Genomes of cultivated and wild <i>Capsicum</i> species provide insights into pepper domestication and population differentiation

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Abstract

Pepper ( Capsicum spp.) is one of the earliest cultivated crops and includes five domesticated species, C. annuum var. annuum, C. chinense, C. frutescens, C. baccatum var. pendulum and C. pubescens. Here, we report a pepper graph pan-genome and a genome variation map of 500 accessions from the five domesticated Capsicum species and close wild relatives. We identify highly differentiated genomic regions among the domesticated peppers that underlie their natural variations in flowering time, characteristic flavors, and unique resistances to biotic and abiotic stresses. Domestication sweeps detected in C. annuum var. annuum and C. baccatum var. pendulum are mostly different, and the common domestication traits, including fruit size, shape and pungency, are achieved mainly through the selection of distinct genomic regions between these two cultivated species. Introgressions from C. baccatum into C. chinense and C. frutescens are detected, including those providing genetic sources for various biotic and abiotic stress tolerances.

Abstract

Existing genetics and genomics studies of peppers mainly focus on single species. Here, the authors report a pepper graph pan-genome and a genome variation map of 500 accessions from five domesticated species and close wild relatives to reveal their domestication, introgression and population differentiation.

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Most cited references 89

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Author and article information

Contributors

Shan Wu:

ORCID: http://orcid.org/0000-0001-8539-3808

sw728@cornell.edu

Zhangjun Fei:

ORCID: http://orcid.org/0000-0001-9684-1450

zf25@cornell.edu

Xuexiao Zou:

ORCID: http://orcid.org/0000-0002-7045-8047

zouxuexiao428@163.com

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 7 September 2023

Publication date PMC-release: 7 September 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 5487

Affiliations

[1 ]GRID grid.257160.7, ISNI 0000 0004 1761 0331, Engineering Research Center for Germplasm Innovation and New Varieties Breeding of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, , Hunan Agricultural University, ; Changsha, China

[2 ]GRID grid.5386.8, ISNI 000000041936877X, Boyce Thompson Institute, ; Ithaca, NY USA

[3 ]GRID grid.5386.8, ISNI 000000041936877X, Plant Biology Section, School of Integrative Plant Science, , Cornell University, ; Ithaca, NY USA

[4 ]GRID grid.508985.9, U.S. Department of Agriculture-Agricultural Research Service, , Plant Genetic Resources Conservation Unit, ; Griffin, GA USA

[5 ]GRID grid.410598.1, ISNI 0000 0004 4911 9766, Institute of Vegetable Research, Hunan Academy of Agricultural Science, ; Changsha, China

[6 ]GRID grid.512862.a, U.S. Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, ; Ithaca, NY USA

[7 ]GRID grid.443483.c, ISNI 0000 0000 9152 7385, Present Address: College of Horticulture Science, , Zhejiang A&F University, ; Hangzhou, China

[8 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, Present Address: Department of Vegetable Crops, College of Horticulture and Forestry, , Huazhong Agricultural University, ; Wuhan, China

Author information

Jiantao Zhao http://orcid.org/0000-0002-5140-8220

Honghe Sun http://orcid.org/0000-0002-2468-6823

Xuepeng Sun http://orcid.org/0000-0002-5896-9004

Robert Jarret http://orcid.org/0000-0002-0426-6186

Jin Wang http://orcid.org/0000-0002-4687-1325

Zhoubing Liu http://orcid.org/0000-0002-9431-7812

Shan Wu http://orcid.org/0000-0001-8539-3808

Zhangjun Fei http://orcid.org/0000-0001-9684-1450

Xuexiao Zou http://orcid.org/0000-0002-7045-8047

Article

Publisher ID: 41251

DOI: 10.1038/s41467-023-41251-4

PMC ID: 10484947

PubMed ID: 37679363

SO-VID: 84f127b9-e813-4c99-beae-3ae705edcd0d

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 22 June 2023

Date accepted : 28 August 2023

Funding

Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);

Award ID: 1855585

Award Recipient : Zhangjun Fei

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ScienceOpen disciplines: Uncategorized

Keywords: plant domestication,genome evolution,agricultural genetics,genetic variation

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ScienceOpen disciplines: Uncategorized

Keywords: plant domestication, genome evolution, agricultural genetics, genetic variation

Genomes of cultivated and wild Capsicum species provide insights into pepper domestication and population differentiation

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Most cited references 89

Trimmomatic: a flexible trimmer for Illumina sequence data

IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

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