Globally deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but is bolstered by RMES2.

Durable host plant resistance (HPR) to insect pests is critical for sustainable agriculture. Natural variation exists for aphid HPR in sorghum (Sorghum bicolor), but the genetic architecture and phenotype have not been clarified and characterized for most sources. In order to assess the current threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and additional HPR architecture in globally admixed populations selected under severe sorghum aphid infestation in Haiti. We found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR response typical of gene-for-gene interaction. A second resistant gene, Resistance to Melanaphis sorghi 2 (RMES2), was more frequent than RMES1 resistant alleles in landraces and historic breeding lines. RMES2 contributes early and mid-season aphid resistance in a segregating F2 population; however, RMES1 was only significant with mid-season fitness. In a fixed population with high sorghum aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of severe antagonistic pleiotropy. Associations with resistance colocated with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, an HPR source vulnerable to biotype shift via selection pressure (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2), which may be staving off a biotype shift and is critical for sustainable sorghum production.

The recent evolutionary rescue of a staple crop depended on over half a century of global germplasm exchange.

Rapid environmental change can lead to population extinction or evolutionary rescue. The global staple crop sorghum (Sorghum bicolor) has recently been threatened by a global outbreak of an aggressive new biotype of sugarcane aphid (SCA; Melanaphis sacchari). We characterized genomic signatures of adaptation in a Haitian breeding population that had rapidly adapted to SCA infestation, conducting evolutionary population genomics analyses on 296 Haitian lines versus 767 global accessions. Genome scans and geographic analyses suggest that SCA adaptation has been conferred by a globally rare East African allele of RMES1, which spread to breeding programs in Africa, Asia, and the Americas. De novo genome sequencing revealed potential causative variants at RMES1. Markers developed from the RMES1 sweep predicted resistance in eight independent commercial and public breeding programs. These findings demonstrate the value of evolutionary genomics to develop adaptive trait technology and highlight the benefits of global germplasm exchange to facilitate evolutionary rescue.

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.