Using Genome-Wide Predictions to Assess the Phenotypic Variation of a Barley ( Hordeum sp.) Gene Bank Collection for Important Agronomic Traits and Passport Information.

Abstract

Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley collection of the Federal ex situ Genebank for Agricultural and Horticultural Crops with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.

Plant phenotyping relevance

ゲノムワイド予測によって未表現型化アクセスionsの農業形質を推定し、交差検証で予測性能を評価しており、植物表現型推定手法が研究の中心である。

Code and data availability

The paper reuses previously published, publicly deposited phenotypic (FT, PH, TGW) and genomic (306,049 SNPs for 20,454 barley accessions) datasets, explicitly linked in its data availability statement via two DOIs. No author analysis code or trained model checkpoints are disclosed; supplementary material is not shown.