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Kapoor C, Anamika, Mukesh Sankar S, Singh SP, Singh N, Kumar S. Omics-driven utilization of wild relatives for empowering pre-breeding in pearl millet. PLANTA 2024; 259:155. [PMID: 38750378 DOI: 10.1007/s00425-024-04423-0] [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: 12/17/2023] [Accepted: 04/25/2024] [Indexed: 05/23/2024]
Abstract
MAIN CONCLUSION Pearl millet wild relatives harbour novel alleles which could be utilized to broaden genetic base of cultivated species. Genomics-informed pre-breeding is needed to speed up introgression from wild to cultivated gene pool in pearl millet. Rising episodes of intense biotic and abiotic stresses challenge pearl millet production globally. Wild relatives provide a wide spectrum of novel alleles which could address challenges posed by climate change. Pre-breeding holds potential to introgress novel diversity in genetically narrow cultivated Pennisetum glaucum from diverse gene pool. Practical utilization of gene pool diversity remained elusive due to genetic intricacies. Harnessing promising traits from wild pennisetum is limited by lack of information on underlying candidate genes/QTLs. Next-Generation Omics provide vast scope to speed up pre-breeding in pearl millet. Genomic resources generated out of draft genome sequence and improved genome assemblies can be employed to utilize gene bank accessions effectively. The article highlights genetic richness in pearl millet and its utilization with a focus on harnessing next-generation Omics to empower pre-breeding.
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Affiliation(s)
- Chandan Kapoor
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
| | - Anamika
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - S Mukesh Sankar
- ICAR-Indian Institute of Spices Research, Kozhikode, Kerala, 673012, India
| | - S P Singh
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Nirupma Singh
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Sudhir Kumar
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
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2
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Adu GB, Awuku FJ, Garcia-Oliveira AL, Amegbor IK, Nelimor C, Nboyine J, Karikari B, Atosona B, Manigben KA, Aboyadana PA. DArTseq-based SNP markers reveal high genetic diversity among early generation fall armyworm tolerant maize inbred lines. PLoS One 2024; 19:e0294863. [PMID: 38630672 PMCID: PMC11023204 DOI: 10.1371/journal.pone.0294863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/09/2023] [Indexed: 04/19/2024] Open
Abstract
Diversity analysis using molecular markers serves as a powerful tool in unravelling the intricacies of inclusivity within various populations and is an initial step in the assessment of populations and the development of inbred lines for host plant resistance in maize. This study was conducted to assess the genetic diversity and population structure of 242 newly developed S3 inbred lines using 3,305 single nucleotide polymorphism (SNP) markers and to also assess the level of homozygosity achieved in each of the inbred lines. A total of 1,184 SNP markers were found highly informative, with a mean polymorphic information content (PIC) of 0.23. Gene diversity was high among the inbred lines, ranging from 0.04 to 0.50, with an average of 0.27. The residual heterozygosity of the 242 S3 inbred lines averaged 8.8%, indicating moderately low heterozygosity levels among the inbred lines. Eighty-four percent of the 58,322 pairwise kinship coefficients among the inbred lines were near zero (0.00-0.05), with only 0.3% of them above 0.50. These results revealed that many of the inbred lines were distantly related, but none were redundant, suggesting each inbred line had a unique genetic makeup with great potential to provide novel alleles for maize improvement. The admixture-based structure analysis, principal coordinate analysis, and neighbour-joining clustering were concordant in dividing the 242 inbred lines into three subgroups based on the pedigree and selection history of the inbred lines. These findings could guide the effective use of the newly developed inbred lines and their evaluation in quantitative genetics and molecular studies to identify candidate lines for breeding locally adapted fall armyworm tolerant varieties in Ghana and other countries in West and Central Africa.
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Affiliation(s)
| | | | - Ana Luisa Garcia-Oliveira
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- Department of Molecular Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Isaac Kodzo Amegbor
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
- Faculty of Natural and Agricultural Sciences, Department of Plant Breeding, University of the Free State, Bloemfontein, South Africa
| | - Charles Nelimor
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Jerry Nboyine
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
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Schoemaker DL, Qiu Y, de Leon N, Hirsch CN, Kaeppler SM. Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize. G3 (BETHESDA, MD.) 2023; 14:jkad256. [PMID: 37950891 PMCID: PMC10755172 DOI: 10.1093/g3journal/jkad256] [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: 06/09/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/13/2023]
Abstract
The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.
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Affiliation(s)
- Dylan L Schoemaker
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Yinjie Qiu
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Shawn M Kaeppler
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin—Madison, Middleton, WI 53562, USA
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4
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Flint-Garcia S, Feldmann MJ, Dempewolf H, Morrell PL, Ross-Ibarra J. Diamonds in the not-so-rough: Wild relative diversity hidden in crop genomes. PLoS Biol 2023; 21:e3002235. [PMID: 37440605 PMCID: PMC10368281 DOI: 10.1371/journal.pbio.3002235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/25/2023] [Indexed: 07/15/2023] Open
Abstract
Crop production is becoming an increasing challenge as the global population grows and the climate changes. Modern cultivated crop species are selected for productivity under optimal growth environments and have often lost genetic variants that could allow them to adapt to diverse, and now rapidly changing, environments. These genetic variants are often present in their closest wild relatives, but so are less desirable traits. How to preserve and effectively utilize the rich genetic resources that crop wild relatives offer while avoiding detrimental variants and maladaptive genetic contributions is a central challenge for ongoing crop improvement. This Essay explores this challenge and potential paths that could lead to a solution.
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Affiliation(s)
- Sherry Flint-Garcia
- Plant Genetics Research Unit, United States Department of Agriculture, Agricultural Research Service, Columbia, Missouri, United States of America
| | - Mitchell J. Feldmann
- Department of Plant Sciences, University of California, Davis, California, United States of America
| | | | - Peter L. Morrell
- Department of Agronomy and Plant Genetics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, Center for Population Biology, and Genome Center, University of California, Davis, California, United States of America
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Shahwar D, Ahn N, Kim D, Ahn W, Park Y. Mutagenesis-based plant breeding approaches and genome engineering: A review focused on tomato. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108473. [PMID: 37716439 DOI: 10.1016/j.mrrev.2023.108473] [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: 03/09/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023]
Abstract
Breeding is the most important and efficient method for crop improvement involving repeated modification of the genetic makeup of a plant population over many generations. In this review, various accessible breeding approaches, such as conventional breeding and mutation breeding (physical and chemical mutagenesis and insertional mutagenesis), are discussed with respect to the actual impact of research on the economic improvement of tomato agriculture. Tomatoes are among the most economically important fruit crops consumed worldwide because of their high nutritional content and health-related benefits. Additionally, we summarize mutation-based mapping approaches, including Mutmap and MutChromeSeq, for the efficient mapping of several genes identified by random indel mutations that are beneficial for crop improvement. Difficulties and challenges in the adaptation of new genome editing techniques that provide opportunities to demonstrate precise mutations are also addressed. Lastly, this review focuses on various effective and convenient genome editing tools, such as RNA interference (RNAi), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR/Cas9), and their potential for the improvement of numerous desirable traits to allow the development of better varieties of tomato and other horticultural crops.
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Affiliation(s)
- Durre Shahwar
- Department of Horticultural Bioscience, Pusan National University, Miryang 50463, Republic of Korea
| | - Namju Ahn
- Daenong Seed Company, Hwasun-gun 58155, Republic of Korea
| | - Donghyun Kim
- Daenong Seed Company, Hwasun-gun 58155, Republic of Korea
| | - Wooseong Ahn
- Daenong Seed Company, Hwasun-gun 58155, Republic of Korea
| | - Younghoon Park
- Department of Horticultural Bioscience, Pusan National University, Miryang 50463, Republic of Korea.
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6
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Oliveira TP, Obšteter J, Pocrnic I, Heslot N, Gorjanc G. A method for partitioning trends in genetic mean and variance to understand breeding practices. Genet Sel Evol 2023; 55:36. [PMID: 37268883 DOI: 10.1186/s12711-023-00804-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 04/17/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual paths due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. METHODS First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. RESULTS We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension. CONCLUSIONS We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different selection paths interact within a breeding programme and how they can be optimised.
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Affiliation(s)
- Thiago P Oliveira
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.
| | - Jana Obšteter
- Agricultural Institute of Slovenia, Ljubljana, Slovenia
| | - Ivan Pocrnic
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | | | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
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7
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Reyes VP, Kitony JK, Nishiuchi S, Makihara D, Doi K. Utilization of Genotyping-by-Sequencing (GBS) for Rice Pre-Breeding and Improvement: A Review. Life (Basel) 2022; 12:1752. [PMID: 36362909 PMCID: PMC9694628 DOI: 10.3390/life12111752] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 09/29/2023] Open
Abstract
Molecular markers play a crucial role in the improvement of rice. To benefit from these markers, genotyping is carried out to identify the differences at a specific position in the genome of individuals. The advances in sequencing technologies have led to the development of different genotyping techniques such as genotyping-by-sequencing. Unlike PCR-fragment-based genotyping, genotyping-by-sequencing has enabled the parallel sequencing and genotyping of hundreds of samples in a single run, making it more cost-effective. Currently, GBS is being used in several pre-breeding programs of rice to identify beneficial genes and QTL from different rice genetic resources. In this review, we present the current advances in the utilization of genotyping-by-sequencing for the development of rice pre-breeding materials and the improvement of existing rice cultivars. The challenges and perspectives of using this approach are also highlighted.
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Affiliation(s)
- Vincent Pamugas Reyes
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | | | - Shunsaku Nishiuchi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
| | - Daigo Makihara
- International Center for Research and Education in Agriculture, Nagoya University, Nagoya 464-8601, Japan
| | - Kazuyuki Doi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
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8
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Yang CJ, Ladejobi O, Mott R, Powell W, Mackay I. Analysis of historical selection in winter wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3005-3023. [PMID: 35864201 PMCID: PMC9482581 DOI: 10.1007/s00122-022-04163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.
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Affiliation(s)
- Chin Jian Yang
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Olufunmilayo Ladejobi
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Richard Mott
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Wayne Powell
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Ian Mackay
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK.
- IMplant Consultancy Ltd, Chelmsford, UK.
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9
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Li Y, Shi F, Lin Z, Robinson H, Moody D, Rattey A, Godoy J, Mullan D, Keeble-Gagnere G, Hayden MJ, Tibbits JFG, Daetwyler HD. Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes. FRONTIERS IN PLANT SCIENCE 2022; 13:786452. [PMID: 35783964 PMCID: PMC9240786 DOI: 10.3389/fpls.2022.786452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.
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Affiliation(s)
- Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Fan Shi
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | | | | | | | | | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | | | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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10
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Shao J, Hao Y, Wang L, Xie Y, Zhang H, Bai J, Wu J, Fu J. Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure. PLANTS (BASEL, SWITZERLAND) 2022; 11:1298. [PMID: 35631723 PMCID: PMC9144439 DOI: 10.3390/plants11101298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.
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Affiliation(s)
- Jing Shao
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
- Gansu Provincial Key Laboratory of Aridland Crop Science, Lanzhou 730070, China
| | - Yangfan Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Lanfen Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Yuxin Xie
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Hongwei Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Jiangping Bai
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Gansu Provincial Key Laboratory of Aridland Crop Science, Lanzhou 730070, China
| | - Jing Wu
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Junjie Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
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11
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Theoretical and experimental assessment of genome-based prediction in landraces of allogamous crops. Proc Natl Acad Sci U S A 2022; 119:e2121797119. [PMID: 35486687 PMCID: PMC9170147 DOI: 10.1073/pnas.2121797119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SignificanceGenetic variation inherent in landraces is essential for broadening the genetic diversity of our crops. This study pioneers the development of a theoretical framework to link molecular inventories of plant genetic resources to phenotypic variation, allowing an informed choice of landraces and their crossing partners. We show that genome-based prediction of genetic values can be implemented successfully in landrace-derived material, despite a strongly reduced level of relatedness compared with elite germplasm. Theoretical derivations are validated with unique experimental data collected on two different landraces. Our results are a pivotal contribution toward the optimization of genome-enabled prebreeding schemes.
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12
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Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, Heumez E, Bouchet S, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:895-914. [PMID: 34988629 DOI: 10.1007/s00122-021-04005-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 05/15/2023]
Abstract
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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Affiliation(s)
- Pauline Robert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - François-Xavier Oury
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, EstréesMons, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Jacques Le Gouis
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France.
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13
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Bari MAA, Zheng P, Viera I, Worral H, Szwiec S, Ma Y, Main D, Coyne CJ, McGee RJ, Bandillo N. Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Front Genet 2022; 12:707754. [PMID: 35003202 PMCID: PMC8740293 DOI: 10.3389/fgene.2021.707754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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Affiliation(s)
- Md Abdullah Al Bari
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ping Zheng
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Indalecio Viera
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Hannah Worral
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Stephen Szwiec
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, United States
| | - Rebecca J McGee
- USDA-ARS Grain Legume Genetics and Physiology Research, Pullman, WA, United States
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
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14
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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15
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Lara LADC, Pocrnic I, Oliveira TDP, Gaynor RC, Gorjanc G. Temporal and genomic analysis of additive genetic variance in breeding programmes. Heredity (Edinb) 2022; 128:21-32. [PMID: 34912044 PMCID: PMC8733024 DOI: 10.1038/s41437-021-00485-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/24/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.
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Affiliation(s)
- Letícia A de C Lara
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK.
| | - Ivan Pocrnic
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK
| | - Thiago de P Oliveira
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK
| | - R Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK
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16
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Rio S, Gallego-Sánchez L, Montilla-Bascón G, Canales FJ, Isidro Y Sánchez J, Prats E. Genomic prediction and training set optimization in a structured Mediterranean oat population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3595-3609. [PMID: 34341832 DOI: 10.1007/s00122-021-03916-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/13/2021] [Indexed: 05/22/2023]
Abstract
The strong genetic structure observed in Mediterranean oats affects the predictive ability of genomic prediction as well as the performance of training set optimization methods. In this study, we investigated the efficiency of genomic prediction and training set optimization in a highly structured population of cultivars and landraces of cultivated oat (Avena sativa) from the Mediterranean basin, including white (subsp. sativa) and red (subsp. byzantina) oats, genotyped using genotype-by-sequencing markers and evaluated for agronomic traits in Southern Spain. For most traits, the predictive abilities were moderate to high with little differences between models, except for biomass for which Bayes-B showed a substantial gain compared to other models. The consistency between the structure of the training population and the population to be predicted was key to the predictive ability of genomic predictions. The predictive ability of inter-subspecies predictions was indeed much lower than that of intra-subspecies predictions for all traits. Regarding training set optimization, the linear mixed model optimization criteria (prediction error variance (PEVmean) and coefficient of determination (CDmean)) performed better than the heuristic approach "partitioning around medoids," even under high population structure. The superiority of CDmean and PEVmean could be explained by their ability to adapt the representation of each genetic group according to those represented in the population to be predicted. These results represent an important step towards the implementation of genomic prediction in oat breeding programs and address important issues faced by the genomic prediction community regarding population structure and training set optimization.
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Affiliation(s)
- Simon Rio
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain.
| | - Luis Gallego-Sánchez
- Institute for Sustainable Agriculture, Spanish Research Council (CSIC), Córdoba, Spain
| | | | - Francisco J Canales
- Institute for Sustainable Agriculture, Spanish Research Council (CSIC), Córdoba, Spain
| | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Elena Prats
- Institute for Sustainable Agriculture, Spanish Research Council (CSIC), Córdoba, Spain
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17
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Bohra A, Kilian B, Sivasankar S, Caccamo M, Mba C, McCouch SR, Varshney RK. Reap the crop wild relatives for breeding future crops. Trends Biotechnol 2021; 40:412-431. [PMID: 34629170 DOI: 10.1016/j.tibtech.2021.08.009] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 02/07/2023]
Abstract
Crop wild relatives (CWRs) have provided breeders with several 'game-changing' traits or genes that have boosted crop resilience and global agricultural production. Advances in breeding and genomics have accelerated the identification of valuable CWRs for use in crop improvement. The enhanced genetic diversity of breeding pools carrying optimum combinations of favorable alleles for targeted crop-growing regions is crucial to sustain genetic gain. In parallel, growing sequence information on wild genomes in combination with precise gene-editing tools provide a fast-track route to transform CWRs into ideal future crops. Data-informed germplasm collection and management strategies together with adequate policy support will be equally important to improve access to CWRs and their sustainable use to meet food and nutrition security targets.
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Affiliation(s)
- Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), 208024 Kanpur, India
| | | | - Shoba Sivasankar
- International Atomic Energy Agency (IAEA), Vienna International Centre, 1400 Vienna, Austria
| | | | - Chikelu Mba
- Food and Agriculture Organization of the United Nations (FAO), Rome 00153, Italy
| | - Susan R McCouch
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14850, USA.
| | - Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA 6150, Australia.
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18
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Engels JMM, Ebert AW. A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. II. Strengths and Weaknesses of the Current System and Recommendations for Its Improvement. PLANTS (BASEL, SWITZERLAND) 2021; 10:1904. [PMID: 34579439 PMCID: PMC8472064 DOI: 10.3390/plants10091904] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/05/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023]
Abstract
In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections.
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19
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Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. Genomic Prediction across Structured Hybrid Populations and Environments in Maize. PLANTS 2021; 10:plants10061174. [PMID: 34207722 PMCID: PMC8227059 DOI: 10.3390/plants10061174] [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: 03/24/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022]
Abstract
Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.
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Affiliation(s)
- Dongdong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Zhenxiang Xu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Riliang Gu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Pingxi Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jialiang Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Dengxiang Du
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jianhua Wang
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
| | - Guoying Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
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20
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Powell OM, Voss-Fels KP, Jordan DR, Hammer G, Cooper M. Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:663565. [PMID: 34149761 PMCID: PMC8211918 DOI: 10.3389/fpls.2021.663565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/13/2021] [Indexed: 05/26/2023]
Abstract
Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.
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Affiliation(s)
- Owen M. Powell
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
| | - David R. Jordan
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
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21
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Prasanna BM, Cairns JE, Zaidi PH, Beyene Y, Makumbi D, Gowda M, Magorokosho C, Zaman-Allah M, Olsen M, Das A, Worku M, Gethi J, Vivek BS, Nair SK, Rashid Z, Vinayan MT, Issa AB, San Vicente F, Dhliwayo T, Zhang X. Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1729-1752. [PMID: 33594449 PMCID: PMC7885763 DOI: 10.1007/s00122-021-03773-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 01/09/2021] [Indexed: 05/03/2023]
Abstract
Intensive public sector breeding efforts and public-private partnerships have led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars for the stress-prone environments in the tropics. Maize (Zea mays L.) plays a critical role in ensuring food and nutritional security, and livelihoods of millions of resource-constrained smallholders. However, maize yields in the tropical rainfed environments are now increasingly vulnerable to various climate-induced stresses, especially drought, heat, waterlogging, salinity, cold, diseases, and insect pests, which often come in combinations to severely impact maize crops. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private sector institutions, has been intensively engaged over the last four decades in breeding elite tropical maize germplasm with tolerance to key abiotic and biotic stresses, using an extensive managed stress screening network and on-farm testing system. This has led to the successful development and deployment of an array of elite stress-tolerant maize cultivars across sub-Saharan Africa, Asia, and Latin America. Further increasing genetic gains in the tropical maize breeding programs demands judicious integration of doubled haploidy, high-throughput and precise phenotyping, genomics-assisted breeding, breeding data management, and more effective decision support tools. Multi-institutional efforts, especially public-private alliances, are key to ensure that the improved maize varieties effectively reach the climate-vulnerable farming communities in the tropics, including accelerated replacement of old/obsolete varieties.
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Affiliation(s)
- Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya.
| | | | - P H Zaidi
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | | | - Mike Olsen
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Aparna Das
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | - Mosisa Worku
- International Maize and Wheat Improvement Center (CIMMYT), ICRAF Campus, UN Avenue, Gigiri, P.O.Box 1041-00621, Nairobi, Kenya
| | | | - B S Vivek
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Sudha K Nair
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - Zerka Rashid
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
| | - M T Vinayan
- CIMMYT, ICRISAT Campus, Patancheru, Greater Hyderabad, Telangana, India
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22
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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23
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Tehseen MM, Kehel Z, Sansaloni CP, Lopes MDS, Amri A, Kurtulus E, Nazari K. Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan. PLANTS 2021; 10:plants10030558. [PMID: 33809650 PMCID: PMC8001917 DOI: 10.3390/plants10030558] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/28/2021] [Accepted: 03/13/2021] [Indexed: 11/16/2022]
Abstract
Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.
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Affiliation(s)
| | - Zakaria Kehel
- International Center for Agricultural Research in the Dry Areas (ICARDA), ICARDA-PreBreeding & Genebank Operations, Biodiversity and Crop Improvement Program, P.O. Box 10000 Rabat, Morocco; (Z.K.); (A.A.)
| | - Carolina P. Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, Texcoco C.P. 56237, Mexico;
| | - Marta da Silva Lopes
- Sustainable Field Crops Programme, IRTA (Institute for Food and Agricultural Research and Technology), 25198 Lleida, Spain;
| | - Ahmed Amri
- International Center for Agricultural Research in the Dry Areas (ICARDA), ICARDA-PreBreeding & Genebank Operations, Biodiversity and Crop Improvement Program, P.O. Box 10000 Rabat, Morocco; (Z.K.); (A.A.)
| | - Ezgi Kurtulus
- International Center for Agricultural Research in the Dry Areas (ICARDA), Biodiversity and Crop Improvement Program, Regional Cereal Rust Research Center (RCRRC), P.O. Box 35661 Menemen, Izmir, Turkey;
| | - Kumarse Nazari
- International Center for Agricultural Research in the Dry Areas (ICARDA), Biodiversity and Crop Improvement Program, Regional Cereal Rust Research Center (RCRRC), P.O. Box 35661 Menemen, Izmir, Turkey;
- Correspondence:
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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25
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Chatham LA, Juvik JA. Linking anthocyanin diversity, hue, and genetics in purple corn. G3 (BETHESDA, MD.) 2021; 11:jkaa062. [PMID: 33585872 PMCID: PMC8022952 DOI: 10.1093/g3journal/jkaa062] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 11/30/2020] [Indexed: 01/13/2023]
Abstract
While maize with anthocyanin-rich pericarp (purple corn) is rising in popularity as a source of natural colorant for foods and beverages, information on color range and stability-factors associated with anthocyanin decorations and compositional profiles-is currently limited. Furthermore, to maximize the scalability and meet growing demands, both anthocyanin concentrations and agronomic performance must improve in purple corn varieties. Using the natural anthocyanin diversity present in a purple corn landrace, Apache Red, we generated a population with variable flavonoid profiles-flavanol-anthocyanin condensed forms (0-83%), acylated anthocyanins (2-72%), pelargonidin-derived anthocyanins (5-99%), C-glycosyl flavone co-pigments up to 1904 µg/g, and with anthocyanin content up to 1598 µg/g. Each aspect of the flavonoid profiles was found to play a role in either the resulting extract hue or intensity. With genotyping-by-sequencing of this population, we mapped aspects of the flavonoid profile. Major quantitative trait loci (QTLs) for anthocyanin type were found near loci previously identified only in aleurone-pigmented maize varieties [Purple aleurone1 (Pr1) and Anthocyanin acyltransferase1 (Aat1)]. A QTL near P1 (Pericarp color1) was found for both flavone content and flavanol-anthocyanin condensed forms. A significant QTL associated with peonidin-derived anthocyanins near a candidate S-adenosylmethionine-dependent methyltransferase was also identified, warranting further investigation. Mapping total anthocyanin content produced signals near Aat1, the aleurone-associated bHLH R1 (Colored1), the plant color-associated MYB, Pl1 (Purple plant1), the aleurone-associated recessive intensifier, In1 (Intensifier1), and several previously unidentified candidates. This population represents one of the most anthocyanin diverse pericarp-pigmented maize varieties characterized to date. Moreover, the candidates identified here will serve as branching points for future research studying the genetic and molecular processes determining anthocyanin profile in pericarp.
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Affiliation(s)
- Laura A Chatham
- Department of Crop Sciences, University of Illinois at Urbana Champaign, Champaign, IL 61801, USA
| | - John A Juvik
- Department of Crop Sciences, University of Illinois at Urbana Champaign, Champaign, IL 61801, USA
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Beche E, Gillman JD, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo AM. Genomic prediction using training population design in interspecific soybean populations. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:15. [PMID: 37309481 PMCID: PMC10236090 DOI: 10.1007/s11032-021-01203-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/11/2021] [Indexed: 06/14/2023]
Abstract
Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01203-6.
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Affiliation(s)
- Eduardo Beche
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | | | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD USA
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois, and USDA-Agricultural Research Service (retired), 1101 W. Peabody Dr., Urbana, IL 61801 USA
| | - Tim Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, Georg-August-Universität, Göttingen, Germany
| | - Jared Decker
- Division of Animal Science, University of Missouri, Columbia, MO USA
| | - Grover Shannon
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | - Andrew M. Scaboo
- Division of Plant Science, University of Missouri, Columbia, MO USA
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Pandey MK, Chaudhari S, Jarquin D, Janila P, Crossa J, Patil SC, Sundravadana S, Khare D, Bhat RS, Radhakrishnan T, Hickey JM, Varshney RK. Genome-based trait prediction in multi- environment breeding trials in groundnut. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:3101-3117. [PMID: 32809035 PMCID: PMC7547976 DOI: 10.1007/s00122-020-03658-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/03/2020] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K 'Axiom_Arachis' SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400-0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.
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Affiliation(s)
- Manish K Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
| | - Sunil Chaudhari
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Diego Jarquin
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Pasupuleti Janila
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Sudam C Patil
- Mahatma Phule Krishi Vidyapeeth (MPKV), Jalgaon, India
| | | | - Dhirendra Khare
- Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur, India
| | - Ramesh S Bhat
- University of Agricultural Sciences (UAS)-Dharwad, Dharwad, India
| | | | - John M Hickey
- The Roslin Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
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Discovery of beneficial haplotypes for complex traits in maize landraces. Nat Commun 2020; 11:4954. [PMID: 33009396 PMCID: PMC7532167 DOI: 10.1038/s41467-020-18683-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 09/06/2020] [Indexed: 12/17/2022] Open
Abstract
Genetic variation is of crucial importance for crop improvement. Landraces are valuable sources of diversity, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we map haplotype-trait associations at high resolution in ~1000 doubled-haploid lines derived from three maize landraces to make their native diversity for early development traits accessible for elite germplasm improvement. A comparative genomic analysis of the discovered haplotypes in the landrace-derived lines and a panel of 65 breeding lines, both genotyped with 600k SNPs, points to untapped beneficial variation for target traits in the landraces. The superior phenotypic performance of lines carrying favorable landrace haplotypes as compared to breeding lines with alternative haplotypes confirms these findings. Stability of haplotype effects across populations and environments as well as their limited effects on undesired traits indicate that our strategy has high potential for harnessing beneficial haplotype variation for quantitative traits from genetic resources. Genetic variations present in landraces are critical for crop genetic improvement. Here, the authors map haplotype-trait associations in ~1000 doubled haploid lines derived from three European maize landraces and identify beneficial haplotypes for quantitative traits that are not present in breeding lines.
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Selective Loss of Diversity in Doubled-Haploid Lines from European Maize Landraces. G3-GENES GENOMES GENETICS 2020; 10:2497-2506. [PMID: 32467127 PMCID: PMC7341142 DOI: 10.1534/g3.120.401196] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Maize landraces are well adapted to their local environments and present valuable sources of genetic diversity for breeding and conservation. But the maintenance of open-pollinated landraces in ex-situ programs is challenging, as regeneration of seed can often lead to inbreeding depression and the loss of diversity due to genetic drift. Recent reports suggest that the production of doubled-haploid (DH) lines from landraces may serve as a convenient means to preserve genetic diversity in a homozygous form that is immediately useful for modern breeding. The production of doubled-haploid (DH) lines presents an extreme case of inbreeding which results in instantaneous homozygosity genome-wide. Here, we analyzed the effect of DH production on genetic diversity, using genome-wide SNP data from hundreds of individuals of five European landraces and their related DH lines. In contrast to previous findings, we observe a dramatic loss of diversity at both the haplotype level and that of individual SNPs. We identify thousands of SNPs that exhibit allele frequency differences larger than expected under models of neutral genetic drift and document losses of shared haplotypes. We find evidence consistent with selection at functional sites that are potentially involved in the diversity differences between landrace and DH populations. Although we were unable to uncover more details about the mode of selection, we conclude that landrace DH lines may be a valuable tool for the introduction of variation into maize breeding programs but come at the cost of decreased genetic diversity.
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Origin Specific Genomic Selection: A Simple Process To Optimize the Favorable Contribution of Parents to Progeny. G3-GENES GENOMES GENETICS 2020; 10:2445-2455. [PMID: 32430306 PMCID: PMC7341124 DOI: 10.1534/g3.120.401132] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Modern crop breeding is in constant demand for new genetic diversity as part of the arms race with genetic gain. The elite gene pool has limited genetic variation and breeders are trying to introduce novelty from unadapted germplasm, landraces and wild relatives. For polygenic traits, currently available approaches to introgression are not ideal, as there is a demonstrable bias against exotic alleles during selection. Here, we propose a partitioned form of genomic selection, called Origin Specific Genomic Selection (OSGS), where we identify and target selection on favorable exotic alleles. Briefly, within a population derived from a bi-parental cross, we isolate alleles originating from the elite and exotic parents, which then allows us to separate out the predicted marker effects based on the allele origins. We validated the usefulness of OSGS using two nested association mapping (NAM) datasets: barley NAM (elite-exotic) and maize NAM (elite-elite), as well as by computer simulation. Our results suggest that OSGS works well in its goal to increase the contribution of favorable exotic alleles in bi-parental crosses, and it is possible to extend the approach to broader multi-parental populations.
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31
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Salgotra RK, Stewart CN. Functional Markers for Precision Plant Breeding. Int J Mol Sci 2020; 21:E4792. [PMID: 32640763 PMCID: PMC7370099 DOI: 10.3390/ijms21134792] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/19/2020] [Accepted: 07/02/2020] [Indexed: 01/24/2023] Open
Abstract
Advances in molecular biology including genomics, high-throughput sequencing, and genome editing enable increasingly faster and more precise cultivar development. Identifying genes and functional markers (FMs) that are highly associated with plant phenotypic variation is a grand challenge. Functional genomics approaches such as transcriptomics, targeting induced local lesions in genomes (TILLING), homologous recombinant (HR), association mapping, and allele mining are all strategies to identify FMs for breeding goals, such as agronomic traits and biotic and abiotic stress resistance. The advantage of FMs over other markers used in plant breeding is the close genomic association of an FM with a phenotype. Thereby, FMs may facilitate the direct selection of genes associated with phenotypic traits, which serves to increase selection efficiencies to develop varieties. Herein, we review the latest methods in FM development and how FMs are being used in precision breeding for agronomic and quality traits as well as in breeding for biotic and abiotic stress resistance using marker assisted selection (MAS) methods. In summary, this article describes the use of FMs in breeding for development of elite crop cultivars to enhance global food security goals.
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Affiliation(s)
- Romesh K. Salgotra
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu, Chatha, Jammu 190008, India
| | - C. Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA
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32
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Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
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33
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Allier A, Teyssèdre S, Lehermeier C, Moreau L, Charcosset A. Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genomics 2020; 21:349. [PMID: 32393177 PMCID: PMC7216646 DOI: 10.1186/s12864-020-6756-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/23/2020] [Indexed: 11/10/2022] Open
Abstract
Background The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. Results We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses. Conclusion Results of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.
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Affiliation(s)
- Antoine Allier
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France. .,RAGT2n, Statistical Genetics Unit, 12510, Druelle, France.
| | | | | | - Laurence Moreau
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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Kumar S, Hilario E, Deng CH, Molloy C. Turbocharging introgression breeding of perennial fruit crops: a case study on apple. HORTICULTURE RESEARCH 2020; 7:47. [PMID: 32257233 PMCID: PMC7109137 DOI: 10.1038/s41438-020-0270-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 02/13/2020] [Accepted: 02/18/2020] [Indexed: 05/08/2023]
Abstract
The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.
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Affiliation(s)
- Satish Kumar
- The New Zealand Institute for Plant and Food Research Limited, Hawkes Bay Research Centre, Havelock North, New Zealand
| | - Elena Hilario
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
| | - Cecilia H. Deng
- The New Zealand Institute for Plant and Food Research Limited, Mount Albert Research Centre, Auckland, New Zealand
| | - Claire Molloy
- The New Zealand Institute for Plant and Food Research Limited, Hawkes Bay Research Centre, Havelock North, New Zealand
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35
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Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C, Prasanna BM, Olsen MS, Wang G, Zhang A. Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants. PLANT COMMUNICATIONS 2020; 1:100005. [PMID: 33404534 PMCID: PMC7747995 DOI: 10.1016/j.xplc.2019.100005] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- CIMMYT-China Tropical Maize Research Center, Foshan University, Foshan 528231, China
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Xiaogang Liu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junjie Fu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jiankang Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Changling Huang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Boddupalli M. Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Michael S. Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Guoying Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Aimin Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
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Hölker AC, Mayer M, Presterl T, Bolduan T, Bauer E, Ordas B, Brauner PC, Ouzunova M, Melchinger AE, Schön CC. European maize landraces made accessible for plant breeding and genome-based studies. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3333-3345. [PMID: 31559526 PMCID: PMC6820615 DOI: 10.1007/s00122-019-03428-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 09/17/2019] [Indexed: 05/05/2023]
Abstract
KEY MESSAGE Doubled-haploid libraries from landraces capture native genetic diversity for a multitude of quantitative traits and make it accessible for breeding and genome-based studies. Maize landraces comprise large allelic diversity. We created doubled-haploid (DH) libraries from three European flint maize landraces and characterized them with respect to their molecular diversity, population structure, trait means, variances, and trait correlations. In total, 899 DH lines were evaluated using high-quality genotypic and multi-environment phenotypic data from up to 11 environments. The DH lines covered 95% of the molecular variation present in 35 landraces of an earlier study and represent the original three landrace populations in an unbiased manner. A comprehensive analysis of the target trait plant development at early growth stages as well as other important agronomic traits revealed large genetic variation for line per se and testcross performance. The majority of the 378 DH lines evaluated as testcrosses outperformed the commercial hybrids for early development. For total biomass yield, we observed a yield gap of 15% between mean testcross yield of the commercial hybrids and mean testcross yield of the DH lines. The DH lines also exhibited genetic variation for undesirable traits like root lodging and tillering, but correlations with target traits early development and yield were low or nonsignificant. The presented diversity atlas is a valuable, publicly available resource for genome-based studies to identify novel trait variation and evaluate the prospects of genomic prediction in landrace-derived material.
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Affiliation(s)
- Armin C Hölker
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | | | | | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Bernardo Ordas
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), 36080, Pontevedra, Spain
| | - Pedro C Brauner
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
- Maize Breeding, KWS SAAT SE, 37574, Einbeck, Germany
| | | | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
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Gedil M, Menkir A. An Integrated Molecular and Conventional Breeding Scheme for Enhancing Genetic Gain in Maize in Africa. FRONTIERS IN PLANT SCIENCE 2019; 10:1430. [PMID: 31781144 PMCID: PMC6851238 DOI: 10.3389/fpls.2019.01430] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 10/15/2019] [Indexed: 05/22/2023]
Abstract
Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers' fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers' livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners.
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Affiliation(s)
- Melaku Gedil
- Bioscience Center and Maize Improvement Program, International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Abebe Menkir
- Maize Improvement Program, International Institute of Tropical Agriculture, Ibadan, Nigeria
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Brauner PC, Schipprack W, Utz HF, Bauer E, Mayer M, Schön CC, Melchinger AE. Testcross performance of doubled haploid lines from European flint maize landraces is promising for broadening the genetic base of elite germplasm. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:1897-1908. [PMID: 30877313 DOI: 10.1007/s00122-019-03325-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 03/11/2019] [Indexed: 05/19/2023]
Abstract
Selected doubled haploid lines averaged similar testcross performance as their original landraces, and the best of them approached the yields of elite inbreds, demonstrating their potential to broaden the narrow genetic diversity of the flint germplasm pool. Maize landraces represent a rich source of genetic diversity that remains largely idle because the high genetic load and performance gap to elite germplasm hamper their use in modern breeding programs. Production of doubled haploid (DH) lines can mitigate problems associated with the use of landraces in pre-breeding. Our objective was to assess in comparison with modern materials the testcross performance (TP) of the best 89 out of 389 DH lines developed from six landraces and evaluated in previous studies for line per se performance (LP). TP with a dent tester was evaluated for the six original landraces, ~ 15 DH lines from each landrace selected for LP, and six elite flint inbreds together with nine commercial hybrids for grain and silage traits. Mean TP of the DH lines rarely differed significantly from TP of their corresponding landrace, which averaged in comparison with the mean TP of the elite flint inbreds ~ 20% lower grain yield and ~ 10% lower dry matter and methane yield. Trait correlations of DH lines closely agreed with the literature; correlation of TP with LP was zero for grain yield, underpinning the need to evaluate TP in addition to LP. For all traits, we observed substantial variation for TP among the DH lines and the best showed similar TP yields as the elite inbreds. Our results demonstrate the high potential of landraces for broadening the narrow genetic base of the flint heterotic pool and the usefulness of the DH technology for exploiting idle genetic resources from gene banks.
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Affiliation(s)
- Pedro C Brauner
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - Wolfgang Schipprack
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - H Friedrich Utz
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany.
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Usefulness Criterion and Post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression. G3-GENES GENOMES GENETICS 2019; 9:1469-1479. [PMID: 30819823 PMCID: PMC6505154 DOI: 10.1534/g3.119.400129] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Predicting the usefulness of crosses in terms of expected genetic gain and genetic diversity is of interest to secure performance in the progeny and to maintain long-term genetic gain in plant breeding. A wide range of crossing schemes are possible including large biparental crosses, backcrosses, four-way crosses, and synthetic populations. In silico progeny simulations together with genome-based prediction of quantitative traits can be used to guide mating decisions. However, the large number of multi-parental combinations can hinder the use of simulations in practice. Analytical solutions have been proposed recently to predict the distribution of a quantitative trait in the progeny of biparental crosses using information of recombination frequency and linkage disequilibrium between loci. Here, we extend this approach to obtain the progeny distribution of more complex crosses including two to four parents. Considering agronomic traits and parental genome contribution as jointly multivariate normally distributed traits, the usefulness criterion parental contribution (UCPC) enables to (i) evaluate the expected genetic gain for agronomic traits, and at the same time (ii) evaluate parental genome contributions to the selected fraction of progeny. We validate and illustrate UCPC in the context of multiple allele introgression from a donor into one or several elite recipients in maize (Zea mays L.). Recommendations regarding the interest of two-way, three-way, and backcrosses were derived depending on the donor performance. We believe that the computationally efficient UCPC approach can be useful for mate selection and allocation in many plant and animal breeding contexts.
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40
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Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:669-686. [PMID: 30569365 DOI: 10.1007/s00122-018-3270-8] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 12/12/2018] [Indexed: 05/05/2023]
Abstract
Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.
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Affiliation(s)
- Kai Peter Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Ben John Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
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41
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Rincent R, Charpentier JP, Faivre-Rampant P, Paux E, Le Gouis J, Bastien C, Segura V. Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar. G3 (BETHESDA, MD.) 2018; 8:3961-3972. [PMID: 30373914 PMCID: PMC6288839 DOI: 10.1534/g3.118.200760] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 10/20/2018] [Indexed: 11/29/2022]
Abstract
Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.
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Affiliation(s)
| | - Jean-Paul Charpentier
- BioForA, INRA, ONF, 45075 Orléans, France
- GenoBois analytical platform, INRA, 45075 Orléans, France
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42
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Gonzalez MY, Weise S, Zhao Y, Philipp N, Arend D, Börner A, Oppermann M, Graner A, Reif JC, Schulthess AW. Unbalanced historical phenotypic data from seed regeneration of a barley ex situ collection. Sci Data 2018; 5:180278. [PMID: 30512010 PMCID: PMC6278694 DOI: 10.1038/sdata.2018.278] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 10/15/2018] [Indexed: 01/21/2023] Open
Abstract
The scarce knowledge on phenotypic characterization restricts the usage of genetic diversity of plant genetic resources in research and breeding. We describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions hosted at the Federal ex situ Genebank for Agricultural and Horticultural Plant Species. The dataset gathers records for three traits with agronomic relevance: flowering time, plant height and thousand grain weight. This information was collected for seven decades for winter and spring barley during the seed regeneration routine. The curated data represent a source for research on genetics and genomics of adaptive and yield related traits in cereals due to the importance of barley as model organism. This data could be used to predict the performance of non-phenotyped individuals in other collections through genomic prediction. Moreover, the dataset empowers the utilization of phenotypic diversity of genetic resources for crop improvement.
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Affiliation(s)
- Maria Y Gonzalez
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Stephan Weise
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Yusheng Zhao
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Norman Philipp
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Daniel Arend
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Andreas Börner
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Markus Oppermann
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Andreas Graner
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
| | - Albert W Schulthess
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany
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Brauner PC, Müller D, Schopp P, Böhm J, Bauer E, Schön CC, Melchinger AE. Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces. Genetics 2018; 210:1185-1196. [PMID: 30257934 PMCID: PMC6283160 DOI: 10.1534/genetics.118.301286] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Accepted: 09/24/2018] [Indexed: 12/24/2022] Open
Abstract
Thousands of maize landraces are stored in seed banks worldwide. Doubled-haploid libraries (DHL) produced from landraces harness their rich genetic diversity for future breeding. We investigated the prospects of genomic prediction (GP) for line per se performance in DHL from six European landraces and 53 elite flint (EF) lines by comparing four scenarios: GP within a single library (sL); GP between pairs of libraries (LwL); and GP among combined libraries, either including (cLi) or excluding (cLe) lines from the training set (TS) that belong to the same DHL as the prediction set. For scenario sL, with N = 50 lines in the TS, the prediction accuracy (ρ) among seven agronomic traits varied from -0.53 to 0.57 for the DHL and reached up to 0.74 for the EF lines. For LwL, ρ was close to zero for all DHL and traits. Whereas scenario cLi showed improved ρ values compared to sL, ρ for cLe remained at the low level observed for LwL. Forecasting ρ with deterministic equations yielded inflated values compared to empirical estimates of ρ for the DHL, but conserved the ranking. In conclusion, GP is promising within DHL, but large TS sizes (N > 100) are needed to achieve decent prediction accuracy because LD between QTL and markers is the primary source of information that can be exploited by GP. Since production of DHL from landraces is expensive, we recommend GP only for very large DHL produced from a few highly preselected landraces.
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Affiliation(s)
- Pedro C Brauner
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
| | - Dominik Müller
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
| | - Pascal Schopp
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
| | - Juliane Böhm
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
| | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
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Selection on Expected Maximum Haploid Breeding Values Can Increase Genetic Gain in Recurrent Genomic Selection. G3-GENES GENOMES GENETICS 2018; 8:1173-1181. [PMID: 29434032 PMCID: PMC5873908 DOI: 10.1534/g3.118.200091] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genomic selection (GS) offers the possibility to estimate the effects of genome-wide molecular markers, which can be used to calculate genomic estimated breeding values (GEBVs) for individuals without phenotypes. GEBVs can serve as a selection criterion in recurrent GS, maximizing single-cycle but not necessarily long-term genetic gain. As simple genome-wide sums, GEBVs do not take into account other genomic information, such as the map positions of loci and linkage phases of alleles. Therefore, we herein propose a novel selection criterion called expected maximum haploid breeding value (EMBV). EMBV predicts the expected performance of the best among a limited number of gametes that a candidate contributes to the next generation, if selected. We used simulations to examine the performance of EMBV in comparison with GEBV as well as the recently proposed criterion optimal haploid value (OHV) and weighted GS. We considered different population sizes, numbers of selected candidates, chromosome numbers and levels of dominant gene action. Criterion EMBV outperformed GEBV after about 5 selection cycles, achieved higher long-term genetic gain and maintained higher diversity in the population. The other selection criteria showed the potential to surpass both GEBV and EMBV in advanced cycles of the breeding program, but yielded substantially lower genetic gain in early to intermediate cycles, which makes them unattractive for practical breeding. Moreover, they were largely inferior in scenarios with dominant gene action. Overall, EMBV shows high potential to be a promising alternative selection criterion to GEBV for recurrent genomic selection.
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45
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Tanaka R, Iwata H. Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:93-105. [PMID: 28986680 DOI: 10.1007/s00122-017-2988-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 09/19/2017] [Indexed: 06/07/2023]
Abstract
A new pre-breeding strategy based on an optimization algorithm is proposed and evaluated via simulations. This strategy can find superior genotypes with less phenotyping effort. Genomic prediction is a promising approach to search for superior genotypes among a large number of accessions in germplasm collections preserved in gene banks. When some accessions are phenotyped and genotyped, a prediction model can be built, and the genotypic values of the remaining accessions can be predicted from their marker genotypes. In this study, we focused on the application of genomic prediction to pre-breeding, and propose a novel strategy that would reduce the cost of phenotyping needed to discover better accessions. We regarded the exploration of superior genotypes with genomic prediction as an optimization problem, and introduced Bayesian optimization to solve it. Bayesian optimization, that samples unobserved inputs according to the expected improvement (EI) as a selection criterion, seemed to be beneficial in pre-breeding. The EI depends on the predicted distribution of genotypic values, whereas usual selection depends only on the point estimate. We simulated a search for the best genotype among candidate genotypes and showed that the EI-based strategy required fewer genotypes to identify the best genotype than the usual and random selection strategy. Therefore, Bayesian optimization can be useful for applying genomic prediction to pre-breeding and would reduce the number of phenotyped accessions needed to find the best accession among a large number of candidates.
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Affiliation(s)
- Ryokei Tanaka
- 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
| | - Hiroyoshi Iwata
- 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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46
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Kusmec A, de Leon N, Schnable PS. Harnessing Phenotypic Plasticity to Improve Maize Yields. FRONTIERS IN PLANT SCIENCE 2018; 9:1377. [PMID: 30283485 PMCID: PMC6156439 DOI: 10.3389/fpls.2018.01377] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 08/29/2018] [Indexed: 05/07/2023]
Abstract
Plants can produce different phenotypes when exposed to different environments. Understanding the genetic basis of these plastic responses is crucial for crop breeding efforts. We discuss two recent studies that suggest that yield plasticity in maize has been under selection but is controlled by different genes than yield.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, United States
| | - Patrick S. Schnable
- Department of Agronomy, Iowa State University, Ames, IA, United States
- Plant Sciences Institute, Iowa State University, Ames, IA, United States
- *Correspondence: Patrick S. Schnable,
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47
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Zhang A, Wang H, Beyene Y, Semagn K, Liu Y, Cao S, Cui Z, Ruan Y, Burgueño J, San Vicente F, Olsen M, Prasanna BM, Crossa J, Yu H, Zhang X. Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations. FRONTIERS IN PLANT SCIENCE 2017; 8:1916. [PMID: 29167677 PMCID: PMC5683035 DOI: 10.3389/fpls.2017.01916] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Accepted: 10/23/2017] [Indexed: 05/20/2023]
Abstract
Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2 ), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2 , TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG /h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG /h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.
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Affiliation(s)
- Ao Zhang
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hongwu Wang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kassa Semagn
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Yubo Liu
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Zhenhai Cui
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
| | - Yanye Ruan
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Haiqiu Yu
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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48
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Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. TRENDS IN PLANT SCIENCE 2017; 22:961-975. [PMID: 28965742 DOI: 10.1016/j.tplants.2017.08.011] [Citation(s) in RCA: 550] [Impact Index Per Article: 78.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 08/05/2017] [Accepted: 08/28/2017] [Indexed: 05/17/2023]
Abstract
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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Affiliation(s)
- José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico.
| | | | - Jaime Cuevas
- Universidad de Quintana Roo, Quintana Roo, 77019, Mexico
| | | | - Diego Jarquín
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, 321 Keim Hall, Lincoln, NE 68503-0915, USA
| | - Gustavo de Los Campos
- Department of Epidemiology & Biostatistics, Michigan State University, 909 Fee Road, Room B601, East Lansing, MI 48824, USA
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | | | | | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico
| | - Manish Roorkiwal
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Telangana, India
| | - Jessica Rutkoski
- International Rice Research Institute, Los Baños, 4030, Philippines
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Telangana, India.
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49
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Mayer M, Unterseer S, Bauer E, de Leon N, Ordas B, Schön CC. Is there an optimum level of diversity in utilization of genetic resources? TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:2283-2295. [PMID: 28780586 PMCID: PMC5641276 DOI: 10.1007/s00122-017-2959-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/26/2017] [Indexed: 05/05/2023]
Abstract
Capitalizing upon the genomic characteristics of long-term random mating populations, sampling from pre-selected landraces is a promising approach for broadening the genetic base of elite germplasm for quantitative traits. Genome-enabled strategies for harnessing untapped allelic variation of landraces are currently evolving. The success of such approaches depends on the choice of source material. Thus, the analysis of different strategies for sampling allelic variation from landraces and their impact on population diversity and linkage disequilibrium (LD) is required to ensure the efficient utilization of diversity. We investigated the impact of different sampling strategies on diversity parameters and LD based on high-density genotypic data of 35 European maize landraces each represented by more than 20 individuals. On average, five landraces already captured ~95% of the molecular diversity of the entire dataset. Within landraces, absence of pronounced population structure, consistency of linkage phases and moderate to low LD levels were found. When combining data of up to 10 landraces, LD decay distances decreased to a few kilobases. Genotyping 24 individuals per landrace with 5k SNPs was sufficient for obtaining representative estimates of diversity and LD levels to allow an informed pre-selection of landraces. Integrating results from European with Central and South American landraces revealed that European landraces represent a unique and diverse spectrum of allelic variation. Sampling strategies for harnessing allelic variation from landraces depend on the study objectives. If the focus lies on the improvement of elite germplasm for quantitative traits, we recommend sampling from pre-selected landraces, as it yields a wide range of diversity, allows optimal marker imputation, control for population structure and avoids the confounding effects of strong adaptive alleles.
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Affiliation(s)
- Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Sandra Unterseer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA, 53706
| | - Bernardo Ordas
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), 36080, Pontevedra, Spain
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
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50
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Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat Genet 2017; 49:1297-1303. [PMID: 28854179 DOI: 10.1038/ng.3920] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 06/23/2017] [Indexed: 11/08/2022]
Abstract
The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
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