Admas Alemu, Lorena Batista, Pawan K. Singh, Alf Ceplitis & Aakash Chawade

Theoretical and Applied Genetics volume 136, Article number: 92

Published: 3 April 2023

Key message

Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models.

Abstract

Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r² < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.

See https://link.springer.com/article/10.1007/s00122-023-04352-8

Figure 3: Genomic prediction with haplotype-tagged SNPs across the two independent populations tested in RR-BLUP model. A Prediction accuracy of models trained with breeding lines and tested in genebank lines (scenario 6 and 14). B Prediction accuracy with the genebank lines used as training and breeding lines as test sets (scenario 7 and 15). FHB Fusarium head blight; SPL Spike length; SPW Spike width; FLA Flag leaf area; LD Linkage disequilibrium