Predicting enviromically adapted varieties with big data.

ABSTRACT:

Background

Breeding programs prioritize the average performance of a genotype across environments and may overlook promising candidates for specific environments. To address this challenge, we propose a genomic prediction framework to select high-yielding genotypes tailored to individual environments.

Results

We compiled winter wheat grain yield data from 13,285 genotypes-6,766 lines and 6,519 hybrids-evaluated in yield plots at 31 central european sites from 2010 to 2022. With integrated genomic data, we show that only as the size of the training dataset increase, convolutional neural networks benchmark competitive to superior compared with traditional genomic best linear unbiased predictions (GBLUP) in predicting average genotype performance of lines. We then extend our pr

SUBMITTER: Gogna A 

PROVIDER: S-EPMC12838137 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature