Study Details Efforts to Predict Corn Traits Based on Genomic and Data Analytics

A new study published in Plant Biotechnology Journal details the efforts of Iowa State University agronomy professor Jianming Yu in predicting corn traits based on genomics and data analytics. Yu has devoted much of his research into "turbo charging" the seemingly endless amount of genetic stocks contained in the world's seed banks.

The study focused on predicting eight corn traits based on the shoot apical meristem (SAM), a microscopic stem cell niche that generates all the above-ground organs of the plant. The researchers used their analytical approach to predict traits in 2,687 diverse maize inbred varieties based on a model they developed from studying 369 inbred varieties that had been grown and had their shoot apical meristems pictured and measured under the microscope. The researchers then validated their predictions with data obtained from 488 inbreds to determine their prediction accuracy ranged from 37% to 57% across the eight traits they studied.

Yu said that plant breeders can bump up the accuracy of those genomic predictions by increasing the number of plants per inbred for measurement and findings-improved prediction algorithms. More importantly, plant breeders can finetune their selection process for which inbreds to study closely by leveraging the "U values," a statistical concept that accounts for the reliability of estimates. Yu also said the study shows that implementing a selection process that accounts for prediction and statistical reliability can help plant breeders zero in on desirable crop genetics faster.

For more details, read the article at the Iowa State University News Service.