Evidence map›Paper›PMID 42086854›Full record

ArticleNature genetics2026

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon.

Honghe Sun, Jie Zhang, Shengjin Liao, Shaogui Guo, Zhe Zhou, Xuebo Zhao, Shan Wu, Jiantao Zhao, Guoyi Gong, Jinfang Wang and 16 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Honghe Sun *Boyce Thompson Institute, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0002-2468-6823
Jie Zhang *State Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Shengjin Liao *State Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Shaogui Guo *State Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.ORCID http://orcid.org/0000-0003-3224-6550
Zhe Zhou *Boyce Thompson Institute, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0003-4239-6720
Xuebo ZhaoBoyce Thompson Institute, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0001-5661-9652
Shan WuBoyce Thompson Institute, Cornell University, Ithaca, NY, USA.
Jiantao ZhaoBoyce Thompson Institute, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0002-5140-8220
Guoyi GongState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Jinfang WangState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Maoying LiState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Yongtao YuState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Yi RenState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Shouwei TianState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Shaofang LiState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Haiying ZhangState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Sue A HammarDepartment of Horticulture, Graduate Program in Plant Breeding, Genetics and Biotechnology, Michigan State University, East Lansing, MI, USA.
Cecilia McGregorDepartment of Horticulture, University of Georgia, Athens, GA, USA.
Robert JarretU.S. Department of Agriculture-Agricultural Research Service, Plant Genetic Resources Conservation Unit, Griffin, GA, USA.
Patrick WechterCoastal Research and Education Center, Clemson University, Charleston, SC, USA.
Sandra E BranhamCoastal Research and Education Center, Clemson University, Charleston, SC, USA.
Chandrasekar KousikU.S. Department of Agriculture-Agricultural Research Service, U.S. Vegetable Lab, Charleston, SC, USA.
Amnon LeviU.S. Department of Agriculture-Agricultural Research Service, U.S. Vegetable Lab, Charleston, SC, USA.
Rebecca GrumetDepartment of Horticulture, Graduate Program in Plant Breeding, Genetics and Biotechnology, Michigan State University, East Lansing, MI, USA.ORCID http://orcid.org/0000-0003-3880-9705
Zhangjun FeiBoyce Thompson Institute, Cornell University, Ithaca, NY, USA. zf25@cornell.edu.ORCID http://orcid.org/0000-0001-9684-1450
Yong XuState Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China. xuyong@nercv.org.ORCID http://orcid.org/0000-0002-3278-5215

Funding

United States Department of Agriculture | National Institute of Food and Agriculture (NIFA) 2015-51181-24285United States Department of Agriculture | National Institute of Food and Agriculture (NIFA) 2020-51181-32139
6 · The paper itself

Abstract

Pangenomes are increasingly important for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here we present a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated genomes representing all seven species. This super-pangenome captures approximately 1 million structural variants (SVs), enabling accurate variant genotyping across 914 accessions. Broader sampling within the pangenome provides insights into watermelon genome evolution and the origin of cultivated watermelon. Incorporating SVs into genome-wide association studies improves mapping resolution and reveals a copy number variant upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, these findings and genomic resources establish a foundation for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crops.

Indexed as

CitrullusEvolution, MolecularGenome, PlantPlant BreedingChromosome MappingDNA Copy Number VariationsGenetic VariationGenome-Wide Association StudyGenomicsGenotypePhenotypeQuantitative Trait Loci

Identifiers

PMID42086854

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.