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

l markers with small effects. But many economically important traits are controlled by a large number of genes with small effects. Because the genomic prediction model captures all these small-effect genes, predictability is vastly improved."

Without genomic prediction, breeders must grow all possible crosses in the field to select the best cross (hybrid). For example, for 1000 inbred parents, the total number of crosses would be 499500.

"It is impossible to grow these many crosses in the field," Xu said. "However, with the genomic prediction technology, we can grow only, say, 500 crosses, then predict all the 499500 potential crosses, and select the best crosses based on the predicted values of these hybrids."

Xu noted that genomic prediction is particularly useful for predicting hybrids because hybrid DNA sequences are determined by their inbred parents.

"More cost-saving can be achieved because we do not need to measure the DNA sequences of the hybrids," he said. "Knowing the genotypes of the parents makes it possible to immediately know the genotype of the hybrid. Indeed, there is no need to measure the genotype of the hybrid. It is fully predicted by the model."

When the researchers incorporated "dominance" and "epistasis" into their prediction model, they found that predictability was improved. In genetics, dominance describes the joint action of two different alleles (copies) of a gene. For example, if one copy of a gene has a value of 1 and the other copy has a value of 2, the joint effect of the two alleles may be 4, indicating that the two alleles are not additive. In this case, dominance has occurred. Epistasis refers to any type of gene-gene interaction.

"By incorporating dominance and epistasis, we took into account all available information for prediction," Xu said. "It led to a more accurate prediction of a trait value."

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Contact: Iqbal Pittalwala
iqbal@ucr.edu
951-827-6050
University of California - Riverside
Source:Eurekalert  

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