Climate change poses threats to resource-limited agriculture through altered pest-disease patterns and increasing abiotic stresses. The advent of rapid and cost-effective DNA sequencing has propelled large-scale germplasm sequencing initiatives and Genomics-assisted breeding (GAB). Genomic selection (GS) emerged as a powerful GAB method reducing selection cycle time and increasing precision in breeding for climate change resilience. Among various DNA markers used for GS, SNPs have gained prominence due to their abundance within plant genomes. However, their PIC value and biallelic nature have constrained the resolution of SNP-marker associations in comparison to SSR markers. To overcome this limitation, the construction of SNP-based haplotypes has been proposed. This has opened promising avenues for harnessing haplotypes in GS2.

Haplotypes represent sets of SNPs on the same chromosome organized into blocks with minimal historical recombination, ensuring that polymorphic SNPs within a haplotype are inherited together. Fitting haplotypes with statistically significant associations to phenotypes as fixed effects in GS models could further improve prediction accuracies due to their high PIC value. The use of haplotypes may enhance GEBVs by better capturing both LD and local high-order allelic interactions. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs1. Hence, this approach could ultimately help further increasing selection gain per unit of time.

A recent GS study that compared the prediction ability computed from haplotypes and SNPs in a set of 383 advanced lines and cultivars of wheat established the superiority of haplotype-based predictions over SNP-based predictions for all studied traits i.e., yield, test weight and protein content. As compared to the individual SNPs, the combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3% and 16.8%, respectively3. In a study comparing genomic prediction methods for important traits in canola, maize, wheat, and soybean datasets, researchers investigated the efficacy of using single SNPs versus haplotype blocks. Various approaches to construct haplotype blocks were examined, including those based on LD, physical distance, and marker adjacency. The findings suggest that haplotype blocks can enhance prediction accuracy, particularly in settings with low marker density4. These studies demonstrated haplotypes outperform SNPs in enhancing prediction accuracies in GS.

The availability of long-read sequencing technologies is also accelerating the discovery of haplotypes, which improves genome assembly. Numerous studies have demonstrated that haplotype-based Genomic Selection yields higher genomic prediction efficiency compared to using SNPs alone, encouraging researchers to increasingly adopt haplotype-assisted genomic prediction in crop improvement programs. Additionally, advances in high-throughput phenotyping would enhance the discovery and subsequent application of superior haplotypes in breeding climate resilient crops.

References: 1. ALEMU, A., BATISTA, L., SINGH, P. K., CEPLITIS, A. AND CHAWADE, A., 2023, Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat. Theor. Appl. Genet., 136(4): 92. 2. BHAT, J.A., YU, D., BOHRA, A., GANIE, S.A. AND VARSHNEY, R.K., 2021, Features and applications of haplotypes in crop breeding. Commun. Biol., 4(1): 1266. 3. SALLAM, A.H., CONLEY, E., PRAKAPENKA, D., DA, Y. AND ANDERSON, J.A., 2020, Improving prediction accuracy using multi-allelic haplotype prediction and training population optimization in wheat. G3-GENES GENOM. GENET., 10(7): 2265-2273. 4. WEBER, S. E., FRISCH, M., SNOWDON, R. J. AND VOSS-FELS, K. P., 2023, Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. Front. Plant Sci., 14: 1217589.