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Meger J, Ulaszewski B, Pałucka M, Kozioł C, Burczyk J. Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash ( Fraxinus excelsior L.) populations. Evol Appl 2024; 17:e13694. [PMID: 38707993 PMCID: PMC11069026 DOI: 10.1111/eva.13694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/28/2024] [Accepted: 04/10/2024] [Indexed: 05/07/2024] Open
Abstract
The increase in introduced insect pests and pathogens due to anthropogenic environmental changes has become a major concern for tree species worldwide. Common ash (Fraxinus excelsior L.) is one of such species facing a significant threat from the invasive fungal pathogen Hymenoscyphus fraxineus. Some studies have indicated that the susceptibility of ash to the pathogen is genetically determined, providing some hope for accelerated breeding programs that are aimed at increasing the resistance of ash populations. To address this challenge, we used a genomic selection strategy to identify potential genetic markers that are associated with resistance to the pathogen causing ash dieback. Through genome-wide association studies (GWAS) of 300 common ash individuals from 30 populations across Poland (ddRAD, dataset A), we identified six significant SNP loci with a p-value ≤1 × 10-4 associated with health status. To further evaluate the effectiveness of GWAS markers in predicting health status, we considered two genomic prediction scenarios. Firstly, we conducted cross-validation on dataset A. Secondly, we trained markers on dataset A and tested them on dataset B, which involved whole-genome sequencing of 20 individuals from two populations. Genomic prediction analysis revealed that the top SNPs identified via GWAS exhibited notably higher prediction accuracies compared to randomly selected SNPs, particularly with a larger number of SNPs. Cross-validation analyses using dataset A showcased high genomic prediction accuracy, predicting tree health status with over 90% accuracy across the top SNP sets ranging from 500 to 10,000 SNPs from the GWAS datasets. However, no significant results emerged for health status when the model trained on dataset A was tested on dataset B. Our findings illuminate potential genetic markers associated with resistance to ash dieback, offering support for future breeding programs in Poland aimed at combating ash dieback and bolstering conservation efforts for this invaluable tree species.
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Affiliation(s)
- Joanna Meger
- Department of Genetics, Faculty of Biological SciencesKazimierz Wielki UniversityBydgoszczPoland
| | - Bartosz Ulaszewski
- Department of Genetics, Faculty of Biological SciencesKazimierz Wielki UniversityBydgoszczPoland
| | | | | | - Jarosław Burczyk
- Department of Genetics, Faculty of Biological SciencesKazimierz Wielki UniversityBydgoszczPoland
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Kerr SC, Shehnaz S, Paudel L, Manivannan MS, Shaw LM, Johnson A, Velasquez JTJ, Tanurdžić M, Cazzonelli CI, Varkonyi-Gasic E, Prentis PJ. Advancing tree genomics to future proof next generation orchard production. FRONTIERS IN PLANT SCIENCE 2024; 14:1321555. [PMID: 38312357 PMCID: PMC10834703 DOI: 10.3389/fpls.2023.1321555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 12/26/2023] [Indexed: 02/06/2024]
Abstract
The challenges facing tree orchard production in the coming years will be largely driven by changes in the climate affecting the sustainability of farming practices in specific geographical regions. Identifying key traits that enable tree crops to modify their growth to varying environmental conditions and taking advantage of new crop improvement opportunities and technologies will ensure the tree crop industry remains viable and profitable into the future. In this review article we 1) outline climate and sustainability challenges relevant to horticultural tree crop industries, 2) describe key tree crop traits targeted for improvement in agroecosystem productivity and resilience to environmental change, and 3) discuss existing and emerging genomic technologies that provide opportunities for industries to future proof the next generation of orchards.
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Affiliation(s)
- Stephanie C Kerr
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Saiyara Shehnaz
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Lucky Paudel
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Mekaladevi S Manivannan
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Lindsay M Shaw
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
- School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, QLD, Australia
| | - Amanda Johnson
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Jose Teodoro J Velasquez
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Miloš Tanurdžić
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | | | - Erika Varkonyi-Gasic
- The New Zealand Institute for Plant and Food Research Limited, Auckland, New Zealand
| | - Peter J Prentis
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
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Ni F, Li Z, Huang J. Worldwide productivity and research trend on fruit quality: a bibliometric study. FRONTIERS IN PLANT SCIENCE 2024; 14:1294989. [PMID: 38264033 PMCID: PMC10803653 DOI: 10.3389/fpls.2023.1294989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024]
Abstract
Introduction As one of the important sources of food for human beings, fruits have been extensively studied. To better guide basic and applied research, it is urgent to conduct a systematic analysis of these studies based on extensive literature collection. Methods Based on the Web of Science Core Collection database, this study uses R language and CiteSpace to conduct bibliometric analysis and data mining on the literatures related to fruit quality from January 2013 to June 2023. Results The results indicated that among various fruits, tomatoes have been most frequently studied with special interests in photosynthesis, fruit development, and molecular breeding. The research direction primarily focused on fruit resistance and storage characteristics. Among the indicators related to fruit quality, antioxidant activity has the highest co-occurrence with other indicators of fruit quality, especially with nutrients such as anthocyanins, phenolic substances, sugars, and fruit firmness. Discussion Currently, adaptation to stress and antioxidant activity are recognized as prominent research focal points in this field. Fruit morphology, particularly fruit size, irrigation methods, application of molecular technology, and infection prevention, represent potential areas of interests in future research on fruit quality.
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Affiliation(s)
- Fei Ni
- Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
- College of Agriculture and Forestry Ecology, Shaoyang University, Shaoyang, China
| | - Ziwei Li
- Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jianzi Huang
- Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
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Tchounke B, Sanchez L, Bell JM, Cros D. Mate selection: A useful approach to maximize genetic gain and control inbreeding in genomic and conventional oil palm (Elaeis guineensis Jacq.) hybrid breeding. PLoS Comput Biol 2023; 19:e1010290. [PMID: 37695766 PMCID: PMC10513302 DOI: 10.1371/journal.pcbi.1010290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/21/2023] [Accepted: 07/31/2023] [Indexed: 09/13/2023] Open
Abstract
Genomic selection (GS) is an effective method for the genetic improvement of complex traits in plants and animals. Optimization approaches could be used in conjunction with GS to further increase its efficiency and to limit inbreeding, which can increase faster with GS. Mate selection (MS) typically uses a metaheuristic optimization algorithm, simulated annealing, to optimize the selection of individuals and their matings. However, in species with long breeding cycles, this cannot be studied empirically. Here, we investigated this aspect with forward genetic simulations on a high-performance computing cluster and massively parallel computing, considering the oil palm hybrid breeding example. We compared MS and simple methods of inbreeding management (limitation of the number of individuals selected per family, prohibition of self-fertilization and combination of these two methods), in terms of parental inbreeding and genetic progress over four generations of genomic selection and phenotypic selection. The results showed that, compared to the conventional method without optimization, MS could lead to significant decreases in inbreeding and increases in annual genetic progress, with the magnitude of the effect depending on MS parameters and breeding scenarios. The optimal solution retained by MS differed by five breeding characteristics from the conventional solution: selected individuals covering a broader range of genetic values, fewer individuals selected per full-sib family, decreased percentage of selfings, selfings preferentially made on the best individuals and unbalanced number of crosses among selected individuals, with the better an individual, the higher the number of times he is mated. Stronger slowing-down in inbreeding could be achieved with other methods but they were associated with a decreased genetic progress. We recommend that breeders use MS, with preliminary analyses to identify the proper parameters to reach the goals of the breeding program in terms of inbreeding and genetic gain.
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Affiliation(s)
- Billy Tchounke
- Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon
| | | | - Joseph Martin Bell
- Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ. Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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De Mori G, Cipriani G. Marker-Assisted Selection in Breeding for Fruit Trait Improvement: A Review. Int J Mol Sci 2023; 24:ijms24108984. [PMID: 37240329 DOI: 10.3390/ijms24108984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
Breeding fruit species is time-consuming and expensive. With few exceptions, trees are likely the worst species to work with in terms of genetics and breeding. Most are characterized by large trees, long juvenile periods, and intensive agricultural practice, and environmental variability plays an important role in the heritability evaluations of every single important trait. Although vegetative propagation allows for the production of a significant number of clonal replicates for the evaluation of environmental effects and genotype × environment interactions, the spaces required for plant cultivation and the intensity of work necessary for phenotypic surveys slow down the work of researchers. Fruit breeders are very often interested in fruit traits: size, weight, sugar and acid content, ripening time, fruit storability, and post-harvest practices, among other traits relevant to each individual species. The translation of trait loci and whole-genome sequences into diagnostic genetic markers that are effective and affordable for use by breeders, who must choose genetically superior parents and subsequently choose genetically superior individuals among their progeny, is one of the most difficult tasks still facing tree fruit geneticists. The availability of updated sequencing techniques and powerful software tools offered the opportunity to mine tens of fruit genomes to find out sequence variants potentially useful as molecular markers. This review is devoted to analysing what has been the role of molecular markers in assisting breeders in selection processes, with an emphasis on the fruit traits of the most important fruit crops for which examples of trustworthy molecular markers have been developed, such as the MDo.chr9.4 marker for red skin colour in apples, the CCD4-based marker CPRFC1, and LG3_13.146 marker for flesh colour in peaches, papayas, and cherries, respectively.
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Affiliation(s)
- Gloria De Mori
- Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Guido Cipriani
- Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy
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Schaller A, Vanderzande S, Peace C. Deducing genotypes for loci of interest from SNP array data via haplotype sharing, demonstrated for apple and cherry. PLoS One 2023; 18:e0272888. [PMID: 36749762 PMCID: PMC9904487 DOI: 10.1371/journal.pone.0272888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Breeders, collection curators, and other germplasm users require genetic information, both genome-wide and locus-specific, to effectively manage their genetically diverse plant material. SNP arrays have become the preferred platform to provide genome-wide genetic profiles for elite germplasm and could also provide locus-specific genotypic information. However, genotypic information for loci of interest such as those within PCR-based DNA fingerprinting panels and trait-predictive DNA tests is not readily extracted from SNP array data, thus creating a disconnect between historic and new data sets. This study aimed to establish a method for deducing genotypes at loci of interest from their associated SNP haplotypes, demonstrated for two fruit crops and three locus types: quantitative trait loci Ma and Ma3 for acidity in apple, apple fingerprinting microsatellite marker GD12, and Mendelian trait locus Rf for sweet cherry fruit color. Using phased data from an apple 8K SNP array and sweet cherry 6K SNP array, unique haplotypes spanning each target locus were associated with alleles of important breeding parents. These haplotypes were compared via identity-by-descent (IBD) or identity-by-state (IBS) to haplotypes present in germplasm important to U.S. apple and cherry breeding programs to deduce target locus alleles in this germplasm. While IBD segments were confidently tracked through pedigrees, confidence in allele identity among IBS segments used a shared length threshold. At least one allele per locus was deduced for 64-93% of the 181 individuals. Successful validation compared deduced Rf and GD12 genotypes with reported and newly obtained genotypes. Our approach can efficiently merge and expand genotypic data sets, deducing missing data and identifying errors, and is appropriate for any crop with SNP array data and historic genotypic data sets, especially where linkage disequilibrium is high. Locus-specific genotypic information extracted from genome-wide SNP data is expected to enhance confidence in management of genetic resources.
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Affiliation(s)
- Alexander Schaller
- Department of Horticulture, Washington State University, Pullman, WA, United States of America
| | - Stijn Vanderzande
- Department of Horticulture, Washington State University, Pullman, WA, United States of America
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA, United States of America
- * E-mail:
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Jahed KR, Hirst PM. Fruit growth and development in apple: a molecular, genomics and epigenetics perspective. FRONTIERS IN PLANT SCIENCE 2023; 14:1122397. [PMID: 37123845 PMCID: PMC10130390 DOI: 10.3389/fpls.2023.1122397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/13/2023] [Indexed: 05/03/2023]
Abstract
Fruit growth and development are physiological processes controlled by several internal and external factors. This complex regulatory mechanism comprises a series of events occurring in a chronological order over a growing season. Understanding the underlying mechanism of fruit development events, however, requires consideration of the events occurring prior to fruit development such as flowering, pollination, fertilization, and fruit set. Such events are interrelated and occur in a sequential order. Recent advances in high-throughput sequencing technology in conjunction with improved statistical and computational methods have empowered science to identify some of the major molecular components and mechanisms involved in the regulation of fruit growth and have supplied encouraging successes in associating genotypic differentiation with phenotypic observations. As a result, multiple approaches have been developed to dissect such complex regulatory machinery and understand the genetic basis controlling these processes. These methods include transcriptomic analysis, quantitative trait loci (QTLs) mapping, whole-genome approach, and epigenetics analyses. This review offers a comprehensive overview of the molecular, genomic and epigenetics perspective of apple fruit growth and development that defines the final fruit size and provides a detailed analysis of the mechanisms by which fruit growth and development are controlled. Though the main emphasis of this article is on the molecular, genomic and epigenetics aspects of fruit growth and development, we will also deliver a brief overview on events occurring prior to fruit growth.
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Yang X, Wu B, Liu J, Zhang Z, Wang X, Zhang H, Ren X, Zhang X, Wang Y, Wu T, Xu X, Han Z, Zhang X. A single QTL harboring multiple genetic variations leads to complicated phenotypic segregation in apple flesh firmness and crispness. PLANT CELL REPORTS 2022; 41:2379-2391. [PMID: 36208306 DOI: 10.1007/s00299-022-02929-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Within a QTL, the genetic recombination and interactions among five and two functional variations at MdbHLH25 and MdWDR5A caused much complicated phenotype segregation in apple FFR and FCR. The storability of climacteric fruit like apple is a quantitative trait. We previously identified 62 quantitative trait loci (QTLs) associating flesh firmness retainability (FFR) and flesh crispness retainability (FCR), but only a few functional genetic variations were identified and validated. The genetic variation network controlling fruit storability is far to be understood and diagnostic markers are needed for molecular breeding. We previously identified overlapped QTLs F16.1/H16.2 for FFR and FCR using an F1 population derived from 'Zisai Pearl' × 'Red Fuji'. In this study, five and two single-nucleotide polymorphisms (SNPs) were identified on the candidate genes MdbHLH25 and MdWDR5A within the QTL region. The SNP1 A allele at MdbHLH25 promoter reduced the expression and SNP2 T allele and/or SNP4/5 GT alleles at the exons attenuated the function of MdbHLH25 by downregulating the expression of the target genes MdACS1, which in turn led to a reduction in ethylene production and maintenance of higher flesh crispness. The SNPs did not alter the protein-protein interaction between MdbHLH25 and MdWDR5A. The joint effect of SNP genotype combinations by the SNPs on MdbHLH25 (SNP1, SNP2, and SNP4) and MdWDR5A (SNPi and SNPii) led to a much broad spectrum of phenotypic segregation in FFR and FCR. Together, the dissection of these genetic variations contributes to understanding the complicated effects of a QTL and provides good potential for marker development in molecular breeding.
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Affiliation(s)
- Xianglong Yang
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Bei Wu
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Jing Liu
- College of Horticultural Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066600, China
| | - Zhongyan Zhang
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Xuan Wang
- College of Horticultural Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066600, China
| | - Haie Zhang
- College of Horticultural Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066600, China
| | - Xuejun Ren
- Testing and Analysis Center, Hebei Normal University of Science and Technology, Qinhuangdao, 066600, China
| | - Xi Zhang
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Yi Wang
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Ting Wu
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Xuefeng Xu
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Zhenhai Han
- College of Horticulture, China Agricultural University, Beijing, 100193, China
| | - Xinzhong Zhang
- College of Horticulture, China Agricultural University, Beijing, 100193, China.
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Wilkinson MJ, Yamashita R, James ME, Bally ISE, Dillon NL, Ali A, Hardner CM, Ortiz-Barrientos D. The influence of genetic structure on phenotypic diversity in the Australian mango (Mangifera indica) gene pool. Sci Rep 2022; 12:20614. [PMID: 36450793 PMCID: PMC9712640 DOI: 10.1038/s41598-022-24800-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/21/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic selection is a promising breeding technique for tree crops to accelerate the development of new cultivars. However, factors such as genetic structure can create spurious associations between genotype and phenotype due to the shared history between populations with different trait values. Genetic structure can therefore reduce the accuracy of the genotype to phenotype map, a fundamental requirement of genomic selection models. Here, we employed 272 single nucleotide polymorphisms from 208 Mangifera indica accessions to explore whether the genetic structure of the Australian mango gene pool explained variation in trunk circumference, fruit blush colour and intensity. Multiple population genetic analyses indicate the presence of four genetic clusters and show that the most genetically differentiated cluster contains accessions imported from Southeast Asia (mainly those from Thailand). We find that genetic structure was strongly associated with three traits: trunk circumference, fruit blush colour and intensity in M. indica. This suggests that the history of these accessions could drive spurious associations between loci and key mango phenotypes in the Australian mango gene pool. Incorporating such genetic structure in associations between genotype and phenotype can improve the accuracy of genomic selection, which can assist the future development of new cultivars.
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Affiliation(s)
- Melanie J Wilkinson
- School of Biological Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia.
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Risa Yamashita
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Maddie E James
- School of Biological Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian S E Bally
- Queensland Department of Agriculture and Fisheries, Mareeba, QLD, 4880, Australia
| | - Natalie L Dillon
- Queensland Department of Agriculture and Fisheries, Mareeba, QLD, 4880, Australia
| | - Asjad Ali
- Queensland Department of Agriculture and Fisheries, Mareeba, QLD, 4880, Australia
| | - Craig M Hardner
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Daniel Ortiz-Barrientos
- School of Biological Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, 4072, Australia
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Khan A, Korban SS. Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3961-3985. [PMID: 35441862 DOI: 10.1007/s00122-022-04093-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/29/2022] [Indexed: 06/14/2023]
Abstract
Climate change, large monocultures of disease-susceptible cultivars, overuse of pesticides, and the emergence of new pathogens or pathogenic strains causing economic losses are all major threats to our environment, health, food, and nutritional supply. Temperate tree fruit crops belonging to the Rosaceae family are the most economically important and widely grown fruit crops. These long-lived crops are under attack from many different pathogens, incurring major economic losses. Multiple chemical sprays to control various diseases annually is a common practice, resulting in significant input costs, as well as environmental and health concerns. Breeding for disease resistance has been undertaken primarily in pome fruit crops (apples and pears) for a few fungal and bacterial diseases, and to a lesser extent in some stone fruit crops. These breeding efforts have taken multiple decades due to the biological constraints and complex genetics of these tree fruit crops. Over the past couple of decades, major advances have been made in genetic and physical mapping, genomics, biotechnology, genome sequencing, and phenomics, along with accumulation of large germplasm collections in repositories. These valuable resources offer opportunities to make significant advances in greatly reducing the time needed to either develop new cultivars or modify existing economic cultivars for enhanced resistance to multiple diseases. This review will cover current knowledge, challenges, and opportunities in breeding for disease resistance in temperate tree fruit crops.
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Affiliation(s)
- Awais Khan
- Plant Pathology and Plant-Microbe Biology Section, Cornell University, Geneva, NY, 14456, USA.
| | - Schuyler S Korban
- Department of Natural Sciences and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
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11
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Hardner CM, Fikere M, Gasic K, da Silva Linge C, Worthington M, Byrne D, Rawandoozi Z, Peace C. Multi-environment genomic prediction for soluble solids content in peach ( Prunus persica). FRONTIERS IN PLANT SCIENCE 2022; 13:960449. [PMID: 36275520 PMCID: PMC9583944 DOI: 10.3389/fpls.2022.960449] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/01/2022] [Indexed: 06/16/2023]
Abstract
Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.
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Affiliation(s)
- Craig M. Hardner
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Mulusew Fikere
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Ksenija Gasic
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Cassia da Silva Linge
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Margaret Worthington
- Faculty Horticulture, University of Arkansas System Division of Agriculture, Fayetteville, AR, United States
| | - David Byrne
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Zena Rawandoozi
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA, United States
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Kumar S, Molloy C, Hunt M, Deng CH, Wiedow C, Andre C, Dare A, McGhie T. GWAS provides new insights into the genetic mechanisms of phytochemicals production and red skin colour in apple. HORTICULTURE RESEARCH 2022; 9:uhac218. [PMID: 36479587 PMCID: PMC9720448 DOI: 10.1093/hr/uhac218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/19/2022] [Indexed: 06/17/2023]
Abstract
Understanding the genetic architecture of apple phytochemicals, and their interplay with conventional selection traits, is critical for the development of new apple cultivars with enhanced health benefits. Apple accessions (n = 344) used for this genome-wide association study (GWAS) represented the wide diversity of metabolic profiles in the domesticated and wild Malus genepools. Fruit samples were phenotyped for 34 metabolites, including a stable vitamin C glycoside "ascorbic acid 2-β-glucoside" (AA-2βG), and the accessions were genotyped using the Apple 20 K SNP Array. Several fruit quality traits, including red skin over-colour (OCOL), were also assessed. Wild Malus accessions showed at least 2-fold higher average content of several metabolites (e.g. ascorbic acid, chlorogenic acid, phloridzin, and trilobatin) than Malus domestica accessions. Several new genomic regions and potential candidate genes underpinning the genetic diversity of apple phytochemicals were identified. The percentage of phenotypic variance explained by the best SNP ranged between 3% and 21% for the different metabolites. Novel association signals for OCOL in the syntenic regions on chromosomes 13 and 16 suggested that whole genome duplication has played a role in the evolution of apple red skin colour. Genetic correlations between phytochemicals and sensory traits were moderate. This study will assist in the selection of Malus accessions with specific phytochemical profiles to establish innovative genomics-based breeding strategies for the development of apple cultivars with enhanced nutritional value.
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Affiliation(s)
| | - Claire Molloy
- The New Zealand Institute for Plant and Food Research Limited, Hawke’s Bay Research Centre, Havelock North 4130, New Zealand
| | - Martin Hunt
- The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North 4410, New Zealand
| | - Cecilia Hong Deng
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland 1025, New Zealand
| | - Claudia Wiedow
- The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North 4410, New Zealand
| | - Christelle Andre
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland 1025, New Zealand
| | - Andrew Dare
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland 1025, New Zealand
| | - Tony McGhie
- The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North 4410, New Zealand
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13
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Sohi HS, Gill MIS, Chhuneja P, Arora NK, Maan SS, Singh J. Construction of Genetic Linkage Map and Mapping QTL Specific to Leaf Anthocyanin Colouration in Mapping Population 'Allahabad Safeda' × 'Purple Guava (Local)' of Guava ( Psidium guajava L.). PLANTS (BASEL, SWITZERLAND) 2022; 11:2014. [PMID: 35956491 PMCID: PMC9370526 DOI: 10.3390/plants11152014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
In the present investigation, F1 hybrids were developed in guava (Psidium guajava L.) by crossing high leaf-anthocyanin reflective-index (ARI1) content cultivars purple guava (local) 'PG', 'CISH G-1' and low leaf-ARI1 content cultivar Seedless 'SL' with Allahabad Safeda 'AS'. On the basis of phenotypic observations, high ARI1 content was observed in the cross 'AS' × 'PG' (0.214). Further, an SSR-markers-based genetic linkage map was developed from a mapping population of 238 F1 individuals derived from cross 'AS' × 'PG'. The linkage map comprised 11 linkage groups (LGs), spanning 1601.7 cM with an average marker interval distance of 29.61 cM between adjacent markers. Five anthocyanin-content related gene-specific markers from apple were tested for parental polymorphism in the genotypes 'AS' and 'PG'. Subsequently, a marker, viz., 'MdMYB10F1', revealed a strong association with leaf anthocyanin content in the guava mapping population. QTL (qARI-6-1) on LG6 explains much of the variation (PVE = 11.51% with LOD = 4.67) in levels of leaf anthocyanin colouration. This is the first report of amplification/utilization of apple anthocyanin-related genes in guava. The genotypic data generated from the genetic map can be further exploited in future for the enrichment of linkage maps and for identification of complex quantitative trait loci (QTLs) governing economically important fruit quality traits in guava.
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Affiliation(s)
- Harjot Singh Sohi
- Department of Fruit Science, College of Horticulture and Forestry Punjab Agricultural University, Ludhiana 141004, India; (M.I.S.G.); (N.K.A.); (S.S.M.)
| | - Manav Indra Singh Gill
- Department of Fruit Science, College of Horticulture and Forestry Punjab Agricultural University, Ludhiana 141004, India; (M.I.S.G.); (N.K.A.); (S.S.M.)
| | - Parveen Chhuneja
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India;
| | - Naresh Kumar Arora
- Department of Fruit Science, College of Horticulture and Forestry Punjab Agricultural University, Ludhiana 141004, India; (M.I.S.G.); (N.K.A.); (S.S.M.)
| | - Sukhjinder Singh Maan
- Department of Fruit Science, College of Horticulture and Forestry Punjab Agricultural University, Ludhiana 141004, India; (M.I.S.G.); (N.K.A.); (S.S.M.)
| | - Jagmohan Singh
- Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;
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14
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Liu W, Chen Z, Jiang S, Wang Y, Fang H, Zhang Z, Chen X, Wang N. Research Progress on Genetic Basis of Fruit Quality Traits in Apple ( Malus × domestica). FRONTIERS IN PLANT SCIENCE 2022; 13:918202. [PMID: 35909724 PMCID: PMC9330611 DOI: 10.3389/fpls.2022.918202] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/23/2022] [Indexed: 06/01/2023]
Abstract
Identifying the genetic variation characteristics of phenotypic traits is important for fruit tree breeding. During the long-term evolution of fruit trees, gene recombination and natural mutation have resulted in a high degree of heterozygosity. Apple (Malus × domestica Borkh.) shows strong ecological adaptability and is widely cultivated, and is among the most economically important fruit crops worldwide. However, the high level of heterozygosity and large genome of apple, in combination with its perennial life history and long juvenile phase, complicate investigation of the genetic basis of fruit quality traits. With continuing augmentation in the apple genomic resources available, in recent years important progress has been achieved in research on the genetic variation of fruit quality traits. This review focuses on summarizing recent genetic studies on apple fruit quality traits, including appearance, flavor, nutritional, ripening, and storage qualities. In addition, we discuss the mapping of quantitative trait loci, screening of molecular markers, and mining of major genes associated with fruit quality traits. The overall aim of this review is to provide valuable insights into the mechanisms of genetic variation and molecular breeding of important fruit quality traits in apple.
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Affiliation(s)
- Wenjun Liu
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an, China
- Collaborative Innovation Center of Fruit & Vegetable Quality and Efficient Production, Tai’an, China
| | - Zijing Chen
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an, China
- Collaborative Innovation Center of Fruit & Vegetable Quality and Efficient Production, Tai’an, China
| | - Shenghui Jiang
- Engineering Laboratory of Genetic Improvement of Horticultural Crops of Shandong Province, College of Horticulture, Qingdao Agricultural University, Qingdao, China
| | - Yicheng Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Hongcheng Fang
- State Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, College of Forestry, Shandong Agricultural University, Tai’an, China
| | - Zongying Zhang
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an, China
- Collaborative Innovation Center of Fruit & Vegetable Quality and Efficient Production, Tai’an, China
| | - Xuesen Chen
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an, China
- Collaborative Innovation Center of Fruit & Vegetable Quality and Efficient Production, Tai’an, China
| | - Nan Wang
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an, China
- Collaborative Innovation Center of Fruit & Vegetable Quality and Efficient Production, Tai’an, China
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15
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Jung M, Keller B, Roth M, Aranzana MJ, Auwerkerken A, Guerra W, Al-Rifaï M, Lewandowski M, Sanin N, Rymenants M, Didelot F, Dujak C, Font i Forcada C, Knauf A, Laurens F, Studer B, Muranty H, Patocchi A. Genetic architecture and genomic predictive ability of apple quantitative traits across environments. HORTICULTURE RESEARCH 2022; 9:uhac028. [PMID: 35184165 PMCID: PMC8976694 DOI: 10.1093/hr/uhac028] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/09/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
Implementation of genomic tools is desirable to increase the efficiency of apple breeding. Recently, the multi-environment apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic predictive ability, and studying genotype by environment interactions (G × E). So far, only two phenological traits were investigated using the apple REFPOP, although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding. Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic predictive abilities of 0.18-0.88 were estimated using main-effect univariate, main-effect multivariate, multi-environment univariate, and multi-environment multivariate models. The G × E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.
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Affiliation(s)
- Michaela Jung
- Agroscope, Breeding Research Group, 8820 Wädenswil, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - Beat Keller
- Agroscope, Breeding Research Group, 8820 Wädenswil, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - Morgane Roth
- Agroscope, Breeding Research Group, 8820 Wädenswil, Switzerland
- GAFL, INRAE, 84140 Montfavet, France
| | - Maria José Aranzana
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), 08140 Caldes de Montbui, Barcelona, Spain
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | | | | | - Mehdi Al-Rifaï
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Mariusz Lewandowski
- The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
| | | | - Marijn Rymenants
- Better3fruit N.V., 3202 Rillaar, Belgium
- Laboratory for Plant Genetics and Crop Improvement, KU Leuven, B-3001 Leuven, Belgium
| | | | - Christian Dujak
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Carolina Font i Forcada
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), 08140 Caldes de Montbui, Barcelona, Spain
| | - Andrea Knauf
- Agroscope, Breeding Research Group, 8820 Wädenswil, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - François Laurens
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bruno Studer
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - Hélène Muranty
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Andrea Patocchi
- Agroscope, Breeding Research Group, 8820 Wädenswil, Switzerland
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16
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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17
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Perry A, Wachowiak W, Beaton J, Iason G, Cottrell J, Cavers S. Identifying and testing marker-trait associations for growth and phenology in three pine species: Implications for genomic prediction. Evol Appl 2022; 15:330-348. [PMID: 35233251 PMCID: PMC8867712 DOI: 10.1111/eva.13345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/02/2022] Open
Abstract
In tree species, genomic prediction offers the potential to forecast mature trait values in early growth stages, if robust marker-trait associations can be identified. Here we apply a novel multispecies approach using genotypes from a new genotyping array, based on 20,795 single nucleotide polymorphisms (SNPs) from three closely related pine species (Pinus sylvestris, Pinus uncinata and Pinus mugo), to test for associations with growth and phenology data from a common garden study. Predictive models constructed using significantly associated SNPs were then tested and applied to an independent multisite field trial of P. sylvestris and the capability to predict trait values was evaluated. One hundred and eighteen SNPs showed significant associations with the traits in the pine species. Common SNPs (MAF > 0.05) associated with bud set were only found in genes putatively involved in growth and development, whereas those associated with growth and budburst were also located in genes putatively involved in response to environment and, to a lesser extent, reproduction. At one of the two independent sites, the model we developed produced highly significant correlations between predicted values and observed height data (YA, height 2020: r = 0.376, p < 0.001). Predicted values estimated with our budburst model were weakly but positively correlated with duration of budburst at one of the sites (GS, 2015: r = 0.204, p = 0.034; 2018: r = 0.205, p = 0.034-0.037) and negatively associated with budburst timing at the other (YA: r = -0.202, p = 0.046). Genomic prediction resulted in the selection of sets of trees whose mean height was taller than the average for each site. Our results provide tentative support for the capability of prediction models to forecast trait values in trees, while highlighting the need for caution in applying them to trees grown in different environments.
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Affiliation(s)
- Annika Perry
- UK Centre for Ecology & Hydrology EdinburghPenicuikUK
| | - Witold Wachowiak
- Institute of Environmental BiologyFaculty of BiologyAdam Mickiewicz University in PoznańPoznańPoland
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18
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Zahid G, Aka Kaçar Y, Dönmez D, Küden A, Giordani T. Perspectives and recent progress of genome-wide association studies (GWAS) in fruits. Mol Biol Rep 2022; 49:5341-5352. [PMID: 35064403 DOI: 10.1007/s11033-021-07055-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Earlier next-generation sequencing technologies are being vastly used to explore, administer, and investigate the gene space with accurate profiling of nucleotide variations in the germplasm. OVERVIEW AND PROGRESS: Recently, novel advancements in high-throughput sequencing technologies allow a genotyping-by-sequencing approach that has opened up new horizons for extensive genotyping exploiting single-nucleotide-polymorphisms (SNPs). This method acts as a bridge to support and minimize a genotype to phenotype gap allowing genetic selection at the genome-wide level, named genomic selection that could facilitate the selection of traits also in the pomology sector. In addition to this, genome-wide genotyping is a prerequisite for genome-wide association studies that have been used successfully to discover the genes, which control polygenic traits including the genetic loci, associated with the trait of interest in fruit crops. AIMS AND PROSPECTS This review article emphasizes the role of genome-wide approaches to unlock and explore the genetic potential along with the detection of SNPs affecting the phenotype of fruit crops and highlights the prospects of genome-wide association studies in fruits.
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Affiliation(s)
- Ghassan Zahid
- Department of Biotechnology, Institute of Natural and Applied Sciences, Çukurova University, 01330, Adana, Turkey.
| | - Yıldız Aka Kaçar
- Department of Horticulture, Faculty of Agriculture, Çukurova University, 01330, Adana, Turkey
| | - Dicle Dönmez
- Biotechnology Research and Application Center, Çukurova University, 01330, Adana, Turkey
| | - Ayzin Küden
- Department of Horticulture, Faculty of Agriculture, Çukurova University, 01330, Adana, Turkey
| | - Tommaso Giordani
- Department of Agriculture, Food and Environment, University of Pisa, 56124, Pisa, Italy
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19
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Cañas-Gutiérrez GP, Sepulveda-Ortega S, López-Hernández F, Navas-Arboleda AA, Cortés AJ. Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From "Criollo" "Elite Trees" via Half-Sib Seedling Rootstocks. FRONTIERS IN PLANT SCIENCE 2022; 13:843099. [PMID: 35685008 PMCID: PMC9171141 DOI: 10.3389/fpls.2022.843099] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/10/2022] [Indexed: 05/11/2023]
Abstract
Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite 'plus trees', and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability-h 2, and genetic-estimated breeding values-GEBVs) across landraces, "criollo", "plus trees" of the super-food fruit tree crop avocado (Persea americana Mill.), and their open-pollinated (OP) half-sib seedling families. Specifically, we used a genomic best linear unbiased prediction (G-BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado "criollo" "plus trees." Estimated additive genetic parameters were validated at a 5-year-old common garden trial (i.e., provenance test), in which 22 OP half-sib seedlings from 82 elite "plus trees" served as rootstocks for the cv. Hass clone. Heritability (h 2) scores in the "criollo" "plus trees" ranged from 0.28 to 0.51. The highest h 2 values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2-0.8) and 0.51 (CI 0.2-0.8), respectively. The h 2 scores for the agronomic traits ranged from 0.34 (CI 0.2-0.6) to 0.39 (CI 0.2-0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits' weight, respectively, exhibited h 2 scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite "criollo" "plus trees" may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from "plus trees" via OP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.
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Affiliation(s)
- Gloria Patricia Cañas-Gutiérrez
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- Corporation for Biological Research (CIB), Unit of Phytosanity and Biological Control, Medellín, Colombia
- *Correspondence: Gloria Patricia Cañas-Gutiérrez,
| | - Stella Sepulveda-Ortega
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | | | - Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- Andrés J. Cortés,
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20
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Cazenave X, Petit B, Lateur M, Nybom H, Sedlak J, Tartarini S, Laurens F, Durel CE, Muranty H. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. G3 (BETHESDA, MD.) 2021; 12:6459174. [PMID: 34893831 PMCID: PMC9210277 DOI: 10.1093/g3journal/jkab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 11/12/2022]
Abstract
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
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Affiliation(s)
- Xabi Cazenave
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bernard Petit
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Marc Lateur
- Plant Breeding and Biodiversity, Centre Wallon de Recherches Agronomiques, Gembloux, Belgium
| | - Hilde Nybom
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Kristianstad, Sweden
| | - Jiri Sedlak
- Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s.r.o, Holovousy, Czech Republic
| | - Stefano Tartarini
- Department of Agricultural Sciences, University of Bologna, Bologna, Italy
| | - François Laurens
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Charles-Eric Durel
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Hélène Muranty
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France,Corresponding author:
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21
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Brault C, Doligez A, Cunff L, Coupel-Ledru A, Simonneau T, Chiquet J, This P, Flutre T. Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine. G3-GENES GENOMES GENETICS 2021; 11:6325507. [PMID: 34544146 PMCID: PMC8496232 DOI: 10.1093/g3journal/jkab248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022]
Abstract
Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.
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Affiliation(s)
- Charlotte Brault
- Institut Français de la Vigne et du Vin, Montpellier F-34398, France.,UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Agnès Doligez
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Le Cunff
- Institut Français de la Vigne et du Vin, Montpellier F-34398, France.,UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Aude Coupel-Ledru
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier 34000, France
| | - Thierry Simonneau
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier 34000, France
| | | | - Patrice This
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Timothée Flutre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
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22
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Rios EF, Andrade MHML, Resende MFR, Kirst M, de Resende MDV, de Almeida Filho JE, Gezan SA, Munoz P. Genomic prediction in family bulks using different traits and cross-validations in pine. G3-GENES GENOMES GENETICS 2021; 11:6321952. [PMID: 34544139 PMCID: PMC8496210 DOI: 10.1093/g3journal/jkab249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022]
Abstract
Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5–20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.
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Affiliation(s)
- Esteban F Rios
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | | | - Marcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Matias Kirst
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA
| | - Marcos D V de Resende
- EMBRAPA Café/Department of Statistics, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa 36570-000, Brazil
| | | | | | - Patricio Munoz
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
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23
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Fugeray-Scarbel A, Bastien C, Dupont-Nivet M, Lemarié S. Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience. Front Genet 2021; 12:629737. [PMID: 34305998 PMCID: PMC8301370 DOI: 10.3389/fgene.2021.629737] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 06/11/2021] [Indexed: 11/25/2022] Open
Abstract
The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.
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Affiliation(s)
| | | | | | | | - Stéphane Lemarié
- Université Grenoble Alpes, INRAE, CNRS, Grenoble INP, GAEL, Grenoble, France
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24
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Jia D, Wu P, Shen F, Li W, Zheng X, Wang Y, Yuan Y, Zhang X, Han Z. Genetic variation in the promoter of an R2R3-MYB transcription factor determines fruit malate content in apple (Malus domestica Borkh.). PLANT PHYSIOLOGY 2021; 186:549-568. [PMID: 33624810 PMCID: PMC8154052 DOI: 10.1093/plphys/kiab098] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 02/13/2021] [Indexed: 05/25/2023]
Abstract
Deciphering the mechanism of malate accumulation in apple (Malus domestica Borkh.) fruits can help to improve their flavor quality and enhance their benefits for human health. Here, we analyzed malate content as a quantitative trait that is determined mainly by genetic effects. In a previous study, we identified an R2R3-MYB transcription factor named MdMYB44 that was a candidate gene in qtl08.1 (quantitative trait locus mapped to chromosome 8) of fruit malate content. In the present study, we established that MdMYB44 negatively regulates fruit malate accumulation by repressing the promoter activity of the malate-associated genes Ma1 (Al-Activated Malate Transporter 9), Ma10 (P-type ATPase 10), MdVHA-A3 (V-type ATPase A3), and MdVHA-D2 (V-type ATPase D2). Two single-nucleotide polymorphisms (SNPs) in the MdMYB44 promoter, SNP A/G and SNP T/-, were experimentally shown to associate with fruit malate content. The TATA-box in the MdMYB44 promoter in the presence of SNP A enhances the basal activity of the MdMYB44 promoter. The binding of a basic-helix-loop-helix transcription factor MdbHLH49 to the MdMYB44 promoter was enhanced by the presence of SNP T, leading to increased MdMYB44 transcript levels and reduced malate accumulation. Furthermore, MdbHLH49 interacts with MdMYB44 and enhances MdMYB44 activity. The two SNPs could be used in combination to select for sour or non-sour apples, providing a valuable tool for the selection of fruit acidity by the apple breeding industry. This research is important for understanding the complex molecular mechanisms of fruit malate accumulation and accelerating the development of germplasm innovation in apple species and cultivars.
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Affiliation(s)
- Dongjie Jia
- Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
| | - Peng Wu
- Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
| | - Fei Shen
- Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wei Li
- College of Horticulture, Institute for Horticultural Plants, China Agricultural University, Beijing 100193, China
| | - Xiaodong Zheng
- Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
| | - Yongzhang Wang
- Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
| | - Yongbing Yuan
- Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
| | - Xinzhong Zhang
- College of Horticulture, Institute for Horticultural Plants, China Agricultural University, Beijing 100193, China
| | - Zhenhai Han
- College of Horticulture, Institute for Horticultural Plants, China Agricultural University, Beijing 100193, China
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25
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O'Connor KM, Hayes BJ, Hardner CM, Alam M, Henry RJ, Topp BL. Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population. BMC Genomics 2021; 22:370. [PMID: 34016055 PMCID: PMC8139092 DOI: 10.1186/s12864-021-07694-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/10/2021] [Indexed: 02/06/2023] Open
Abstract
Background Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. Results Narrow-sense heritability of yield and yield stability was low (h2=0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r=0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474g/year, more than double that of traditional breeding methods (226g/year), due to the halving of generation length from 8 to 4years. Conclusions The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07694-z.
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Affiliation(s)
- Katie M O'Connor
- Queensland Department of Agriculture and Fisheries, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia. .,Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia.
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4072, Australia
| | - Craig M Hardner
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4072, Australia
| | - Mobashwer Alam
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia
| | - Robert J Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, 4072, Australia
| | - Bruce L Topp
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia
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26
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Wu B, Shen F, Wang X, Zheng WY, Xiao C, Deng Y, Wang T, Yu Huang Z, Zhou Q, Wang Y, Wu T, Feng Xu X, Hai Han Z, Zhong Zhang X. Role of MdERF3 and MdERF118 natural variations in apple flesh firmness/crispness retainability and development of QTL-based genomics-assisted prediction. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1022-1037. [PMID: 33319456 PMCID: PMC8131039 DOI: 10.1111/pbi.13527] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 10/29/2020] [Accepted: 12/06/2020] [Indexed: 05/24/2023]
Abstract
Retention of flesh texture attributes during cold storage is critical for the long-term maintenance of fruit quality. The genetic variations determining flesh firmness and crispness retainability are not well understood. The objectives of this study are to identify gene markers based on quantitative trait loci (QTLs) and to develop genomics-assisted prediction (GAP) models for apple flesh firmness and crispness retainability. Phenotype data of 2664 hybrids derived from three Malus domestica cultivars and a M. asiatica cultivar were collected in 2016 and 2017. The phenotype segregated considerably with high broad-sense heritability of 83.85% and 83.64% for flesh firmness and crispness retainability, respectively. Fifty-six candidate genes were predicted from the 62 QTLs identified using bulked segregant analysis and RNA-seq. The genotype effects of the markers designed on each candidate gene were estimated. The genomics-predicted values were obtained using pyramiding marker genotype effects and overall mean phenotype values. Fivefold cross-validation revealed that the prediction accuracy was 0.5541 and 0.6018 for retainability of flesh firmness and crispness, respectively. An 8-bp deletion in the MdERF3 promoter disrupted MdDOF5.3 binding, reduced MdERF3 expression, relieved the inhibition on MdPGLR3, MdPME2, and MdACO4 expression, and ultimately decreased flesh firmness and crispness retainability. A 3-bp deletion in the MdERF118 promoter decreased its expression by disrupting the binding of MdRAVL1, which increased MdPGLR3 and MdACO4 expression and reduced flesh firmness and crispness retainability. These results provide insights regarding the genetic variation network regulating flesh firmness and crispness retainability, and the GAP models can assist in apple breeding.
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Affiliation(s)
- Bei Wu
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Fei Shen
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Xuan Wang
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Wen Yan Zheng
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Chen Xiao
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Yang Deng
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Ting Wang
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Zhen Yu Huang
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Qian Zhou
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Yi Wang
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Ting Wu
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Xue Feng Xu
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Zhen Hai Han
- College of HorticultureChina Agricultural UniversityBeijingChina
| | - Xin Zhong Zhang
- College of HorticultureChina Agricultural UniversityBeijingChina
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27
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Tan K, Zhang H, Li S, Ma H, Zheng H. Lipid nutritional quality of marine and freshwater bivalves and their aquaculture potential. Crit Rev Food Sci Nutr 2021; 62:6990-7014. [PMID: 33847542 DOI: 10.1080/10408398.2021.1909531] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Omega-3 Long-chain polyunsaturated fatty acids (n-3 LC-PUFA) are beneficial to human health. Since the industrial revolution, with the tremendous increase of human population, the supply of natural n-3 LC-PUFA is far lower than the nutritional need of n-3 LC-PUFA. Therefore, a new alternative source of natural n-3 LC-PUFA is urgently needed to reduce the supply and demand gap of n-3 LC-PUFA. Mollusks, mainly bivalves, are rich in n-3 LC-PUFA, but the information of bivalves' lipid profile is not well organized. Therefore, this study aims to analyze the published fatty acid profiles of bivalves and reveal the potential of bivalve aquaculture in meeting the nutritional needs of human for n-3 LC-PUFA. There are growing evidence show that the nutritional quality of bivalve lipid is not only species-specific, but also geographical specific. To date, bivalve aquaculture has not been evenly practiced across the globe. It can be seen that aquaculture is predominant in Asia, especially China. Unlike fish aquaculture, bivalve aquaculture does not rely on fishmeal and fish oil inputs, so it has better room for expansion. In order to unleash the full potential of bivalve aquaculture, there are some challenges need to be addressed, including recurrent mass mortalities of farmed bivalves, food safety and food security issues. The information of this article is very useful to provide an overview of lipid nutritional quality of bivalves, and reveal the potential of bivalve aquaculture in meeting the growing demand of human for n-3 LC-PUFA.
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Affiliation(s)
- Karsoon Tan
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou, China.,Mariculture Research Center for Subtropical Shellfish & Algae of Guangdong Province, Shantou, China.,STU-UMT Joint Shellfish Research Laboratory, Shantou University, Shantou, China
| | - Hongkuan Zhang
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou, China.,Mariculture Research Center for Subtropical Shellfish & Algae of Guangdong Province, Shantou, China.,STU-UMT Joint Shellfish Research Laboratory, Shantou University, Shantou, China
| | - Shengkang Li
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou, China.,Mariculture Research Center for Subtropical Shellfish & Algae of Guangdong Province, Shantou, China.,STU-UMT Joint Shellfish Research Laboratory, Shantou University, Shantou, China
| | - Hongyu Ma
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou, China.,Mariculture Research Center for Subtropical Shellfish & Algae of Guangdong Province, Shantou, China.,STU-UMT Joint Shellfish Research Laboratory, Shantou University, Shantou, China
| | - Huaiping Zheng
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Sciences, Shantou University, Shantou, China.,Mariculture Research Center for Subtropical Shellfish & Algae of Guangdong Province, Shantou, China.,STU-UMT Joint Shellfish Research Laboratory, Shantou University, Shantou, China
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28
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Howard NP, Troggio M, Durel CE, Muranty H, Denancé C, Bianco L, Tillman J, van de Weg E. Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls. BMC Genomics 2021; 22:246. [PMID: 33827434 PMCID: PMC8028180 DOI: 10.1186/s12864-021-07565-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 03/30/2021] [Indexed: 11/23/2022] Open
Abstract
Background Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls. Results Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe. Conclusions The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07565-7.
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Affiliation(s)
- Nicholas P Howard
- Institut für Biologie und Umweltwissenschaften, Carl von Ossietzky Univ., Oldenburg, Germany.,Department of Horticultural Science, Univ. of Minnesota, St Paul, USA
| | | | - Charles-Eric Durel
- Université d'Angers, Institut Agro, INRAE, IRHS, SFR 4207 QuaSaV, Beaucouzé, France
| | - Hélène Muranty
- Université d'Angers, Institut Agro, INRAE, IRHS, SFR 4207 QuaSaV, Beaucouzé, France
| | - Caroline Denancé
- Université d'Angers, Institut Agro, INRAE, IRHS, SFR 4207 QuaSaV, Beaucouzé, France
| | - Luca Bianco
- Fondazione Edmund Mach, San Michele all'Adige, TN, Italy
| | - John Tillman
- Department of Horticultural Science, Univ. of Minnesota, St Paul, USA
| | - Eric van de Weg
- Department of Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.
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29
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Minamikawa MF, Kunihisa M, Noshita K, Moriya S, Abe K, Hayashi T, Katayose Y, Matsumoto T, Nishitani C, Terakami S, Yamamoto T, Iwata H. Tracing founder haplotypes of Japanese apple varieties: application in genomic prediction and genome-wide association study. HORTICULTURE RESEARCH 2021; 8:49. [PMID: 33642580 PMCID: PMC7917097 DOI: 10.1038/s41438-021-00485-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/28/2020] [Accepted: 01/03/2021] [Indexed: 05/21/2023]
Abstract
Haplotypes provide useful information for genomics-based approaches, genomic prediction, and genome-wide association study. As a small number of superior founders have contributed largely to the breeding history of fruit trees, the information of founder haplotypes may be relevant for performing the genomics-based approaches in these plants. In this study, we proposed a method to estimate 14 haplotypes from 7 founders and automatically trace the haplotypes forward to apple parental (185 varieties) and breeding (659 F1 individuals from 16 full-sib families) populations based on 11,786 single-nucleotide polymorphisms, by combining multiple algorithms. Overall, 92% of the single-nucleotide polymorphisms information in the parental and breeding populations was characterized by the 14 founder haplotypes. The use of founder haplotype information improved the accuracy of genomic prediction in 7 traits and the resolution of genome-wide association study in 13 out of 27 fruit quality traits analyzed in this study. We also visualized the significant propagation of the founder haplotype with the largest genetic effect in genome-wide association study over the pedigree tree of the parental population. These results suggest that the information of founder haplotypes can be useful for not only genetic improvement of fruit quality traits in apples but also for understanding the selection history of founder haplotypes in the breeding program of Japanese apple varieties.
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Affiliation(s)
- Mai F Minamikawa
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Miyuki Kunihisa
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605, Japan
| | - Koji Noshita
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Shigeki Moriya
- Division of Apple Research, Institute of Fruit Tree and Tea Science, NARO, 92-24 Shimokuriyagawa Nabeyashiki, Morioka, Iwate, 020-0123, Japan
| | - Kazuyuki Abe
- Division of Apple Research, Institute of Fruit Tree and Tea Science, NARO, 92-24 Shimokuriyagawa Nabeyashiki, Morioka, Iwate, 020-0123, Japan
| | - Takeshi Hayashi
- Institute of Crop Science, NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Yuichi Katayose
- Institute of Crop Science, NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Toshimi Matsumoto
- Institute of Crop Science, NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, Japan
- Institute of Agrobiological Sciences, NARO, 1-2 Owashi, Tsukuba, Ibaraki, 305-8634, Japan
| | - Chikako Nishitani
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605, Japan
| | - Shingo Terakami
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605, Japan
| | - Toshiya Yamamoto
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan.
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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Reyes-Herrera PH, Muñoz-Baena L, Velásquez-Zapata V, Patiño L, Delgado-Paz OA, Díaz-Diez CA, Navas-Arboleda AA, Cortés AJ. Inheritance of Rootstock Effects in Avocado ( Persea americana Mill.) cv. Hass. FRONTIERS IN PLANT SCIENCE 2020; 11:555071. [PMID: 33424874 PMCID: PMC7785968 DOI: 10.3389/fpls.2020.555071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 11/17/2020] [Indexed: 05/16/2023]
Abstract
Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015-2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a "genetic prediction" model to calculate narrow-sense heritabilities (h 2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in "Hass" avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.
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Affiliation(s)
- Paula H. Reyes-Herrera
- Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA)—CI Tibaitatá, Mosquera, Colombia
| | - Laura Muñoz-Baena
- Department of Microbiology and Immunology, Western University, London, ON, Canada
| | - Valeria Velásquez-Zapata
- Department of Plant Pathology and Microbiology, Interdepartmental Bioinformatics and Computational Biology, Iowa State University, Ames, IA, United States
| | - Laura Patiño
- Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA)—CI La Selva, Rionegro, Colombia
| | - Oscar A. Delgado-Paz
- Facultad de Ingenierías, Universidad Católica de Oriente—UCO, Rionegro, Antioquia
| | - Cipriano A. Díaz-Diez
- Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA)—CI La Selva, Rionegro, Colombia
| | | | - Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA)—CI La Selva, Rionegro, Colombia
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Adoption and Optimization of Genomic Selection To Sustain Breeding for Apricot Fruit Quality. G3-GENES GENOMES GENETICS 2020; 10:4513-4529. [PMID: 33067307 PMCID: PMC7718743 DOI: 10.1534/g3.120.401452] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.
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Jung M, Roth M, Aranzana MJ, Auwerkerken A, Bink M, Denancé C, Dujak C, Durel CE, Font I Forcada C, Cantin CM, Guerra W, Howard NP, Keller B, Lewandowski M, Ordidge M, Rymenants M, Sanin N, Studer B, Zurawicz E, Laurens F, Patocchi A, Muranty H. The apple REFPOP-a reference population for genomics-assisted breeding in apple. HORTICULTURE RESEARCH 2020; 7:189. [PMID: 33328447 PMCID: PMC7603508 DOI: 10.1038/s41438-020-00408-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 08/25/2020] [Accepted: 09/06/2020] [Indexed: 05/16/2023]
Abstract
Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions. Here we present an apple reference population: the apple REFPOP, a large collection formed of 534 genotypes planted in six European countries, as a unique tool to accelerate apple breeding. The population consisted of 269 accessions and 265 progeny from 27 parental combinations, representing the diversity in cultivated apple and current European breeding material, respectively. A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95. Based on the genotypic data, linkage disequilibrium was low and population structure was weak. Two well-studied phenological traits of horticultural importance were measured. We found marker-trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date, respectively. With decreasing SNP density, the detection of significant marker-trait associations varied depending on trait architecture. Regardless of the trait, 10,000 SNPs sufficed to maximize genomic prediction ability. We confirm the suitability of the apple REFPOP design for genomics-assisted breeding, especially for breeding programs using related germplasm, and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era.
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Affiliation(s)
- Michaela Jung
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092, Zurich, Switzerland
- Breeding Research group, Agroscope, 8820, Wädenswil, Switzerland
| | - Morgane Roth
- Breeding Research group, Agroscope, 8820, Wädenswil, Switzerland
- GAFL, INRAE, 84140, Montfavet, France
| | - Maria José Aranzana
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), 08140, Caldes de Montbui, Barcelona, Spain
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | | | - Marco Bink
- Biometris, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands
- Hendrix Genetics Research, Technology and Services B.V., PO Box 114, 5830AC, Boxmeer, The Netherlands
| | - Caroline Denancé
- IRHS, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 49071, Beaucouzé, France
| | - Christian Dujak
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, 08193, Bellaterra, Barcelona, Spain
| | - Charles-Eric Durel
- IRHS, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 49071, Beaucouzé, France
| | - Carolina Font I Forcada
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), 08140, Caldes de Montbui, Barcelona, Spain
| | - Celia M Cantin
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), 08140, Caldes de Montbui, Barcelona, Spain
- ARAID (Fundación Aragonesa para la Investigación y el Desarrollo), 50018, Zaragoza, Spain
| | | | - Nicholas P Howard
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA
- Institute of Biology and Environmental Sciences, University of Oldenburg, 26129, Oldenburg, Germany
| | - Beat Keller
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092, Zurich, Switzerland
- Breeding Research group, Agroscope, 8820, Wädenswil, Switzerland
| | | | - Matthew Ordidge
- School of Agriculture, Policy and Development, University of Reading, Whiteknights, RG6 6AR, Reading, UK
| | - Marijn Rymenants
- Better3fruit N.V., 3202, Rillaar, Belgium
- Biometris, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands
- Laboratory for Plant Genetics and Crop Improvement, KU Leuven, B-3001, Leuven, Belgium
| | - Nadia Sanin
- Research Centre Laimburg, 39040, Auer, Italy
| | - Bruno Studer
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, 8092, Zurich, Switzerland
| | - Edward Zurawicz
- Research Institute of Horticulture, 96-100, Skierniewice, Poland
| | - François Laurens
- IRHS, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 49071, Beaucouzé, France
| | - Andrea Patocchi
- Breeding Research group, Agroscope, 8820, Wädenswil, Switzerland
| | - Hélène Muranty
- IRHS, Université d'Angers, INRAE, Institut Agro, SFR 4207 QuaSaV, 49071, Beaucouzé, France.
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Zheng W, Shen F, Wang W, Wu B, Wang X, Xiao C, Tian Z, Yang X, Yang J, Wang Y, Wu T, Xu X, Han Z, Zhang X. Quantitative trait loci-based genomics-assisted prediction for the degree of apple fruit cover color. THE PLANT GENOME 2020; 13:e20047. [PMID: 33217219 DOI: 10.1002/tpg2.20047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 06/22/2020] [Indexed: 06/11/2023]
Abstract
Apple fruit cover color is an important appearance trait determining fruit quality, high degree of fruit cover color or completely red fruit skin is also the ultimate breeding goal. MdMYB1 has repeatedly been reported as a major gene controlling apple fruit cover color. There are also multiple minor-effect genes affecting degree of fruit cover color (DFC). This study was to identify genome-wide quantitative trait loci (QTLs) and to develop genomics-assisted prediction for apple DFC. The DFC phenotype data of 9,422 hybrids from five full-sib families of Malus asiatica 'Zisai Pearl', M. domestica 'Red Fuji', 'Golden Delicious', and 'Jonathan' were collected in 2014-2017. The phenotype varied considerably among hybrids with the same MdMYB1 genotype. Ten QTLs for DFC were identified using MapQTL and bulked segregant analysis via sequencing. From these QTLs, ten candidate genes were predicted, including MdMYB1 from a year-stable QTL on chromosome 9 of 'Zisai Pearl' and 'Red Fuji'. Then, kompetitive allele-specific polymerase chain reaction (KASP) markers were designed on these candidate genes and 821 randomly selected hybrids were genotyped. The genotype effects of the markers were estimated. MdMYB1-1 (represented by marker H162) exhibited a partial dominant allelic effect on MdMYB1-2 and showed non-allelic epistasis on markers H1245 and G6. Finally, a non-additive QTL-based genomics assisted prediction model was established for DFC. The Pearson's correlation coefficient between the genomic predicted value and the observed phenotype value was 0.5690. These results can be beneficial for apple genomics-assisted breeding and may provide insights for understanding the mechanism of fruit coloration.
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Affiliation(s)
- Wenyan Zheng
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Fei Shen
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Wuqian Wang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Bei Wu
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Xuan Wang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Chen Xiao
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Zhendong Tian
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Xianglong Yang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Jing Yang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Yi Wang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Ting Wu
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Xuefeng Xu
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Zhenhai Han
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
| | - Xinzhong Zhang
- College of Horticulture, China Agricultural University, No. 2 Yuanmingyuan West Road, Beijing, China, 100193
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Yamashita H, Uchida T, Tanaka Y, Katai H, Nagano AJ, Morita A, Ikka T. Genomic predictions and genome-wide association studies based on RAD-seq of quality-related metabolites for the genomics-assisted breeding of tea plants. Sci Rep 2020; 10:17480. [PMID: 33060786 PMCID: PMC7562905 DOI: 10.1038/s41598-020-74623-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/14/2020] [Indexed: 12/01/2022] Open
Abstract
Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic breeding. Because tea plants are bred using conventional methods, we evaluated the potential of genomic predictions (GPs) and genome-wide association studies (GWASs) for the genetic breeding of tea quality-related metabolites using genome-wide single nucleotide polymorphisms (SNPs) detected from restriction site-associated DNA sequencing of 150 tea accessions. The present GP, based on genome-wide SNPs, and six models produced moderate prediction accuracy values (r) for the levels of most catechins, represented by ( -)-epigallocatechin gallate (r = 0.32-0.41) and caffeine (r = 0.44-0.51), but low r values for free amino acids and chlorophylls. Integrated analysis of GWAS and GP detected potential candidate genes for each metabolite using 80-160 top-ranked SNPs that resulted in the maximum cumulative prediction value. Applying GPs and GWASs to tea accession traits will contribute to genomics-assisted tea breeding.
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Affiliation(s)
- Hiroto Yamashita
- Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan
- United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagito, Gifu, 501-1193, Japan
| | - Tomoki Uchida
- Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan
| | - Yasuno Tanaka
- Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan
- United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagito, Gifu, 501-1193, Japan
| | - Hideyuki Katai
- Shizuoka Prefectural Research Institute of Agriculture and Forestry, Tea Research Center, 1706-11 Kurasawa, Kikugawa, Shizuoka, 439-0002, Japan
- Shizuoka Prefecture Chubu Agriculture and Forestry Office, 2-20 Ariake-cho, Suruga-ku, Shizuoka, 422-8031, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, 1-5 Yokotani, Seta Oe-cho, Otsu, Shiga, 520-2194, Japan
| | - Akio Morita
- Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan
- Institute for Tea Science, Shizuoka University, 836 Ohya, Shizuoka, 422-8529, Japan
| | - Takashi Ikka
- Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan.
- Institute for Tea Science, Shizuoka University, 836 Ohya, Shizuoka, 422-8529, Japan.
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Ban S, Xu K. Identification of two QTLs associated with high fruit acidity in apple using pooled genome sequencing analysis. HORTICULTURE RESEARCH 2020; 7:171. [PMID: 33082977 PMCID: PMC7546611 DOI: 10.1038/s41438-020-00393-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 08/16/2020] [Accepted: 08/30/2020] [Indexed: 06/01/2023]
Abstract
Acidity is a critical component determining apple fruit quality. Previous studies reported two major acidity quantitative trait loci (QTLs) on linkage groups (LGs) 16 (Ma) and 8 (Ma3), respectively, and their homozygous genotypes mama and ma3ma3 usually confer low titratable acidity (TA) (<3.0 mg ml-1) to apple fruit. However, apples of genotypes Ma- (MaMa and Mama) or Ma3- (Ma3Ma3 and Ma3ma3) frequently show an acidity range spanning both regular (TA 3.0-10.0 mg ml-1) and high (TA > 10 mg ml-1) acidity levels. To date, the genetic control for high-acidity apples remains essentially unknown. In order to map QTLs associated with high acidity, two genomic DNA pools, one for high acidity and the other for regular acidity, were created in an interspecific F1 population Royal Gala (Malus domestica) × PI 613988 (M. sieversii) of 191 fruit-bearing progenies. By Illumina paired-end sequencing of the high and regular acidity pools, 1,261,640 single-nucleotide variants (SNVs) commonly present in both pools were detected. Using allele frequency directional difference and density (AFDDD) mapping approach, one region on chromosome 4 and another on chromosome 6 were identified to be putatively associated with high acidity, and were named Ma6 and Ma4, respectively. Trait association analysis of DNA markers independently developed from the Ma6 and Ma4 regions confirmed the mapping of Ma6 and Ma4. In the background of MaMa, 20.6% of acidity variation could be explained by Ma6, 28.5% by Ma4, and 50.7% by the combination of both. The effects of Ma6 and Ma4 in the background of Mama were also significant, but lower. These findings provide important genetic insight into high acidity in apple.
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Affiliation(s)
- Seunghyun Ban
- Horticulture Section, School of Integrative Plant Science, Cornell Agritech, Cornell University, Geneva, NY 14456 USA
| | - Kenong Xu
- Horticulture Section, School of Integrative Plant Science, Cornell Agritech, Cornell University, Geneva, NY 14456 USA
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Roth M, Muranty H, Di Guardo M, Guerra W, Patocchi A, Costa F. Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple. HORTICULTURE RESEARCH 2020; 7:148. [PMID: 32922820 PMCID: PMC7459338 DOI: 10.1038/s41438-020-00370-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/18/2020] [Accepted: 07/24/2020] [Indexed: 05/11/2023]
Abstract
Texture is a complex trait and a major component of fruit quality in apple. While the major effect of MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the 'Gala' × 'Pink Lady' family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a 'tailored training population' can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, Zurich, Switzerland
- Present Address: GAFL, INRAE, 84140 Montfavet, France
| | - Hélène Muranty
- IRHS, INRAE, Agrocampus-Ouest, Université d’Angers, SFR 4207 QuaSaV, Beaucouzé, France
| | - Mario Di Guardo
- Department of Genomics and Biology of Fruit Crops, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Italy
- Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy
| | - Walter Guerra
- Research Centre Laimburg, Laimburg 6, 39040 Auer, Italy
| | - Andrea Patocchi
- Plant Breeding Research Division, Agroscope, Wädenswil, Zurich, Switzerland
| | - Fabrizio Costa
- Department of Genomics and Biology of Fruit Crops, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Italy
- Center Agriculture Food Environment, University of Trento, Via Mach 1, 38010 San Michele all’Adige, Italy
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Liu J, Shen F, Xiao Y, Fang H, Qiu C, Li W, Wu T, Xu X, Wang Y, Zhang X, Han Z. Genomics-assisted prediction of salt and alkali tolerances and functional marker development in apple rootstocks. BMC Genomics 2020; 21:550. [PMID: 32778069 PMCID: PMC7430842 DOI: 10.1186/s12864-020-06961-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 07/29/2020] [Indexed: 12/18/2022] Open
Abstract
Background Saline, alkaline, and saline-alkaline stress severely affect plant growth and development. The tolerance of plants to these stressors has long been important breeding objectives, especially for woody perennials like apple. The aims of this study were to identify quantitative trait loci (QTLs) and to develop genomics-assisted prediction models for salt, alkali, and salt-alkali tolerance in apple rootstock. Results A total of 3258 hybrids derived from the apple rootstock cultivars ‘Baleng Crab’ (Malus robusta Rehd., tolerant) × ‘M9’ (M. pumila Mill., sensitive) were used to identify 17, 13, and two QTLs for injury indices of salt, alkali, and salt–alkali stress via bulked segregant analysis. The genotype effects of single nucleotide polymorphism (SNP) markers designed on candidate genes in each QTL interval were estimated. The genomic predicted value of an individual hybrid was calculated by adding the sum of all marker genotype effects to the mean phenotype value of the population. The prediction accuracy was 0.6569, 0.6695, and 0.5834 for injury indices of salt, alkali, and salt–alkali stress, respectively. SNP182G on MdRGLG3, which changes a leucine to an arginine at the vWFA-domain, conferred tolerance to salt, alkali, and salt-alkali stress. SNP761A on MdKCAB, affecting the Kv_beta domain that cooperated with the linked allelic variation SNP11, contributed to salt, alkali, and salt–alkali tolerance in apple rootstock. Conclusions The genomics-assisted prediction models can potentially be used in breeding saline, alkaline, and saline-alkaline tolerant apple rootstocks. The QTLs and the functional markers may provide insight for future studies into the genetic variation of plant abiotic stress tolerance.
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Affiliation(s)
- Jing Liu
- College of Horticulture, China Agricultural University, Beijing, China
| | - Fei Shen
- College of Horticulture, China Agricultural University, Beijing, China
| | - Yao Xiao
- College of Horticulture, China Agricultural University, Beijing, China
| | - Hongcheng Fang
- College of Horticulture, China Agricultural University, Beijing, China
| | - Changpeng Qiu
- College of Horticulture, China Agricultural University, Beijing, China
| | - Wei Li
- College of Horticulture, China Agricultural University, Beijing, China
| | - Ting Wu
- College of Horticulture, China Agricultural University, Beijing, China
| | - Xuefeng Xu
- College of Horticulture, China Agricultural University, Beijing, China
| | - Yi Wang
- College of Horticulture, China Agricultural University, Beijing, China
| | - Xinzhong Zhang
- College of Horticulture, China Agricultural University, Beijing, China.
| | - Zhenhai Han
- College of Horticulture, China Agricultural University, Beijing, China
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Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species. PLoS One 2020; 15:e0232201. [PMID: 32520936 PMCID: PMC7286500 DOI: 10.1371/journal.pone.0232201] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 04/08/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. RESULTS As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. CONCLUSIONS Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits.
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Kumar S, Hilario E, Deng CH, Molloy C. Turbocharging introgression breeding of perennial fruit crops: a case study on apple. HORTICULTURE RESEARCH 2020; 7:47. [PMID: 32257233 PMCID: PMC7109137 DOI: 10.1038/s41438-020-0270-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 02/13/2020] [Accepted: 02/18/2020] [Indexed: 05/08/2023]
Abstract
The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.
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Affiliation(s)
- Satish Kumar
- The New Zealand Institute for Plant and Food Research Limited, Hawkes Bay Research Centre, Havelock North, New Zealand
| | - Elena Hilario
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
| | - Cecilia H. Deng
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
| | - Claire Molloy
- The New Zealand Institute for Plant and Food Research Limited, Hawkes Bay Research Centre, Havelock North, New Zealand
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Parkinson JE, Baker AC, Baums IB, Davies SW, Grottoli AG, Kitchen SA, Matz MV, Miller MW, Shantz AA, Kenkel CD. Molecular tools for coral reef restoration: Beyond biomarker discovery. Conserv Lett 2019. [DOI: 10.1111/conl.12687] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Affiliation(s)
- John Everett Parkinson
- SECORE International Miami Florida
- Department of Integrative BiologyUniversity of South Florida Tampa Florida
| | - Andrew C. Baker
- Department of Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric ScienceUniversity of Miami Miami Florida
| | - Iliana B. Baums
- Department of BiologyPennsylvania State University University Park Pennsylvania
| | | | | | - Sheila A. Kitchen
- Department of BiologyPennsylvania State University University Park Pennsylvania
| | - Mikhail V. Matz
- Department of Integrative BiologyUniversity of Texas at Austin Austin Texas
| | | | - Andrew A. Shantz
- Department of Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric ScienceUniversity of Miami Miami Florida
| | - Carly D. Kenkel
- Department of Biological SciencesUniversity of Southern California Los Angeles California
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Foster TM, Bassil NV, Dossett M, Leigh Worthington M, Graham J. Genetic and genomic resources for Rubus breeding: a roadmap for the future. HORTICULTURE RESEARCH 2019; 6:116. [PMID: 31645970 PMCID: PMC6804857 DOI: 10.1038/s41438-019-0199-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 08/17/2019] [Accepted: 08/27/2019] [Indexed: 05/09/2023]
Abstract
Rubus fruits are high-value crops that are sought after by consumers for their flavor, visual appeal, and health benefits. To meet this demand, production of red and black raspberries (R. idaeus L. and R. occidentalis L.), blackberries (R. subgenus Rubus), and hybrids, such as Boysenberry and marionberry, is growing worldwide. Rubus breeding programmes are continually striving to improve flavor, texture, machine harvestability, and yield, provide pest and disease resistance, improve storage and processing properties, and optimize fruits and plants for different production and harvest systems. Breeders face numerous challenges, such as polyploidy, the lack of genetic diversity in many of the elite cultivars, and until recently, the relative shortage of genetic and genomic resources available for Rubus. This review will highlight the development of continually improving genetic maps, the identification of Quantitative Trait Loci (QTL)s controlling key traits, draft genomes for red and black raspberry, and efforts to improve gene models. The development of genetic maps and markers, the molecular characterization of wild species and germplasm, and high-throughput genotyping platforms will expedite breeding of improved cultivars. Fully sequenced genomes and accurate gene models facilitate identification of genes underlying traits of interest and enable gene editing technologies such as CRISPR/Cas9.
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Affiliation(s)
- Toshi M. Foster
- The New Zealand Institute for Plant and Food Research (PFR) Ltd, 55 Old Mill Road, Motueka, New Zealand
| | - Nahla V. Bassil
- USDA ARS National Clonal Germplasm Repository (NCGR), 33447 Peoria Rd., Corvallis, OR USA
| | - Michael Dossett
- Blueberry Council (in Partnership with Agriculture and Agri-Food Canada) Agassiz Food Research Centre, Columbia, BC V0M 1A0 Canada
| | - Margaret Leigh Worthington
- Department of Horticulture, University of Arkansas, 316 Plant Science Building, Fayetteville, AR 72701 USA
| | - Julie Graham
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA Scotland
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McClure KA, Gong Y, Song J, Vinqvist-Tymchuk M, Campbell Palmer L, Fan L, Burgher-MacLellan K, Zhang Z, Celton JM, Forney CF, Migicovsky Z, Myles S. Genome-wide association studies in apple reveal loci of large effect controlling apple polyphenols. HORTICULTURE RESEARCH 2019; 6:107. [PMID: 31645962 PMCID: PMC6804656 DOI: 10.1038/s41438-019-0190-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 07/19/2019] [Accepted: 07/24/2019] [Indexed: 05/03/2023]
Abstract
Apples are a nutritious food source with significant amounts of polyphenols that contribute to human health and wellbeing, primarily as dietary antioxidants. Although numerous pre- and post-harvest factors can affect the composition of polyphenols in apples, genetics is presumed to play a major role because polyphenol concentration varies dramatically among apple cultivars. Here we investigated the genetic architecture of apple polyphenols by combining high performance liquid chromatography (HPLC) data with ~100,000 single nucleotide polymorphisms (SNPs) from two diverse apple populations. We found that polyphenols can vary in concentration by up to two orders of magnitude across cultivars, and that this dramatic variation was often predictable using genetic markers and frequently controlled by a small number of large effect genetic loci. Using GWAS, we identified candidate genes for the production of quercitrin, epicatechin, catechin, chlorogenic acid, 4-O-caffeoylquinic acid and procyanidins B1, B2, and C1. Our observation that a relatively simple genetic architecture underlies the dramatic variation of key polyphenols in apples suggests that breeders may be able to improve the nutritional value of apples through marker-assisted breeding or gene editing.
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Affiliation(s)
- Kendra A. McClure
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3 Canada
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - YuiHui Gong
- College of Horticulture, South China Agriculture University, Guangzhou, 510642 China
| | - Jun Song
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - Melinda Vinqvist-Tymchuk
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - Leslie Campbell Palmer
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - Lihua Fan
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - Karen Burgher-MacLellan
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - ZhaoQi Zhang
- College of Horticulture, South China Agriculture University, Guangzhou, 510642 China
| | - Jean-Marc Celton
- IRHS, Agrocampus-Ouest, INRA, Université d’Angers, SFR 4207 QuaSaV, Beaucouzé, France
| | - Charles F. Forney
- Agriculture and Agri-Food Canada, Kentville Research and Development Centre, Kentville, NS B4N 1J5 Canada
| | - Zoë Migicovsky
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3 Canada
| | - Sean Myles
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3 Canada
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High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow. PLoS One 2019; 14:e0210928. [PMID: 31246947 PMCID: PMC6597046 DOI: 10.1371/journal.pone.0210928] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 04/19/2019] [Indexed: 12/14/2022] Open
Abstract
High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.
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Kumar S, Kirk C, Deng CH, Shirtliff A, Wiedow C, Qin M, Wu J, Brewer L. Marker-trait associations and genomic predictions of interspecific pear (Pyrus) fruit characteristics. Sci Rep 2019; 9:9072. [PMID: 31227781 PMCID: PMC6588632 DOI: 10.1038/s41598-019-45618-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 06/11/2019] [Indexed: 12/15/2022] Open
Abstract
Interspecific pear (Pyrus spp.) hybrid populations are often used to develop novel cultivars. Pear cultivar breeding is a lengthy process because of long juvenility and the subsequent time required for reliable fruit phenotyping. Molecular techniques such as genome-wide association (GWA) and genomic selection (GS) provide an opportunity to fast-forward the development of high-value cultivars. We evaluated the genetic architecture of 10 pear fruit phenotypes (including sensory traits) and the potential of GS using genotyping-by-sequencing of 550 hybrid seedlings from nine interrelated full-sib families. Results from GWA suggested a complex polygenic nature of all 10 traits as the maximum variance explained by each marker was less than 4% of the phenotypic variance. The effect-size of SNPs for each trait suggested many genes of small effect and few of moderate effect. Some genomic regions associated with pear sensory traits were similar to those reported for apple - possibly a result of high synteny between the apple and pear genomes. The average (across nine families) GS accuracy varied from 0.32 (for crispness) to 0.62 (for sweetness), with an across-trait average of 0.42. Further efforts are needed to develop larger genotype-phenotype datasets in order to predict fruit phenotypes of untested seedlings with sufficient efficiency.
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Affiliation(s)
- Satish Kumar
- The New Zealand Institute for Plant and Food Research Limited, Hawke's Bay Research Centre, Havelock North, New Zealand.
| | - Chris Kirk
- The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North, New Zealand
| | - Cecilia Hong Deng
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
| | - Angela Shirtliff
- The New Zealand Institute for Plant and Food Research Limited, Motueka Research Centre, Motueka, New Zealand
| | - Claudia Wiedow
- The New Zealand Institute for Plant and Food Research Limited, Palmerston North Research Centre, Palmerston North, New Zealand
| | - Mengfan Qin
- Centre of Pear Engineering Technology Research, Nanjing Agricultural University, Nanjing, 210095, China
| | - Jun Wu
- Centre of Pear Engineering Technology Research, Nanjing Agricultural University, Nanjing, 210095, China
| | - Lester Brewer
- The New Zealand Institute for Plant and Food Research Limited, Motueka Research Centre, Motueka, New Zealand
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An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction. G3-GENES GENOMES GENETICS 2019; 9:1355-1369. [PMID: 30819822 PMCID: PMC6505148 DOI: 10.1534/g3.119.400126] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.), an active area of research aims to develop statistical models for the prediction of univariate and multivariate traits in GS. However, most of the models developed so far are for univariate and continuous (Gaussian) traits. Therefore, to overcome the lack of multivariate statistical models for genome-based prediction by improving the original version of the BMTME, we propose an improved Bayesian multi-trait and multi-environment (BMTME) R package for analyzing breeding data with multiple traits and multiple environments. We also introduce Bayesian multi-output regressor stacking (BMORS) functions that are considerably efficient in terms of computational resources. The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way. We illustrate the use of the BMTME with real toy datasets to show all the facilities that the software offers the user. However, for large datasets, the BME() and BMTME() functions of the BMTME R package are very intense in terms of computing time; on the other hand, less intensive computing is required with BMORS functions BMORS() and BMORS_Env() that are also included in the BMTME package.
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Montanari S, Bianco L, Allen BJ, Martínez-García PJ, Bassil NV, Postman J, Knäbel M, Kitson B, Deng CH, Chagné D, Crepeau MW, Langley CH, Evans K, Dhingra A, Troggio M, Neale DB. Development of a highly efficient Axiom™ 70 K SNP array for Pyrus and evaluation for high-density mapping and germplasm characterization. BMC Genomics 2019; 20:331. [PMID: 31046664 PMCID: PMC6498479 DOI: 10.1186/s12864-019-5712-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
Background Both a source of diversity and the development of genomic tools, such as reference genomes and molecular markers, are equally important to enable faster progress in plant breeding. Pear (Pyrus spp.) lags far behind other fruit and nut crops in terms of employment of available genetic resources for new cultivar development. To address this gap, we designed a high-density, high-efficiency and robust single nucleotide polymorphism (SNP) array for pear, with the main objectives of conducting genetic diversity and genome-wide association studies. Results By applying a two-step design process, which consisted of the construction of a first ‘draft’ array for the screening of a small subset of samples, we were able to identify the most robust and informative SNPs to include in the Applied Biosystems™ Axiom™ Pear 70 K Genotyping Array, currently the densest SNP array for pear. Preliminary evaluation of this 70 K array in 1416 diverse pear accessions from the USDA National Clonal Germplasm Repository (NCGR) in Corvallis, OR identified 66,616 SNPs (93% of all the tiled SNPs) as high quality and polymorphic (PolyHighResolution). We further used the Axiom Pear 70 K Genotyping Array to construct high-density linkage maps in a bi-parental population, and to make a direct comparison with available genotyping-by-sequencing (GBS) data, which suggested that the SNP array is a more robust method of screening for SNPs than restriction enzyme reduced representation sequence-based genotyping. Conclusions The Axiom Pear 70 K Genotyping Array, with its high efficiency in a widely diverse panel of Pyrus species and cultivars, represents a valuable resource for a multitude of molecular studies in pear. The characterization of the USDA-NCGR collection with this array will provide important information for pear geneticists and breeders, as well as for the optimization of conservation strategies for Pyrus. Electronic supplementary material The online version of this article (10.1186/s12864-019-5712-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sara Montanari
- Department of Plant Sciences, University of California, Davis, CA, USA.
| | - Luca Bianco
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Trento, Italy
| | - Brian J Allen
- Department of Plant Sciences, University of California, Davis, CA, USA
| | | | - Nahla V Bassil
- USDA Agricultural Research Service, National Clonal Germplasm Repository, Corvallis, OR, USA
| | - Joseph Postman
- USDA Agricultural Research Service, National Clonal Germplasm Repository, Corvallis, OR, USA
| | - Mareike Knäbel
- Palmerston North Research Centre, The New Zealand Institute for Plant & Food Research Limited (PFR), Palmerston North, New Zealand
| | - Biff Kitson
- Motueka Research Centre, The New Zealand Institute for Plant & Food Research Limited (PFR), Motueka, New Zealand
| | - Cecilia H Deng
- Auckland Research Centre, The New Zealand Institute for Plant & Food Research Limited (PFR), Auckland, New Zealand
| | - David Chagné
- Palmerston North Research Centre, The New Zealand Institute for Plant & Food Research Limited (PFR), Palmerston North, New Zealand
| | - Marc W Crepeau
- Department of Evolution and Ecology, University of California, Davis, CA, USA
| | - Charles H Langley
- Department of Evolution and Ecology, University of California, Davis, CA, USA
| | - Kate Evans
- Tree Fruit Research and Extension Center, Washington State University, Wenatchee, WA, USA
| | - Amit Dhingra
- Department of Horticulture, Washington State University, Pullman, WA, USA
| | - Michela Troggio
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Trento, Italy
| | - David B Neale
- Department of Plant Sciences, University of California, Davis, CA, USA
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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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Hardner CM, Hayes BJ, Kumar S, Vanderzande S, Cai L, Piaskowski J, Quero-Garcia J, Campoy JA, Barreneche T, Giovannini D, Liverani A, Charlot G, Villamil-Castro M, Oraguzie N, Peace CP. Prediction of genetic value for sweet cherry fruit maturity among environments using a 6K SNP array. HORTICULTURE RESEARCH 2019; 6:6. [PMID: 30603092 PMCID: PMC6312542 DOI: 10.1038/s41438-018-0081-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 06/08/2018] [Accepted: 07/15/2018] [Indexed: 05/21/2023]
Abstract
The timing of fruit maturity is an important trait in sweet cherry production and breeding. Phenotypic variation for phenology of fruit maturity in sweet cherry appears to be under strong genetic control, but that control might be complicated by phenotypic instability across environments. Although such genotype-by-environment interaction (G × E) is a common phenomenon in crop plants, knowledge about it is lacking for fruit maturity timing and other sweet cherry traits. In this study, 1673 genome-wide SNP markers were used to estimate genomic relationships among 597 weakly pedigree-connected individuals evaluated over two seasons at three locations in Europe and one location in the USA, thus sampling eight 'environments'. The combined dataset enabled a single meta-analysis to investigate the environmental stability of genomic predictions. Linkage disequilibrium among marker loci declined rapidly with physical distance, and ordination of the relationship matrix suggested no strong structure among germplasm. The most parsimonious G × E model allowed heterogeneous genetic variance and pairwise covariances among environments. Narrow-sense genomic heritability was very high (0.60-0.83), as was accuracy of predicted breeding values (>0.62). Average correlation of additive effects among environments was high (0.96) and breeding values were highly correlated across locations. Results indicated that genomic models can be used in cherry to accurately predict date of fruit maturity for untested individuals in new environments. Limited G × E for this trait indicated that phenotypes of individuals will be stable across similar environments. Equivalent analyses for other sweet cherry traits, for which multiple years of data are commonly available among breeders and cultivar testers, would be informative for predicting performance of elite selections and cultivars in new environments.
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Affiliation(s)
- Craig M. Hardner
- University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072 Australia
| | - Ben J. Hayes
- University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072 Australia
| | - Satish Kumar
- The New Zealand Institute for Plant and Food Research Limited, Hawke’s Bay Research Centre, Hastings, 4130 New Zealand
| | - Stijn Vanderzande
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Lichun Cai
- Department of Horticulture, Michigan State University, East Lansing, MI 48824 USA
| | - Julia Piaskowski
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - José Quero-Garcia
- UMR 1332 BFP, INRA, University of Bordeaux, 33140 Nouvelle-Aquitaine, France
| | - José Antonio Campoy
- UMR 1332 BFP, INRA, University of Bordeaux, 33140 Nouvelle-Aquitaine, France
| | - Teresa Barreneche
- UMR 1332 BFP, INRA, University of Bordeaux, 33140 Nouvelle-Aquitaine, France
| | - Daniela Giovannini
- Council for Agricultural Research and Economics (CREA), Fruit Unit of Forlì, Via la Canapona, 1 bis, 47121 Emilia-Romagna, Italy
| | - Alessandro Liverani
- Council for Agricultural Research and Economics (CREA), Fruit Unit of Forlì, Via la Canapona, 1 bis, 47121 Emilia-Romagna, Italy
| | - Gérard Charlot
- Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL), 751 Chemin de Balandran, 30127 Bellegarde, France
| | - Miguel Villamil-Castro
- University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072 Australia
| | - Nnadozie Oraguzie
- Department of Horticulture, Washington State University, Irrigated Agriculture Research and Extension Center, 24106N Bunn Road, Prosser, WA 99350 USA
| | - Cameron P. Peace
- Department of Horticulture, Washington State University, Pullman, WA 99164 USA
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Chagné D, Vanderzande S, Kirk C, Profitt N, Weskett R, Gardiner SE, Peace CP, Volz RK, Bassil NV. Validation of SNP markers for fruit quality and disease resistance loci in apple ( Malus × domestica Borkh.) using the OpenArray® platform. HORTICULTURE RESEARCH 2019; 6:30. [PMID: 30854208 PMCID: PMC6395728 DOI: 10.1038/s41438-018-0114-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 11/01/2018] [Accepted: 12/12/2018] [Indexed: 05/22/2023]
Abstract
Genome mapping has promised much to tree fruit breeding during the last 10 years. Nevertheless, one of the greatest challenges remaining to tree fruit geneticists is the translation of trait loci and whole genome sequences into diagnostic genetic markers that are efficient and cost-effective for use by breeders, who must select genetically optimal parents and subsequently select genetically superior individuals among their progeny. To take this translational step, we designed the apple International RosBREED SNP Consortium OpenArray v1.0 (IRSCOA v1.0) assay using a set of 128 apple single nucleotide polymorphisms (SNPs) linked to fruit quality and pest and disease resistance trait loci. The Thermo Fisher Scientific OpenArray® technology enables multiplexed screening of SNP markers using a real-time PCR instrument with fluorescent probe-based Taqman® assays. We validated the apple IRSCOA v1.0 multi-trait assay by screening 240 phenotyped individuals from the Plant & Food Research apple cultivar breeding programme. This set of individuals comprised commercial and heritage cultivars, elite selections, and families segregating for traits of importance to breeders. In total, 33 SNP markers of the IRSCOA v1.0 were validated for use in marker-assisted selection (MAS) for the scab resistances Rvi2/Vh2, Rvi4/Vh4, Rvi6/Vf, fire blight resistance MR5/RLP1, powdery mildew resistance Pl2, fruit firmness, skin colour, flavour intensity, and acidity. The availability of this set of validated trait-associated SNP markers, which can be used individually on multiple genotyping platforms available to various apple breeding programmes or re-designed using the flanking sequences, represents a large translational genetics step from genomics to crop improvement of apple.
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Affiliation(s)
- David Chagné
- The New Zealand Institute for Plant & Food Research Ltd (Plant & Food Research), Palmerston North Research Centre, Palmerston North, New Zealand
| | - Stijn Vanderzande
- Department of Horticulture, Washington State University, Pullman, WA USA
| | - Chris Kirk
- The New Zealand Institute for Plant & Food Research Ltd (Plant & Food Research), Palmerston North Research Centre, Palmerston North, New Zealand
| | - Natalie Profitt
- Plant & Food Research, Hawke’s Bay Research Centre, Havelock North, New Zealand
| | - Rosemary Weskett
- Plant & Food Research, Hawke’s Bay Research Centre, Havelock North, New Zealand
| | - Susan E. Gardiner
- The New Zealand Institute for Plant & Food Research Ltd (Plant & Food Research), Palmerston North Research Centre, Palmerston North, New Zealand
| | - Cameron P. Peace
- Department of Horticulture, Washington State University, Pullman, WA USA
| | - Richard K. Volz
- Plant & Food Research, Hawke’s Bay Research Centre, Havelock North, New Zealand
| | - Nahla V. Bassil
- USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR USA
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