Genomic selection for agronomical phenotypes using genome-wide SNPs and SVs in pearl millet

Pearl millet is an essential crop worldwide, with noteworthy resilience to abiotic stress, yet the advancement of its breeding remains constrained by the underutilization of molecular-assisted breeding techniques. In this study, we collected 1,455,924 single nucleotide polymorphism (SNP) and 124,532 structural variant (SV) markers primarily from a pearl millet inbred germplasm association panel consisting of 242 accessions including 120 observed phenotypes, mostly related to the yield. Our findings revealed that the SV markers had the capacity to capture genetic diversity not discerned by SNP markers. Furthermore, no correlation in heritability was observed between SNP and SV markers associated with the same phenotype. The assessment of the nine genomic prediction models revealed that SV markers performed better than SNP markers. When using the SV markers as the predictor variable, the genomic BLUP model achieved the best performance, while using the SNP markers, Bayesian methods outperformed the others. The integration of these models enabled the identification of eight candidate accessions with high genomic estimated breeding values (GEBV) across nine phenotypes using SNP markers. Four candidate accessions were identified with high GEBV across 22 phenotypes using SV markers. Notably, accession 'P23' emerged as a consistent candidate predicted based on both SNP and SV markers specifically for panicle number. These findings contribute valuable insights into the potential of utilizing both SNP and SV markers for genomic prediction in pearl millet breeding. Moreover, the identification of promising candidate accessions, such as 'P23', underscores the accelerated prospects of molecular breeding initiatives for enhancing pearl millet varieties.