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Flay C, Symonds VV, Storey R, Davy M, Datson P. Mapping QTL associated with resistance to Pseudomonas syringae pv. actinidiae in kiwifruit ( Actinidia chinensis var. chinensis). FRONTIERS IN PLANT SCIENCE 2024; 14:1255506. [PMID: 38596713 PMCID: PMC11003357 DOI: 10.3389/fpls.2023.1255506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 10/25/2023] [Indexed: 04/11/2024]
Abstract
Pseudomonas syringae pv. actinidiae (Psa) is a bacterial pathogen of kiwifruit. This pathogen causes leaf-spotting, cane dieback, wilting, cankers (lesions), and in severe cases, plant death. Families of diploid A. chinensis seedlings grown in the field show a range of susceptibilities to the disease with up to 100% of seedlings in some families succumbing to Psa. But the effect of selection for field resistance to Psa on the alleles that remain in surviving seedlings has not been assessed. The objective of this work was to analyse, the effect of plant removal from Psa on the allele frequency of an incomplete-factorial-cross population. This population was founded using a range of genotypically distinct diploid A. chinensis var. chinensis parents to make 28 F1 families. However, because of the diversity of these families, low numbers of surviving individuals, and a lack of samples from dead individuals, standard QTL mapping approaches were unlikely to yield good results. Instead, a modified bulk segregant analysis (BSA) overcame these drawbacks while reducing the costs of sampling and sample processing, and the complexity of data analysis. Because the method was modified, part one of this work was used to determine the signal strength required for a QTL to be detected with BSA. Once QTL detection accuracy was known, part two of this work analysed the 28 families from the incomplete-factorial-cross population that had multiple individuals removed due to Psa infection. Each family was assigned to one of eight bulks based on a single parent that contributed to the families. DNA was extracted in bulk by grinding sampled leaf discs together before DNA extraction. Each sample bulk was compared against a bulk made up of WGS data from the parents contributing to the sample bulk. The deviation in allele frequency from the expected allele frequency within surviving populations using the modified BSA method was able to identify 11 QTLs for Psa that were present in at least two analyses. The identification of these Psa resistance QTL will enable marker development to selectively breed for resistance to Psa in future kiwifruit breeding programs.
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Affiliation(s)
- Casey Flay
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - V. Vaughan Symonds
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
| | - Roy Storey
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - Marcus Davy
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
| | - Paul Datson
- The New Zealand Institute for Plant and Food Research Limited, Te Puke, New Zealand
- Kiwifruit Breeding Centre, Te Puke, New Zealand
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Bilton TP, Sharma SK, Schofield MR, Black MA, Jacobs JME, Bryan GJ, Dodds KG. Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:64. [PMID: 38430392 PMCID: PMC10908621 DOI: 10.1007/s00122-024-04568-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/30/2024] [Indexed: 03/03/2024]
Abstract
KEY MESSAGE An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.
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Affiliation(s)
- Timothy P Bilton
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand.
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
| | - Sanjeev Kumar Sharma
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee, UK
| | - Matthew R Schofield
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
| | - Michael A Black
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Glenn J Bryan
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee, UK
| | - Ken G Dodds
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
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Pégard M, Barre P, Delaunay S, Surault F, Karagić D, Milić D, Zorić M, Ruttink T, Julier B. Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1196134. [PMID: 37476178 PMCID: PMC10354441 DOI: 10.3389/fpls.2023.1196134] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023]
Abstract
China's and Europe's dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.
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Affiliation(s)
| | | | | | | | - Djura Karagić
- Login EKO doo, Bulevar Zorana Đinđića 125, Novi Beograd, Serbia
| | - Dragan Milić
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Miroslav Zorić
- Login EKO doo, Bulevar Zorana Đinđića 125, Novi Beograd, Serbia
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Machado IP, DoVale JC, Sabadin F, Fritsche-Neto R. On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops. FRONTIERS IN PLANT SCIENCE 2023; 14:1164555. [PMID: 37332727 PMCID: PMC10272588 DOI: 10.3389/fpls.2023.1164555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023]
Abstract
The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.
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Affiliation(s)
| | - Júlio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, Brazil
| | - Felipe Sabadin
- School of Plant and Environmental Sciences, Virginia Tech: Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Roberto Fritsche-Neto
- LSU AgCenter, Louisiana State University Agricultural Center, Baton Rouge, LA, United States
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Zhang F, Kang J, Long R, Li M, Sun Y, He F, Jiang X, Yang C, Yang X, Kong J, Wang Y, Wang Z, Zhang Z, Yang Q. Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa. HORTICULTURE RESEARCH 2022; 10:uhac225. [PMID: 36643744 PMCID: PMC9832841 DOI: 10.1093/hr/uhac225] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/25/2022] [Indexed: 06/17/2023]
Abstract
Fall dormancy (FD) is an essential trait to overcome winter damage and for alfalfa (Medicago sativa) cultivar selection. The plant regrowth height after autumn clipping is an indirect way to evaluate FD. Transcriptomics, proteomics, and quantitative trait locus mapping have revealed crucial genes correlated with FD; however, these genes cannot predict alfalfa FD very well. Here, we conducted genomic prediction of FD using whole-genome SNP markers based on machine learning-related methods, including support vector machine (SVM) regression, and regularization-related methods, such as Lasso and ridge regression. The results showed that using SVM regression with linear kernel and the top 3000 genome-wide association study (GWAS)-associated markers achieved the highest prediction accuracy for FD of 64.1%. For plant regrowth height, the prediction accuracy was 59.0% using the 3000 GWAS-associated markers and the SVM linear model. This was better than the results using whole-genome markers (25.0%). Therefore, the method we explored for alfalfa FD prediction outperformed the other models, such as Lasso and ElasticNet. The study suggests the feasibility of using machine learning to predict FD with GWAS-associated markers, and the GWAS-associated markers combined with machine learning would benefit FD-related traits as well. Application of the methodology may provide potential targets for FD selection, which would accelerate genetic research and molecular breeding of alfalfa with optimized FD.
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Affiliation(s)
- Fan Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA, 99163
| | - Junmei Kang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Ruicai Long
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Mingna Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Yan Sun
- Department of Turf Science and Engineering, College of Grassland Science and Technology, China Agricultural University, Beijing, China, 100193
| | - Fei He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Xueqian Jiang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Changfu Yang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Xijiang Yang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Jie Kong
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Yiwen Wang
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia, 3052
| | - Zhen Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Zhiwu Zhang
- Corresponding author: Zhiwu Zhang (, Phone (Office): 509-335-2899, Fax: 509-335-8674) or Qingchuan Yang (, Phone: 010-62815996, Fax: 010-62815996)
| | - Qingchuan Yang
- Corresponding author: Zhiwu Zhang (, Phone (Office): 509-335-2899, Fax: 509-335-8674) or Qingchuan Yang (, Phone: 010-62815996, Fax: 010-62815996)
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Melo ATO, Hale I. Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP. Bioinformatics 2020; 35:1783-1785. [PMID: 30321264 PMCID: PMC6513162 DOI: 10.1093/bioinformatics/bty873] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 09/25/2018] [Accepted: 10/13/2018] [Indexed: 12/15/2022] Open
Abstract
Summary GBS-SNP-CROP is a bioinformatics pipeline originally developed to support the cost-effective genome-wide characterization of plant genetic resources through paired-end genotyping-by-sequencing (GBS), particularly in the absence of a reference genome. Since its 2016 release, the pipeline’s functionality has greatly expanded, its computational efficiency has improved, and its applicability to a broad set of genomic studies for both plants and animals has been demonstrated. This note details the suite of improvements to date, as realized in GBS-SNP-CROP v.4.0, with specific attention paid to a new integrated metric that facilitates reliable variant identification despite the complications of homologs. Using the new de novo GBS read simulator GBS-Pacecar, also introduced in this note, results show an improvement in overall pipeline accuracy from 66% (v.1.0) to 84% (v.4.0), with a time saving of ∼70%. Both GBS-SNP-CROP versions significantly outperform TASSEL-UNEAK; and v.4.0 resolves the issue of non-overlapping variant calls observed between UNEAK and v.1.0. Availability and implementation GBS-SNP-CROP source code and user manual are available at https://github.com/halelab/GBS-SNP-CROP. The GBS read simulator GBS-Pacecar is available at https://github.com/halelab/GBS-Pacecar. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arthur T O Melo
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA
| | - Iago Hale
- Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA
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Liu XP, Hawkins C, Peel MD, Yu LX. Genetic Loci Associated with Salt Tolerance in Advanced Breeding Populations of Tetraploid Alfalfa Using Genome-Wide Association Studies. THE PLANT GENOME 2019; 12:180026. [PMID: 30951087 DOI: 10.3835/plantgenome2018.05.0026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Many agricultural lands in the western United States consist of soil with high concentrations of salt, which is detrimental to alfalfa ( L.) growth and production, especially in the region where water resource is limited. Developing alfalfa varieties with salt tolerance is imperative for sustainable production under increasing soil salinity. In the present study, we used advanced alfalfa breeding populations and evaluated five traits related to salt tolerance including biomass dry weight (DW) and fresh weight (FW), plant height (PH), leaf relative water content (RWC), and stomatal conductance (SC) under control and salt stress. Stress susceptibility index (SSI) of each trait and single-nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS) were used for genome-wide association studies (GWAS) to identify loci associated with salt tolerance. A total of 53 significant SNPs associated with salt tolerance were identified and they were located at 49 loci through eight chromosomes. A Basic Local Alignment Search Tool (BLAST) search of the regions surrounding the SNPs revealed 21 putative candidate genes associated with salt tolerance. The genetic architecture for traits related to salt tolerance characterized in this report could help in understanding the genetic mechanism by which salt stress affects plant growth and production in alfalfa. The markers and candidate genes identified in the present study would be useful for marker-assisted selection (MAS) in breeding salt-tolerant alfalfa after validation of the markers.
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