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Sreedasyam A, Lovell JT, Mamidi S, Khanal S, Jenkins JW, Plott C, Bryan KB, Li Z, Shu S, Carlson J, Goodstein D, De Santiago L, Kirkbride RC, Calleja S, Campbell T, Koebernick JC, Dever JK, Scheffler JA, Pauli D, Jenkins JN, McCarty JC, Williams M, Boston L, Webber J, Udall JA, Chen ZJ, Bourland F, Stiller WN, Saski CA, Grimwood J, Chee PW, Jones DC, Schmutz J. Genome resources for three modern cotton lines guide future breeding efforts. NATURE PLANTS 2024; 10:1039-1051. [PMID: 38816498 PMCID: PMC11208153 DOI: 10.1038/s41477-024-01713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/27/2024] [Indexed: 06/01/2024]
Abstract
Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars ('UGA230', 'UA48' and 'CSX8308') and updating the 'TM-1' cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from 'Pima' cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.
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Affiliation(s)
- Avinash Sreedasyam
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
- DOE Joint Genome Institute, Berkeley, CA, USA.
| | - John T Lovell
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- DOE Joint Genome Institute, Berkeley, CA, USA
| | - Sujan Mamidi
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Sameer Khanal
- Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Tifton, GA, USA
| | - Jerry W Jenkins
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Christopher Plott
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Kempton B Bryan
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, USA
| | - Zhigang Li
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, USA
| | | | | | | | - Luis De Santiago
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Ryan C Kirkbride
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | | | - Todd Campbell
- USDA-ARS, Coastal Plains Soil Water and Plant Research Center, Florence, SC, USA
| | - Jenny C Koebernick
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, USA
| | - Jane K Dever
- Texas A&M AgriLife Research, Lubbock, TX, USA
- Pee Dee Research and Education Center, Clemson University, Florence, SC, USA
| | | | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, USA
| | - Johnie N Jenkins
- USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS, USA
| | - Jack C McCarty
- USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS, USA
| | - Melissa Williams
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - LoriBeth Boston
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Jenell Webber
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Joshua A Udall
- USDA-ARS, Crop Germplasm Research Unit, College Station, TX, USA
| | - Z Jeffrey Chen
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Fred Bourland
- Northeast Research and Extension Center (NEREC), University of Arkansas, Keiser, AR, USA
| | - Warwick N Stiller
- CSIRO Agriculture and Food Cotton Research Unit, Narrabri, New South Wales, Australia
| | - Christopher A Saski
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, USA
| | - Jane Grimwood
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Peng W Chee
- Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Tifton, GA, USA
| | - Don C Jones
- Agriculture and Environmental Research Cotton Incorporated, Cary, NC, USA
| | - Jeremy Schmutz
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
- DOE Joint Genome Institute, Berkeley, CA, USA.
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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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Manivannan A, Cheeran Amal T. Deciphering the complex cotton genome for improving fiber traits and abiotic stress resilience in sustainable agriculture. Mol Biol Rep 2023; 50:6937-6953. [PMID: 37349608 DOI: 10.1007/s11033-023-08565-4] [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: 12/15/2022] [Accepted: 05/31/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND Understanding the complex cotton genome is of paramount importance in devising a strategy for sustainable agriculture. Cotton is probably the most economically important cash crop known for its cellulose-rich fiber content. The cotton genome has become an ideal model for deciphering polyploidization due to its polyploidy, setting it apart from other major crops. However, the main challenge in understanding the functional and regulatory functions of many genes in cotton is still the complex cotton polyploidy genome, which is not limited to a single role. Cotton production is vulnerable to the sensitive effects of climate change, which can alter or aggravate soil, pests, and diseases. Thus, conventional plant breeding coupled with advanced technologies has led to substantial progress being made in cotton production. GENOMICS APPROACHES IN COTTON In the frontier areas of genomics research, cotton genomics has gained momentum accomplished by robust high-throughput sequencing platforms combined with novel computational tools to make the cotton genome more tractable. Advances in long-read sequencing have allowed for the generation of the complete set of cotton gene transcripts giving incisive scientific knowledge in cotton improvement. In contrast, the integration of the latest sequencing platforms has been used to generate multiple high-quality reference genomes in diploid and tetraploid cotton. While pan-genome and 3D genomic studies are still in the early stages in cotton, it is anticipated that rapid advances in sequencing, assembly algorithms, and analysis pipelines will have a greater impact on advanced cotton research. CONCLUSIONS This review article briefly compiles substantial contributions in different areas of the cotton genome, which include genome sequencing, genes, and their molecular regulatory networks in fiber development and stress tolerance mechanism. This will greatly help us in understanding the robust genomic organization which in turn will help unearth candidate genes for functionally important agronomic traits.
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Affiliation(s)
- Alagarsamy Manivannan
- ICAR-Central Institute for Cotton Research, Regional Station, Coimbatore, 641 003, Tamil Nadu, India.
| | - Thomas Cheeran Amal
- ICAR-Central Institute for Cotton Research, Regional Station, Coimbatore, 641 003, Tamil Nadu, India
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Miller MJ, Song Q, Fallen B, Li Z. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean ( Glycine max). FRONTIERS IN PLANT SCIENCE 2023; 14:1171135. [PMID: 37235007 PMCID: PMC10206060 DOI: 10.3389/fpls.2023.1171135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 05/28/2023]
Abstract
Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean's profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross's offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.
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Affiliation(s)
- Mark J. Miller
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture - Agricultural Research Service, Beltsville, MD, United States
| | - Benjamin Fallen
- Soybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service, Raleigh, NC, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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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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Liu YH, Zhang M, Sze SH, Smith CW, Zhang HB. Analysis of the genes controlling cotton fiber length reveals the molecular basis of plant breeding and the genetic potential of current cultivars for continued improvement. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 321:111318. [PMID: 35696918 DOI: 10.1016/j.plantsci.2022.111318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 05/02/2022] [Accepted: 05/08/2022] [Indexed: 06/15/2023]
Abstract
Stagnated crop improvement has raised questions of whether and how current crop cultivars can be further improved. Genes are the core determinants of performance of all cultivars. Here, we report the molecular basis of plant breeding and address these questions by analyzing 226 GFL genes controlling and accurately predicting fiber length, an important breeding objective trait, in cotton (Gossypium sp.). We first identified the favorable allele and the number of favorable alleles (NFAs) of each GFL gene, calculated the total NFAs of the 226 GFL genes accumulated in 198 advanced breeding lines, and analyzed them against fiber lengths. Fiber lengths of the breeding lines were strongly correlated with the total NFAs of the GFL genes (r = 0.85, P < 0.0001), suggesting that accumulation of the favorable alleles of the genes controlling objective traits is the molecular basis of cotton breeding. Surprisingly, a breeding line with a fiber length of present cultivars having the longest fibers contained only about 51% of the total NFAs of the 226 GFL genes. The genetic potentials of current cultivars were then predicted using linear and non-linear models, respectively, revealing that a breeding line or cultivar with a fiber length of 33.8 mm could be further improved in fiber length by up to 118%. Finally, we showed that the genetic potential of such a breeding line can be realized through gene-based breeding. Therefore, these findings shed light on continued crop improvement in general and provide 740 genic biomarkers desirable for enhanced cotton fiber breeding.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering, and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - C Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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Li Z, Liu S, Conaty W, Zhu QH, Moncuquet P, Stiller W, Wilson I. Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods. Heredity (Edinb) 2022; 129:103-112. [PMID: 35523950 PMCID: PMC9338257 DOI: 10.1038/s41437-022-00537-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 01/26/2023] Open
Abstract
Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped. We aim to develop a new GP-based breeding system that can improve the efficiency of our cotton breeding program. This article presents a GP study on cotton fibre quality and yield traits using 1385 breeding lines from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) cotton breeding program which were genotyped using a high-density SNP chip that generated 12,296 informative SNPs. The aim of this study was twofold: (1) to identify the models and data sources (i.e. genomic and pedigree) that produce the highest prediction accuracies; and (2) to assess the effectiveness of GP as a selection tool in the CSIRO cotton breeding program. The prediction analyses were conducted under various scenarios using different Bayesian predictive models. Results highlighted that the model combining genomic and pedigree information resulted in the best cross validated prediction accuracies: 0.76 for fibre length, 0.65 for fibre strength, and 0.64 for lint yield. Overall, this work represents the largest scale genomic selection studies based on cotton breeding trial data. Prediction accuracies reported in our study indicate the potential of GP as a breeding tool for cotton. The study highlighted the importance of incorporating pedigree and environmental factors in GP models to optimise the prediction performance.
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Affiliation(s)
- Zitong Li
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT, 2601, Australia.
| | - Shiming Liu
- CSIRO Agriculture & Food, Locked Bag 59, Narrabri, NSW, 2390, Australia
| | - Warren Conaty
- CSIRO Agriculture & Food, Locked Bag 59, Narrabri, NSW, 2390, Australia
| | - Qian-Hao Zhu
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT, 2601, Australia
| | | | - Warwick Stiller
- CSIRO Agriculture & Food, Locked Bag 59, Narrabri, NSW, 2390, Australia
| | - Iain Wilson
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT, 2601, Australia
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Billings GT, Jones MA, Rustgi S, Bridges WC, Holland JB, Hulse-Kemp AM, Campbell BT. Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs. PLANTS 2022; 11:plants11111446. [PMID: 35684219 PMCID: PMC9182660 DOI: 10.3390/plants11111446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022]
Abstract
Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.
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Affiliation(s)
- Grant T. Billings
- Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA; (G.T.B.); (J.B.H.)
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Michael A. Jones
- Pee Dee Research and Education Center, Clemson University, Florence, SC 29506, USA; (M.A.J.); (S.R.)
| | - Sachin Rustgi
- Pee Dee Research and Education Center, Clemson University, Florence, SC 29506, USA; (M.A.J.); (S.R.)
| | - William C. Bridges
- Department of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;
| | - James B. Holland
- Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA; (G.T.B.); (J.B.H.)
- Plant Sciences Research Unit, The Agricultural Research Service of U.S. Department of Agriculture, Raleigh, NC 27695, USA
| | - Amanda M. Hulse-Kemp
- Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA; (G.T.B.); (J.B.H.)
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
- Genomics and Bioinformatics Research Unit, The Agricultural Research Service of U.S. Department of Agriculture, Raleigh, NC 27965, USA
- Correspondence: (A.M.H.-K.); (B.T.C.)
| | - B. Todd Campbell
- Coastal Plains Soil, Water, and Plant Research Center, The Agricultural Research Service of U.S. Department of Agriculture, Florence, SC 29501, USA
- Correspondence: (A.M.H.-K.); (B.T.C.)
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Conaty WC, Broughton KJ, Egan LM, Li X, Li Z, Liu S, Llewellyn DJ, MacMillan CP, Moncuquet P, Rolland V, Ross B, Sargent D, Zhu QH, Pettolino FA, Stiller WN. Cotton Breeding in Australia: Meeting the Challenges of the 21st Century. FRONTIERS IN PLANT SCIENCE 2022; 13:904131. [PMID: 35646011 PMCID: PMC9136452 DOI: 10.3389/fpls.2022.904131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.
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Affiliation(s)
| | | | - Lucy M. Egan
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | - Xiaoqing Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Zitong Li
- CSIRO Agriculture and Food, Canberra, ACT, Australia
| | - Shiming Liu
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
| | | | | | | | | | - Brett Ross
- Cotton Seed Distributors Ltd., Wee Waa, NSW, Australia
| | - Demi Sargent
- CSIRO Agriculture and Food, Narrabri, NSW, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Canberra, ACT, Australia
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11
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Shahi D, Guo J, Pradhan S, Khan J, Avci M, Khan N, McBreen J, Bai G, Reynolds M, Foulkes J, Babar MA. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat. BMC Genomics 2022; 23:298. [PMID: 35413795 PMCID: PMC9004054 DOI: 10.1186/s12864-022-08487-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. RESULTS In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. CONCLUSIONS The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.
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Affiliation(s)
- Dipendra Shahi
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | - Jia Guo
- Department of Forest Ecosystem and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, USA
| | - Sumit Pradhan
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | - Jahangir Khan
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | - Muhsin Avci
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | - Naeem Khan
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | - Jordan McBreen
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA
| | | | - Matthew Reynolds
- CIMMYT International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico, El Batan, Texcoco, Mexico
| | - John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Md Ali Babar
- Department of Agronomy, 3105 McCarty Hall B, Gainesville, FL, 32611, USA.
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12
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Song C, Acuña T, Adler-Agmon M, Rachmilevitch S, Barak S, Fait A. Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance. HORTICULTURE RESEARCH 2022; 9:uhac061. [PMID: 35531316 PMCID: PMC9071376 DOI: 10.1093/hr/uhac061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.
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Affiliation(s)
- Chao Song
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Tania Acuña
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | | | - Shimon Rachmilevitch
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Simon Barak
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Aaron Fait
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
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13
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Liu YH, Zhang M, Scheuring CF, Cilkiz M, Sze SH, Smith CW, Murray SC, Xu W, Zhang HB. Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 316:111153. [PMID: 35151437 DOI: 10.1016/j.plantsci.2021.111153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
Accurate, simple, rapid, and inexpensive prediction of complex traits controlled by numerous genes is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report a novel method that enables accurate, simple, and rapid prediction of complex traits of individuals or offspring from parents based on the number of favorable alleles (NFAs) of the genes controlling the objective traits. The NFAs of 226 cotton fiber length (GFL) genes and nine maize hybrid grain yield related (ZmF1GY) genes were directly used to predict cotton fiber lengths of individual plants and maize grain yields of F1 hybrids from parents, respectively, using prediction model-based methods as controls. The NFAs of the 226 GFL genes predicted cotton fiber lengths at an accuracy of 0.85, as the model methods and outperforming genomic prediction by 82 % - 170 %. The NFAs of the nine ZmF1GY genes predicted grain yields of maize hybrids from parents at an accuracy of 0.80, outperforming genomic prediction by 67 %. Moreover, the prediction accuracies of these traits were consistent across years, environments, and eco-agricultural systems. Importantly, the accurate prediction of these traits directly using the NFAs of the genes allows breeding to be performed in greenhouse, phytotron, or off-season, without the need of the model training and validation steps essential and costly for model-based genomic or genic prediction. Therefore, this new method dramatically outperforms the current model-based genomic methods used for phenotype prediction and streamlines the process of breeding, thus promising to substantially enhance current plant and animal breeding.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Mustafa Cilkiz
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | - C Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Wenwei Xu
- Texas A&M AgriLife Research, Lubbock, TX 79403, USA
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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14
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Sabadin F, Carvalho HF, Galli G, Fritsche-Neto R. Population-tailored mock genome enables genomic studies in species without a reference genome. Mol Genet Genomics 2021; 297:33-46. [PMID: 34755217 DOI: 10.1007/s00438-021-01831-9] [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: 02/15/2021] [Accepted: 10/28/2021] [Indexed: 11/26/2022]
Abstract
Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.
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Affiliation(s)
- Felipe Sabadin
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | - Humberto Fanelli Carvalho
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | - Giovanni Galli
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
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15
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Fonseca JMO, Klein PE, Crossa J, Pacheco A, Perez-Rodriguez P, Ramasamy P, Klein R, Rooney WL. Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance. THE PLANT GENOME 2021; 14:e20127. [PMID: 34370387 DOI: 10.1002/tpg2.20127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/08/2021] [Indexed: 05/02/2023]
Abstract
Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA-SCA-based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.
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Affiliation(s)
- Jales M O Fonseca
- Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843, USA
| | - Patricia E Klein
- Dep. of Horticultural Sciences, Texas A&M Univ., College Station, TX, 77843, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Él Batán, Mexico
| | - Angela Pacheco
- International Maize and Wheat Improvement Center (CIMMYT), Él Batán, Mexico
| | | | - Perumal Ramasamy
- Agriculture Research Center, Kansas State Univ., Hays, KS, 67601, USA
| | - Robert Klein
- Southern Plains Agricultural Research Center, USDA-ARS, College Station, TX, 77845, USA
| | - William L Rooney
- Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843, USA
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16
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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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17
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Ma J, Cao Y. Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:690059. [PMID: 34335658 PMCID: PMC8319912 DOI: 10.3389/fpls.2021.690059] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/14/2021] [Indexed: 05/21/2023]
Abstract
High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait-marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.
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18
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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19
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Liu YH, Xu Y, Zhang M, Cui Y, Sze SH, Smith CW, Xu S, Zhang HB. Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton. FRONTIERS IN PLANT SCIENCE 2020; 11:583277. [PMID: 33281846 PMCID: PMC7690289 DOI: 10.3389/fpls.2020.583277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/15/2020] [Indexed: 05/03/2023]
Abstract
Accurate phenotype prediction of quantitative traits is paramount to enhanced plant research and breeding. Here, we report the accurate prediction of cotton fiber length, a typical quantitative trait, using 474 cotton (Gossypium ssp.) fiber length (GFL) genes and nine prediction models. When the SNPs/InDels contained in 226 of the GFL genes or the expressions of all 474 GFL genes was used for fiber length prediction, a prediction accuracy of r = 0.83 was obtained, approaching the maximally possible prediction accuracy of a quantitative trait. This has improved by 116%, the prediction accuracies of the fiber length thus far achieved for genomic selection using genome-wide random DNA markers. Moreover, analysis of the GFL genes identified 125 of the GFL genes that are key to accurate prediction of fiber length, with which a prediction accuracy similar to that of all 474 GFL genes was obtained. The fiber lengths of the plants predicted with expressions of the 125 key GFL genes were significantly correlated with those predicted with the SNPs/InDels of the above 226 SNP/InDel-containing GFL genes (r = 0.892, P = 0.000). The prediction accuracies of fiber length using both genic datasets were highly consistent across environments or generations. Finally, we found that a training population consisting of 100-120 plants was sufficient to train a model for accurate prediction of a quantitative trait using the genes controlling the trait. Therefore, the genes controlling a quantitative trait are capable of accurately predicting its phenotype, thereby dramatically improving the ability, accuracy, and efficiency of phenotype prediction and promoting gene-based breeding in cotton and other species.
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Affiliation(s)
- Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Yang Xu
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Yanru Cui
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - C. Wayne Smith
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Shizhong Xu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Shizhong Xu,
| | - Hong-Bin Zhang
- Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
- Hong-Bin Zhang,
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