Success in plant breeding is determined from the ability to select superior plants, but selecting optimal plants is challenging due to the variable field conditions that plants grow in within the field experiments and from year to year or location to location. Advances in genomics have provided plant breeders with a wealth of information to understand the genetic makeup of plants, and recently phenotyping platforms have been developed that can measure plants physical attributes throughout the growing season. While the combination of both genotypic and phenotypic data should increase selection accuracy, there has been limited work to integrate these two disciplines.
In a paper recently published in The Plant Genome, researchers provide insights into how to utilize high-throughput physical measurements of plants along with genetic data to increase yield prediction accuracy. Using high-throughput measurements of plant temperature and light reflectance combined with genomic information, they were able to increase accuracy of yield predictions by up to 7% over standard genomic selection models. With the potential to take an abundance of phenotypic measurements along with numerous genetic markers, scientists can increase the accuracy of their breeding programs, and more rapidly transfer higher performing crops to farmers.
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