas emissions from agriculture,” said
study author Stephen Moose, Alexander Professor of Crop Sciences
at the University of Illinois at Urbana-Champaign.
Using genomic data to predict outcomes in agriculture is both a
promise and challenge for biologists. Researchers are working to
determine how to use vast amounts of genomic data to predict how
organisms respond to changes in nutrition, toxins, and pathogen
exposure—which in turn would inform crop improvement. But the
implications go beyond crops, providing insights in disease
prognosis, epidemiology, and public health.
However, accurately predicting complex outcomes in agriculture
and medicine from genome-scale information remains a significant
challenge.
As a proof-of-concept, the researchers demonstrated that machine
learning models could predict genes of importance for
nitrogen-use-efficiency in corn. A key first step was finding
genes that respond to nitrogen in leaves of both field-grown
corn plants and Arabidopsis, a small flowering plant widely used
as a model organism in plant biology.
Nitrogen is a crucial nutrient for plants and the main component
of fertilizer; crops that use nitrogen more efficiently grow
better and require less fertilizer, which has economic and
environmental benefits.
“We show that focusing on genes whose expression patterns are
evolutionarily conserved across species enhances our ability to
learn and predict ‘genes of importance’ to growth performance
for staple crops, as well as disease outcomes in animals,”
explained Gloria Coruzzi, Carroll & Milton Petrie Professor in
NYU’s Department of Biology and Center for Genomics and Systems
Biology and the paper’s senior author.
The researchers conducted
experiments that tested whether eight “master switch” genes
predicted from the machine learning model actually contribute to
nitrogen-use-efficiency. They showed that altered expression of
these switch genes in Arabidopsis or corn could increase plant
growth in low nitrogen soils, which they tested both in the lab
at NYU and in cornfields at the University of Illinois.
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“Our approach exploits the natural variation of genome-wide
expression and related phenotypes within or across species,” added
Chia-Yi Cheng of NYU’s Center for Genomics and Systems Biology and
National Taiwan University, the lead author of this study. “We show
that paring down our genomic input to genes whose expression
patterns are conserved within and across species is a biologically
principled way to reduce dimensionality of the genomic data, which
significantly improves the ability of our machine learning models to
identify which genes are important to a trait.”
Moreover, the researchers proved that this evolutionarily informed
machine learning approach can be applied to other traits and species
by predicting additional traits in plants, including biomass and
yield in both Arabidopsis and corn. They also showed that this
approach can predict genes of importance to drought resistance in
another staple crop, rice, as well as disease outcomes in animals
through studying mouse models.
“Because we showed that our evolutionarily informed pipeline can
also be applied in animals, this underlines its potential to uncover
genes of importance for any physiological or clinical traits of
interest across biology, agriculture, or medicine,” said Coruzzi.
In addition to Moose, Coruzzi, and Cheng, additional researchers
involved in this study include co-PI Ying Li and Kranthi Varala,
faculty in the Department of Horticulture and Landscape Architecture
at Purdue University, as well as members of their research teams at
NYU, the University of Illinois, and Purdue. The research was
supported by the National Science Foundation’s Plant Genome Research
Program (IOS-1339362), the U.S. Department of Agriculture National
Institute of Food and Agriculture Hatch project (1013620), the USDA-NIFA
predoctoral fellowship (2016-67011025167), and an NSF CompGen
fellowship.
[Source: Steve Moose
ACES media contact: Lauren Quinn]
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