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ARS Home » Southeast Area » Stoneville, Mississippi » Genomics and Bioinformatics Research » Research » Publications at this Location » Publication #395476

Title: Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance in melon

item Vaughn, Justin
item BRANHAM, SANDRA - Clemson University
item ABERNATHY, BRIAN - University Of Georgia
item Hulse-Kemp, Amanda
item Rivers, Adam
item Levi, Amnon
item Wechter, William - Pat

Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/12/2022
Publication Date: 12/22/2022
Citation: Vaughn, J.N., Branham, S.E., Abernathy, B.L., Hulse-Kemp, A.M., Rivers, A.R., Levi, A., Wechter, W.P. 2022. Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance in melon. Nature Genetics.

Interpretive Summary: High-quality full genome sequences are now affordable on a per-lab basis. Graph-based genetics represents an elegant way to incorporate these genomes in routine analysis, but computational approaches related to graphs are still difficult to use. A major motivator of this study was to help the USDA and its collaborators determine what benefits they might hope to see in an applied setting by implementing this novel approach. We wanted to explore the graph-based approach in the “simplest” possible genetic system, a bi-parental cross. The simplicity of the resultant population helped us to better understand the source of relative gains. Our conclusions support recent work, but we also present a computational pipeline to perform end-to-end analysis in a graph-based way. This tool improves genetic information density relative to prior graph-based crop studies, particularly with a low cost-per-sample rate. From a breeding perspective, we also used these genotyping improvements and genomic analysis to understand the mutations that generate fungal resistance. These discoveries will help to make molecular tools to speed melon improvement.

Technical Abstract: The genomic sequences segregating in experimental populations are often highly divergent from the community reference and from one another. Such divergence is problematic under various short-read-based genotyping strategies. In addition, large structural differences are often invisible despite being strong candidates for causal variation. These issues are exacerbated in specialty crop breeding programs with fewer, lower-quality sequence resources. We examined the benefits of complete genomic information, based on long-read assemblies, in a biparental mapping experiment segregating at numerous disease resistance loci in the non-model crop, melon (Cucumis melo). We find that a graph-based approach, which uses both parental genomes, results in 19% more variants callable across the population and raw allele calls with a 2 to 3-fold error-rate reduction, even relative to single reference approaches using a parent genome. We show that structural variation has played a substantial role in shaping two Fusarium wilt resistance loci with known causal genes. We also report on the genetics of powdery mildew resistance, where copy number variation and local recombination suppression are directly interpretable via parental genome alignments. Benefits observed, even in this low-resolution biparental experiment, will inevitably be amplified in more complex populations.