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Skøt L, Nay MM, Grieder C, Frey LA, Pégard M, Öhlund L, Amdahl H, Radovic J, Jaluvka L, Palmé A, Ruttink T, Lloyd D, Howarth CJ, Kölliker R. Including marker x environment interactions improves genomic prediction in red clover ( Trifolium pratense L.). FRONTIERS IN PLANT SCIENCE 2024; 15:1407609. [PMID: 38916032 PMCID: PMC11194335 DOI: 10.3389/fpls.2024.1407609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/20/2024] [Indexed: 06/26/2024]
Abstract
Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.
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Affiliation(s)
- Leif Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Michelle M. Nay
- Division of Plant Breeding, Fodder Plant Breeding, Agroscope, Zurich, Switzerland
| | - Christoph Grieder
- Division of Plant Breeding, Fodder Plant Breeding, Agroscope, Zurich, Switzerland
| | - Lea A. Frey
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | | | | | - Helga Amdahl
- Graminor Breeding Ltd., Bjørke Forsøksgård, Norway
| | | | | | - Anna Palmé
- The Nordic Genetic Resource Centre, Plant Section, Alnarp, Sweden
| | - Tom Ruttink
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - David Lloyd
- Germinal Horizon, Plas Gogerddan, Aberystwyth, United Kingdom
| | - Catherine J. Howarth
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Roland Kölliker
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
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2
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Wang H, Bai Y, Biligetu B. Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection. THE PLANT GENOME 2024; 17:e20431. [PMID: 38263612 DOI: 10.1002/tpg2.20431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 11/29/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024]
Abstract
Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.
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Affiliation(s)
- Hu Wang
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Yuguang Bai
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Bill Biligetu
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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Pégard M, Barre P, Delaunay S, Surault F, Karagić D, Milić D, Zorić M, Ruttink T, Julier B. Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1196134. [PMID: 37476178 PMCID: PMC10354441 DOI: 10.3389/fpls.2023.1196134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023]
Abstract
China's and Europe's dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.
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Affiliation(s)
| | | | | | | | - Djura Karagić
- Login EKO doo, Bulevar Zorana Đinđića 125, Novi Beograd, Serbia
| | - Dragan Milić
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Miroslav Zorić
- Login EKO doo, Bulevar Zorana Đinđića 125, Novi Beograd, Serbia
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Nay MM, Grieder C, Frey LA, Amdahl H, Radovic J, Jaluvka L, Palmé A, Skøt L, Ruttink T, Kölliker R. Multi-location trials and population-based genotyping reveal high diversity and adaptation to breeding environments in a large collection of red clover. FRONTIERS IN PLANT SCIENCE 2023; 14:1128823. [PMID: 36938037 PMCID: PMC10014843 DOI: 10.3389/fpls.2023.1128823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/13/2023] [Indexed: 06/01/2023]
Abstract
Red clover (Trifolium pratense L.) is an outcrossing forage legume that has adapted to a wide range of climatic and growing conditions across Europe. Red clover is valued for its high yield potential and its forage quality. The high amount of genetic diversity present in red clover provides an invaluable, but often poorly characterized resource to improve key traits such as yield, quality, and resistance to biotic and abiotic stresses. In this study, we examined the genetic and phenotypic diversity within a diverse set of 395 diploid red clover accessions via genome wide allele frequency fingerprinting and multi-location field trials across Europe. We found that the genetic structure of accessions mostly reflected their geographic origin and only few cases were detected, where breeders integrated foreign genetic resources into their local breeding pools. The mean dry matter yield of the first main harvesting season ranged from 0.74 kg m-2 in Serbia and Norway to 1.34 kg m-2 in Switzerland. Phenotypic performance of accessions in the multi-location field trials revealed a very strong accession x location interaction. Local adaptation was especially prominent in Nordic red clover accessions that showed a distinct adaptation to the growing conditions and cutting regime of the North. The traits vigor, dry matter yield and plant density were negatively correlated between the trial location in Norway and the locations Great Britain, Switzerland, Czech Republic and Serbia. Notably, breeding material and cultivars generally performed well at the location where they were developed. Our results confirmed that red clover cultivars were bred from regional ecotypes and show a narrow adaptation to regional conditions. Our study can serve as a valuable basis for identifying interesting materials that express the desired characteristics and contribute to the adaptation of red clover to future climatic conditions.
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Affiliation(s)
- Michelle M. Nay
- Fodder Plant Breeding, Division of Plant Breeding, Agroscope, Zurich, Switzerland
| | - Christoph Grieder
- Fodder Plant Breeding, Division of Plant Breeding, Agroscope, Zurich, Switzerland
| | - Lea A. Frey
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Helga Amdahl
- Graminor Breeding Ltd., Bjørke Forsøksgård, Norway
| | | | | | - Anna Palmé
- The Nordic Genetic Resource Centre, Plant Section, Alnarp, Sweden
| | - Leif Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Tom Ruttink
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Roland Kölliker
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
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6
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Filho CCF, Andrade MHML, Nunes JAR, Jarquin DH, Rios EF. Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics. THE PLANT GENOME 2023:e20306. [PMID: 36815221 DOI: 10.1002/tpg2.20306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/17/2022] [Indexed: 06/18/2023]
Abstract
Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.) can take several years as these traits must be evaluated under multiple harvests. Therefore, genotype-by-harvest interaction should be incorporated into genomic prediction models to explore genotypes' adaptability and stability. In this study, we investigated how enviromics could help to predict the genotypic performance under multiharvest alfalfa breeding trials by evaluating 177 families across 11 harvests under four cross-validation scenarios. All scenarios were analyzed using six models in a Bayesian mixed model framework. Our results demonstrate that models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance, indicating better biological explanation when the enviromic information was incorporated. Furthermore, models that accounted for enviromic data led to higher predictive ability (PA) in a reduced number of harvests used in the training data set. The best enviromic models (M2 and M3) outperformed the base model (GBLUP model-M0) for predicting adaptability and persistence across all cross-validation scenarios. Incorporating environmental covariates also provided higher PA for persistence compared with the base model, as predictions increased from 0 to 0.16, 0.20, 0.56, and 0.46 for CV00, CV1, CV0, and CV2. The results also demonstrate that GBLUP without enviromics term has low power to predict persistence, thus the adoption of enviromics is a cheap and efficient alternative to increase accuracy and biological meaning.
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Affiliation(s)
| | | | - José Airton Rodrigues Nunes
- Departamento de Biologia, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil
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7
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Annicchiarico P, Nazzicari N, Bouizgaren A, Hayek T, Laouar M, Cornacchione M, Basigalup D, Monterrubio Martin C, Brummer EC, Pecetti L. Alfalfa genomic selection for different stress-prone growing regions. THE PLANT GENOME 2022; 15:e20264. [PMID: 36222346 DOI: 10.1002/tpg2.20264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop-livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.
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Affiliation(s)
- Paolo Annicchiarico
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Centro di ricerca Zootecnia e Acquacoltura, 29 viale Piacenza, Lodi, 26900, Italy
| | - Nelson Nazzicari
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Centro di ricerca Zootecnia e Acquacoltura, 29 viale Piacenza, Lodi, 26900, Italy
| | - Abdelaziz Bouizgaren
- Institut National de la Recherche Agronomique, Centre Régional de Marrakech, BP 533, Marrakech, 40000, Morocco
| | - Taoufik Hayek
- Institut des Régions Arides, Route du Jorf, Médenine, 4119, Tunisia
| | - Meriem Laouar
- Ecole Nationale Supérieure Agronomique, Dép. de Productions Végétales. Laboratoire d'Amélioration Intégrative des Productions Végétales (C2711100), Rue Hassen Badi, El Harrach 16200, Alger, Algérie
| | - Monica Cornacchione
- Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Santiago del Estero, Jujuy 850, Santiago del Estero, 4200, Argentina
| | - Daniel Basigalup
- Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Manfredi, Ruta Nacional no. 9 km 636, Manfredi, Córdoba, X5988, Argentina
| | - Cristina Monterrubio Martin
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Centro di ricerca Zootecnia e Acquacoltura, 29 viale Piacenza, Lodi, 26900, Italy
| | - Edward Charles Brummer
- Plant Breeding Center, Dep. of Plant Sciences, Univ. of California, Davis, CA, 95616, USA
| | - Luciano Pecetti
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Centro di ricerca Zootecnia e Acquacoltura, 29 viale Piacenza, Lodi, 26900, Italy
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He X, Zhang F, He F, Shen Y, Yu LX, Zhang T, Kang J. Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations. FRONTIERS IN PLANT SCIENCE 2022; 13:1037272. [PMID: 36388566 PMCID: PMC9650308 DOI: 10.3389/fpls.2022.1037272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Alfalfa (Medicago sativa) is one of the most important leguminous forages, widely planted in temperate and subtropical regions. As a homozygous tetraploid, its complex genetic background limits genetic improvement of biomass yield attributes through conventional breeding methods. Genomic selection (GS) could improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib F1 populations, consisting of 149 and 392 individual plants (P149 and P392), were constructed using parents with differences in yield traits, and the yield traits of the F1 populations were measured for several years in multiple environments. Comparisons of individual yields were greatly affected by environments, and the best linear unbiased prediction (BLUP) could accurately represent the original yield data. The two hybrid F1 populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were identified. To develop yield prediction models for a single location and across locations, genotypic and phenotypic data from alfalfa yields in multiple environments were combined with various prediction models. The prediction accuracies of the F1 population, including 149 individuals, were 0.11 to 0.70, and those of the F1 population, consisting of 392 individuals, were 0.14 to 0.67. The BayesC and RF models had the highest average prediction accuracy of 0.60 for two F1 populations. The accuracy of the prediction models for P392 was higher than that of P149. By analyzing multiple prediction models, moderate prediction accuracies are obtained, although accuracies will likely decline across multiple locations. Our study provided evidence that GS can accelerate the improvement of alfalfa yield traits.
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Affiliation(s)
- Xiaofan He
- School of Grassland Science, Beijing Forestry University, Beijing, China
| | - Fan Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fei He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuhua Shen
- College of Chemistry and Life Sciences, Chifeng University, Chifeng, China
| | - Long-Xi Yu
- Plant Germplasm Introduction and Testing Research, United States Department of Agriculture-Agricultural Research Service, Prosser, WA, United States
| | - Tiejun Zhang
- School of Grassland Science, Beijing Forestry University, Beijing, China
| | - Junmei Kang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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9
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Zhang F, Kang J, Long R, Li M, Sun Y, He F, Jiang X, Yang C, Yang X, Kong J, Wang Y, Wang Z, Zhang Z, Yang Q. Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa. HORTICULTURE RESEARCH 2022; 10:uhac225. [PMID: 36643744 PMCID: PMC9832841 DOI: 10.1093/hr/uhac225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/25/2022] [Indexed: 06/17/2023]
Abstract
Fall dormancy (FD) is an essential trait to overcome winter damage and for alfalfa (Medicago sativa) cultivar selection. The plant regrowth height after autumn clipping is an indirect way to evaluate FD. Transcriptomics, proteomics, and quantitative trait locus mapping have revealed crucial genes correlated with FD; however, these genes cannot predict alfalfa FD very well. Here, we conducted genomic prediction of FD using whole-genome SNP markers based on machine learning-related methods, including support vector machine (SVM) regression, and regularization-related methods, such as Lasso and ridge regression. The results showed that using SVM regression with linear kernel and the top 3000 genome-wide association study (GWAS)-associated markers achieved the highest prediction accuracy for FD of 64.1%. For plant regrowth height, the prediction accuracy was 59.0% using the 3000 GWAS-associated markers and the SVM linear model. This was better than the results using whole-genome markers (25.0%). Therefore, the method we explored for alfalfa FD prediction outperformed the other models, such as Lasso and ElasticNet. The study suggests the feasibility of using machine learning to predict FD with GWAS-associated markers, and the GWAS-associated markers combined with machine learning would benefit FD-related traits as well. Application of the methodology may provide potential targets for FD selection, which would accelerate genetic research and molecular breeding of alfalfa with optimized FD.
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Affiliation(s)
- Fan Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA, 99163
| | - Junmei Kang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Ruicai Long
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Mingna Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Yan Sun
- Department of Turf Science and Engineering, College of Grassland Science and Technology, China Agricultural University, Beijing, China, 100193
| | - Fei He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Xueqian Jiang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Changfu Yang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Xijiang Yang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Jie Kong
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Yiwen Wang
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia, 3052
| | - Zhen Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193
| | - Zhiwu Zhang
- Corresponding author: Zhiwu Zhang (, Phone (Office): 509-335-2899, Fax: 509-335-8674) or Qingchuan Yang (, Phone: 010-62815996, Fax: 010-62815996)
| | - Qingchuan Yang
- Corresponding author: Zhiwu Zhang (, Phone (Office): 509-335-2899, Fax: 509-335-8674) or Qingchuan Yang (, Phone: 010-62815996, Fax: 010-62815996)
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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11
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Murad Leite Andrade MH, Acharya JP, Benevenuto J, de Bem Oliveira I, Lopez Y, Munoz P, Resende MFR, Rios EF. Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks. THE PLANT GENOME 2022; 15:e20235. [PMID: 35818699 DOI: 10.1002/tpg2.20235] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/30/2022] [Indexed: 06/15/2023]
Abstract
Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.
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Affiliation(s)
| | - Janam P Acharya
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Juliana Benevenuto
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | | | - Yolanda Lopez
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Patricio Munoz
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Marcio F R Resende
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Esteban F Rios
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
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12
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Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:77-112. [PMID: 35451773 DOI: 10.1007/978-1-0716-2205-6_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.
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Singh L, Pierce C, Santantonio N, Steiner R, Miller D, Reich J, Ray I. Validation of DNA marker-assisted selection for forage biomass productivity under deficit irrigation in alfalfa. THE PLANT GENOME 2022; 15:e20195. [PMID: 35178866 DOI: 10.1002/tpg2.20195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/13/2022] [Indexed: 06/14/2023]
Abstract
Drought and limited irrigation resources threaten agricultural sustainability in many regions of the world. Application of genomic-based breeding strategies may benefit crop variety development for these environments. Here, we provide a first report on the effect of deploying DNA marker-assisted selection (MAS) for the drought resilience quantitative trait in alfalfa (Medicago sativa L.). The goals of this study were to validate the effect of several quantitative trait loci (QTL) associated with alfalfa forage and crown-root (CR) biomass during drought and to determine their potential to improve forage yield of elite germplasm under water-limited conditions. Marker assisted selection was employed to introgress favorable or unfavorable DNA marker alleles affiliated with 10 biomass QTL into three elite backgrounds. Thirty-two populations were developed and evaluated for forage productivity over 3 yr under continuous deficit irrigation management in New Mexico, USA. Significant yield differences (ranging from -13 to 26%) were detected among some MAS-derived populations in all three elite backgrounds. Application of QTL MAS generally resulted in expected phenotypic responses within an elite genetic background that was similar to that in which the QTL were originally identified. However, relative performance of the populations varied substantially across the three genetic backgrounds. These outcomes indicate that QTL MAS can significantly affect forage productivity of elite alfalfa germplasm in drought-stressed environments. However, if biomass QTL are detected in donor germplasm that is genetically dissimilar to targeted elite populations, characterization of donor alleles may be warranted within elite backgrounds of interest to confirm their phenotypic effects prior to implementing MAS-based breeding.
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Affiliation(s)
- Lovepreet Singh
- Dep. of Plant and Environmental Sciences, New Mexico State Univ., Las Cruces, NM, 88003, USA
- Dep. of Plant Science and Landscape Architecture, Univ. of Maryland, College Park, MD, 20742, USA
| | - Chris Pierce
- Dep. of Plant and Environmental Sciences, New Mexico State Univ., Las Cruces, NM, 88003, USA
| | - Nicholas Santantonio
- Dep. of Plant and Environmental Sciences, New Mexico State Univ., Las Cruces, NM, 88003, USA
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Robert Steiner
- Dep. of Economics, Applied Statistics and International Business, New Mexico State Univ., Las Cruces, NM, 88003, USA
| | - Don Miller
- Cal/West Seeds, Woodland, CA, 95695, USA
- Alforex Seeds, Woodland, CA, 95695, USA
| | - Jon Reich
- Cal/West Seeds, Woodland, CA, 95695, USA
- Canaan Agricultural Consulting LLC, Woodland, CA, 95695, USA
| | - Ian Ray
- Dep. of Plant and Environmental Sciences, New Mexico State Univ., Las Cruces, NM, 88003, USA
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14
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Long R, Zhang F, Zhang Z, Li M, Chen L, Wang X, Liu W, Zhang T, Yu LX, He F, Jiang X, Yang X, Yang C, Wang Z, Kang J, Yang Q. Genome assembly of alfalfa cultivar zhongmu-4 and identification of SNPs associated with agronomic traits. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:14-28. [PMID: 35033678 PMCID: PMC9510860 DOI: 10.1016/j.gpb.2022.01.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 12/23/2021] [Accepted: 01/07/2022] [Indexed: 12/21/2022]
Abstract
Alfalfa (Medicago sativa L.) is the most important legume forage crop worldwide with high nutritional value and yield. For a long time, the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits. We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4, a cultivar widely cultivated in China, and a comprehensive database of genomic variations based on resequencing of 220 germplasms. Approximate 2.74 Gb contigs (N50 of 2.06 Mb), accounting for 88.39% of the estimated genome, were assembled, and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes. A total of 34,922 allelic genes were identified from the allele-aware genome. We observed the expansion of gene families, especially those related to the nitrogen metabolism, and the increase of repetitive elements including transposable elements, which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula. Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe, suggesting that geography may influence alfalfa genetic divergence during local adaption. Genome-wide association studies identified 101 single nucleotide polymorphisms (SNPs) associated with 27 agronomic traits. Two candidate genes were predicted to be correlated with fall dormancy and salt response. We believe that the allele-aware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.
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Affiliation(s)
- Ruicai Long
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Fan Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, United States
| | - Mingna Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lin Chen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xue Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Wenwen Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Tiejun Zhang
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China
| | - Long-Xi Yu
- United States Department of Agriculture-Agricultural Research Service, Plant and Germplasm Introduction and Testing Research, Prosser, WA, 99350, United States
| | - Fei He
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xueqian Jiang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xijiang Yang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Changfu Yang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zhen Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Junmei Kang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Qingchuan Yang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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15
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Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa ( Medicago sativa L.). Cells 2021; 10:cells10123372. [PMID: 34943880 PMCID: PMC8699225 DOI: 10.3390/cells10123372] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.
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16
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Paliwal R, Adegboyega TT, Abberton M, Faloye B, Oyatomi O. Potential of genomics for the improvement of underutilized legumes in sub‐Saharan Africa. LEGUME SCIENCE 2021; 3. [PMID: 0 DOI: 10.1002/leg3.69] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Affiliation(s)
- Rajneesh Paliwal
- Genetic Resources Center International Institute of Tropical Agriculture Ibadan Nigeria
| | | | - Michael Abberton
- Genetic Resources Center International Institute of Tropical Agriculture Ibadan Nigeria
| | - Ben Faloye
- Genetic Resources Center International Institute of Tropical Agriculture Ibadan Nigeria
| | - Olaniyi Oyatomi
- Genetic Resources Center International Institute of Tropical Agriculture Ibadan Nigeria
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17
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Blanco-Pastor JL, Liberal IM, Sakiroglu M, Wei Y, Brummer EC, Andrew RL, Pfeil BE. Annual and perennial Medicago show signatures of parallel adaptation to climate and soil in highly conserved genes. Mol Ecol 2021; 30:4448-4465. [PMID: 34217151 DOI: 10.1111/mec.16061] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 06/24/2021] [Accepted: 06/29/2021] [Indexed: 12/24/2022]
Abstract
Human induced environmental change may require rapid adaptation of plant populations and crops, but the genomic basis of environmental adaptation remain poorly understood. We analysed polymorphic loci from the perennial crop Medicago sativa (alfalfa or lucerne) and the annual legume model species M. truncatula to search for a common set of candidate genes that might contribute to adaptation to abiotic stress in both annual and perennial Medicago species. We identified a set of candidate genes of adaptation associated with environmental gradients along the distribution of the two Medicago species. Candidate genes for each species were detected in homologous genomic linkage blocks using genome-environment (GEA) and genome-phenotype association analyses. Hundreds of GEA candidate genes were species-specific, of these, 13.4% (M. sativa) and 24% (M. truncatula) were also significantly associated with phenotypic traits. A set of 168 GEA candidates were shared by both species, which was 25.4% more than expected by chance. When combined, they explained a high proportion of variance for certain phenotypic traits associated with adaptation. Genes with highly conserved functions dominated among the shared candidates and were enriched in gene ontology terms that have shown to play a central role in drought avoidance and tolerance mechanisms by means of cellular shape modifications and other functions associated with cell homeostasis. Our results point to the existence of a molecular basis of adaptation to abiotic stress in Medicago determined by highly conserved genes and gene functions. We discuss these results in light of the recently proposed omnigenic model of complex traits.
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Affiliation(s)
- José Luis Blanco-Pastor
- Department of Biological and Environmental Sciences, University of Gothenburg, Göteborg, Sweden.,INRAE, Centre Nouvelle-Aquitaine-Poitiers, UR4 (URP3F), Lusignan, France
| | - Isabel M Liberal
- Department of Biological and Environmental Sciences, University of Gothenburg, Göteborg, Sweden.,Real Jardín Botánico de Madrid (RJB-CSIC), Madrid, Spain
| | - Muhammet Sakiroglu
- Department of Bioengineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Yanling Wei
- Plant Breeding Center, Department of Plant Sciences, University of California, Davis, Davis, CA, USA
| | - E Charles Brummer
- Plant Breeding Center, Department of Plant Sciences, University of California, Davis, Davis, CA, USA
| | - Rose L Andrew
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | - Bernard E Pfeil
- Department of Biological and Environmental Sciences, University of Gothenburg, Göteborg, Sweden
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18
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Abstract
A suitable pairwise relatedness estimation is key to genetic studies. Several methods are proposed to compute relatedness in autopolyploids based on molecular data. However, unlike diploids, autopolyploids still need further studies considering scenarios with many linked molecular markers with known dosage. In this study, we provide guidelines for plant geneticists and breeders to access trustworthy pairwise relatedness estimates. To this end, we simulated populations considering different ploidy levels, meiotic pairings patterns, number of loci and alleles, and inbreeding levels. Analysis were performed to access the accuracy of distinct methods and to demonstrate the usefulness of molecular marker in practical situations. Overall, our results suggest that at least 100 effective biallelic molecular markers are required to have good pairwise relatedness estimation if methods based on correlation is used. For this number of loci, current methods based on multiallelic markers show lower performance than biallelic ones. To estimate relatedness in cases of inbreeding or close relationships (as parent-offspring, full-sibs, or half-sibs) is more challenging. Methods to estimate pairwise relatedness based on molecular markers, for different ploidy levels or pedigrees were implemented in the AGHmatrix R package.
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Aono AH, Costa EA, Rody HVS, Nagai JS, Pimenta RJG, Mancini MC, Dos Santos FRC, Pinto LR, Landell MGDA, de Souza AP, Kuroshu RM. Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance. Sci Rep 2020; 10:20057. [PMID: 33208862 PMCID: PMC7676261 DOI: 10.1038/s41598-020-77063-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
Abstract
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.
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Affiliation(s)
- Alexandre Hild Aono
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Estela Araujo Costa
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - Hugo Vianna Silva Rody
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - James Shiniti Nagai
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - Ricardo José Gonzaga Pimenta
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Melina Cristina Mancini
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Luciana Rossini Pinto
- Advanced Center of Sugarcane Agrobusiness Technological Research, Agronomic Institute of Campinas (IAC), Ribeirão Preto, SP, Brazil
| | | | - Anete Pereira de Souza
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil.
- Department of Plant Biology, Institute of Biology (IB), University of Campinas (UNICAMP), Campinas, SP, Brazil.
| | - Reginaldo Massanobu Kuroshu
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil.
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20
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Zhao X, Nie G, Yao Y, Ji Z, Gao J, Wang X, Jiang Y. Natural variation and genomic prediction of growth, physiological traits, and nitrogen-use efficiency in perennial ryegrass under low-nitrogen stress. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:6670-6683. [PMID: 32827031 DOI: 10.1093/jxb/eraa388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/17/2020] [Indexed: 06/11/2023]
Abstract
Genomic prediction of nitrogen-use efficiency (NUE) has not previously been studied in perennial grass species exposed to low-N stress. Here, we conducted a genomic prediction of physiological traits and NUE in 184 global accessions of perennial ryegrass (Lolium perenne) in response to a normal (7.5 mM) and low (0.75 mM) supply of N. After 21 d of treatment under greenhouse conditions, significant variations in plant height increment (ΔHT), leaf fresh weight (LFW), leaf dry weight (LDW), chlorophyll index (Chl), chlorophyll fluorescence, leaf N and carbon (C) contents, C/N ratio, and NUE were observed in accessions , but to a greater extent under low-N stress. Six genomic prediction models were applied to the data, namely the Bayesian method Bayes C, Bayesian LASSO, Bayesian Ridge Regression, Ridge Regression-Best Linear Unbiased Prediction, Reproducing Kernel Hilbert Spaces, and randomForest. These models produced similar prediction accuracy of traits within the normal or low-N treatments, but the accuracy differed between the two treatments. ΔHT, LFW, LDW, and C were predicted slightly better under normal N with a mean Pearson r-value of 0.26, compared with r=0.22 under low N, while the prediction accuracies for Chl, N, C/N, and NUE were significantly improved under low-N stress with a mean r=0.45, compared with r=0.26 under normal N. The population panel contained three population structures, which generally had no effect on prediction accuracy. The moderate prediction accuracies obtained for N, C, and NUE under low-N stress are promising, and suggest a feasible means by which germplasm might be initially assessed for further detailed studies in breeding programs.
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Affiliation(s)
- Xiongwei Zhao
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Gang Nie
- Department of Grassland Science, Animal Science and Technology College, Sichuan Agricultural University, Chengdu, Sichuan Province, China
| | - Yanyu Yao
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
| | - Zhongjie Ji
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
| | - Jianhua Gao
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Xingchun Wang
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Yiwei Jiang
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
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Lin S, Medina CA, Boge B, Hu J, Fransen S, Norberg S, Yu LX. Identification of genetic loci associated with forage quality in response to water deficit in autotetraploid alfalfa (Medicago sativa L.). BMC PLANT BIOLOGY 2020; 20:303. [PMID: 32611315 PMCID: PMC7328273 DOI: 10.1186/s12870-020-02520-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 06/24/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND Alfalfa has been cultivated in many regions around the world as an important forage crop due to its nutritive value to livestock and ability to adapt to various environments. However, the genetic basis by which plasticity of quality-relevant traits influence alfalfa adaption to different water conditions remain largely unknown. RESULTS In the present study, 198 accessions of alfalfa of the core collection for drought tolerance were evaluated for 26 forage quality traits in a field trial under an imposed deficit irrigation gradient. Regression analysis between quality traits and water stress revealed that values of fiber-related traits were negatively correlated with values of energy-related traits as water deficit increased. More than one hundred significant markers associated with forage quality under different water treatments were identified using genome-wide association studies with genotyping by sequencing. Among them, 131 markers associated with multiple traits in all the water deficit treatments. Most of the associated markers were dependent to the levels of water deficit, suggesting genetic controls for forage quality traits were dependent to the stress treatment. Twenty-four loci associated with forage quality were annotated to functional genes that may play roles in cell development or in response to water stress. CONCLUSIONS This study addressed the genetic base of phenotypic variation of forage quality traits under water deficit. The SNP markers identified in this study will be useful in marker-assisted selection for the genetic improvement of alfalfa with enhanced drought tolerance while maintaining forage quality.
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Affiliation(s)
- Sen Lin
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, 24106 N Bunn Road, Prosser, WA, 99350, USA
| | - Cesar Augusto Medina
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, 24106 N Bunn Road, Prosser, WA, 99350, USA
| | - Bill Boge
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, 24106 N Bunn Road, Prosser, WA, 99350, USA
| | - Jinguo Hu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, 24106 N Bunn Road, Prosser, WA, 99350, USA
| | - Steven Fransen
- Irrigated Agriculture Extension and Research Center, Washington State University, 24106 N Bunn Road, Prosser, Washington, USA
| | - Steven Norberg
- Washington State University Franklin County Extension Office, 404 West Clark Street, Pasco, Washington, USA
| | - Long-Xi Yu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, 24106 N Bunn Road, Prosser, WA, 99350, USA.
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22
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Hay Yield and Water Use Efficiency of Alfalfa under Different Irrigation and Fungicide Regimes in a Semiarid Climate. WATER 2020. [DOI: 10.3390/w12061721] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Alfalfa is one of the most nutritive and high-yielding forage legumes planted in rotation with cereal crops across the United States. Under semiarid and arid climates with limited water resources, sustainable management of the available resources is required. The objective of this study was to investigate the effect of different irrigation regimes and fungicide applications on alfalfa in the high desert region of the Colorado Plateau of the U.S. Field experiments were conducted during the 2012–2014 period. Alfalfa was planted in fall 2012, uniformly irrigated for crop establishment and subjected to seven different irrigation regimes after the first cut in spring 2013. Alfalfa was treated by fungicide application and was harvested at 10% blooming. The maximum amounts of applied water were 350, 300, 208, and 312 mm, respectively, during the first, second, third, and fourth regrowth cycles in 2013, and 373, 282, 198, and 246 mm in 2014 for the respective regrowth cycles in 2014; the seasonal applied irrigation amount varied from 711 to 1171 mm in 2013 and from 328 to 1100 mm in 2014. The results showed non-significant effect of fungicide application on the forage yield. Alfalfa forage yield was significantly affected by the irrigation regimes and showed a third order polynomial relationship with the applied irrigation amounts during each regrowth cycle and on seasonal scale. Forage yield decreased from the first cut to the fourth cut and the annual forage varied from 10.6 to 25.7 Mg/ha for the treated alfalfa and from 11.5 to 25.6 Mg/ha for the non-treated alfalfa. Forage yield at each cut accounted for 39.6, 24.2, 17.6, and 18.6% of the 2013 season forage yield and 31.4, 23.8, 21.6, and 23.2% of the 2014 season forage yield, for the first, second, third, and fourth cut, respectively. Alfalfa water use efficiency varied from 0.06 to 3.3 kg/m3. The relationships developed in this study could be used by forage growers, crop consultants, and project managers for decision making and planning to improve the productivity of water under the semiarid and arid climate of New Mexico and the surrounding regions.
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Medina CA, Hawkins C, Liu XP, Peel M, Yu LX. Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa ( Medicago sativa L.). Int J Mol Sci 2020; 21:ijms21093361. [PMID: 32397526 PMCID: PMC7247575 DOI: 10.3390/ijms21093361%20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 05/28/2023] Open
Abstract
Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.
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Affiliation(s)
- Cesar Augusto Medina
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
| | - Charles Hawkins
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
- Current address: Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Xiang-Ping Liu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
- Current address: College of Animal Science & Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing 163316, Heilongjiang, China
| | - Michael Peel
- United States Department of Agriculture-Agricultural Research Service, Forage and Range Research Lab, Logan, UT 84322, USA;
| | - Long-Xi Yu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
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Medina CA, Hawkins C, Liu XP, Peel M, Yu LX. Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa ( Medicago sativa L.). Int J Mol Sci 2020; 21:E3361. [PMID: 32397526 PMCID: PMC7247575 DOI: 10.3390/ijms21093361] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 12/13/2022] Open
Abstract
Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.
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Affiliation(s)
- Cesar Augusto Medina
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
| | - Charles Hawkins
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
- Current address: Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Xiang-Ping Liu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
- Current address: College of Animal Science & Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing 163316, Heilongjiang, China
| | - Michael Peel
- United States Department of Agriculture-Agricultural Research Service, Forage and Range Research Lab, Logan, UT 84322, USA;
| | - Long-Xi Yu
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA; (C.A.M.); (C.H.); (X.-P.L.)
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Arojju SK, Cao M, Zulfi Jahufer MZ, Barrett BA, Faville MJ. Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass. G3 (BETHESDA, MD.) 2020; 10:695-708. [PMID: 31792009 PMCID: PMC7003077 DOI: 10.1534/g3.119.400880] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 11/25/2019] [Indexed: 11/24/2022]
Abstract
Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.
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Affiliation(s)
- Sai Krishna Arojju
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Mingshu Cao
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - M Z Zulfi Jahufer
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Brent A Barrett
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Marty J Faville
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
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Adhikari L, Makaju SO, Missaoui AM. QTL mapping of flowering time and biomass yield in tetraploid alfalfa (Medicago sativa L.). BMC PLANT BIOLOGY 2019; 19:359. [PMID: 31419945 PMCID: PMC6697951 DOI: 10.1186/s12870-019-1946-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 07/26/2019] [Indexed: 05/20/2023]
Abstract
BACKGROUND The genetic and genomic basis of flowering time and biomass yield in alfalfa (Medicago sativa L.) remains poorly understood mainly due to the autopolyploid nature of the species and the lack of adequate genomic resources. We constructed linkage maps using genotyping-by-sequencing (GBS) based single dose allele (SDA) SNP and mapped alfalfa timing of flowering (TOF), spring yield (SY), and cumulative summer biomass (CSB) in a pseudo-testcross F1 population derived from a fall dormant (3010) and a non-dormant (CW 1010) cultivars. We analyzed the quantitative trait loci (QTL) to identify conserved genomic regions and detected molecular markers and potential candidate genes associated with the traits to improve alfalfa and provide genomic resources for the future studies. RESULTS This study showed that both fall dormant and non-dormant alfalfa cultivars harbored QTL for early and late flowering, suggesting that flowering time in alfalfa is not an indicator of its fall dormancy (FD) levels. A weak phenotypic correlation between the flowering time and fall dormancy (FD) in F1 and checks also corroborated that alfalfa FD and TOF are not the predictors of one another. The relationship between flowering time and alfalfa biomass yield was not strong, but the non-dormant had relatively more SY than dormant. Therefore, selecting superior alfalfa cultivars that are non-dormant, winter-hardy, and early flowering would allow for an early spring harvest with enhanced biomass. In this study, we found 25 QTL for TOF, 17 for SY and six QTL for CSB. Three TOF related QTL were stable and four TOF QTL were detected in the corresponding genomic locations of the flowering QTL of M. truncatula, an indication of possible evolutionarily conserved regions. The potential candidate genes for the SNP sequences of QTL regions were identified for all three traits and these genes would be potential targets for further molecular studies. CONCLUSIONS This research showed that variation in alfalfa flowering time after spring green up has no association with dormancy levels. Here we reported QTL, markers, and potential candidate genes associated with spring flowering time and biomass yield of alfalfa, which constitute valuable genomic resources for improving these traits via marker-assisted selection (MAS).
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Affiliation(s)
- Laxman Adhikari
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, The University of Georgia, Athens, GA, USA
| | - Shiva Om Makaju
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, The University of Georgia, Athens, GA, USA
| | - Ali M Missaoui
- Institute of Plant Breeding, Genetics and Genomics and Department of Crop and Soil Sciences, The University of Georgia, Athens, GA, USA.
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Annicchiarico P, Nazzicari N, Pecetti L, Romani M, Russi L. Pea genomic selection for Italian environments. BMC Genomics 2019; 20:603. [PMID: 31331290 PMCID: PMC6647272 DOI: 10.1186/s12864-019-5920-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 06/20/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction). RESULTS Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits. CONCLUSIONS Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection.
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Affiliation(s)
- Paolo Annicchiarico
- Council for Agricultural Research and Economics (CREA), Research Center for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy
| | - Nelson Nazzicari
- Council for Agricultural Research and Economics (CREA), Research Center for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy
| | - Luciano Pecetti
- Council for Agricultural Research and Economics (CREA), Research Center for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy
| | - Massimo Romani
- Council for Agricultural Research and Economics (CREA), Research Center for Animal Production and Aquaculture, viale Piacenza 29, 26900 Lodi, Italy
| | - Luigi Russi
- Department of Agricultural, Food and Environmental Science, University of Perugia, Borgo XX Giugno 74, 06121 Perugia, Italy
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Hawkins C, Yu LX. Recent progress in alfalfa (Medicago sativa L.) genomics and genomic selection. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.cj.2018.01.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Kulkarni KP, Tayade R, Asekova S, Song JT, Shannon JG, Lee JD. Harnessing the Potential of Forage Legumes, Alfalfa, Soybean, and Cowpea for Sustainable Agriculture and Global Food Security. FRONTIERS IN PLANT SCIENCE 2018; 9:1314. [PMID: 30283466 PMCID: PMC6157451 DOI: 10.3389/fpls.2018.01314] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/20/2018] [Indexed: 05/18/2023]
Abstract
Substantial improvements in access to food and increased purchasing power are driving many people toward consuming nutrition-rich foods causing an unprecedented demand for protein food worldwide, which is expected to rise further. Forage legumes form an important source of feed for livestock and have potential to provide a sustainable solution for food and protein security. Currently, alfalfa is a commercially grown source of forage and feed in many countries. However, soybean and cowpea also have the potential to provide quality forage and fodder for animal use. The cultivation of forage legumes is under threat from changing climatic conditions, indicating the need for breeding cultivars that can sustain and acclimatize to the negative effects of climate change. Recent progress in genetic and genomic tools have facilitated the identification of quantitative trait loci and genes/alleles that can aid in developing forage cultivars through genomics-assisted breeding. Furthermore, transgenic technology can be utilized to manipulate the genetic makeup of plants to improve forage digestibility for better animal performance. In this article, we assess the genetic potential of three important legume crops, alfalfa, soybean, and cowpea in supplying quality fodder and feed for livestock. In addition, we examine the impact of climate change on forage quality and discuss efforts made in enhancing the adaptation of the plant to the abiotic stress conditions. Subsequently, we suggest the application of integrative approaches to achieve adequate forage production amid the unpredictable climatic conditions.
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Affiliation(s)
| | - Rupesh Tayade
- School of Applied Biosciences, Kyungpook National University, Daegu, South Korea
| | - Sovetgul Asekova
- Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration, Miryang, South Korea
| | - Jong Tae Song
- School of Applied Biosciences, Kyungpook National University, Daegu, South Korea
| | - J. Grover Shannon
- National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, United States
| | - Jeong-Dong Lee
- School of Applied Biosciences, Kyungpook National University, Daegu, South Korea
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Jia C, Zhao F, Wang X, Han J, Zhao H, Liu G, Wang Z. Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa ( Medicago sativa). FRONTIERS IN PLANT SCIENCE 2018; 9:1220. [PMID: 30177947 PMCID: PMC6109793 DOI: 10.3389/fpls.2018.01220] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/30/2018] [Indexed: 05/31/2023]
Abstract
Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.
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Affiliation(s)
- Congjun Jia
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xuemin Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jianlin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
- International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Haiming Zhao
- Institute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Guibo Liu
- Institute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Zan Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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31
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Perennial Grain Legume Domestication Phase I: Criteria for Candidate Species Selection. SUSTAINABILITY 2018. [DOI: 10.3390/su10030730] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato. Genetics 2018. [PMID: 29514860 DOI: 10.1534/genetics.118.300685] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
As one of the world's most important food crops, the potato (Solanum tuberosum L.) has spurred innovation in autotetraploid genetics, including in the use of SNP arrays to determine allele dosage at thousands of markers. By combining genotype and pedigree information with phenotype data for economically important traits, the objectives of this study were to (1) partition the genetic variance into additive vs. nonadditive components, and (2) determine the accuracy of genome-wide prediction. Between 2012 and 2017, a training population of 571 clones was evaluated for total yield, specific gravity, and chip fry color. Genomic covariance matrices for additive (G), digenic dominant (D), and additive × additive epistatic (G#G) effects were calculated using 3895 markers, and the numerator relationship matrix (A) was calculated from a 13-generation pedigree. Based on model fit and prediction accuracy, mixed model analysis with G was superior to A for yield and fry color but not specific gravity. The amount of additive genetic variance captured by markers was 20% of the total genetic variance for specific gravity, compared to 45% for yield and fry color. Within the training population, including nonadditive effects improved accuracy and/or bias for all three traits when predicting total genotypic value. When six F1 populations were used for validation, prediction accuracy ranged from 0.06 to 0.63 and was consistently lower (0.13 on average) without allele dosage information. We conclude that genome-wide prediction is feasible in potato and that it will improve selection for breeding value given the substantial amount of nonadditive genetic variance in elite germplasm.
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Faville MJ, Ganesh S, Cao M, Jahufer MZZ, Bilton TP, Easton HS, Ryan DL, Trethewey JAK, Rolston MP, Griffiths AG, Moraga R, Flay C, Schmidt J, Tan R, Barrett BA. Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:703-720. [PMID: 29264625 PMCID: PMC5814531 DOI: 10.1007/s00122-017-3030-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 11/25/2017] [Indexed: 05/06/2023]
Abstract
KEY MESSAGE Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.
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Affiliation(s)
- Marty J Faville
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand.
| | - Siva Ganesh
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Mingshu Cao
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - M Z Zulfi Jahufer
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Timothy P Bilton
- AgResearch Ltd, Invermay Agricultural Centre, PB 50034, Mosgiel, New Zealand
| | - H Sydney Easton
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Douglas L Ryan
- AgResearch Ltd, Ruakura Research Centre, PB 3123, Hamilton, New Zealand
- PGG Wrightson Seeds Ltd, Ruakura Research Centre, Hamilton, New Zealand
| | - Jason A K Trethewey
- AgResearch Ltd, Lincoln Science Centre, PB 4749, Lincoln, New Zealand
- Lincoln Agritech, PO Box 69 133, Lincoln, New Zealand
| | - M Philip Rolston
- AgResearch Ltd, Lincoln Science Centre, PB 4749, Lincoln, New Zealand
| | - Andrew G Griffiths
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Roger Moraga
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Casey Flay
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Jana Schmidt
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Rachel Tan
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Brent A Barrett
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
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Annicchiarico P, Nazzicari N, Wei Y, Pecetti L, Brummer EC. Genotyping-by-Sequencing and Its Exploitation for Forage and Cool-Season Grain Legume Breeding. FRONTIERS IN PLANT SCIENCE 2017; 8:679. [PMID: 28536584 PMCID: PMC5423274 DOI: 10.3389/fpls.2017.00679] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 04/13/2017] [Indexed: 05/21/2023]
Abstract
Genotyping-by-Sequencing (GBS) may drastically reduce genotyping costs compared with single nucleotide polymorphism (SNP) array platforms. However, it may require optimization for specific crops to maximize the number of available markers. Exploiting GBS-generated markers may require optimization, too (e.g., to cope with missing data). This study aimed (i) to compare elements of GBS protocols on legume species that differ for genome size, ploidy, and breeding system, and (ii) to show successful applications and challenges of GBS data on legume species. Preliminary work on alfalfa and Medicago truncatula suggested the greater interest of ApeKI over PstI:MspI DNA digestion. We compared KAPA and NEB Taq polymerases in combination with primer extensions that were progressively more selective on restriction sites, and found greater number of polymorphic SNP loci in pea, white lupin and diploid alfalfa when adopting KAPA with a non-selective primer. This protocol displayed a slight advantage also for tetraploid alfalfa (where SNP calling requires higher read depth). KAPA offered the further advantage of more uniform amplification than NEB over fragment sizes and GC contents. The number of GBS-generated polymorphic markers exceeded 6,500 in two tetraploid alfalfa reference populations and a world collection of lupin genotypes, and 2,000 in different sets of pea or lupin recombinant inbred lines. The predictive ability of GBS-based genomic selection was influenced by the genotype missing data threshold and imputation, as well as by the genomic selection model, with the best model depending on traits and data sets. We devised a simple method for comparing phenotypic vs. genomic selection in terms of predicted yield gain per year for same evaluation costs, whose application to preliminary data for alfalfa and pea in a hypothetical selection scenario for each crop indicated a distinct advantage of genomic selection.
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Affiliation(s)
- Paolo Annicchiarico
- Centro di Ricerca per le Produzioni Foraggere e Lattiero-Casearie (FLC), CREALodi, Italy
| | - Nelson Nazzicari
- Centro di Ricerca per le Produzioni Foraggere e Lattiero-Casearie (FLC), CREALodi, Italy
| | - Yanling Wei
- Centro di Ricerca per le Produzioni Foraggere e Lattiero-Casearie (FLC), CREALodi, Italy
- Plant Breeding Center, Department of Plant Sciences, University of California, Davis, DavisCA, USA
| | - Luciano Pecetti
- Centro di Ricerca per le Produzioni Foraggere e Lattiero-Casearie (FLC), CREALodi, Italy
| | - Edward C. Brummer
- Plant Breeding Center, Department of Plant Sciences, University of California, Davis, DavisCA, USA
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Sakiroglu M, Brummer EC. Identification of loci controlling forage yield and nutritive value in diploid alfalfa using GBS-GWAS. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:261-268. [PMID: 27662844 DOI: 10.1007/s00122-016-2782-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 08/30/2016] [Indexed: 05/23/2023]
Abstract
We attempted to identify genomic regions controlling forage yield and nutritive value in alfalfa. Several candidate genes and associated genetic markers were identified that could potentially be useful for alfalfa breeding to more efficiently develop improved cultivars. Alfalfa is one of the most widely cultivated forage legumes worldwide and improving alfalfa forage yield and nutritive value is a major global breeding goal. Genotyping-by-sequencing (GBS) provides cost-effective molecular marker genotyping for genome-wide association studies (GWAS). Using more than 15,000 genome-wide single nucleotide polymorphisms (SNP) identified from GBS, we conducted a GWAS to investigate forage yield and nutritive value-related traits. We have detected a number of associations for all the traits evaluated and a number of associations detected were located on the Medicago truncatula genome. The SNP in a coding region of a cell wall biosynthesis gene was associated with several cell wall-related traits, and we suggest that it may be the causative polymorphism. Two other SNPs residing in meristematic development and early growth genes were found to associate with the total biomass yield. None of the SNPs associated with regrowth after harvest or with spring regrowth were mapped to the M. truncatula genome, possibly reflecting the fact that M. truncatula is an annual species related to alfalfa that typically has limited ability to regrow. The alleles we identify with the major impact on forage yield and nutritive value can be rapidly incorporated into our breeding program.
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Affiliation(s)
| | - E Charles Brummer
- Department of Plant Sciences, Plant Breeding Center, The University of California, Davis, CA, 95616, USA
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36
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Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Singh PK, Singh GP, Prabhu KV. Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding. Front Genet 2016; 7:221. [PMID: 28083016 PMCID: PMC5186759 DOI: 10.3389/fgene.2016.00221] [Citation(s) in RCA: 130] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 12/12/2016] [Indexed: 12/31/2022] Open
Abstract
Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.
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Affiliation(s)
- Javaid A Bhat
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - Sajad Ali
- National Research Centre for Plant Biotechnology New Delhi, India
| | - Romesh K Salgotra
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu Chatha, India
| | - Zahoor A Mir
- National Research Centre for Plant Biotechnology New Delhi, India
| | - Sutapa Dutta
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - Vasudha Jadon
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - Anshika Tyagi
- National Research Centre for Plant Biotechnology New Delhi, India
| | - Muntazir Mushtaq
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu Chatha, India
| | - Neelu Jain
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - Pradeep K Singh
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - Gyanendra P Singh
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
| | - K V Prabhu
- Division of Genetics, Indian Agricultural Research Institute New Delhi, India
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Auinger HJ, Schönleben M, Lehermeier C, Schmidt M, Korzun V, Geiger HH, Piepho HP, Gordillo A, Wilde P, Bauer E, Schön CC. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2016; 129:2043-2053. [PMID: 27480157 PMCID: PMC5069347 DOI: 10.1007/s00122-016-2756-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 07/15/2016] [Indexed: 05/02/2023]
Abstract
KEY MESSAGE Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.
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Affiliation(s)
- Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Manfred Schönleben
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Christina Lehermeier
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Malthe Schmidt
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Viktor Korzun
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Hartwig H Geiger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, 70599, Stuttgart, Germany
| | - Andres Gordillo
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Peer Wilde
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany.
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Grinberg NF, Lovatt A, Hegarty M, Lovatt A, Skøt KP, Kelly R, Blackmore T, Thorogood D, King RD, Armstead I, Powell W, Skøt L. Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations. FRONTIERS IN PLANT SCIENCE 2016; 7:133. [PMID: 26904088 PMCID: PMC4751346 DOI: 10.3389/fpls.2016.00133] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 01/25/2016] [Indexed: 05/23/2023]
Abstract
Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.
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Affiliation(s)
| | - Alan Lovatt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Matt Hegarty
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Andi Lovatt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Kirsten P. Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Rhys Kelly
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Tina Blackmore
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Danny Thorogood
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Ross D. King
- Manchester Institute of Biotechnology, University of ManchesterManchester, UK
| | - Ian Armstead
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - Wayne Powell
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
- CGIAR Consortium, CGIAR Consortium OfficeMontpellier, France
| | - Leif Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
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Annicchiarico P, Nazzicari N, Li X, Wei Y, Pecetti L, Brummer EC. Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 2015; 16:1020. [PMID: 26626170 PMCID: PMC4667460 DOI: 10.1186/s12864-015-2212-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 11/13/2015] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. RESULTS Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection. CONCLUSIONS Genomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems.
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Affiliation(s)
- Paolo Annicchiarico
- Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, 29 viale Piacenza, 26900, Lodi, Italy.
| | - Nelson Nazzicari
- Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, 29 viale Piacenza, 26900, Lodi, Italy.
| | - Xuehui Li
- Department of Plant Sciences, North Dakota State University, 1340 Administration Avenue, Fargo, ND, 58108, USA.
| | - Yanling Wei
- Plant Sciences Department, University of California, Davis, Plant Breeding Center, One Shields Avenue, Davis, CA, 95616, USA.
| | - Luciano Pecetti
- Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, 29 viale Piacenza, 26900, Lodi, Italy.
| | - E Charles Brummer
- Plant Sciences Department, University of California, Davis, Plant Breeding Center, One Shields Avenue, Davis, CA, 95616, USA.
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