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Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses

Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley.

Maria Y. GonzalezYusheng ZhaoYong JiangNils SteinAntje HabekussJochen C. Reif & Albert W. Schulthess

Theoretical and Applied Genetics July 2021; vol. 134:2181–2196

Key message

Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.

Abstract

Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

 

See: https://link.springer.com/article/10.1007/s00122-021-03815-0

 

Figure 6: Comparison among genome-wide prediction using W-BLUP and marker-assisted selection (MAS) for non-phenotyped winter barley accessions maintained at the IPK genebank. W-BLUP based on first 10 SNPs for BaYMV, and first 20 SNPs for BaMMV having the lowest P-values for associations during GWAS. MAS stand for a the highest associated marker for BaYMV susceptibility and for the most associated markers at chromosomes 3H b and 4H c for BaMMV susceptibility. Each diagram was divided into I-IV quadrants, each quadrant included the respective percentage of accessions. Culling levels defined by vertical lines separated the 10% less susceptible genotypes detected by genomic prediction. Culling levels delineated by horizontal lines stand for the population mean, and separated the susceptible accessions (upper) from the less susceptible ones (lower) according to allelic effects in MAS

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