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Sensing what Plants Sense: Integrated Framework helps Scientists Explain Biology and Predict Crop Performance

Sensing what Plants Sense: Integrated Framework helps Scientists Explain Biology and Predict Crop Performance

Scientists have invested great time and effort into making connections between a plant's genotype, or its genetic makeup, and its phenotype, or the plant's observable traits. Understanding a plant's genome helps plant biologists predict how that plant will perform in the real world, which can be useful for breeding crop varieties that will produce high yields or resist stress.

But  play a role as well. Plants with the same genotype will perform differently when grown in different environments. A new study led by an Iowa State University scientist uses advanced data analytics to help scientists understand how the environment interacts with genomics in corn, wheat and oats. The results could lead to more accurate and faster models that will allow plant breeders to develop crop varieties with desirable traits.

The study was published recently in the peer-reviewed academic journal Molecular Plant.

Jianming Yu, a professor of agronomy and the Pioneer Distinguished Chair in Maize Breeding, said the study sheds light on , or the ability of crops to adapt to . This could help plant breeders get a better understanding of how "shapable" plant species are, or how much potential they have to perform well in different environments.

"We knew that genetic performance is context dependent. It's not static; it's dependent on environmental conditions," said Xianran Li, an adjunct associate professor and the first author of the study. "Two alleles of a gene perform differently in one environment but the same in another. What is challenging is to understand the interplay between genes and the environment under the natural field conditions. The obvious obstacle is that natural environments are much more complex than controlled laboratory conditions. How can we detect the major signals  perceive?"

The study made use of previously gathered data on the three crop species from across the globe. A group of 17 scientists from four institutions contributed to the current study, but a much larger group of scientists carried out the initial experiments that generated the data. The dataset included 282 inbred lines of corn evaluated in the United States and Puerto Rico; 288 inbred lines of wheat evaluated in Africa, India and Middle Eastern countries; and 433 inbred populations of oats evaluated in the United States and Canada. The data included environmental conditions such as temperature and availability of sunlight. The phenotypic data analyzed in the study included yields, plant height and flowering time, or the window of time during which the plant reaches the reproductive stage.

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