Communications of the ACM
Scientists Improve Yield Predictions Based on Seedling Data
Michigan State University scientists used plant RNA data from 2-week-old corn seedlings to determine that adult crop trait predictions can be improved with accuracy that rivals current approaches using DNA.
Credit: Kurt Stepnitz
Michigan State University (MSU) scientists have computationally improved the accuracy of crop yield forecasts according to genetic data from corn seedlings.
MSU's Shinhan Shiu and colleagues created a gene expression-based model that incorporated plant RNA, which yielded information that DNA alone cannot provide.
Conventional techniques using genetic marker-based models identify only one of 14 known genes associated with flowering time as critical. Shiu's RNA-based method returned five.
Sais Shiu, "Not only does this help in selection of breeding lines with desirable traits, but also enhances our understanding of the mechanisms involved in these processes."
From MSU Today
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