Biology subjects

Pressoir, G.

Publications and source records attributed to Pressoir, G..

6 recordsLinked to original sources

bioRxiv2026

Flywheel Genomics: Simultaneous trait discovery and genetic gain in plant breeding

Genomic mapping has yielded extensive catalogs of quantitative trait loci underlying agronomic traits, yet translating these discoveries into breeding gains remains inefficient. Here, we introduce Flywheel Genomics, a framework that integrates trait discovery directly within rapid cycling breeding populations. Using empirical data from a smallholder-oriented sorghum breeding program, we demonstrate that recurrent intermating and selection maintain genetic diversity, effective population size, and recombination while reducing confounding from plant height and maturity. Within this population, we resolve loci underlying simple adaptive and complex environmentally responsive traits and generate large segregating populations for mapping and near-isogenic lines for locus validation. We further demonstrate applicability in a public wheat breeding program, where known agronomic loci were readily detected. Simulations show that rapid cycling better preserves the population genetic properties required for Flywheel Genomics than conventional pure line development. By integrating discovery with improvement, Flywheel Genomics reframes breeding programs as engines of both crop improvement and genetic insight.

genetics↗

bioRxiv2026

Phenotyping replication is a major determinant of genomic predictive ability in sweet sorghum (Sorghum bicolor Moench)

Genomic selection can increase the rate of genetic gain in crop breeding programs, but its effectiveness depends on the reliability of phenotypic data, the size and composition of the training population (TP), and the statistical model used to estimate genomic breeding values. These design choices are especially important in resource-limited breeding programs, where additional replication, larger TPs, and more extensive genotyping compete for the same resources. Using empirical data from a sweet sorghum [Sorghum bicolor (L.) Moench] breeding population, developed by CHIBAS, we evaluated the effects of phenotyping replication, TP size, training-validation genomic relatedness, and genomic prediction (GP) model on predictive ability (PA). Grain yield, plant height, stem weight, and total soluble solids were evaluated across three field environments. Few studies in sorghum have examined these factors together with comparable empirical rigor. Increasing replication improved genomic heritability and PA for all traits and environments, with the largest gains observed for grain yield. Larger TPs and increased training-validation genomic relatedness also improved PA, but their effects were most significant when phenotype estimates were based on multiple replicates. Different GP models had comparable PA across all evaluated traits, with a few exceptions. These findings provide practical guidance for optimizing genomic selection in resource-limited sorghum breeding programs. ARTICLE SUMMARYGenomic selection can accelerate breeding only when the phenotypes used to train predictive models are reliable. Using a sweet sorghum breeding population evaluated in three Haitian field environments, we quantified how replication number, training population size, training-validation genomic relatedness, and prediction model affected genomic predictive ability for grain yield, plant height, stem weight, and total soluble solids. Replication increased genomic heritability and predictive ability for all traits, with the strongest effects for grain yield. Larger and more connected training populations improved prediction, particularly when evaluated with greater replication. These results provide practical guidance for resource-limited breeding programs. Core ideasO_LIIn this empirical sweet sorghum breeding population, phenotyping replication was the dominant factor explaining variation in genomic predictive ability across traits and environments. C_LIO_LIThe benefit of larger training populations and greater training-validation genomic relatedness increased when phenotype estimates were based on more replicates. C_LIO_LIGrain yield, the most environmentally sensitive trait evaluated, showed the largest response to improved replication and training-population design. C_LIO_LIBayesian models, rrBLUP, and GBLUP showed similar predictive abilities across traits and environments, suggesting that phenotyping and experimental design may be more important than model complexity. C_LI

genomics↗

bioRxiv2025

Ancient pangenomic origins of noncanonical NLR genes underlying the recent evolutionary rescue of a staple crop

Evolutionary rescue occurs when populations in deteriorating environments avoid extinction by rapid adaptation. The recent evolutionary rescue of the cereal crop sorghum via RMES1 aphid resistance is among a few known in situ cases, but its pangenomic origins and molecular basis is not yet known. Here, we describe the behavioral effects, molecular endophenotypes, and pangenomic evolution underlying this evolutionary rescue. Analysis of near-isogenic lines show that RMES1 disrupts phloem feeding via global immunity activation of conserved defense networks. A growth-to-defense transition is evidenced by extensive transcriptome remodeling (>15% of expressed genes) and mediated by salicylic acid signaling. Nucleotide-binding leucine-rich repeat (NLR) immune receptor genes at the RMES1 locus harbored on a large tandem duplication have extensive copy number variation across the sorghum pangenome. The likely causative NLRs (RMES1A and RMES1B) were inferred from expression and structural variation. The NLRs encoded at RMES1 lack an N-terminal signaling domain and have nucleotide-binding domain sequence variation expected to result in a loss of ATP binding, suggesting RMES1 NLRs function via a noncanonical mechanism. The RMES1 NLR family is shared across the grass super-pangenome and includes the brown planthopper resistance gene BPH40 in Oryza sativa, which is syntenic to RMES1. Finally, k-mer analysis of RMES1 haplotypes in the sorghum pangenome established the East African origin of rare standing variation for resistance. Thus, the birth-and-death process at an ancient gene cluster generated pangenomic variation that was recruited to activate coordinated defense pathways and provide evolutionary rescue.

genetics↗

bioRxiv2023

Globally-deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but being 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 has not been clarified for most sources. To assess the threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and contributing HPR architecture in globally-admixed populations selected under severe 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, RMES2, were 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 aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of significant antagonistic pleiotropy. Associations with resistance co-located with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, a vulnerable HPR source (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2) which may be staving off a biotype shift. HIGHLIGHTThe globally-deployed sorghum aphid resistance gene, RMES1, reduces aphid reproduction and therefore is vulnerable to a biotype shift. A second major gene, RMES2, and cyanogenesis may increase global durability of resistance.

plant biology↗

bioRxiv2021

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

Rapid environmental change can lead to extinction of populations or evolutionary rescue via genetic adaptation. In the past several years, smallholder and commercial cultivation of sorghum (Sorghum bicolor), a global cereal and forage crop, has been threatened by a global outbreak of an aggressive new biotype of sugarcane aphid (SCA; Melanaphis sacchari). Here we characterized genomic signatures of adaptation in a Haitian sorghum breeding population, which had been recently founded from admixed global germplasm, extensively intercrossed, and subjected to intense selection under SCA infestation. We conducted evolutionary population genomics analyses of 296 post-selection Haitian lines compared to 767 global accessions at 159,683 single nucleotide polymorphisms. Despite intense selection, the Haitian population retains high nucleotide diversity through much of the genome due to diverse founders and an intercrossing strategy. A genome-wide fixation (FST) scan and geographic analyses suggests that adaptation to SCA in Haiti is conferred by a globally-rare East African allele of RMES1, which has also spread to other breeding programs in Africa, Asia, and the Americas. De novo genome sequencing data for SCA resistant and susceptible lines revealed putative causative variants at RMES1. Convenient low-cost markers were developed from the RMES1 selective sweep and successfully predicted resistance in independent U.S. x African breeding lines and eight U.S. commercial and public breeding programs, demonstrating the global relevance of the findings. Together, the findings highlight the potential of evolutionary genomics to develop adaptive trait breeding technology and the value of global germplasm exchange to facilitate evolutionary rescue.

evolutionary biology↗

bioRxiv2019

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 increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗