Researchers hunt for hard-to-spot genetic links to improve crop and livestock breeding

Genetics is the cornerstone of breeding, but genomes-to-phenomes research powered by innovative statistical analysis and modeling can provide deeper insight that will be essential to meeting future agricultural production needs, said Schnable.

“There are enormous challenges facing the world in terms of food supply. Agriculture was invented in a world different than the one we’ll be facing in the coming decades,” he said.

Developing crops and livestock that are more resilient to diseases and changing weather conditions is complex and urgent, said distinguished professor of animal science Jack Dekkers. Deeper data analysis can speed up adaptation efforts.

“Breeding progress comes faster when you have a better understanding of the genetic basis of the traits you’re trying to improve,” Dekkers said.

Hexbyte Glen Cove Statistical detective work

The statistical detective work of searching for associations between genomic and phenomic data sets often tangles with environmental variation and other confounding factors. In the study of the derecho-downed corn, researchers initially weren’t able to detect the genetic causes of root lodging. Statisticians only found the genes of interest after including in their models the condition of neighboring plants, which impacted the effects of the extreme winds, Schnable said.

Associate professors of statistics Vivekananda Roy and Somak Dutta analyzed the post-derecho corn data. Their models integrate multiple methods of scanning large data sets, account for numerous sources of variation and employ strategies for more efficient processing. Their approach is like knowing the Dewey Decimal System when you’re looking for a library book instead of searching shelf-by-shelf, Roy said.

“If you know how the books are arranged, that’s a lot better,” he said.

More information:
Zihao Zheng et al, The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture, Plant Physiology (2023). DOI: 10.1093/plphys/kiad194