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Mbo Nkoulou LF, Ngalle HB, Cros D, Adje COA, Fassinou NVH, Bell J, Achigan-Dako EG. Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species. FRONTIERS IN PLANT SCIENCE 2022; 13:953133. [PMID: 36388523 PMCID: PMC9650417 DOI: 10.3389/fpls.2022.953133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.
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Affiliation(s)
- Luther Fort Mbo Nkoulou
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
- Institute of Agricultural Research for Development, Centre de Recherche Agricole de Mbalmayo (CRAM), Mbalmayo, Cameroon
| | - Hermine Bille Ngalle
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - David Cros
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche (UMR) Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales (AGAP) Institut, Montpellier, France
- Unité Mixte de Recherche (UMR) Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales (AGAP) Institut, University of Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Agro, Montpellier, France
| | - Charlotte O. A. Adje
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
| | - Nicodeme V. H. Fassinou
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
| | - Joseph Bell
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Enoch G. Achigan-Dako
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
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Voorrips RE, Tumino G. PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. BMC Bioinformatics 2022; 23:442. [PMID: 36274121 PMCID: PMC9590153 DOI: 10.1186/s12859-022-04989-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 10/16/2022] [Indexed: 11/18/2022] Open
Abstract
Background For genetic analyses, multi-allelic markers have an advantage over bi-allelic markers like SNPs (single nucleotide polymorphisms) in that they carry more information about the genetic constitution of individuals. This is especially the case in polyploids, where individuals carry more than two alleles at each locus. Haploblocks are multi-allelic markers that can be derived by phasing sets of closely-linked SNP markers. Phased haploblocks, similarly to other multi-allelic markers, will therefore be advantageous in genetic tasks like linkage mapping, QTL mapping and genome-wide association studies. Results We present a new method to reconstruct haplotypes from SNP dosages derived from genotyping arrays, which is applicable to polyploids. This method is implemented in the software package PolyHaplotyper. In contrast to existing packages for polyploids it makes use of full-sib families among the samples to guide the haplotyping process. We show that in this situation it is much more accurate than other available software, using experimental hexaploid data and simulated tetraploid data. Conclusions Our method and the software package PolyHaplotyper in which it is implemented extend the available tools for haplotyping in polyploids. They perform especially well in situations where one or more full-sib families are present. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04989-0.
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The Contribution of JAK2 46/1 Haplotype in the Predisposition to Myeloproliferative Neoplasms. Int J Mol Sci 2022; 23:ijms232012582. [PMID: 36293440 PMCID: PMC9604447 DOI: 10.3390/ijms232012582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/17/2022] Open
Abstract
Haplotype 46/1 (GGCC) consists of a set of genetic variations distributed along chromosome 9p.24.1, which extend from the Janus Kinase 2 gene to Insulin like 4. Marked by four jointly inherited variants (rs3780367, rs10974944, rs12343867, and rs1159782), this haplotype has a strong association with the development of BCR-ABL1-negative myeloproliferative neoplasms (MPNs) because it precedes the acquisition of the JAK2V617F variant, a common genetic alteration in individuals with these hematological malignancies. It is also described as one of the factors that increases the risk of familial MPNs by more than five times, 46/1 is associated with events related to inflammatory dysregulation, splenomegaly, splanchnic vein thrombosis, Budd–Chiari syndrome, increases in RBC count, platelets, leukocytes, hematocrit, and hemoglobin, which are characteristic of MPNs, as well as other findings that are still being elucidated and which are of great interest for the etiopathological understanding of these hematological neoplasms. Considering these factors, the present review aims to describe the main findings and discussions involving the 46/1 haplotype, and highlights the molecular and immunological aspects and their relevance as a tool for clinical practice and investigation of familial cases.
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Akagi T, Jung K, Masuda K, Shimizu KK. Polyploidy before and after domestication of crop species. CURRENT OPINION IN PLANT BIOLOGY 2022; 69:102255. [PMID: 35870416 DOI: 10.1016/j.pbi.2022.102255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/01/2022] [Accepted: 05/27/2022] [Indexed: 06/15/2023]
Abstract
Recent advances in the genomics of polyploid species answer some of the long-standing questions about the role of polyploidy in crop species. Here, we summarize the current literature to reexamine scenarios in which polyploidy played a role both before and after domestication. The prevalence of polyploidy can help to explain environmental robustness in agroecosystems. This review also clarifies the molecular basis of some agriculturally advantageous traits of polyploid crops, including yield increments in polyploid cotton via subfunctionalization, modification of a separated sexuality to selfing in polyploid persimmon via neofunctionalization, and transition to a selfing system via nonfunctionalization combined with epistatic interaction between duplicated S-loci. The rapid progress in genomics and genetics is discussed along with how this will facilitate functional studies of understudied polyploid crop species.
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Affiliation(s)
- Takashi Akagi
- Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan.
| | - Katharina Jung
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zürich, Switzerland
| | - Kanae Masuda
- Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
| | - Kentaro K Shimizu
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zürich, Switzerland; Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka, 244-0813 Totsuka-ward, Yokohama, Japan.
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Schrinner S, Serra Mari R, Finkers R, Arens P, Usadel B, Marschall T, Klau GW. Genetic polyploid phasing from low-depth progeny samples. iScience 2022; 25:104461. [PMID: 35692633 PMCID: PMC9184567 DOI: 10.1016/j.isci.2022.104461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/20/2022] [Accepted: 05/16/2022] [Indexed: 11/08/2022] Open
Abstract
An important challenge in genome assembly is haplotype phasing, that is, to reconstruct the different haplotype sequences of an individual genome. Phasing becomes considerably more difficult with increasing ploidy, which makes polyploid phasing a notoriously hard computational problem. We present a novel genetic phasing method for plant breeding with the aim to phase two deep-sequenced parental samples with the help of a large number of progeny samples sequenced at low depth. The key ideas underlying our approach are to (i) integrate the individually weak Mendelian progeny signals with a Bayesian log-likelihood model, (ii) cluster alleles according to their likelihood of co-occurrence, and (iii) assign them to haplotypes via an interval scheduling approach. We show on two deep-sequenced parental and 193 low-depth progeny potato samples that our approach computes high-quality sparse phasings and that it scales to whole genomes. Allows phasing of autopolyploid species through genetic information of progenies High number of low-depth progeny samples yields significant markers for phasing Informative variant types (simplex-nulliplex) phasable with high confidence Continuity not limited by read connectivity, but rather by the recombination rate
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Affiliation(s)
- Sven Schrinner
- Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Serra Mari
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Richard Finkers
- Plant Breeding, Wageningen University & Research, Wageningen, the Netherlands.,Gennovation B.V., Agro Business Park 10, 6708 PW, Wageningen, The Netherlands
| | - Paul Arens
- Plant Breeding, Wageningen University & Research, Wageningen, the Netherlands
| | - Björn Usadel
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Forschungszentrum Jülich, Institute of Bio and Geosciences, Bioinformatics (IBG-4), Jülich, Germany.,Bioeconomy Science Center, c/o Forschungszentrum, Jülich, Germany.,Biological Data Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gunnar W Klau
- Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Jubin C, Griebel S, Beissinger T. Improving genomic tools for outcrossing crops. MOLECULAR PLANT 2021; 14:538-540. [PMID: 33753305 DOI: 10.1016/j.molp.2021.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Cathy Jubin
- Division of Plant Breeding Methodology & Center for Integrated Breeding Research, Department of Crop Science, University of Göttingen, 37075 Göttingen, Germany
| | - Stefanie Griebel
- Division of Plant Breeding Methodology & Center for Integrated Breeding Research, Department of Crop Science, University of Göttingen, 37075 Göttingen, Germany
| | - Timothy Beissinger
- Division of Plant Breeding Methodology & Center for Integrated Breeding Research, Department of Crop Science, University of Göttingen, 37075 Göttingen, Germany.
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7
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Chen M, Fan W, Ji F, Hua H, Liu J, Yan M, Ma Q, Fan J, Wang Q, Zhang S, Liu G, Sun Z, Tian C, Zhao F, Zheng J, Zhang Q, Chen J, Qiu J, Wei X, Chen Z, Zhang P, Pei D, Yang J, Huang X. Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq. MOLECULAR PLANT 2021; 14:556-570. [PMID: 33429094 DOI: 10.1016/j.molp.2021.01.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/07/2020] [Accepted: 01/06/2021] [Indexed: 05/27/2023]
Abstract
Many important crops (e.g., tuber, root, and tree crops) are cross-pollinating. For these crops, no inbred lines are available for genetic study and breeding because they are self-incompatible, clonally propagated, or have a long generation time, making the identification of agronomically important genes difficult, particularly in crops with a complex autopolyploid genome. In this study, we developed a method, OutcrossSeq, for mapping agronomically important loci in outcrossing crops based on whole-genome low-coverage resequencing of a large genetic population, and designed three computation algorithms in OutcrossSeq for different types of outcrossing populations. We applied OutcrossSeq to a tuberous root crop (sweet potato, autopolyploid), a tree crop (walnut tree, highly heterozygous diploid), and hybrid crops (double-cross populations) to generate high-density genotype maps for the outcrossing populations, which enable precise identification of genomic loci underlying important agronomic traits. Candidate causative genes at these loci were detected based on functional clues. Taken together, our results indicate that OutcrossSeq is a robust and powerful method for identifying agronomically important genes in heterozygous species, including polyploids, in a cost-efficient way. The OutcrossSeq software and its instruction manual are available for downloading at www.xhhuanglab.cn/tool/OutcrossSeq.html.
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Affiliation(s)
- Mengjiao Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Weijuan Fan
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
| | - Feiyang Ji
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Hua Hua
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Mengxiao Yan
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
| | - Qingguo Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Jiongjiong Fan
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Shufeng Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Guiling Liu
- Tai'an Academy of Agricultural Sciences, Tai'an 271000, Shandong, China
| | - Zhe Sun
- Tai'an Academy of Agricultural Sciences, Tai'an 271000, Shandong, China
| | - Changgeng Tian
- Tai'an Academy of Agricultural Sciences, Tai'an 271000, Shandong, China
| | - Fengling Zhao
- Tai'an Academy of Agricultural Sciences, Tai'an 271000, Shandong, China
| | - Jianli Zheng
- Tai'an Academy of Agricultural Sciences, Tai'an 271000, Shandong, China
| | - Qi Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiaxin Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ziru Chen
- National Genomics Data Center, Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Peng Zhang
- CAS Center for Excellence of Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China.
| | - Dong Pei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China.
| | - Jun Yang
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, China.
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China.
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Peace CP, Bianco L, Troggio M, van de Weg E, Howard NP, Cornille A, Durel CE, Myles S, Migicovsky Z, Schaffer RJ, Costes E, Fazio G, Yamane H, van Nocker S, Gottschalk C, Costa F, Chagné D, Zhang X, Patocchi A, Gardiner SE, Hardner C, Kumar S, Laurens F, Bucher E, Main D, Jung S, Vanderzande S. Apple whole genome sequences: recent advances and new prospects. HORTICULTURE RESEARCH 2019; 6:59. [PMID: 30962944 PMCID: PMC6450873 DOI: 10.1038/s41438-019-0141-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 03/15/2019] [Accepted: 03/15/2019] [Indexed: 05/19/2023]
Abstract
In 2010, a major scientific milestone was achieved for tree fruit crops: publication of the first draft whole genome sequence (WGS) for apple (Malus domestica). This WGS, v1.0, was valuable as the initial reference for sequence information, fine mapping, gene discovery, variant discovery, and tool development. A new, high quality apple WGS, GDDH13 v1.1, was released in 2017 and now serves as the reference genome for apple. Over the past decade, these apple WGSs have had an enormous impact on our understanding of apple biological functioning, trait physiology and inheritance, leading to practical applications for improving this highly valued crop. Causal gene identities for phenotypes of fundamental and practical interest can today be discovered much more rapidly. Genome-wide polymorphisms at high genetic resolution are screened efficiently over hundreds to thousands of individuals with new insights into genetic relationships and pedigrees. High-density genetic maps are constructed efficiently and quantitative trait loci for valuable traits are readily associated with positional candidate genes and/or converted into diagnostic tests for breeders. We understand the species, geographical, and genomic origins of domesticated apple more precisely, as well as its relationship to wild relatives. The WGS has turbo-charged application of these classical research steps to crop improvement and drives innovative methods to achieve more durable, environmentally sound, productive, and consumer-desirable apple production. This review includes examples of basic and practical breakthroughs and challenges in using the apple WGSs. Recommendations for "what's next" focus on necessary upgrades to the genome sequence data pool, as well as for use of the data, to reach new frontiers in genomics-based scientific understanding of apple.
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Affiliation(s)
- Cameron P. Peace
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Luca Bianco
- Computational Biology, Fondazione Edmund Mach, San Michele all’Adige, TN 38010 Italy
| | - Michela Troggio
- Department of Genomics and Biology of Fruit Crops, Fondazione Edmund Mach, San Michele all’Adige, TN 38010 Italy
| | - Eric van de Weg
- Plant Breeding, Wageningen University and Research, Wageningen, 6708PB The Netherlands
| | - Nicholas P. Howard
- Department of Horticultural Science, University of Minnesota, St. Paul, MN 55108 USA
- Institut für Biologie und Umweltwissenschaften, Carl von Ossietzky Universität, 26129 Oldenburg, Germany
| | - Amandine Cornille
- GQE – Le Moulon, Institut National de la Recherche Agronomique, University of Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Charles-Eric Durel
- Institut National de la Recherche Agronomique, Institut de Recherche en Horticulture et Semences, UMR 1345, 49071 Beaucouzé, France
| | - Sean Myles
- Department of Plant, Food and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3 Canada
| | - Zoë Migicovsky
- Department of Plant, Food and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3 Canada
| | - Robert J. Schaffer
- The New Zealand Institute for Plant and Food Research Ltd, Motueka, 7198 New Zealand
- School of Biological Sciences, University of Auckland, Auckland, 1142 New Zealand
| | - Evelyne Costes
- AGAP, INRA, CIRAD, Montpellier SupAgro, University of Montpellier, Montpellier, France
| | - Gennaro Fazio
- Plant Genetic Resources Unit, USDA ARS, Geneva, NY 14456 USA
| | - Hisayo Yamane
- Laboratory of Pomology, Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502 Japan
| | - Steve van Nocker
- Department of Horticulture, Michigan State University, East Lansing, MI 48824 USA
| | - Chris Gottschalk
- Department of Horticulture, Michigan State University, East Lansing, MI 48824 USA
| | - Fabrizio Costa
- Department of Genomics and Biology of Fruit Crops, Fondazione Edmund Mach, San Michele all’Adige, TN 38010 Italy
| | - David Chagné
- The New Zealand Institute for Plant and Food Research Ltd (Plant & Food Research), Palmerston North Research Centre, Palmerston North, 4474 New Zealand
| | - Xinzhong Zhang
- College of Horticulture, China Agricultural University, 100193 Beijing, China
| | | | - Susan E. Gardiner
- The New Zealand Institute for Plant and Food Research Ltd (Plant & Food Research), Palmerston North Research Centre, Palmerston North, 4474 New Zealand
| | - Craig Hardner
- Queensland Alliance of Agriculture and Food Innovation, University of Queensland, St Lucia, 4072 Australia
| | - Satish Kumar
- New Cultivar Innovation, Plant and Food Research, Havelock North, 4130 New Zealand
| | - Francois Laurens
- Institut National de la Recherche Agronomique, Institut de Recherche en Horticulture et Semences, UMR 1345, 49071 Beaucouzé, France
| | - Etienne Bucher
- Institut National de la Recherche Agronomique, Institut de Recherche en Horticulture et Semences, UMR 1345, 49071 Beaucouzé, France
- Agroscope, 1260 Changins, Switzerland
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Sook Jung
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Stijn Vanderzande
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
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