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Two-week Old Corn Seedlings' RNA Data Produce Reliable Yield Predictions
Posted by: Prof. Dr. M. Raupp (IP Logged)
Date: January 18, 2020 07:14AM

Researchers from Michigan State University (MSU) used information in the
corn's RNA to create models and make accurate predictions of its phenotypic
traits prior to the corn's full growth and development. The new information
documented in their study can help other researchers understand the
mechanisms involved in the plant processes and in the selection of breeding
lines with desirable traits without having to go through years of
traditional breeding selection.

Lead scientist Shinhan Shiu recognized the challenge of connecting the DNA
information, or genotype, to physical traits, or phenotype. He also knew
that computational approaches can be used to resolve evolution and genome
biology questions. He believed that finding the answers to the challenges
were keys to understanding how genetic information can be translated into
outward traits. Thus, there is an opportunity to predict the desirable
traits just from the DNA and the RNA without having to grow crops through
traditional breeding and directly measure the traits, which can take months
or years.

The MSU team proved that accurate crop predictions are possible by just
using plant RNA data from two-week-old corn seedlings. RNA, being a part of
the DNA, is only one step closer to the traits that DNA influences. With the
help of machine learning approaches, they found that the RNA can potentially
serve as a blueprint that offers predictions. They also found that RNA
measurements can provide additional information that DNA cannot provide
alone. The gene expression-based model they developed was able to identify
five of the 14 important known genes linked to flowering time, unlike
genetic marker-based models that can identify only one of the 14.

The scientists said that this is helpful for new breeding programs and may
help improve ways to do genetic testing, as they were able to make accurate
flowering and yield predictions prior to the plant's developing its seed or
flower organisms. Their findings, they said, were complementary to genetic
marker-based predictions and were able to identify gene expression-trait
associations that are not explained by genetic markers.


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