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Statistical model predicts performance of hybrid rice
Date:8/13/2014

RIVERSIDE, Calif. Genomic prediction, a new field of quantitative genetics, is a statistical approach to predicting the value of an economically important trait in a plant, such as yield or disease resistance. The method works if the trait is heritable, as many traits tend to be, and can be performed early in the life cycle of the plant, helping reduce costs.

Now a research team led by plant geneticists at the University of California, Riverside and Huazhong Agricultural University, China, has used the method to predict the performance of hybrid rice (for example, the yield, growth-rate and disease resistance). The new technology could potentially revolutionize hybrid breeding in agriculture.

The study, published online this week in the Proceedings of the National Academy of Sciences, is a pilot research project on rice. The technology can be easily extended, however, to other crops such as maize.

"Rice and maize are two main crops that depend on hybrid breeding," said Shizhong Xu, a professor of genetics in the UC Riverside Department of Botany and Plant Sciences, who co-led the research project. "If we can identify many high-performance hybrids in these crops and use these hybrids, we can substantially increase grain production to achieve global food security."

Genomic prediction uses genome-wide markers to predict future individuals or species. These markers are genes or DNA sequences with known locations on a chromosome. Genomic prediction differs from traditional predictions in that it skips the marker-detection step. The method simply uses all markers of the entire genome to predict a trait.

"Classical marker-assisted selection only uses markers that have large effects on the trait," Xu explained. "It ignores al
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Contact: Iqbal Pittalwala
iqbal@ucr.edu
951-827-6050
University of California - Riverside
Source:Eurekalert  

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