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Bose S, Banerjee S, Kumar S, Saha A, Nandy D, Hazra S. Review of applications of artificial intelligence (AI) methods in crop research. J Appl Genet 2024; 65:225-240. [PMID: 38216788 DOI: 10.1007/s13353-023-00826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/14/2024]
Abstract
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.
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Affiliation(s)
- Suvojit Bose
- Department of Vegetables and Spice Crops, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, 736165, West Bengal, India
| | | | - Soumya Kumar
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Akash Saha
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Debalina Nandy
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Soham Hazra
- Department of Agriculture, Brainware University, Barasat, 700125, West Bengal, India.
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Vondracek K, Altpeter F, Liu T, Lee S. Advances in genomics and genome editing for improving strawberry ( Fragaria ×ananassa). Front Genet 2024; 15:1382445. [PMID: 38706796 PMCID: PMC11066249 DOI: 10.3389/fgene.2024.1382445] [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: 02/05/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024] Open
Abstract
The cultivated strawberry, Fragaria ×ananassa, is a recently domesticated fruit species of economic interest worldwide. As such, there is significant interest in continuous varietal improvement. Genomics-assisted improvement, including the use of DNA markers and genomic selection have facilitated significant improvements of numerous key traits during strawberry breeding. CRISPR/Cas-mediated genome editing allows targeted mutations and precision nucleotide substitutions in the target genome, revolutionizing functional genomics and crop improvement. Genome editing is beginning to gain traction in the more challenging polyploid crops, including allo-octoploid strawberry. The release of high-quality reference genomes and comprehensive subgenome-specific genotyping and gene expression profiling data in octoploid strawberry will lead to a surge in trait discovery and modification by using CRISPR/Cas. Genome editing has already been successfully applied for modification of several strawberry genes, including anthocyanin content, fruit firmness and tolerance to post-harvest disease. However, reports on many other important breeding characteristics associated with fruit quality and production are still lacking, indicating a need for streamlined genome editing approaches and tools in Fragaria ×ananassa. In this review, we present an overview of the latest advancements in knowledge and breeding efforts involving CRISPR/Cas genome editing for the enhancement of strawberry varieties. Furthermore, we explore potential applications of this technology for improving other Rosaceous plant species.
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Affiliation(s)
- Kaitlyn Vondracek
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Wimauma, FL, United States
- University of Florida, Horticultural Sciences Department, Institute of Food and Agricultural Sciences, Gainesville, FL, United States
| | - Fredy Altpeter
- University of Florida, Agronomy Department, Institute of Food and Agricultural Sciences, Gainesville, FL, United States
| | - Tie Liu
- University of Florida, Horticultural Sciences Department, Institute of Food and Agricultural Sciences, Gainesville, FL, United States
| | - Seonghee Lee
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Wimauma, FL, United States
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Jee E, Do E, Gil CS, Kim S, Lee SY, Lee S, Ku KM. Analysis of volatile organic compounds in Korean-bred strawberries: insights for improving fruit flavor. FRONTIERS IN PLANT SCIENCE 2024; 15:1360050. [PMID: 38562564 PMCID: PMC10982345 DOI: 10.3389/fpls.2024.1360050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024]
Abstract
Introduction The strawberry industry in South Korea has witnessed a significant 65% growth over the past decade, surpassing other fruits and vegetables in production value. While sweetness and acidity are well-recognized flavor determinants, the role of volatile organic compounds (VOCs) in defining the desirable flavor profiles of strawberries is also crucial. However, existing research has predominantly concentrated on a limited range of commercial cultivars, neglecting the broader spectrum of strawberry varieties. Methods This study embarked on developing a comprehensive VOC database for a diverse array of strawberry cultivars sourced both domestically and internationally. A total of 61 different strawberry cultivars from Korea (45), the USA (7), Japan (8), and France (1) were analyzed for their VOC content using Tenax TA Thermo Desorption tubes and Gas Chromatography-Mass Spectrometry (GC-MS). In addition to VOC profiling, heritability was assessed using one-way ANOVA to compare means among multiple groups, providing insights into the genetic basis of flavor differences. Results and discussion The analysis identified 122 compounds categorized into esters, alcohols, terpenes, and lactones, with esters constituting the majority (46.5%) of total VOCs in Korean cultivars. 'Arihyang', 'Sunnyberry', and 'Kingsberry' exhibited the highest diversity of VOCs detected (97 types), whereas 'Seolhong' showed the highest overall concentration (57.5mg·kg-1 FW). Compared to the USA cultivars, which were abundant in γ-decalactone (a peach-like fruity aroma), most domestic cultivars lacked this compound. Notably, 'Misohyang' displayed a high γ-decalactone content, highlighting its potential as breeding germplasm to improve flavor in Korean strawberries. The findings underscore the importance of a comprehensive VOC analysis across different strawberry cultivars to understand flavor composition. The significant variation in VOC content among the cultivars examined opens avenues for targeted breeding strategies. By leveraging the distinct VOC profiles, particularly the presence of γ-decalactone, breeders can develop new strawberry varieties with enhanced flavor profiles, catering to consumer preferences for both domestic and international markets.
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Affiliation(s)
- Eungu Jee
- Department of Plant Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
| | - Eunsu Do
- Department of Plant Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
| | - Chan Saem Gil
- Department of Horticulture, College of Industrial Science, Kongju National University, Yesan, Republic of Korea
| | - Seolah Kim
- National Institute of Horticultural and Herbal Science, Rural Development Administration, Wanju, Republic of Korea
| | - Sun Yi Lee
- National Institute of Horticultural and Herbal Science, Rural Development Administration, Wanju, Republic of Korea
| | - Seonghee Lee
- Gulf Coast Research and Education Center, Horticultural Sciences Department, Institute of Food and Agricultural Science, University of Florida, Wimauma, FL, United States
| | - Kang-Mo Ku
- Department of Plant Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
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Hardigan MA, Feldmann MJ, Carling J, Zhu A, Kilian A, Famula RA, Cole GS, Knapp SJ. A medium-density genotyping platform for cultivated strawberry using DArTag technology. THE PLANT GENOME 2023; 16:e20399. [PMID: 37940627 DOI: 10.1002/tpg2.20399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/22/2023] [Indexed: 11/10/2023]
Abstract
Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high-density genetic marker platforms designed for strawberry. We developed mid-density panels of molecular inversion probes (MIPs) to be deployed with the "DArTag" marker platform to provide a low-cost, high-throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome-specific amplicon alignments to the FaRR1 assembly and subsequent alignment-based variant calls with strong concordance to DArT's alignment-free, count-based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low-amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33, respectively.
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Affiliation(s)
- Michael A Hardigan
- USDA-ARS, Horticultural Crops Production and Genetic Improvement Research Unit, Corvallis, Oregon, USA
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Jason Carling
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Anyu Zhu
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Andrzej Kilian
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Randi A Famula
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California Davis, Davis, California, USA
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Denoyes B, Prohaska A, Petit J, Rothan C. Deciphering the genetic architecture of fruit color in strawberry. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:6306-6320. [PMID: 37386925 PMCID: PMC10627153 DOI: 10.1093/jxb/erad245] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/28/2023] [Indexed: 07/01/2023]
Abstract
Fruits of Fragaria species usually have an appealing bright red color due to the accumulation of anthocyanins, water-soluble flavonoid pigments. Octoploid cultivated strawberry (Fragaria × ananassa) is a major horticultural crop for which fruit color and associated nutritional value are main breeding targets. Great diversity in fruit color intensity and pattern is observed not only in cultivated strawberry but also in wild relatives such as its octoploid progenitor F. chiloensis or the diploid woodland strawberry F. vesca, a model for fruit species in the Rosaceae. This review examines our understanding of fruit color formation in strawberry and how ongoing developments will advance it. Natural variations of fruit color as well as color changes during fruit development or in response to several cues have been used to explore the anthocyanin biosynthetic pathway and its regulation. So far, the successful identification of causal genetic variants has been largely driven by the availability of high-throughput genotyping tools and high-quality reference genomes of F. vesca and F. × ananassa. The current completion of haplotype-resolved genomes of F. × ananassa combined with QTL mapping will accelerate the exploitation of the untapped genetic diversity of fruit color and help translate the findings into strawberry improvement.
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Affiliation(s)
- Béatrice Denoyes
- INRAE and Univ. of Bordeaux, UMR 1332 Biologie du Fruit et Pathologie, F-33140 Villenave d’Ornon, France
| | - Alexandre Prohaska
- INRAE and Univ. of Bordeaux, UMR 1332 Biologie du Fruit et Pathologie, F-33140 Villenave d’Ornon, France
- INVENIO, MIN de Brienne, Bordeaux, France
| | - Johann Petit
- INRAE and Univ. of Bordeaux, UMR 1332 Biologie du Fruit et Pathologie, F-33140 Villenave d’Ornon, France
| | - Christophe Rothan
- INRAE and Univ. of Bordeaux, UMR 1332 Biologie du Fruit et Pathologie, F-33140 Villenave d’Ornon, France
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Yada B, Musana P, Chelangat DM, Osaru F, Anyanga MO, Katungisa A, Oloka BM, Ssali RT, Mugisa I. Breeding Cultivars for Resistance to the African Sweetpotato Weevils, Cylas puncticollis and Cylas brunneus, in Uganda: A Review of the Current Progress. INSECTS 2023; 14:837. [PMID: 37999036 PMCID: PMC10671729 DOI: 10.3390/insects14110837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023]
Abstract
In sub-Saharan Africa, sweetpotato weevils are the major pests of cultivated sweetpotato, causing estimated losses of between 60% and 100%, primarily during dry spells. The predominantly cryptic feeding behavior of Cylas spp. within their roots makes their control difficult, thus, host plant resistance is one of the most promising lines of protection against these pests. However, limited progress has been made in cultivar breeding for weevil resistance, partly due to the complex hexaploid genome of sweetpotato, which complicates conventional breeding, in addition to the limited number of genotypes with significant levels of resistance for use as sources of resistance. Pollen sterility, cross incompatibility, and poor seed set and germination in sweetpotato are also common challenges in improving weevil resistance. The accurate phenotyping of sweetpotato weevil resistance to enhance the efficiency of selection has been equally difficult. Genomics-assisted breeding, though in its infancy stages in sweetpotato, has a potential application in overcoming some of these barriers. However, it will require the development of more genomic infrastructure, particularly single-nucleotide polymorphism markers (SNPs) and robust next-generation sequencing platforms, together with relevant statistical procedures for analyses. With the recent advances in genomics, we anticipate that genomic breeding for sweetpotato weevil resistance will be expedited in the coming years. This review sheds light on Uganda's efforts, to date, to breed against the Cylas puncticollis (Boheman) and Cylas brunneus (Fabricius) species of African sweetpotato weevil.
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Affiliation(s)
- Benard Yada
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Paul Musana
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Doreen M. Chelangat
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Florence Osaru
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Milton O. Anyanga
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Arnold Katungisa
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
| | - Bonny M. Oloka
- Department of Horticultural Science, North Carolina State University, Raleigh, NC 27695, USA
| | | | - Immaculate Mugisa
- National Crops Resources Research Institute (NaCRRI), NARO, Kampala 999123, Uganda
- Department of Agricultural Production, Makerere University, Kampala 999123, Uganda
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Islam MS, Corak K, McCord P, Hulse-Kemp AM, Lipka AE. A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane. FRONTIERS IN PLANT SCIENCE 2023; 14:1205999. [PMID: 37600177 PMCID: PMC10433174 DOI: 10.3389/fpls.2023.1205999] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/03/2023] [Indexed: 08/22/2023]
Abstract
The sugarcane ratooning ability (RA) is the most important target trait for breeders seeking to enhance the profitability of sugarcane production by reducing the planting cost. Understanding the genetics governing the RA could help breeders by identifying molecular markers that could be used for genomics-assisted breeding (GAB). A replicated field trial was conducted for three crop cycles (plant cane, first ratoon, and second ratoon) using 432 sugarcane clones and used for conducting genome-wide association and genomic prediction of five sugar and yield component traits of the RA. The RA traits for economic index (EI), stalk population (SP), stalk weight (SW), tonns of cane per hectare (TCH), and tonns of sucrose per hectare (TSH) were estimated from the yield and sugar data. A total of six putative quantitative trait loci and eight nonredundant single-nucleotide polymorphism (SNP) markers were associated with all five tested RA traits and appear to be unique. Seven putative candidate genes were colocated with significant SNPs associated with the five RA traits. The genomic prediction accuracies for those tested traits were moderate and ranged from 0.21 to 0.36. However, the models fitting fixed effects for the most significant associated markers for each respective trait did not give any advantages over the standard models without fixed effects. As a result of this study, more robust markers could be used in the future for clone selection in sugarcane, potentially helping resolve the genetic control of the RA in sugarcane.
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Affiliation(s)
| | - Keo Corak
- Genomics and Bioinformatics Research Unit, USDA-ARS, Raleigh, NC, United States
| | - Per McCord
- Sugarcane Field Station, USDA-ARS, Canal Point, FL, United States
- Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA, United States
| | - Amanda M. Hulse-Kemp
- Genomics and Bioinformatics Research Unit, USDA-ARS, Raleigh, NC, United States
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, United States
| | - Alexander E. Lipka
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, IL, United States
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Li Z, Gao E, Zhou J, Han W, Xu X, Gao X. Applications of deep learning in understanding gene regulation. CELL REPORTS METHODS 2023; 3:100384. [PMID: 36814848 PMCID: PMC9939384 DOI: 10.1016/j.crmeth.2022.100384] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.
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Affiliation(s)
- Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Elva Gao
- The KAUST School, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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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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10
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Bhattarai G, Olaoye D, Mou B, Correll JC, Shi A. Mapping and selection of downy mildew resistance in spinach cv. whale by low coverage whole genome sequencing. FRONTIERS IN PLANT SCIENCE 2022; 13:1012923. [PMID: 36275584 PMCID: PMC9583407 DOI: 10.3389/fpls.2022.1012923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Spinach (Spinacia oleracea) is a popular leafy vegetable crop and commercial production is centered in California and Arizona in the US. The oomycete Peronospora effusa causes the most important disease in spinach, downy mildew. A total of nineteen races of P. effusa are known, with more than 15 documented in the last three decades, and the regular emergence of new races is continually overcoming the genetic resistance to the pathogen. This study aimed to finely map the downy mildew resistance locus RPF3 in spinach, identify single nucleotide polymorphism (SNP) markers associated with the resistance, refine the candidate genes responsible for the resistance, and evaluate the prediction performance using multiple machine learning genomic prediction (GP) methods. Segregating progeny population developed from a cross of resistant cultivar Whale and susceptible cultivar Viroflay to race 5 of P. effusa was inoculated under greenhouse conditions to determine downy mildew disease response across the panel. The progeny panel and the parents were resequenced at low coverage (1x) to identify genome wide SNP markers. Association analysis was performed using disease response phenotype data and SNP markers in TASSEL, GAPIT, and GENESIS programs and mapped the race 5 resistance loci (RPF3) to 1.25 and 2.73 Mb of Monoe-Viroflay chromosome 3 with the associated SNP in the 1.25 Mb region was 0.9 Kb from the NBS-LRR gene SOV3g001250. The RPF3 locus in the 1.22-1.23 Mb region of Sp75 chromosome 3 is 2.41-3.65 Kb from the gene Spo12821 annotated as NBS-LRR disease resistance protein. This study extended our understanding of the genetic basis of downy mildew resistance in spinach cultivar Whale and mapped the RPF3 resistance loci close to the NBS-LRR gene providing a target to pursue functional validation. Three SNP markers efficiently selected resistance based on multiple genomic selection (GS) models. The results from this study have added new genomic resources, generated an informed basis of the RPF3 locus resistant to spinach downy mildew pathogen, and developed markers and prediction methods to select resistant lines.
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Affiliation(s)
- Gehendra Bhattarai
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Dotun Olaoye
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Beiquan Mou
- Crop Improvement and Protection Research Unit, United States Department of Agriculture, Agricultural Research Service, Salinas, CA, United States
| | - James C. Correll
- Department of Plant Pathology, University of Arkansas, Fayetteville, AR, United States
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
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Bhattarai G, Shi A, Mou B, Correll JC. Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods. HORTICULTURE RESEARCH 2022; 9:uhac205. [PMID: 36467269 PMCID: PMC9715576 DOI: 10.1093/hr/uhac205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Downy mildew, commercially the most important disease of spinach, is caused by the obligate oomycete Peronospora effusa. In the past two decades, new pathogen races have repeatedly overcome the resistance used in newly released cultivars, urging the need for more durable resistance. Commercial spinach cultivars are bred with major R genes to impart resistance to downy mildew pathogens and are effective against some pathogen races/isolates. This work aimed to evaluate the worldwide USDA spinach germplasm collections and commercial cultivars for resistance to downy mildew pathogen in the field condition under natural inoculum pressure and conduct genome wide association analysis (GWAS) to identify resistance-associated genomic regions (alleles). Another objective was to evaluate the prediction accuracy (PA) using several genomic prediction (GP) methods to assess the potential implementation of genomic selection (GS) to improve spinach breeding for resistance to downy mildew pathogen. More than four hundred diverse spinach genotypes comprising USDA germplasm accessions and commercial cultivars were evaluated for resistance to downy mildew pathogen between 2017-2019 in Salinas Valley, California and Yuma, Arizona. GWAS was performed using single nucleotide polymorphism (SNP) markers identified via whole genome resequencing (WGR) in GAPIT and TASSEL programs; detected 14, 12, 5, and 10 significantly associated SNP markers with the resistance from four tested environments, respectively; and the QTL alleles were detected at the previously reported region of chromosome 3 in three of the four experiments. In parallel, PA was assessed using six GP models and seven unique marker datasets for field resistance to downy mildew pathogen across four tested environments. The results suggest the suitability of GS to improve field resistance to downy mildew pathogen. The QTL, SNP markers, and PA estimates provide new information in spinach breeding to select resistant plants and breeding lines through marker-assisted selection (MAS) and GS, eventually helping to accumulate beneficial alleles for durable disease resistance.
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12
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Senger E, Osorio S, Olbricht K, Shaw P, Denoyes B, Davik J, Predieri S, Karhu S, Raubach S, Lippi N, Höfer M, Cockerton H, Pradal C, Kafkas E, Litthauer S, Amaya I, Usadel B, Mezzetti B. Towards smart and sustainable development of modern berry cultivars in Europe. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1238-1251. [PMID: 35751152 DOI: 10.1111/tpj.15876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.
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Affiliation(s)
- Elisa Senger
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Sonia Osorio
- Departamento de Biología Molecular y Bioquímica, Instituto de Hortofruticultura Subtropical y Mediterránea 'La Mayora', Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Campus de Teatinos, Málaga, Spain
| | | | - Paul Shaw
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Béatrice Denoyes
- Université de Bordeaux, UMR BFP, INRAE, Villenave d'Ornon, France
| | - Jahn Davik
- Department of Molecular Plant Biology, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
| | - Stefano Predieri
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Saila Karhu
- Natural Resources Institute Finland (Luke), Turku, Finland
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Nico Lippi
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Monika Höfer
- Institute of Breeding Research on Fruit Crops, Federal Research Centre for Cultivated Plants (JKI), Dresden, Germany
| | - Helen Cockerton
- Genetics, Genomics and Breeding Department, NIAB, East Malling, UK
| | - Christophe Pradal
- CIRAD and UMR AGAP Institute, Montpellier, France
- INRIA and LIRMM, University Montpellier, CNRS, Montpellier, France
| | - Ebru Kafkas
- Department of Horticulture, Faculty of Agriculture, Çukurova University, Balcalı, Adana, Turkey
| | | | - Iraida Amaya
- Unidad Asociada deI + D + i IFAPA-CSIC Biotecnología y Mejora en Fresa, Málaga, Spain
- Laboratorio de Genómica y Biotecnología, Centro IFAPA de Málaga, Instituto Andaluz de Investigación y Formación Agraria y Pesquera, Málaga, Spain
| | - Björn Usadel
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bruno Mezzetti
- Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, Ancona, Italy
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13
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Shen F, Bianco L, Wu B, Tian Z, Wang Y, Wu T, Xu X, Han Z, Velasco R, Fontana P, Zhang X. A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple. J Adv Res 2022; 42:149-162. [PMID: 36513410 PMCID: PMC9788957 DOI: 10.1016/j.jare.2022.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/06/2022] [Accepted: 03/22/2022] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Genomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants. OBJECTIVES We attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes. METHODS A novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted. RESULTS By using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively. CONCLUSION The BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.
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Affiliation(s)
- Fei Shen
- College of Horticulture, China Agricultural University, Beijing 100193, China,Research and Innovation Center, Edmund Mach Foundation, 38010 S. Michele all’Adige, Italy,Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Luca Bianco
- Research and Innovation Center, Edmund Mach Foundation, 38010 S. Michele all’Adige, Italy
| | - Bei Wu
- College of Horticulture, China Agricultural University, Beijing 100193, China
| | - Zhendong Tian
- 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,Corresponding authors.
| | - Riccardo Velasco
- Research Centre for Viticulture and Enology, CREA, Conegliano, Italy
| | - Paolo Fontana
- Research and Innovation Center, Edmund Mach Foundation, 38010 S. Michele all’Adige, Italy,Corresponding authors.
| | - Xinzhong Zhang
- College of Horticulture, China Agricultural University, Beijing 100193, China,Corresponding authors.
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14
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Islam MS, McCord PH, Olatoye MO, Qin L, Sood S, Lipka AE, Todd JR. Experimental evaluation of genomic selection prediction for rust resistance in sugarcane. THE PLANT GENOME 2021; 14:e20148. [PMID: 34510803 DOI: 10.1002/tpg2.20148] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/22/2021] [Indexed: 06/13/2023]
Abstract
The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross-validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.
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Affiliation(s)
- Md S Islam
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
| | - Per H McCord
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
- Current address: Irrigated Agriculture Research and Extension Center, WA State Univ., Prosser, WA, USA
| | - Marcus O Olatoye
- Dep. of Crop Sciences, Univ. of Illinois, Urbana-Champaign, IL, USA
| | - Lifang Qin
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
- Current address: Guangxi Univ., Nanning, Guangxi, China
| | - Sushma Sood
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
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15
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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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16
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Liu C, Liu X, Han Y, Wang X, Ding Y, Meng H, Cheng Z. Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber ( Cucumis sativus L.). FRONTIERS IN PLANT SCIENCE 2021; 12:729328. [PMID: 34504510 PMCID: PMC8421847 DOI: 10.3389/fpls.2021.729328] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from -0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.
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Affiliation(s)
- Ce Liu
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Xiaoxiao Liu
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Yike Han
- State Key Laboratory of Vegetable Germplasm Innovation, Tianjin Key Laboratory of Vegetable Breeding Enterprise, Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin, China
| | - Xi'ao Wang
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Yuanyuan Ding
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Huanwen Meng
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Zhihui Cheng
- College of Horticulture, Northwest A&F University, Yangling, China
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17
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Rabier C, Grusea S. Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Charles‐Elie Rabier
- ISE‐MUMR 5554CNRSIRDUniversité de Montpellier France
- IMAGUMR 5149CNRSUniversité de Montpellier France
- LIRMMUMR 5506CNRSUniversité de Montpellier France
| | - Simona Grusea
- Institut de Mathématiques de Toulouse Université de ToulouseINSA de Toulouse France
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18
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Ferrão LFV, Amadeu RR, Benevenuto J, de Bem Oliveira I, Munoz PR. Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding. FRONTIERS IN PLANT SCIENCE 2021; 12:676326. [PMID: 34194453 PMCID: PMC8236943 DOI: 10.3389/fpls.2021.676326] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/12/2021] [Indexed: 05/17/2023]
Abstract
Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to expanding production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on recurrent phenotypic selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberry breeding cycles are costly and time consuming, which results in low genetic gains per unit of time. Motivated by applying molecular markers for a more accurate selection in the early stages of breeding, we performed pioneering genomic selection studies and optimization for its implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based genotyping and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic selection studies and showed for the first time its application in an independent validation set. In this paper, our contribution is three-fold: (i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; (ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; (iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for using genomic selection in blueberry, with the potential to be applied to other polyploid species of a similar background.
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Affiliation(s)
- Luís Felipe V. Ferrão
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Rodrigo R. Amadeu
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Juliana Benevenuto
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Ivone de Bem Oliveira
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
- Hortifrut North America, Inc., Estero, FL, United States
| | - Patricio R. Munoz
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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19
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Zingaretti LM, Monfort A, Pérez-Enciso M. Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9812910. [PMID: 34056620 PMCID: PMC8139333 DOI: 10.34133/2021/9812910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/20/2021] [Indexed: 06/01/2023]
Abstract
Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.
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Affiliation(s)
- Laura M. Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
| | - Amparo Monfort
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA), 08193 Barcelona, Spain
| | - Miguel Pérez-Enciso
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
- ICREA, Passeig de Lluís Companys 23, 08010 Barcelona, Spain
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20
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Fan Z, Plotto A, Bai J, Whitaker VM. Volatiles Influencing Sensory Attributes and Bayesian Modeling of the Soluble Solids-Sweetness Relationship in Strawberry. FRONTIERS IN PLANT SCIENCE 2021; 12:640704. [PMID: 33815448 PMCID: PMC8010315 DOI: 10.3389/fpls.2021.640704] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/01/2021] [Indexed: 05/27/2023]
Abstract
Descriptive analysis via trained sensory panels has great power to facilitate flavor improvement in fresh fruits and vegetables. When paired with an understanding of fruit volatile organic compounds, descriptive analysis can help uncover the chemical drivers of sensory attributes. In the present study, 213 strawberry samples representing 56 cultivars and advanced selections were sampled over seven seasons and subjected to both sensory descriptive and chemical analyses. Principal component analysis and K-cluster analyses of sensory data highlighted three groups of strawberry samples, with one classified as superior with high sweetness and strawberry flavor and low sourness and green flavor. Partial least square models revealed 20 sweetness-enhancing volatile organic compounds and two sweetness-reducing volatiles, many of which overlap with previous consumer sensory studies. Volatiles modulating green, sour, astringent, overripe, woody, and strawberry flavors were also identified. The relationship between soluble solids content (SSC) and sweetness was modeled with Bayesian regression, generating probabilities for sweetness levels from varying levels of soluble solids. A hierarchical Bayesian model with month effects indicated that SSC is most correlated to sweetness toward the end of the fruiting season, making this the best period to make phenotypic selections for soluble solids. Comparing effects from genotypes, harvest months, and their interactions on sensory attributes revealed that sweetness, sourness, and firmness were largely controlled by genetics. These findings help formulate a paradigm for improvement of eating quality in which sensory analyses drive the targeting of chemicals important to consumer-desired attributes, which further drive the development of genetic tools for improvement of flavor.
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Affiliation(s)
- Zhen Fan
- Horticultural Sciences Department, IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
| | - Anne Plotto
- Horticultural Research Laboratory, USDA-ARS, Fort Pierce, FL, United States
| | - Jinhe Bai
- Horticultural Research Laboratory, USDA-ARS, Fort Pierce, FL, United States
| | - Vance M. Whitaker
- Horticultural Sciences Department, IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
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21
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Yamamoto E, Kataoka S, Shirasawa K, Noguchi Y, Isobe S. Genomic Selection for F 1 Hybrid Breeding in Strawberry ( Fragaria × ananassa). FRONTIERS IN PLANT SCIENCE 2021; 12:645111. [PMID: 33747025 PMCID: PMC7969887 DOI: 10.3389/fpls.2021.645111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 02/09/2021] [Indexed: 05/27/2023]
Abstract
Cultivated strawberry is the most widely consumed fruit crop in the world, and therefore, many breeding programs are underway to improve its agronomic traits such as fruit quality. Strawberry cultivars were vegetatively propagated through runners and carried a high risk of infection with viruses and insects. To solve this problem, the development of F1 hybrid seeds has been proposed as an alternative breeding strategy in strawberry. In this study, we conducted a potential assessment of genomic selection (GS) in strawberry F1 hybrid breeding. A total of 105 inbred lines were developed as candidate parents of strawberry F1 hybrids. In addition, 275 parental combinations were randomly selected from the 105 inbred lines and crossed to develop test F1 hybrids for GS model training. These populations were phenotyped for petiole length, leaf area, Brix, fruit hardness, and pericarp color. Whole-genome shotgun sequencing of the 105 inbred lines detected 20,811 single nucleotide polymorphism sites that were provided for subsequent GS analyses. In a GS model construction, inclusion of dominant effects showed a slight advantage in GS accuracy. In the across population prediction analysis, GS models using the inbred lines showed predictability for the test F1 hybrids and vice versa, except for Brix. Finally, the GS models were used for phenotype prediction of 5,460 possible F1 hybrids from 105 inbred lines to select F1 hybrids with high fruit hardness or high pericarp color. These F1 hybrids were developed and phenotyped to evaluate the efficacy of the GS. As expected, F1 hybrids that were predicted to have high fruit hardness or high pericarp color expressed higher observed phenotypic values than the F1 hybrids that were selected for other objectives. Through the analyses in this study, we demonstrated that GS can be applied for strawberry F1 hybrid breeding.
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Affiliation(s)
- Eiji Yamamoto
- Graduate School of Agriculture, Meiji University, Kawasaki, Japan
| | - Sono Kataoka
- Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Tsu, Japan
| | - Kenta Shirasawa
- Department of Frontier Research and Development, Kazusa DNA Research Institute, Kisarazu, Japan
| | - Yuji Noguchi
- Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Tsu, Japan
| | - Sachiko Isobe
- Department of Frontier Research and Development, Kazusa DNA Research Institute, Kisarazu, Japan
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22
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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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23
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Rabier CE, Delmas C. The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection. STATISTICS-ABINGDON 2021. [DOI: 10.1080/02331888.2021.1881785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Charles-Elie Rabier
- ISEM, CNRS, EPHE, IRD, Université de Montpellier, Montpellier, France
- IMAG, CNRS, Université de Montpellier, Montpellier, France
- LIRMM, CNRS, Université de Montpellier, Montpellier, France
| | - Céline Delmas
- INRAE, UR MIAT, Université de Toulouse, Castanet-Tolosan, France
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24
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Tong H, Nikoloski Z. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153354. [PMID: 33385619 DOI: 10.1016/j.jplph.2020.153354] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 05/07/2023]
Abstract
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.
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Affiliation(s)
- Hao Tong
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Bioinformatics and Mathematical Modeling Department, Centre for Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Bioinformatics and Mathematical Modeling Department, Centre for Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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25
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Osorio LF, Gezan SA, Verma S, Whitaker VM. Independent Validation of Genomic Prediction in Strawberry Over Multiple Cycles. Front Genet 2021; 11:596258. [PMID: 33552121 PMCID: PMC7862747 DOI: 10.3389/fgene.2020.596258] [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: 08/18/2020] [Accepted: 12/31/2020] [Indexed: 01/05/2023] Open
Abstract
The University of Florida strawberry (Fragaria × ananassa) breeding program has implemented genomic prediction (GP) as a tool for choosing outstanding parents for crosses over the last five seasons. This has allowed the use of some parents 1 year earlier than with traditional methods, thus reducing the duration of the breeding cycle. However, as the number of breeding cycles increases over time, greater knowledge is needed on how multiple cycles can be used in the practical implementation of GP in strawberry breeding. Advanced selections and cultivars totaling 1,558 unique individuals were tested in field trials for yield and fruit quality traits over five consecutive years and genotyped for 9,908 SNP markers. Prediction of breeding values was carried out using Bayes B models. Independent validation was carried out using separate trials/years as training (TRN) and testing (TST) populations. Single-trial predictive abilities for five polygenic traits averaged 0.35, which was reduced to 0.24 when individuals common across trials were excluded, emphasizing the importance of relatedness among training and testing populations. Training populations including up to four previous breeding cycles increased predictive abilities, likely due to increases in both training population size and relatedness. Predictive ability was also strongly influenced by heritability, but less so by changes in linkage disequilibrium and effective population size. Genotype by year interactions were minimal. A strategy for practical implementation of GP in strawberry breeding is outlined that uses multiple cycles to predict parental performance and accounts for traits not included in GP models when constructing crosses. Given the importance of relatedness to the success of GP in strawberry, future work could focus on the optimization of relatedness in the design of TRN and TST populations to increase predictive ability in the short-term without compromising long-term genetic gains.
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Affiliation(s)
- Luis F Osorio
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
| | - Salvador A Gezan
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL, United States
| | - Sujeet Verma
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
| | - Vance M Whitaker
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
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26
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Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, Barrón-López JA, Martini JWR, Fajardo-Flores SB, Gaytan-Lugo LS, Santana-Mancilla PC, Crossa J. A review of deep learning applications for genomic selection. BMC Genomics 2021; 22:19. [PMID: 33407114 PMCID: PMC7789712 DOI: 10.1186/s12864-020-07319-x] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 12/10/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. CONCLUSIONS The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.
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Affiliation(s)
| | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, Mexico.
| | | | - José Alberto Barrón-López
- Department of Animal Production (DPA), Universidad Nacional Agraria La Molina, Av. La Molina s/n La Molina, 15024, Lima, Peru
| | - Johannes W R Martini
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45, CP 52640, Carretera Mexico-Veracruz, Mexico
| | | | - Laura S Gaytan-Lugo
- School of Mechanical and Electrical Engineering, Universidad de Colima, 28040, Colima, Colima, Mexico
| | | | - José Crossa
- Colegio de Postgraduados, CP 56230, Montecillos, Edo. de México, Mexico.
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45, CP 52640, Carretera Mexico-Veracruz, Mexico.
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27
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Cockerton HM, Karlström A, Johnson AW, Li B, Stavridou E, Hopson KJ, Whitehouse AB, Harrison RJ. Genomic Informed Breeding Strategies for Strawberry Yield and Fruit Quality Traits. FRONTIERS IN PLANT SCIENCE 2021; 12:724847. [PMID: 34675948 PMCID: PMC8525896 DOI: 10.3389/fpls.2021.724847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/01/2021] [Indexed: 05/09/2023]
Abstract
Over the last two centuries, breeders have drastically modified the fruit quality of strawberries through artificial selection. However, there remains significant variation in quality across germplasm with scope for further improvements to be made. We reported extensive phenotyping of fruit quality and yield traits in a multi-parental strawberry population to allow genomic prediction and quantitative trait nucleotide (QTN) identification, thereby enabling the description of genetic architecture to inform the efficacy of implementing advanced breeding strategies. A negative relationship (r = -0.21) between total soluble sugar content and class one yield was identified, indicating a trade-off between these two essential traits. This result highlighted an established dilemma for strawberry breeders and a need to uncouple the relationship, particularly under June-bearing, protected production systems comparable to this study. A large effect of quantitative trait nucleotide was associated with perceived acidity and pH whereas multiple loci were associated with firmness. Therefore, we recommended the implementation of both marker assisted selection (MAS) and genomic prediction to capture the observed variation respectively. Furthermore, we identified a large effect locus associated with a 10% increase in the number of class one fruit and a further 10 QTN which, when combined, are associated with a 27% increase in the number of marketable strawberries. Ultimately, our results suggested that the best method to improve strawberry yield is through selecting parental lines based upon the number of marketable fruits produced per plant. Not only were strawberry number metrics less influenced by environmental fluctuations, but they had a larger additive genetic component when compared with mass traits. As such, selecting using "number" traits should lead to faster genetic gain.
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Affiliation(s)
- Helen M. Cockerton
- Genetics, Genomics and Breeding, NIAB EMR, East Malling, United Kingdom
- University of Kent, Canterbury, United Kingdom
- *Correspondence: Helen M. Cockerton
| | - Amanda Karlström
- Genetics, Genomics and Breeding, NIAB EMR, East Malling, United Kingdom
| | | | - Bo Li
- Genetics, Genomics and Breeding, NIAB EMR, East Malling, United Kingdom
| | | | - Katie J. Hopson
- Genetics, Genomics and Breeding, NIAB EMR, East Malling, United Kingdom
| | | | - Richard J. Harrison
- Genetics, Genomics and Breeding, NIAB EMR, East Malling, United Kingdom
- Cambridge Crop Research, NIAB, Cambridge, United Kingdom
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28
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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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29
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Iezzoni AF, McFerson J, Luby J, Gasic K, Whitaker V, Bassil N, Yue C, Gallardo K, McCracken V, Coe M, Hardner C, Zurn JD, Hokanson S, van de Weg E, Jung S, Main D, da Silva Linge C, Vanderzande S, Davis TM, Mahoney LL, Finn C, Peace C. RosBREED: bridging the chasm between discovery and application to enable DNA-informed breeding in rosaceous crops. HORTICULTURE RESEARCH 2020; 7:177. [PMID: 33328430 PMCID: PMC7603521 DOI: 10.1038/s41438-020-00398-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 07/16/2020] [Accepted: 08/30/2020] [Indexed: 05/05/2023]
Abstract
The Rosaceae crop family (including almond, apple, apricot, blackberry, peach, pear, plum, raspberry, rose, strawberry, sweet cherry, and sour cherry) provides vital contributions to human well-being and is economically significant across the U.S. In 2003, industry stakeholder initiatives prioritized the utilization of genomics, genetics, and breeding to develop new cultivars exhibiting both disease resistance and superior horticultural quality. However, rosaceous crop breeders lacked certain knowledge and tools to fully implement DNA-informed breeding-a "chasm" existed between existing genomics and genetic information and the application of this knowledge in breeding. The RosBREED project ("Ros" signifying a Rosaceae genomics, genetics, and breeding community initiative, and "BREED", indicating the core focus on breeding programs), addressed this challenge through a comprehensive and coordinated 10-year effort funded by the USDA-NIFA Specialty Crop Research Initiative. RosBREED was designed to enable the routine application of modern genomics and genetics technologies in U.S. rosaceous crop breeding programs, thereby enhancing their efficiency and effectiveness in delivering cultivars with producer-required disease resistances and market-essential horticultural quality. This review presents a synopsis of the approach, deliverables, and impacts of RosBREED, highlighting synergistic global collaborations and future needs. Enabling technologies and tools developed are described, including genome-wide scanning platforms and DNA diagnostic tests. Examples of DNA-informed breeding use by project participants are presented for all breeding stages, including pre-breeding for disease resistance, parental and seedling selection, and elite selection advancement. The chasm is now bridged, accelerating rosaceous crop genetic improvement.
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Affiliation(s)
- Amy F Iezzoni
- Michigan State University, East Lansing, MI, 48824, USA.
| | - Jim McFerson
- Washington State University, Wenatchee, WA, 98801, USA
| | - James Luby
- University of Minnesota, St. Paul, MN, 55108, USA
| | | | | | | | - Chengyan Yue
- University of Minnesota, St. Paul, MN, 55108, USA
| | | | | | - Michael Coe
- Cedar Lake Research Group, Portland, OR, 97215, USA
| | | | | | | | - Eric van de Weg
- Wageningen University and Research, 6700 AA, Wageningen, The Netherlands
| | - Sook Jung
- Washington State University, Pullman, WA, 99164, USA
| | - Dorrie Main
- Washington State University, Pullman, WA, 99164, USA
| | | | | | | | | | | | - Cameron Peace
- Washington State University, Pullman, WA, 99164, USA
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30
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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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31
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Li B, Cockerton HM, Johnson AW, Karlström A, Stavridou E, Deakin G, Harrison RJ. Defining strawberry shape uniformity using 3D imaging and genetic mapping. HORTICULTURE RESEARCH 2020; 7:115. [PMID: 32821398 PMCID: PMC7395166 DOI: 10.1038/s41438-020-0337-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/25/2020] [Accepted: 05/17/2020] [Indexed: 05/24/2023]
Abstract
Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters 'by eye' and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A "regular shape" QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries.
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Affiliation(s)
- Bo Li
- NIAB EMR, East Malling, Kent, ME19 6BJ UK
- University of the West of England, Bristol, BS16 1QY UK
| | | | | | | | | | | | - Richard J. Harrison
- NIAB EMR, East Malling, Kent, ME19 6BJ UK
- NIAB, Huntingdon Road, Cambridge, CB3 0LE UK
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Davis TM, Yang Y, Mahoney LL, Frailey DC. A pentaploid-based linkage map of the ancestral octoploid strawberry Fragaria virginiana reveals instances of sporadic hyper-recombination. HORTICULTURE RESEARCH 2020; 7:77. [PMID: 32411378 PMCID: PMC7206004 DOI: 10.1038/s41438-020-0308-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 03/27/2020] [Accepted: 03/30/2020] [Indexed: 05/04/2023]
Abstract
The first high-resolution genetic linkage map of the ancestral octoploid (2n = 8x = 56) strawberry species, Fragaria virginiana, was constructed using segregation data obtained from a pentaploid progeny population. This novel mapping population of size 178 was generated by crossing highly heterozygous F. virginiana hybrid "LB48" as a paternal parent with diploid (2n = 2x = 14) Fragaria vesca "Hawaii 4". The LB48 linkage map comprises 6055 markers genotyped on the Axiom® IStraw90 strawberry SNP array. The map consists of 28 linkage groups (LGs) organized into seven homoeology groups of four LGs each, and excludes a small 29th LG of undefined homoeology. One member of each homoeology group was assignable to an "A" subgenome associated with ancestral diploid Fragaria vesca, while no other subgenomes were defined. Despite an intriguing discrepancy within homoeology group VI, synteny comparisons with the previously published Fragaria ×ananassa DA × MO linkage map revealed substantial agreement. Following initial map construction, examination of crossover distributions revealed that six of the total 5162 (=29 chromosomes/individual × 178 individuals) chromosomes making up the data set exhibited abnormally high crossover counts, ranging from 15 to 48 crossovers per chromosome, as compared with the overall mean of 0.66 crossovers per chromosome. Each of these six hyper-recombinant (HypR) chromosomes occurred in a different LG and in a different individual. When calculated upon exclusion of the six HypR chromosomes, the canonical (i.e., broadly representative) LB48 map had 1851 loci distributed over a total map length of 1873 cM, while their inclusion increased the number of loci by 130, and the overall map length by 91 cM. Discovery of these hyper-recombinant chromosomes points to the existence of a sporadically acting mechanism that, if identified and manipulable, could be usefully harnessed for multiple purposes by geneticists and breeders.
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Affiliation(s)
- Thomas M. Davis
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH 03824 USA
| | - Yilong Yang
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH 03824 USA
| | - Lise L. Mahoney
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH 03824 USA
| | - Daniel C. Frailey
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH 03824 USA
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Whitaker VM, Knapp SJ, Hardigan MA, Edger PP, Slovin JP, Bassil NV, Hytönen T, Mackenzie KK, Lee S, Jung S, Main D, Barbey CR, Verma S. A roadmap for research in octoploid strawberry. HORTICULTURE RESEARCH 2020; 7:33. [PMID: 32194969 PMCID: PMC7072068 DOI: 10.1038/s41438-020-0252-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 01/26/2020] [Indexed: 05/02/2023]
Abstract
The cultivated strawberry (Fragaria × ananassa) is an allo-octoploid species, originating nearly 300 years ago from wild progenitors from the Americas. Since that time the strawberry has become the most widely cultivated fruit crop in the world, universally appealing due to its sensory qualities and health benefits. The recent publication of the first high-quality chromosome-scale octoploid strawberry genome (cv. Camarosa) is enabling rapid advances in genetics, stimulating scientific debate and provoking new research questions. In this forward-looking review we propose avenues of research toward new biological insights and applications to agriculture. Among these are the origins of the genome, characterization of genetic variants, and big data approaches to breeding. Key areas of research in molecular biology will include the control of flowering, fruit development, fruit quality, and plant-pathogen interactions. In order to realize this potential as a global community, investments in genome resources must be continually augmented.
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Affiliation(s)
- Vance M Whitaker
- 1University of Florida, Institute of Food and Agricultural Sciences, Gulf Coast Research and Education Center, Wimauma, Florida 33598 USA
| | - Steven J Knapp
- 2Department of Plant Sciences, University of California, Davis, CA 95616 USA
| | - Michael A Hardigan
- 2Department of Plant Sciences, University of California, Davis, CA 95616 USA
| | - Patrick P Edger
- 3Department of Horticulture, Michigan State University, East Lansing, MI 48824 USA
| | - Janet P Slovin
- USDA-ARS Genetic Improvement of Fruits and Vegetables Laboratory, Beltsville, MA 20705 USA
| | - Nahla V Bassil
- 5USDA-ARS National Clonal Germplasm Repository, Corvallis, OR 97333 USA
| | - Timo Hytönen
- 6Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, 00790 Finland
- 7Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, 00790 Finland
- NIAB EMR, Kent, ME19 6BJ UK
| | - Kathryn K Mackenzie
- 6Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, 00790 Finland
| | - Seonghee Lee
- 1University of Florida, Institute of Food and Agricultural Sciences, Gulf Coast Research and Education Center, Wimauma, Florida 33598 USA
| | - Sook Jung
- 9Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Dorrie Main
- 9Department of Horticulture, Washington State University, Pullman, WA 99164 USA
| | - Christopher R Barbey
- 1University of Florida, Institute of Food and Agricultural Sciences, Gulf Coast Research and Education Center, Wimauma, Florida 33598 USA
| | - Sujeet Verma
- 1University of Florida, Institute of Food and Agricultural Sciences, Gulf Coast Research and Education Center, Wimauma, Florida 33598 USA
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Zingaretti LM, Gezan SA, Ferrão LFV, Osorio LF, Monfort A, Muñoz PR, Whitaker VM, Pérez-Enciso M. Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species. FRONTIERS IN PLANT SCIENCE 2020; 11:25. [PMID: 32117371 PMCID: PMC7015897 DOI: 10.3389/fpls.2020.00025] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/10/2020] [Indexed: 05/21/2023]
Abstract
Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.
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Affiliation(s)
- Laura M. Zingaretti
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, Barcelona, Spain
| | - Salvador Alejandro Gezan
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL, United States
| | - Luis Felipe V. Ferrão
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Luis F. Osorio
- IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
| | - Amparo Monfort
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, Barcelona, Spain
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Barcelona, Spain
| | - Patricio R. Muñoz
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Vance M. Whitaker
- IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, United States
| | - Miguel Pérez-Enciso
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, Barcelona, Spain
- ICREA, Passeig de Lluís Companys 23, Barcelona, Spain
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Lewers KS, Castro P, Hancock JF, Weebadde CK, Die JV, Rowland LJ. Evidence of epistatic suppression of repeat fruiting in cultivated strawberry. BMC PLANT BIOLOGY 2019; 19:386. [PMID: 31488054 PMCID: PMC6729047 DOI: 10.1186/s12870-019-1984-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 08/23/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Consumers purchase fresh strawberries all year long. Extending the fruiting season for new strawberry cultivars is a common breeding goal. Understanding the inheritance of repeat fruiting is key to improving breeding efficiency. Several independent research groups using multiple genotypes and analytic approaches have all identified a single genomic region in strawberry associated with repeat fruiting. Markers mapped to this region were used to evaluate breeding parents from the United States Department of Agriculture - Agricultural Research Service (USDA-ARS) strawberry breeding program at Beltsville, Maryland. RESULTS Markers mapped to repeat fruiting identified once-fruiting genotypes but not repeat-fruiting genotypes. Eleven of twenty-three breeding parents with repeat-fruiting marker profiles were actually once fruiting, indicating at least one additional locus acting epistatically to suppress repeat fruiting. Family segregation ratios could not be predicted reliably by the combined use of parental phenotypes and marker profiles, when using a single-gene model. Expected segregation ratios were calculated for all phenotypic and marker-profile combinations possible from the mapped locus combined with a hypothetical dominant or recessive suppressor locus. Segregation ratios specific to an epistatic suppressor acting on the mapped locus were observed in four families. The segregation ratios for two families were best explained by a dominant suppressor acting on the mapped locus, and, for the other two, by a recessive suppressor. Not all of the observed ratios could be explained by one model or the other, and when multiple families with a common parent were compared, there was no predicted genotype for the common parent that would lead to all of the observed segregation ratios. CONCLUSIONS Considering all lines of evidence in this study and others, repeat-fruiting in commercial strawberry is controlled primarily by a dominant allele at a single locus, previously mapped by multiple groups. At least two additional genes, one dominant and one recessive, exist that act epistatically to suppress repeat fruiting. Environmental effects and/or incomplete penetrance likely affect phenotype through the suppressor loci, rather than the primary mapped locus. One of the dominant suppressors acts only in the first year, the year the plant is germinated from seed, and not after the plant has experienced a winter.
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Affiliation(s)
- K. S. Lewers
- USDA-ARS, Genetic Improvement of Fruits and Vegetables Laboratory, Building 010A BARC- West, 10300 Baltimore Ave., Beltsville, MD 20705-2350 USA
| | - P. Castro
- Department of Genetics, Escuela Técnica Superior de Ingenieros Agrónomos, Edificio Gregor Mendel (C-5), Campus de Rabanales, University of Cordoba, 14071 Córdoba, Spain
| | - J. F. Hancock
- Department of Horticulture, A342C Plant and Soil Sciences Building, Michigan State University, East Lansing, MI 48824-1325 USA
| | - C. K. Weebadde
- Department of Plant, Soil and Microbial Sciences, A384-D Plant and Soil Sciences Building, Michigan State University, East Lansing, MI 48824-1325 USA
| | - J. V. Die
- Department of Genetics, Escuela Técnica Superior de Ingenieros Agrónomos, Edificio Gregor Mendel (C-5), Campus de Rabanales, University of Cordoba, 14071 Córdoba, Spain
| | - L. J. Rowland
- USDA-ARS, Genetic Improvement of Fruits and Vegetables Laboratory, Building 010A BARC- West, 10300 Baltimore Ave., Beltsville, MD 20705-2350 USA
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36
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Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton. Mol Genet Genomics 2019; 295:67-79. [DOI: 10.1007/s00438-019-01599-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/29/2019] [Indexed: 01/25/2023]
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Frouin J, Labeyrie A, Boisnard A, Sacchi GA, Ahmadi N. Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. PLoS One 2019; 14:e0217516. [PMID: 31194746 PMCID: PMC6563978 DOI: 10.1371/journal.pone.0217516] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 05/13/2019] [Indexed: 12/28/2022] Open
Abstract
The high concentration of arsenic (As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.
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Affiliation(s)
- Julien Frouin
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Axel Labeyrie
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | | | | | - Nourollah Ahmadi
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
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Thistlethwaite FR, Ratcliffe B, Klápště J, Porth I, Chen C, Stoehr MU, El-Kassaby YA. Genomic selection of juvenile height across a single-generational gap in Douglas-fir. Heredity (Edinb) 2019; 122:848-863. [PMID: 30631145 PMCID: PMC6781123 DOI: 10.1038/s41437-018-0172-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 11/24/2018] [Accepted: 11/26/2018] [Indexed: 11/30/2022] Open
Abstract
Here, we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. A total of 1321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for (1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, (2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, (3) F1 mature height (HT35) predicting HTJ EBVs in F2, and (4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60, respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.
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Affiliation(s)
- Frances R Thistlethwaite
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Jaroslav Klápště
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Whakarewarewa, Rotorua, 3046, New Zealand
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6, 165 21, Czech Republic
| | - Ilga Porth
- Département des sciences du bois et de la forêt, Université Laval, G1V 0A6, Québec, QC, Canada
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, 74078-3035, USA
| | - Michael U Stoehr
- British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, BC, V8W 9C2, Canada
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
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Michel S, Löschenberger F, Ametz C, Pachler B, Sparry E, Bürstmayr H. Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:1745-1760. [PMID: 30810763 PMCID: PMC6531418 DOI: 10.1007/s00122-019-03312-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/15/2019] [Indexed: 05/10/2023]
Abstract
Large genetic improvement can be achieved by simultaneous genomic selection for grain yield and protein content when combining different breeding strategies in the form of selection indices. Genomic selection has been implemented in many national and international breeding programmes in recent years. Numerous studies have shown the potential of this new breeding tool; few have, however, taken the simultaneous selection for multiple traits into account that is though common practice in breeding programmes. The simultaneous improvement in grain yield and protein content is thereby a major challenge in wheat breeding due to a severe negative trade-off. Accordingly, the potential and limits of multi-trait selection for this particular trait complex utilizing the vast phenotypic and genomic data collected in an applied wheat breeding programme were investigated in this study. Two breeding strategies based on various genomic-selection indices were compared, which (1) aimed to select high-protein genotypes with acceptable yield potential and (2) develop high-yielding varieties, while maintaining protein content. The prediction accuracy of preliminary yield trials could be strongly improved when combining phenotypic and genomic information in a genomics-assisted selection approach, which surpassed both genomics-based and classical phenotypic selection methods both for single trait predictions and in genomic index selection across years. The employed genomic selection indices mitigated furthermore the negative trade-off between grain yield and protein content leading to a substantial selection response for protein yield, i.e. total seed nitrogen content, which suggested that it is feasible to develop varieties that combine a superior yield potential with comparably high protein content, thus utilizing available nitrogen resources more efficiently.
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Affiliation(s)
- Sebastian Michel
- Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
| | | | - Christian Ametz
- Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | - Bernadette Pachler
- Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | - Ellen Sparry
- C&M Seeds, 6180 5th Line, Palmerston, ON, N0G 2P0, Canada
| | - Hermann Bürstmayr
- Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
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40
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pSBVB: A Versatile Simulation Tool To Evaluate Genomic Selection in Polyploid Species. G3-GENES GENOMES GENETICS 2019; 9:327-334. [PMID: 30573468 PMCID: PMC6385978 DOI: 10.1534/g3.118.200942] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Genomic Selection (GS) is the procedure whereby molecular information is used to predict complex phenotypes and it is standard in many animal and plant breeding schemes. However, only a small number of studies have been reported in horticultural crops, and in polyploid species in particular. In this paper, we have developed a versatile forward simulation tool, called polyploid Sequence Based Virtual Breeding (pSBVB), to evaluate GS strategies in polyploids; pSBVB is an efficient gene dropping software that can simulate any number of complex phenotypes, allowing a very flexible modeling of phenotypes suited to polyploids. As input, it takes genotype data from the founder population, which can vary from single nucleotide polymorphisms (SNP) chips up to sequence, a list of causal variants for every trait and their heritabilities, and the pedigree. Recombination rates between homeologous chromosomes can be specified, so that both allo- and autopolyploid species can be considered. The program outputs phenotype and genotype data for all individuals in the pedigree. Optionally, it can produce several genomic relationship matrices that consider exact or approximate genotype values. pSBVB can therefore be used to evaluate GS strategies in polyploid species (say varying SNP density, genetic architecture or population size, among other factors), or to optimize experimental designs for association studies. We illustrate pSBVB with SNP data from tetraploid potato and partial sequence data from octoploid strawberry, and we show that GS is a promising breeding strategy for polyploid species but that the actual advantage critically depends on the underlying genetic architecture. Source code, examples and a complete manual are freely available in GitHub https://github.com/lauzingaretti/pSBVB.
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41
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Nellist CF, Vickerstaff RJ, Sobczyk MK, Marina-Montes C, Wilson FM, Simpson DW, Whitehouse AB, Harrison RJ. Quantitative trait loci controlling Phytophthora cactorum resistance in the cultivated octoploid strawberry ( Fragaria × ananassa). HORTICULTURE RESEARCH 2019; 6:60. [PMID: 31069084 PMCID: PMC6491645 DOI: 10.1038/s41438-019-0136-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 01/17/2019] [Accepted: 01/22/2019] [Indexed: 05/18/2023]
Abstract
The cultivated strawberry, Fragaria × ananassa (Fragaria spp.) is the most economically important global soft fruit. Phytophthora cactorum, a water-borne oomycete causes economic losses in strawberry production globally. A bi-parental cross of octoploid cultivated strawberry segregating for resistance to P. cactorum, the causative agent of crown rot disease, was screened using artificial inoculation. Multiple putative resistance quantitative trait loci (QTL) were identified and mapped. Three major effect QTL (FaRPc6C, FaRPc6D and FaRPc7D) explained 37% of the variation observed. There were no epistatic interactions detected between the three major QTLs. Testing a subset of the mapping population progeny against a range of P. cactorum isolates revealed no significant interaction (p = 0.0593). However, some lines showed higher susceptibility than predicted, indicating that additional undetected factors may affect the expression of some quantitative resistance loci. Using historic crown rot disease score data from strawberry accessions, a preliminary genome-wide association study (GWAS) of 114 individuals revealed an additional locus associated with resistance to P. cactorum. Mining of the Fragaria vesca Hawaii 4 v1.1 genome revealed candidate resistance genes in the QTL regions.
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Affiliation(s)
- Charlotte F. Nellist
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - Robert J. Vickerstaff
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - Maria K. Sobczyk
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - César Marina-Montes
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - Fiona M. Wilson
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - David W. Simpson
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - Adam B. Whitehouse
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
| | - Richard J. Harrison
- Department of Genetics, Genomics and Breeding, NIAB EMR, New Road, East Malling, ME19 6BJ UK
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Aranzana MJ, Decroocq V, Dirlewanger E, Eduardo I, Gao ZS, Gasic K, Iezzoni A, Jung S, Peace C, Prieto H, Tao R, Verde I, Abbott AG, Arús P. Prunus genetics and applications after de novo genome sequencing: achievements and prospects. HORTICULTURE RESEARCH 2019; 6:58. [PMID: 30962943 PMCID: PMC6450939 DOI: 10.1038/s41438-019-0140-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/10/2019] [Accepted: 03/13/2019] [Indexed: 05/04/2023]
Abstract
Prior to the availability of whole-genome sequences, our understanding of the structural and functional aspects of Prunus tree genomes was limited mostly to molecular genetic mapping of important traits and development of EST resources. With public release of the peach genome and others that followed, significant advances in our knowledge of Prunus genomes and the genetic underpinnings of important traits ensued. In this review, we highlight key achievements in Prunus genetics and breeding driven by the availability of these whole-genome sequences. Within the structural and evolutionary contexts, we summarize: (1) the current status of Prunus whole-genome sequences; (2) preliminary and ongoing work on the sequence structure and diversity of the genomes; (3) the analyses of Prunus genome evolution driven by natural and man-made selection; and (4) provide insight into haploblocking genomes as a means to define genome-scale patterns of evolution that can be leveraged for trait selection in pedigree-based Prunus tree breeding programs worldwide. Functionally, we summarize recent and ongoing work that leverages whole-genome sequences to identify and characterize genes controlling 22 agronomically important Prunus traits. These include phenology, fruit quality, allergens, disease resistance, tree architecture, and self-incompatibility. Translationally, we explore the application of sequence-based marker-assisted breeding technologies and other sequence-guided biotechnological approaches for Prunus crop improvement. Finally, we present the current status of publically available Prunus genomics and genetics data housed mainly in the Genome Database for Rosaceae (GDR) and its updated functionalities for future bioinformatics-based Prunus genetics and genomics inquiry.
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Affiliation(s)
- Maria José Aranzana
- IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Cerdanyola del Vallès (Bellaterra), 08193 Barcelona, Spain
| | - Véronique Decroocq
- UMR 1332 BFP, INRA, University of Bordeaux, A3C and Virology Teams, 33882 Villenave-d’Ornon Cedex, France
| | - Elisabeth Dirlewanger
- UMR 1332 BFP, INRA, University of Bordeaux, A3C and Virology Teams, 33882 Villenave-d’Ornon Cedex, France
| | - Iban Eduardo
- IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Cerdanyola del Vallès (Bellaterra), 08193 Barcelona, Spain
| | - Zhong Shan Gao
- Allergy Research Center, Zhejiang University, 310058 Hangzhou, China
| | | | - Amy Iezzoni
- Department of Horticulture, Michigan State University, 1066 Bogue Street, East Lansing, MI 48824-1325 USA
| | - Sook Jung
- Department of Horticulture, Washington State University, Pullman, WA 99164-6414 USA
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA 99164-6414 USA
| | - Humberto Prieto
- Biotechnology Laboratory, La Platina Research Station, Instituto de Investigaciones Agropecuarias, Santa Rosa, 11610 La Pintana, Santiago Chile
| | - Ryutaro Tao
- Laboratory of Pomology, Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502 Japan
| | - Ignazio Verde
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) – Centro di ricerca Olivicoltura, Frutticoltura e Agrumicoltura (CREA-OFA), Rome, Italy
| | - Albert G. Abbott
- University of Kentucky, 106 T. P. Cooper Hall, Lexington, KY 40546-0073 USA
| | - Pere Arús
- IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB, Campus UAB, Edifici CRAG, Cerdanyola del Vallès (Bellaterra), 08193 Barcelona, Spain
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Minamikawa MF, Takada N, Terakami S, Saito T, Onogi A, Kajiya-Kanegae H, Hayashi T, Yamamoto T, Iwata H. Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai). Sci Rep 2018; 8:11994. [PMID: 30097588 PMCID: PMC6086889 DOI: 10.1038/s41598-018-30154-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/25/2018] [Indexed: 12/13/2022] Open
Abstract
Breeding of fruit trees is hindered by their large size and long juvenile period. Genome-wide association study (GWAS) and genomic selection (GS) are promising methods for circumventing this hindrance, but preparing new large datasets for these methods may not always be practical. Here, we evaluated the potential of breeding populations evaluated routinely in breeding programs for GWAS and GS. We used a pear parental population of 86 varieties and breeding populations of 765 trees from 16 full-sib families, which were phenotyped for 18 traits and genotyped for 1,506 single nucleotide polymorphisms (SNPs). The power of GWAS and accuracy of genomic prediction were improved when we combined data from the breeding populations and the parental population. The accuracy of genomic prediction was improved further when full-sib data of the target family were available. The results suggest that phenotype data collected in breeding programs can be beneficial for GWAS and GS when they are combined with genome-wide marker data. The potential of GWAS and GS will be further extended if we can build a system for routine collection of the phenotype and marker genotype data for breeding populations.
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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
| | - Norio Takada
- 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
| | - Toshihiro Saito
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605, Japan
| | - Akio Onogi
- 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
| | - Hiromi Kajiya-Kanegae
- 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
| | - Takeshi Hayashi
- Institute of Crop Science, NARO, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518, 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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Nyine M, Uwimana B, Blavet N, Hřibová E, Vanrespaille H, Batte M, Akech V, Brown A, Lorenzen J, Swennen R, Doležel J. Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana. THE PLANT GENOME 2018; 11:170090. [PMID: 30025016 DOI: 10.3835/plantgenome2017.10.0090] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Improving the efficiency of selection in conventional crossbreeding is a major priority in banana ( spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47- 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.
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Piaskowski J, Hardner C, Cai L, Zhao Y, Iezzoni A, Peace C. Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits. BMC Genet 2018; 19:23. [PMID: 29636022 PMCID: PMC5894190 DOI: 10.1186/s12863-018-0609-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/22/2018] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Sweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years. RESULTS High broad-sense heritabilities of 0.83, 0.77, and 0.76 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic model resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance. CONCLUSIONS Predicted breeding values and genetic values revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.
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Affiliation(s)
- Julia Piaskowski
- Department of Horticulture, Washington State University, Pullman, WA 99164-6414 USA
| | - Craig Hardner
- Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation University of Queensland, Brisbane, Australia
| | - Lichun Cai
- Department of Horticulture, Michigan State University, East Lansing, MI 48824-1325 USA
| | - Yunyang Zhao
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC 28081 USA
| | - Amy Iezzoni
- Department of Horticulture, Michigan State University, East Lansing, MI 48824-1325 USA
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA 99164-6414 USA
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Ben Hassen M, Cao TV, Bartholomé J, Orasen G, Colombi C, Rakotomalala J, Razafinimpiasa L, Bertone C, Biselli C, Volante A, Desiderio F, Jacquin L, Valè G, Ahmadi N. Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:417-435. [PMID: 29138904 PMCID: PMC5787227 DOI: 10.1007/s00122-017-3011-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 11/04/2017] [Indexed: 05/25/2023]
Abstract
KEY MESSAGE Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5-F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r 2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41-0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.
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Affiliation(s)
- M Ben Hassen
- Department of Agriculture and Environmental Sciences, University of Milan, Via Giovanni Celoria, 2, 20133, Milan, Italy
| | - T V Cao
- Cirad, UMR AGAP, Avenue Agropolis, 34398, Montpellier Cedex 5, France
| | - J Bartholomé
- Cirad, UMR AGAP, Avenue Agropolis, 34398, Montpellier Cedex 5, France
| | - G Orasen
- Department of Agriculture and Environmental Sciences, University of Milan, Via Giovanni Celoria, 2, 20133, Milan, Italy
| | - C Colombi
- Fondazione Parco Tecnologico Padano, Via Einstein, Loc. Cascina Codazza, 26900, Lodi, Italy
| | | | | | - C Bertone
- Department of Agriculture and Environmental Sciences, University of Milan, Via Giovanni Celoria, 2, 20133, Milan, Italy
| | - C Biselli
- CREA-Council for Agricultural Research and Economics, Research Center for Genomics and Bioinformatics, Via S. Protaso 302, 29017, Fiorenzuola d'Arda, PC, Italy
| | - A Volante
- CREA-Council for Agricultural Research and Economics, Research Center for Cereal and Industrial Crops, S. S. 11 to Torino Km 2.5, 13100, Vercelli, Italy
| | - F Desiderio
- CREA-Council for Agricultural Research and Economics, Research Center for Genomics and Bioinformatics, Via S. Protaso 302, 29017, Fiorenzuola d'Arda, PC, Italy
| | - L Jacquin
- Cirad, UMR AGAP, Avenue Agropolis, 34398, Montpellier Cedex 5, France
| | - G Valè
- CREA-Council for Agricultural Research and Economics, Research Center for Cereal and Industrial Crops, S. S. 11 to Torino Km 2.5, 13100, Vercelli, Italy
| | - N Ahmadi
- Cirad, UMR AGAP, Avenue Agropolis, 34398, Montpellier Cedex 5, France.
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You Q, Yang X, Peng Z, Xu L, Wang J. Development and Applications of a High Throughput Genotyping Tool for Polyploid Crops: Single Nucleotide Polymorphism (SNP) Array. FRONTIERS IN PLANT SCIENCE 2018; 9:104. [PMID: 29467780 PMCID: PMC5808122 DOI: 10.3389/fpls.2018.00104] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 01/19/2018] [Indexed: 05/18/2023]
Abstract
Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes.
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Affiliation(s)
- Qian You
- Key Laboratory of Sugarcane Biology and Genetic Breeding Ministry of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Xiping Yang
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Ze Peng
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Liping Xu
- Key Laboratory of Sugarcane Biology and Genetic Breeding Ministry of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- *Correspondence: Liping Xu
| | - Jianping Wang
- Agronomy Department, University of Florida, Gainesville, FL, United States
- Plant Molecular and Cellular Biology Program, Genetics Institute, University of Florida, Gainesville, FL, United States
- Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Center for Genomics and Biotechnology, Fujian Agriculture and Forestry University, Fuzhou, China
- Jianping Wang
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48
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He JQ, Harrison RJ, Li B. A novel 3D imaging system for strawberry phenotyping. PLANT METHODS 2017; 13:93. [PMID: 29176998 PMCID: PMC5688821 DOI: 10.1186/s13007-017-0243-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/23/2017] [Indexed: 05/20/2023]
Abstract
BACKGROUND Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human eye to assess most external fruit quality attributes, which is time-consuming and subjective. 3D imaging is a promising high-throughput technique that allows multiple external fruit quality attributes to be measured simultaneously. RESULTS A low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit. A 3D point cloud of the sample was derived and analysed with custom-developed software to estimate berry height, length, width, volume, calyx size, colour and achene number. Analysis of these traits in 100 fruits showed good concordance with manual assessment methods. CONCLUSION This study demonstrates the feasibility of an MVS based 3D imaging system for the rapid and quantitative phenotyping of seven agronomically important external strawberry traits. With further improvement, this method could be applied in strawberry breeding programmes as a cost effective phenotyping technique.
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Affiliation(s)
- Joe Q. He
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
- University of Reading, Whiteknights, Reading, RG6 6AH UK
| | - Richard J. Harrison
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
- University of Reading, Whiteknights, Reading, RG6 6AH UK
| | - Bo Li
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
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Mangandi J, Verma S, Osorio L, Peres NA, van de Weg E, Whitaker VM. Pedigree-Based Analysis in a Multiparental Population of Octoploid Strawberry Reveals QTL Alleles Conferring Resistance to Phytophthora cactorum. G3 (BETHESDA, MD.) 2017; 7:1707-1719. [PMID: 28592652 PMCID: PMC5473751 DOI: 10.1534/g3.117.042119] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 04/03/2017] [Indexed: 02/03/2023]
Abstract
Understanding the genetic architecture of traits in breeding programs can be critical for making genetic progress. Important factors include the number of loci controlling a trait, allele frequencies at those loci, and allele effects in breeding germplasm. To this end, multiparental populations offer many advantages for quantitative trait locus (QTL) analyses compared to biparental populations. These include increased power for QTL detection, the ability to sample a larger number of segregating loci and alleles, and estimation of allele effects across diverse genetic backgrounds. Here, we investigate the genetic architecture of resistance to crown rot disease caused by Phytophthora cactorum in strawberry (Fragaria × ananassa), using connected full-sib families from a breeding population. Clonal replicates of > 1100 seedlings from 139 full-sib families arising from 61 parents were control-inoculated during two consecutive seasons. Subgenome-specific single nucleotide polymorphism (SNP) loci were mapped in allo-octoploid strawberry (2n = 8 × = 56), and FlexQTL software was utilized to perform a Bayesian, pedigree-based QTL analysis. A major locus on linkage group (LG) 7D, which we name FaRPc2, accounts for most of the genetic variation for resistance. Four predominant SNP haplotypes were detected in the FaRPc2 region, two of which are strongly associated with two different levels of resistance, suggesting the presence of multiple resistance alleles. The phenotypic effects of FaRPc2 alleles across trials and across numerous genetic backgrounds make this locus a highly desirable target for genetic improvement of resistance in cultivated strawberry.
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Affiliation(s)
- Jozer Mangandi
- Department of Horticultural Science, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida 33598
| | - Sujeet Verma
- Department of Horticultural Science, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida 33598
| | - Luis Osorio
- Department of Horticultural Science, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida 33598
| | - Natalia A Peres
- Department of Plant Pathology, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida 33598
| | - Eric van de Weg
- Plant Breeding, Wageningen University and Research, 6708 PB, The Netherlands
| | - Vance M Whitaker
- Department of Horticultural Science, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida 33598
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50
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Samad S, Kurokura T, Koskela E, Toivainen T, Patel V, Mouhu K, Sargent DJ, Hytönen T. Additive QTLs on three chromosomes control flowering time in woodland strawberry ( Fragaria vesca L.). HORTICULTURE RESEARCH 2017; 4:17020. [PMID: 28580150 PMCID: PMC5442962 DOI: 10.1038/hortres.2017.20] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 04/21/2017] [Accepted: 04/21/2017] [Indexed: 05/18/2023]
Abstract
Flowering time is an important trait that affects survival, reproduction and yield in both wild and cultivated plants. Therefore, many studies have focused on the identification of flowering time quantitative trait locus (QTLs) in different crops, and molecular control of this trait has been extensively investigated in model species. Here we report the mapping of QTLs for flowering time and vegetative traits in a large woodland strawberry mapping population that was phenotyped both under field conditions and in a greenhouse after flower induction in the field. The greenhouse experiment revealed additive QTLs in three linkage groups (LG), two on both LG4 and LG7, and one on LG6 that explain about half of the flowering time variance in the population. Three of the QTLs were newly identified in this study, and one co-localized with the previously characterized FvTFL1 gene. An additional strong QTL corresponding to previously mapped PFRU was detected in both field and greenhouse experiments indicating that gene(s) in this locus can control the timing of flowering in different environments in addition to the duration of flowering and axillary bud differentiation to runners and branch crowns. Several putative flowering time genes were identified in these QTL regions that await functional validation. Our results indicate that a few major QTLs may control flowering time and axillary bud differentiation in strawberries. We suggest that the identification of causal genes in the diploid strawberry may enable fine tuning of flowering time and vegetative growth in the closely related octoploid cultivated strawberry.
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Affiliation(s)
- Samia Samad
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
- Fondazione Edmund Mach, Research and Innovation Centre, San Michele All'adige, 38010 TN, Italy
| | - Takeshi Kurokura
- Faculty of Agriculture, Utsunomiya University, Tochigi, 321-8505, Japan
| | - Elli Koskela
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
| | - Tuomas Toivainen
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
| | - Vipul Patel
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Katriina Mouhu
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
| | - Daniel James Sargent
- Fondazione Edmund Mach, Research and Innovation Centre, San Michele All'adige, 38010 TN, Italy
- Driscoll’s Genetics Limited, East Malling Enterprise Centre, East Malling, Kent ME19 6BJ, UK
| | - Timo Hytönen
- Department of Agricultural Sciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
- Department of Biosciences, Viikki Plant Science Centre, University of Helsinki, 00014 Helsinki, Finland
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