Genome-Wide DArTSeq Genotyping and Phenotypic Based Assessment of Within and Among Accessions Diversity and Effective Sample Size in the Diverse Sorghum, Pearl Millet, and Pigeonpea Landraces.

Germplasm should be conserved in such a way that the genetic integrity of a given accession is maintained. In most genebanks, landraces constitute a major portion of collections, wherein the extent of genetic diversity within and among landraces of crops vary depending on the extent of outcrossing and selection intensity infused by farmers. In this study, we assessed the level of diversity within and among 108 diverse landraces and wild accessions using both phenotypic and genotypic characterization. This included 36 accessions in each of sorghum, pearl millet, and pigeonpea, conserved at ICRISAT genebank. We genotyped about 15 to 25 individuals within each accession, totaling 1,980 individuals using the DArTSeq approach. This resulted in 45,249, 19,052, and 8,211 high-quality single nucleotide polymorphisms (SNPs) in pearl millet, sorghum, and pigeonpea, respectively. Sorghum had the lowest average phenotypic (0.090) and genotypic (0.135) within accession distances, while pearl millet had the highest average phenotypic (0.227) and genotypic (0.245) distances. Pigeonpea had an average of 0.203 phenotypic and 0.168 genotypic within accession distances. Analysis of molecular variance also confirms the lowest variability within accessions of sorghum (26.3%) and the highest of 80.2% in pearl millet, while an intermediate in pigeonpea (57.0%). The effective sample size required to capture maximum variability and to retain rare alleles while regeneration ranged from 47 to 101 for sorghum, 155 to 203 for pearl millet, and 77 to 89 for pigeonpea accessions. This study will support genebank curators, in understanding the dynamics of population within and among accessions, in devising appropriate germplasm conservation strategies, and aid in their utilization for crop improvement.

A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage-only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57-.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection.