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Nandudu L, Strock C, Ogbonna A, Kawuki R, Jannink JL. Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies. Front Plant Sci 2024; 15:1360729. [PMID: 38562560 PMCID: PMC10982329 DOI: 10.3389/fpls.2024.1360729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 03/07/2024] [Indexed: 04/04/2024]
Abstract
Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management.
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Affiliation(s)
- Leah Nandudu
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Root Crops Department, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Christopher Strock
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Alex Ogbonna
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Robert Kawuki
- Root Crops Department, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Jean-Luc Jannink
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- US Department of Agriculture, Agricultural Research Service (USDA-ARS), Ithaca, NY, United States
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Brzozowski LJ, Campbell MT, Hu H, Yao L, Caffe M, Gutiérrez LA, Smith KP, Sorrells ME, Gore MA, Jannink JL. Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa). Plant Genome 2023; 16:e20370. [PMID: 37539632 DOI: 10.1002/tpg2.20370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 08/05/2023]
Abstract
Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Linxing Yao
- Analytical Resources Core-Bioanalysis and Omics, Colorado State University, Fort Collins, Colorado, USA
| | - Melanie Caffe
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, Saint Paul, Minnesota, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA
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Nandudu L, Kawuki R, Ogbonna A, Kanaabi M, Jannink JL. Genetic dissection of cassava brown streak disease in a genomic selection population. Front Plant Sci 2023; 13:1099409. [PMID: 36714759 PMCID: PMC9880483 DOI: 10.3389/fpls.2022.1099409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
Introduction Cassava brown streak disease (CBSD) is a major threat to food security in East and central Africa. Breeding for resistance against CBSD is the most economical and sustainable way of addressing this challenge. Methods This study seeks to assess the (1) performance of CBSD incidence and severity; (2) identify genomic regions associated with CBSD traits and (3) candidate genes in the regions of interest, in the Cycle 2 population of the National Crops Resources Research Institute. Results A total of 302 diverse clones were screened, revealing that CBSD incidence across growing seasons was 44%. Severity scores for both foliar and root symptoms ranged from 1.28 to 1.99 and 1.75 to 2.28, respectively across seasons. Broad sense heritability ranged from low to high (0.15 - 0.96), while narrow sense heritability ranged from low to moderate (0.03 - 0.61). Five QTLs, explaining approximately 19% phenotypic variation were identified for CBSD severity at 3 months after planting on chromosomes 1, 13, and 18 in the univariate GWAS analysis. Multivariate GWAS analysis identified 17 QTLs that were consistent with the univariate analysis including additional QTLs on chromosome 6. Seventy-seven genes were identified in these regions with functions such as catalytic activity, ATP-dependent activity, binding, response to stimulus, translation regulator activity, transporter activity among others. Discussion These results suggest variation in virulence in the C2 population, largely due to genetics and annotated genes in these QTLs regions may play critical roles in virus initiation and replication, thus increasing susceptibility to CBSD.
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Affiliation(s)
- Leah Nandudu
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- Root crops Department National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Robert Kawuki
- Root crops Department National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Alex Ogbonna
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Michael Kanaabi
- Root crops Department National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Jean-Luc Jannink
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- US Department of Agriculture, Agricultural Research Service (USDA-ARS), Ithaca, NY, United States
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de Andrade LRB, Sousa MBE, Wolfe M, Jannink JL, de Resende MDV, Azevedo CF, de Oliveira EJ. Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones. Front Plant Sci 2022; 13:1071156. [PMID: 36589120 PMCID: PMC9800927 DOI: 10.3389/fpls.2022.1071156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.
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Affiliation(s)
| | | | - Marnin Wolfe
- Department of Crop, Soil and Environment Sciences, Auburn University, Auburn, AL, United States
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- United States Department of Agriculture – Agriculture Research Service, Plant, Soil and Nutrition Research, Ithaca, NY, United States
| | - Marcos Deon Vilela de Resende
- Department of Forestry Engineering, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
- Embrapa Florestas, Colombo, Paraná, Brazil
- Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
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Taagen E, Jordan K, Akhunov E, Sorrells ME, Jannink JL. If It Ain't Broke, Don't Fix It: Evaluating the Effect of Increased Recombination on Response to Selection for Wheat Breeding. G3 Genes|Genomes|Genetics 2022; 12:6798772. [PMID: 36331396 PMCID: PMC9713416 DOI: 10.1093/g3journal/jkac291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Meiotic recombination is a source of allelic diversity, but the low frequency and biased distribution of crossovers that occur during meiosis limits the genetic variation available to plant breeders. Simulation studies previously identified that increased recombination frequency can retain more genetic variation and drive greater genetic gains than wildtype recombination. Our study was motivated by the need to define desirable recombination intervals in regions of the genome with fewer crossovers. We hypothesized that deleterious variants, which can negatively impact phenotypes and occur at higher frequencies in low recombining regions where they are linked in repulsion with favorable loci, may offer a signal for positioning shifts of recombination distributions. Genomic selection breeding simulation models based on empirical wheat data were developed to evaluate increased recombination frequency and changing recombination distribution on response to selection. Comparing high and low values for a range of simulation parameters identified that few combinations retained greater genetic variation and fewer still achieved higher genetic gain than wildtype. More recombination was associated with loss of genomic prediction accuracy, which outweighed the benefits of disrupting repulsion linkages. Irrespective of recombination frequency or distribution and deleterious variant annotation, enhanced response to selection under increased recombination required polygenic trait architecture, high heritability, an initial scenario of more repulsion than coupling linkages, and greater than six cycles of genomic selection. Altogether, the outcomes of this research discourage a controlled recombination approach to genomic selection in wheat as a more efficient path to retaining genetic variation and increasing genetic gains compared to existing breeding methods.
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Affiliation(s)
- Ella Taagen
- Corresponding author: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
| | - Katherine Jordan
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66502, USA
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66502, USA
| | - Eduard Akhunov
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66502, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca NY 14853, USA
- USDA-ARS, R.W. Holley Center, Cornell University, Ithaca, NY 14853, USA
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Bakare MA, Kayondo SI, Aghogho CI, Wolfe MD, Parkes EY, Kulakow P, Egesi C, Jannink JL, Rabbi IY. Parsimonious genotype by environment interaction covariance models for cassava ( Manihot esculenta). Front Plant Sci 2022; 13:978248. [PMID: 36212387 PMCID: PMC9532941 DOI: 10.3389/fpls.2022.978248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/08/2022] [Indexed: 06/16/2023]
Abstract
The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016-2017, 2017-2018, 2018-2019, and 2019-2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance-covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.
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Affiliation(s)
- Moshood A. Bakare
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
- International Institute of Tropical Agriculture, Ibadan, Nigeria
| | | | - Cynthia I. Aghogho
- International Institute of Tropical Agriculture, Ibadan, Nigeria
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Ghana
| | - Marnin D. Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
- Department of Crop, Soil and Environmental Sciences, College of Agriculture, Auburn University, Auburn, AL, United States
| | | | - Peter Kulakow
- International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Chiedozie Egesi
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
- International Institute of Tropical Agriculture, Ibadan, Nigeria
- National Root Crops Research Institute (NRCRI), Umudike, Umuahia, Nigeria
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
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Li Y, Umanzor S, Ng C, Huang M, Marty-Rivera M, Bailey D, Aydlett M, Jannink JL, Lindell S, Yarish C. Skinny kelp ( Saccharina angustissima) provides valuable genetics for the biomass improvement of farmed sugar kelp ( Saccharina latissima). J Appl Phycol 2022; 34:2551-2563. [PMID: 36033835 PMCID: PMC9391627 DOI: 10.1007/s10811-022-02811-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED Saccharina latissima (sugar kelp) is one of the most widely cultivated brown marine macroalgae species in the North Atlantic and the eastern North Pacific Oceans. To meet the expanding demands of the sugar kelp mariculture industry, selecting and breeding sugar kelp that is best suited to offshore farm environments is becoming necessary. To that end, a multi-year, multi-institutional breeding program was established by the U.S. Department of Energy's (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program. Hybrid sporophytes were generated using 203 unique gametophyte cultures derived from wild-collected Saccharina spp. for two seasons of farm trials (2019-2020 and 2020-2021). The wild sporophytes were collected from 10 different locations within the Gulf of Maine (USA) region, including both sugar kelp (Saccharina latissima) and the skinny kelp species (Saccharina angustissima). We harvested 232 common farm plots during these two seasons with available data. We found that farmed kelp plots with skinny kelp as parents had an average increased yield over the mean (wet weight 2.48 ± 0.90 kg m-1 and dry weight 0.32 ± 0.10 kg m-1) in both growing seasons. We also found that blade length positively correlated with biomass in skinny kelp x sugar kelp crosses or pure sugar kelp crosses. The skinny x sugar progenies had significantly longer and narrower blades than the pure sugar kelp progenies in both seasons. Overall, these findings suggest that sugar x skinny kelp crosses provide improved yield compared to pure sugar kelp crosses. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10811-022-02811-1.
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Affiliation(s)
- Yaoguang Li
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315 USA
| | - Schery Umanzor
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315 USA
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Juneau, AK 99775 USA
| | - Crystal Ng
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315 USA
| | - Mao Huang
- Section On Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853 USA
| | - Michael Marty-Rivera
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315 USA
| | - David Bailey
- Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA
| | - Margaret Aydlett
- Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA
| | - Jean-Luc Jannink
- Section On Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853 USA
- United States Department of Agriculture - Agriculture Research Service, Ithaca, NY 14853 USA
| | - Scott Lindell
- Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA
| | - Charles Yarish
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315 USA
- Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA
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Bakare MA, Kayondo SI, Aghogho CI, Wolfe MD, Parkes EY, Kulakow P, Egesi C, Rabbi IY, Jannink JL. Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods. PLoS One 2022; 17:e0268189. [PMID: 35849556 PMCID: PMC9292083 DOI: 10.1371/journal.pone.0268189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/23/2022] [Indexed: 11/19/2022] Open
Abstract
Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.
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Affiliation(s)
- Moshood A. Bakare
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States of America
- * E-mail: (J-LJ); (MAB)
| | | | - Cynthia I. Aghogho
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Ghana
| | - Marnin D. Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States of America
| | | | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Chiedozie Egesi
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States of America
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- National Root Crops Research Institute Umudike, Umuahia, Nigeria
| | | | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States of America
- USDA-ARS Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States of America
- * E-mail: (J-LJ); (MAB)
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Morales N, Ogbonna AC, Ellerbrock BJ, Bauchet GJ, Tantikanjana T, Tecle IY, Powell AF, Lyon D, Menda N, Simoes CC, Saha S, Hosmani P, Flores M, Panitz N, Preble RS, Agbona A, Rabbi I, Kulakow P, Peteti P, Kawuki R, Esuma W, Kanaabi M, Chelangat DM, Uba E, Olojede A, Onyeka J, Shah T, Karanja M, Egesi C, Tufan H, Paterne A, Asfaw A, Jannink JL, Wolfe M, Birkett CL, Waring DJ, Hershberger JM, Gore MA, Robbins KR, Rife T, Courtney C, Poland J, Arnaud E, Laporte MA, Kulembeka H, Salum K, Mrema E, Brown A, Bayo S, Uwimana B, Akech V, Yencho C, de Boeck B, Campos H, Swennen R, Edwards JD, Mueller LA. Breedbase: a digital ecosystem for modern plant breeding. G3 Genes|Genomes|Genetics 2022; 12:6564228. [PMID: 35385099 PMCID: PMC9258556 DOI: 10.1093/g3journal/jkac078] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/14/2022] [Indexed: 01/17/2023]
Abstract
Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.
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Affiliation(s)
- Nicolas Morales
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | - Alex C Ogbonna
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | | | | | | | | | | | - David Lyon
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | - Naama Menda
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | - Surya Saha
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ezenwanyi Uba
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Adeyemi Olojede
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Joseph Onyeka
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | | | | | - Chiedozie Egesi
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- IITA Ibadan , 200001 Ibadan, Nigeria
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Hale Tufan
- Cornell University , Ithaca, NY 14853, USA
| | | | | | - Jean-Luc Jannink
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | - Clay L Birkett
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | - David J Waring
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | | | | | - Trevor Rife
- Kansas State University , Manhattan, KS 66506, USA
| | | | - Jesse Poland
- Kansas State University , Manhattan, KS 66506, USA
| | | | | | | | | | | | | | | | | | | | - Craig Yencho
- North Carolina State University (NCSU) , Raleigh, NC 27695, USA
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10
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Rabbi IY, Kayondo SI, Bauchet G, Yusuf M, Aghogho CI, Ogunpaimo K, Uwugiaren R, Smith IA, Peteti P, Agbona A, Parkes E, Lydia E, Wolfe M, Jannink JL, Egesi C, Kulakow P. Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava. Plant Mol Biol 2022; 109:195-213. [PMID: 32734418 PMCID: PMC9162993 DOI: 10.1007/s11103-020-01038-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 07/20/2020] [Indexed: 05/05/2023]
Abstract
More than 40 QTLs associated with 14 stress-related, quality and agro-morphological traits were identified. A catalogue of favourable SNP markers for MAS and a list of candidate genes are provided. Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5130 clones developed at the International Institute of Tropical Agriculture-Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type) were investigated. We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.
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Affiliation(s)
- Ismail Yusuf Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria.
| | - Siraj Ismail Kayondo
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | | | - Muyideen Yusuf
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Cynthia Idhigu Aghogho
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Kayode Ogunpaimo
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Ruth Uwugiaren
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Ikpan Andrew Smith
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Prasad Peteti
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Elizabeth Parkes
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
| | - Ezenwaka Lydia
- National Root Crops Research Institute (NRCRI), PMB 7006, Umudike, 440221, Nigeria
| | - Marnin Wolfe
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, 14850, USA
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, 14850, USA
- United States Department of Agriculture - Agriculture Research Service, Ithaca, NY, 14850, USA
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
- National Root Crops Research Institute (NRCRI), PMB 7006, Umudike, 440221, Nigeria
- Global Development Department, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, 14850, USA
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, 200001, Oyo State, Nigeria
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11
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Brzozowski LJ, Campbell MT, Hu H, Caffe M, Gutiérrez LA, Smith KP, Sorrells ME, Gore MA, Jannink JL. Generalizable approaches for genomic prediction of metabolites in plants. Plant Genome 2022; 15:e20205. [PMID: 35470586 DOI: 10.1002/tpg2.20205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Melanie Caffe
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57006, USA
| | - Lucı A Gutiérrez
- Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Dep. of Agronomy & Plant Genetics, Univ. of Minnesota, St. Paul, MN, 55108, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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12
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Yao E, Blake VC, Cooper L, Wight CP, Michel S, Cagirici HB, Lazo GR, Birkett CL, Waring DJ, Jannink JL, Holmes I, Waters AJ, Eickholt DP, Sen TZ. GrainGenes: a data-rich repository for small grains genetics and genomics. Database (Oxford) 2022; 2022:6591224. [PMID: 35616118 PMCID: PMC9216595 DOI: 10.1093/database/baac034] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/01/2022] [Accepted: 04/26/2022] [Indexed: 05/16/2023]
Abstract
As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.
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Affiliation(s)
- Eric Yao
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Victoria C Blake
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Plant Sciences and Plant Pathology, Montana State University, 119 Plant Biosciences Building, Bozeman, MT 59717, USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 1500 SW Jefferson Way, Corvallis, OR 97331, USA
| | - Charlene P Wight
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada
| | - Steve Michel
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - H Busra Cagirici
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Gerard R Lazo
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Clay L Birkett
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
| | - David J Waring
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Ian Holmes
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Amanda J Waters
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - David P Eickholt
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - Taner Z Sen
- *Corresponding author: Tel: +1 (510) 559-5982; Fax: + 1 (510) 559-5963;
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13
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Brzozowski LJ, Hu H, Campbell MT, Broeckling CD, Caffe M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.). G3 (Bethesda) 2022; 12:6459173. [PMID: 34893823 PMCID: PMC9210299 DOI: 10.1093/g3journal/jkab419] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022]
Abstract
Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Corey D Broeckling
- Bioanalysis and Omics Center of the Analytical Resources Core, Colorado State University, Fort Collins, CO 80523 USA
| | - Melanie Caffe
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57006, USA
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kevin P Smith
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853 USA
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14
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Huang M, Robbins KR, Li Y, Umanzor S, Marty-Rivera M, Bailey D, Yarish C, Lindell S, Jannink JL. Simulation of sugar kelp (Saccharina latissima) breeding guided by practices to accelerate genetic gains. G3 (Bethesda) 2022; 12:jkac003. [PMID: 35088860 PMCID: PMC8895986 DOI: 10.1093/g3journal/jkac003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/17/2021] [Indexed: 11/18/2022]
Abstract
Though Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.
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Affiliation(s)
- Mao Huang
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Yaoguang Li
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - Schery Umanzor
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Juneau, AK 99775, USA
| | - Michael Marty-Rivera
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - David Bailey
- Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Charles Yarish
- Department of Ecology & Evolutionary Biology, University of Connecticut, Stamford, CT 06901-2315, USA
| | - Scott Lindell
- Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
- United States Department of Agriculture—Agriculture Research Service, Ithaca, NY 14853, USA
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15
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Chan AW, Villwock SS, Williams AL, Jannink JL. Sexual dimorphism and the effect of wild introgressions on recombination in cassava (Manihot esculenta Crantz) breeding germplasm. G3 (Bethesda) 2022; 12:jkab372. [PMID: 34791172 PMCID: PMC8728042 DOI: 10.1093/g3journal/jkab372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 09/29/2021] [Indexed: 01/09/2023]
Abstract
Recombination has essential functions in meiosis, evolution, and breeding. The frequency and distribution of crossovers dictate the generation of new allele combinations and can vary across species and between sexes. Here, we examine recombination landscapes across the 18 chromosomes of cassava (Manihot esculenta Crantz) with respect to male and female meioses and known introgressions from the wild relative Manihot glaziovii. We used SHAPEIT2 and duoHMM to infer crossovers from genotyping-by-sequencing data and a validated multigenerational pedigree from the International Institute of Tropical Agriculture cassava breeding germplasm consisting of 7020 informative meioses. We then constructed new genetic maps and compared them to an existing map previously constructed by the International Cassava Genetic Map Consortium. We observed higher recombination rates in females compared to males, and lower recombination rates in M. glaziovii introgression segments on chromosomes 1 and 4, with suppressed recombination along the entire length of the chromosome in the case of the chromosome 4 introgression. Finally, we discuss hypothesized mechanisms underlying our observations of heterochiasmy and crossover suppression and discuss the broader implications for plant breeding.
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Affiliation(s)
- Ariel W Chan
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Seren S Villwock
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Amy L Williams
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- RW Holley Center for Agriculture and Health, United States Department of Agriculture—Agricultural Research Service, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853, USA
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16
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Phumichai C, Aiemnaka P, Nathaisong P, Hunsawattanakul S, Fungfoo P, Rojanaridpiched C, Vichukit V, Kongsil P, Kittipadakul P, Wannarat W, Chunwongse J, Tongyoo P, Kijkhunasatian C, Chotineeranat S, Piyachomkwan K, Wolfe MD, Jannink JL, Sorrells ME. Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava. Theor Appl Genet 2022; 135:145-171. [PMID: 34661695 DOI: 10.1007/s00122-021-03956-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.
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Affiliation(s)
- Chalermpol Phumichai
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand.
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand.
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand.
| | - Pornsak Aiemnaka
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Piyaporn Nathaisong
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Sirikan Hunsawattanakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand
| | - Phasakorn Fungfoo
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | | | - Vichan Vichukit
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Pasajee Kongsil
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Piya Kittipadakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Wannasiri Wannarat
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Julapark Chunwongse
- Department of Horticulture, Faculty of Agriculture Kamphaeng Saen, Kasetsart University, Nakhon Pathom, 73140, Thailand
| | - Pumipat Tongyoo
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
| | - Chookiat Kijkhunasatian
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Sunee Chotineeranat
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Kuakoon Piyachomkwan
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Marnin D Wolfe
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture - Agriculture Research Service, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
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17
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Wolfe MD, Chan AW, Kulakow P, Rabbi I, Jannink JL. Corrigendum to: Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices. Genetics 2021; 220:6489724. [PMID: 35100367 PMCID: PMC9097245 DOI: 10.1093/genetics/iyab225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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18
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theor Appl Genet 2021. [PMID: 34643760 DOI: 10.25739/8p1e-0931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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19
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theor Appl Genet 2021; 134:4043-4054. [PMID: 34643760 PMCID: PMC8580906 DOI: 10.1007/s00122-021-03946-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/05/2021] [Indexed: 05/26/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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20
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Jordan KW, Bradbury PJ, Miller ZR, Nyine M, He F, Fraser M, Anderson J, Mason E, Katz A, Pearce S, Carter AH, Prather S, Pumphrey M, Chen J, Cook J, Liu S, Rudd JC, Wang Z, Chu C, Ibrahim AMH, Turkus J, Olson E, Nagarajan R, Carver B, Yan L, Taagen E, Sorrells M, Ward B, Ren J, Akhunova A, Bai G, Bowden R, Fiedler J, Faris J, Dubcovsky J, Guttieri M, Brown-Guedira G, Buckler E, Jannink JL, Akhunov ED. Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation. G3 (Bethesda) 2021; 12:6423995. [PMID: 34751373 PMCID: PMC9210282 DOI: 10.1093/g3journal/jkab390] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/21/2021] [Indexed: 12/04/2022]
Abstract
To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10−14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.
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Affiliation(s)
- Katherine W Jordan
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Peter J Bradbury
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Zachary R Miller
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Moses Nyine
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Fei He
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Max Fraser
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jim Anderson
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Esten Mason
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Andrew Katz
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Stephen Pearce
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Samuel Prather
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Michael Pumphrey
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Jianli Chen
- Department of Plant Sciences, University of Idaho, Aberdeen, ID, 83210, USA
| | - Jason Cook
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| | - Shuyu Liu
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Jackie C Rudd
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Zhen Wang
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Chenggen Chu
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Amir M H Ibrahim
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Jonathan Turkus
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Eric Olson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Ragupathi Nagarajan
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Brett Carver
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Liuling Yan
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Ellie Taagen
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Mark Sorrells
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Brian Ward
- USDA-ARS, Plant Science Research Unit, Raleigh, NC, 27695, USA
| | - Jie Ren
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,Integrative Genomics Facility, Kansas State University, Manhattan, KS, 66506 USA
| | - Alina Akhunova
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,Integrative Genomics Facility, Kansas State University, Manhattan, KS, 66506 USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Robert Bowden
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Jason Fiedler
- USDA-ARS, Cereal Crops Research Unit, Fargo, ND, 58102, USA
| | - Justin Faris
- USDA-ARS, Cereal Crops Research Unit, Fargo, ND, 58102, USA
| | - Jorge Dubcovsky
- Department of Plant Sciences, University of California-Davis, Davis, CA, 95616, USA
| | - Mary Guttieri
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | | | - Ed Buckler
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Eduard D Akhunov
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
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21
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Wolfe MD, Chan AW, Kulakow P, Rabbi I, Jannink JL. Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices. Genetics 2021; 219:6363799. [PMID: 34740244 PMCID: PMC8570794 DOI: 10.1093/genetics/iyab122] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/13/2021] [Indexed: 11/14/2022] Open
Abstract
Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land, and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects, and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances, and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses was accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal the new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).
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Affiliation(s)
- Marnin D Wolfe
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Ariel W Chan
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850, USA.,USDA-ARS, Ithaca, NY 14850, USA
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22
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Lozano R, Booth GT, Omar BY, Li B, Buckler ES, Lis JT, Del Carpio DP, Jannink JL. RNA polymerase mapping in plants identifies intergenic regulatory elements enriched in causal variants. G3 (Bethesda) 2021; 11:6364897. [PMID: 34499719 PMCID: PMC8527479 DOI: 10.1093/g3journal/jkab273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/04/2021] [Indexed: 12/14/2022]
Abstract
Control of gene expression is fundamental at every level of cell function. Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have only been studied in a handful of research experiments in plants. PRO-Seq analysis in cassava (Manihot esculenta) identified peaks of transcriptionally engaged RNA polymerase at both the 5' and 3' end of genes, consistent with paused or slowly moving Polymerase. In addition, we identified divergent transcription at intergenic sites. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many intergenic regulatory element (IRE) candidates. These sites showed distinct patterns of methylation and nucleotide conservation based on genomic evolutionary rate profiling (GERP). SNPs within these IRE candidates explained significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, strongly suggesting a functional importance of these sites. Maize GRO-Seq data showed RNA polymerase occupancy at IREs consistent with patterns in cassava. Furthermore, these IREs in maize significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation, and were enriched for reported eQTL. Our results suggest that bidirectional transcription can identify intergenic genomic regions in plants that play an important role in transcription regulation and whose identification has the potential to aid crop improvement.
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Affiliation(s)
- Roberto Lozano
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Gregory T Booth
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | | | - Bo Li
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Science, Beijing 100101, China
| | - Edward S Buckler
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - John T Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Dunia Pino Del Carpio
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
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23
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de Sousa K, van Etten J, Poland J, Fadda C, Jannink JL, Kidane YG, Lakew BF, Mengistu DK, Pè ME, Solberg SØ, Dell'Acqua M. Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment. Commun Biol 2021; 4:944. [PMID: 34413464 PMCID: PMC8376984 DOI: 10.1038/s42003-021-02463-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers' knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.
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Affiliation(s)
- Kauê de Sousa
- Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar, Norway
- Digital Inclusion, Bioversity International, Montpellier, France
| | - Jacob van Etten
- Digital Inclusion, Bioversity International, Montpellier, France
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, USA
| | - Carlo Fadda
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi, Kenya
| | - Jean-Luc Jannink
- College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY, USA
| | - Yosef Gebrehawaryat Kidane
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi, Kenya
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Basazen Fantahun Lakew
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi, Kenya
- Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia
| | - Dejene Kassahun Mengistu
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi, Kenya
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Mario Enrico Pè
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Svein Øivind Solberg
- Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Matteo Dell'Acqua
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.
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24
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Ozimati AA, Esuma W, Alicai T, Jannink JL, Egesi C, Kawuki R. Outlook of Cassava Brown Streak Disease Assessment: Perspectives of the Screening Methods of Breeders and Pathologists. Front Plant Sci 2021; 12:648436. [PMID: 34290720 PMCID: PMC8288188 DOI: 10.3389/fpls.2021.648436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
Cassava production and productivity in Eastern, Central, and Southern Africa are ravaged by cassava brown streak disease (CBSD), causing yield losses of up to 100% when susceptible varieties are grown. Efforts to develop CBSD-resistant clones are underway. However, the methods for screening CBSD resistance currently vary between breeders and pathologists, with the limited empirical data to support their choices. In this study, we used the empirical CBSD foliar and root necrosis data from two breeding populations, termed cycle zero (C0) and cycle one (C1), to assess and compare the effectiveness of the CBSD screening methods of breeders vs. pathologists. On the one hand, the estimates of broad-sense heritability (H 2) for the CBSD root necrosis assessment of breeder ranged from 0.15 to 0.87, while for the assessment method of pathologists, H 2 varied from 0.00 to 0.71 in C0 clones. On the other hand, the marker-based heritability estimates (h 2) for C0 ranged from 0.00 to 0.70 for the assessment method of breeders and from 0.00 to 0.63 for the assessment method of pathologists. For cycle one (C1) population, where both foliar and root necrosis data were analyzed for clones assessed at clonal evaluation trials (CETs) and advanced yield trials (AYTs), H 2 varied from 0.10 to 0.59 for the assessment method of breeders, while the H 2 values ranged from 0.09 to 0.35 for the CBSD computation method of pathologists. In general, higher correlations were recorded for foliar severity from the assessment method of breeders (r = 0.4, p ≤ 0.01 for CBSD3s and r = 0.37, p ≤ 0.01 for CBSD6s) in C1 clones evaluated at both clonal and advanced breeding stages than from the approach of pathologists. Ranking of top 10 C1 clones by their indexed best linear unbiased predictors (BLUPs) for CBSD foliar and root necrosis showed four overlapping clones between clonal and advanced selection stages for the method of breeders; meanwhile, only a clone featured in both clonal and advanced selection stages from the CBSD assessment method of pathologists. Overall, the CBSD assessment method of breeders was more effective than the assessment method of pathologists, and thus, it justifies its continued use in CBSD resistance breeding.
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Affiliation(s)
- Alfred A. Ozimati
- Root Crops Program, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Williams Esuma
- Root Crops Program, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Titus Alicai
- Root Crops Program, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Jean-Luc Jannink
- College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | - Chiedozie Egesi
- College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | - Robert Kawuki
- Root Crops Program, National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
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25
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Wolfe MD, Jannink JL, Kantar MB, Santantonio N. Multi-Species Genomics-Enabled Selection for Improving Agroecosystems Across Space and Time. Front Plant Sci 2021; 12:665349. [PMID: 34249037 PMCID: PMC8261054 DOI: 10.3389/fpls.2021.665349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/12/2021] [Indexed: 05/27/2023]
Abstract
Plant breeding has been central to global increases in crop yields. Breeding deserves praise for helping to establish better food security, but also shares the responsibility of unintended consequences. Much work has been done describing alternative agricultural systems that seek to alleviate these externalities, however, breeding methods and breeding programs have largely not focused on these systems. Here we explore breeding and selection strategies that better align with these more diverse spatial and temporal agricultural systems.
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Affiliation(s)
- Marnin D. Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- United States Department of Agriculture – Agriculture Research Service, Ithaca, NY, United States
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science, University of Hawai‘i at Mānoa, Honolulu, HI, United States
| | - Nicholas Santantonio
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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26
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Campbell MT, Hu H, Yeats TH, Brzozowski LJ, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices. Front Genet 2021; 12:643733. [PMID: 33868378 PMCID: PMC8044359 DOI: 10.3389/fgene.2021.643733] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.
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Affiliation(s)
- Malachy T. Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Trevor H. Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Lauren J. Brzozowski
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Melanie Caffe-Treml
- Seed Technology Lab 113, Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, United States
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin P. Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - Mark E. Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Michael A. Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- R.W. Holley Center for Agriculture & Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY, United States
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27
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Campbell MT, Hu H, Yeats TH, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.). Genetics 2021; 217:iyaa043. [PMID: 33789350 PMCID: PMC8045723 DOI: 10.1093/genetics/iyaa043] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 12/08/2020] [Indexed: 12/13/2022] Open
Abstract
Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.
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Affiliation(s)
- Malachy T Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- R.W. Holley Center for Agriculture & Health US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853, USA
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28
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Yonis BO, Pino Del Carpio D, Wolfe M, Jannink JL, Kulakow P, Rabbi I. Improving root characterisation for genomic prediction in cassava. Sci Rep 2020; 10:8003. [PMID: 32409788 PMCID: PMC7224197 DOI: 10.1038/s41598-020-64963-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 04/23/2020] [Indexed: 11/08/2022] Open
Abstract
Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.
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Affiliation(s)
| | - Dunia Pino Del Carpio
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
- Department of Jobs, Precincts and Regions, AgriBio, Centre for AgriBioscience, Bundoora, Australia
| | - Marnin Wolfe
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - Jean-Luc Jannink
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
- US Department of Agriculture - Agricultural Research Service (USDA-ARS), Ithaca, NY, USA
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.
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29
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Blake VC, Woodhouse MR, Lazo GR, Odell SG, Wight CP, Tinker NA, Wang Y, Gu YQ, Birkett CL, Jannink JL, Matthews DE, Hane DL, Michel SL, Yao E, Sen TZ. GrainGenes: centralized small grain resources and digital platform for geneticists and breeders. Database (Oxford) 2020; 2019:5513438. [PMID: 31210272 DOI: 10.1093/database/baz065] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/18/2019] [Accepted: 04/22/2019] [Indexed: 11/13/2022]
Abstract
GrainGenes (https://wheat.pw.usda.gov or https://graingenes.org) is an international centralized repository for curated, peer-reviewed datasets useful to researchers working on wheat, barley, rye and oat. GrainGenes manages genomic, genetic, germplasm and phenotypic datasets through a dynamically generated web interface for facilitated data discovery. Since 1992, GrainGenes has served geneticists and breeders in both the public and private sectors on six continents. Recently, several new datasets were curated into the database along with new tools for analysis. The GrainGenes homepage was enhanced by making it more visually intuitive and by adding links to commonly used pages. Several genome assemblies and genomic tracks are displayed through the genome browsers at GrainGenes, including the Triticum aestivum (bread wheat) cv. 'Chinese Spring' IWGSC RefSeq v1.0 genome assembly, the Aegilops tauschii (D genome progenitor) Aet v4.0 genome assembly, the Triticum turgidum ssp. dicoccoides (wild emmer wheat) cv. 'Zavitan' WEWSeq v.1.0 genome assembly, a T. aestivum (bread wheat) pangenome, the Hordeum vulgare (barley) cv. 'Morex' IBSC genome assembly, the Secale cereale (rye) select 'Lo7' assembly, a partial hexaploid Avena sativa (oat) assembly and the Triticum durum cv. 'Svevo' (durum wheat) RefSeq Release 1.0 assembly. New genetic maps and markers were added and can be displayed through CMAP. Quantitative trait loci, genetic maps and genes from the Wheat Gene Catalogue are indexed and linked through the Wheat Information System (WheatIS) portal. Training videos were created to help users query and reach the data they need. GSP (Genome Specific Primers) and PIECE2 (Plant Intron Exon Comparison and Evolution) tools were implemented and are available to use. As more small grains reference sequences become available, GrainGenes will play an increasingly vital role in helping researchers improve crops.
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Affiliation(s)
- Victoria C Blake
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Margaret R Woodhouse
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Gerard R Lazo
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Sarah G Odell
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA.,Department of Plant Sciences, University of California, Davis, CA, USA
| | - Charlene P Wight
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Yi Wang
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Yong Q Gu
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Clay L Birkett
- Robert Holley Center, United States Department of Agriculture-Agricultural Research Service, Ithaca, NY, USA
| | - Jean-Luc Jannink
- Robert Holley Center, United States Department of Agriculture-Agricultural Research Service, Ithaca, NY, USA.,Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
| | - Dave E Matthews
- Robert Holley Center, United States Department of Agriculture-Agricultural Research Service, Ithaca, NY, USA
| | - David L Hane
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Steve L Michel
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA
| | - Eric Yao
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA.,Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Taner Z Sen
- Western Regional Research Center, Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Albany, CA, USA.,Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA
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30
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Kaya HB, Akdemir D, Lozano R, Cetin O, Sozer Kaya H, Sahin M, Smith JL, Tanyolac B, Jannink JL. Genome wide association study of 5 agronomic traits in olive (Olea europaea L.). Sci Rep 2019; 9:18764. [PMID: 31822760 PMCID: PMC6904458 DOI: 10.1038/s41598-019-55338-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 11/05/2019] [Indexed: 01/08/2023] Open
Abstract
Olive (Olea europaea L.) is one of the most economically and historically important fruit crops worldwide. Genetic progress for valuable agronomic traits has been slow in olive despite its importance and benefits. Advances in next generation sequencing technologies provide inexpensive and highly reproducible genotyping approaches such as Genotyping by Sequencing, enabling genome wide association study (GWAS). Here we present the first comprehensive GWAS study on olive using GBS. A total of 183 accessions (FULL panel) were genotyped using GBS, 94 from the Turkish Olive GenBank Resource (TOGR panel) and 89 from the USDA-ARS National Clonal Germplasm Repository (NCGR panel) in the USA. After filtering low quality and redundant markers, GWAS was conducted using 24,977 SNPs in FULL, TOGR and NCGR panels. In total, 52 significant associations were detected for leaf length, fruit weight, stone weight and fruit flesh to pit ratio using the MLM_K. Significant GWAS hits were mapped to their positions and 19 candidate genes were identified within a 10-kb distance of the most significant SNP. Our findings provide a framework for the development of markers and identification of candidate genes that could be used in olive breeding programs.
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Affiliation(s)
- Hilal Betul Kaya
- Department of Bioengineering, Faculty of Engineering, Manisa Celal Bayar University, Manisa, Turkey.
- School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, USA.
| | - Deniz Akdemir
- Cornell Statistical Consulting Unit, Cornell University, Ithaca, NY, USA
| | - Roberto Lozano
- School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, USA
| | | | | | | | - Jenny L Smith
- National Clonal Germplasm Repository, USDA-ARS, One Shields Avenue, Davis, CA, USA
| | - Bahattin Tanyolac
- Department of Bioengineering, Faculty of Engineering, Ege University, Bornova, Izmir, Turkey
| | - Jean-Luc Jannink
- School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, USA
- USDA ARS, Robert W. Holley Center for Agriculture & Health, Ithaca, NY, USA
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31
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Wolfe MD, Bauchet GJ, Chan AW, Lozano R, Ramu P, Egesi C, Kawuki R, Kulakow P, Rabbi I, Jannink JL. Historical Introgressions from a Wild Relative of Modern Cassava Improved Important Traits and May Be Under Balancing Selection. Genetics 2019; 213:1237-1253. [PMID: 31624088 PMCID: PMC6893375 DOI: 10.1534/genetics.119.302757] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 10/15/2019] [Indexed: 12/23/2022] Open
Abstract
Introgression of alleles from wild relatives has often been adaptive in plant breeding. However, the significance of historical hybridization events in modern breeding is often not clear. Cassava (Manihot esculenta) is among the most important staple foods in the world, sustaining hundreds of millions of people in the tropics, especially in sub-Saharan Africa. Widespread genotyping makes cassava a model for clonally propagated root and tuber crops in the developing world, and provides an opportunity to study the modern benefits and consequences of historical introgression. We detected large introgressed Manihot glaziovii genome-segments in a collection of 2742 modern cassava landraces and elite germplasm, the legacy of a 1930s era breeding to combat disease epidemics. African landraces and improved varieties were, on average, 3.8% (max 13.6%) introgressed. Introgressions accounted for a significant (mean 20%, max 56%) portion of the heritability of tested traits. M. glaziovii alleles on the distal 10 Mb of chr. 1 increased dry matter and root number. On chr. 4, introgressions in a 20 Mb region improved harvest index and brown streak disease tolerance. We observed the introgression frequency on chr. 1 double over three cycles of selection, and that later stage trials selectively excluded homozygotes from consideration as varieties. This indicates a heterozygous advantage of introgressions. However, we also found that maintaining large recombination-suppressed introgressions in the heterozygous state allowed the accumulation of deleterious mutations. We conclude that targeted recombination of introgressions would increase the efficiency of cassava breeding by allowing simultaneous fixation of beneficial alleles and purging of genetic load.
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Affiliation(s)
- Marnin D Wolfe
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, New York 14850
| | | | - Ariel W Chan
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, New York 14850
| | - Roberto Lozano
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, New York 14850
| | - Punna Ramu
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14850
| | - Chiedozie Egesi
- International Programs, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York 14850
- National Root Crops Research Institute (NRCRI), Umudike, Umuahia, 440221, Nigeria
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria
| | - Robert Kawuki
- National Root Crops Resources Research Institute, Namulonge, Uganda
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, New York 14850
- United States Department of Agriculture - Agriculture Research Service, Ithaca, New York 14850
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32
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Carlson MO, Montilla-Bascon G, Hoekenga OA, Tinker NA, Poland J, Baseggio M, Sorrells ME, Jannink JL, Gore MA, Yeats TH. Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat ( Avena sativa L.). G3 (Bethesda) 2019; 9:2963-2975. [PMID: 31296616 PMCID: PMC6723141 DOI: 10.1534/g3.119.400228] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/08/2019] [Indexed: 01/06/2023]
Abstract
Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.
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Affiliation(s)
- Maryn O Carlson
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | | | | | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, and
| | - Matheus Baseggio
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- R.W. Holley Center for Agriculture and Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
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Sun J, Poland JA, Mondal S, Crossa J, Juliana P, Singh RP, Rutkoski JE, Jannink JL, Crespo-Herrera L, Velu G, Huerta-Espino J, Sorrells ME. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theor Appl Genet 2019; 132:1705-1720. [PMID: 30778634 DOI: 10.1007/s00122-019-03309-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 02/06/2019] [Indexed: 05/18/2023]
Abstract
Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.
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Affiliation(s)
- Jin Sun
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jesse A Poland
- Department of Plant Pathology and Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - Jessica E Rutkoski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- International Rice Research Institute, 4030, Los Baños, Philippines
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS R.W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - Govindan Velu
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56237, Texcoco, CP, Mexico
| | - Julio Huerta-Espino
- Campo Experimental Valle de México INIFAP, Apdo. Postal 10, 56230, Chapingo, Edo. de México, Mexico
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
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Wang DR, Guadagno CR, Mao X, Mackay DS, Pleban JR, Baker RL, Weinig C, Jannink JL, Ewers BE. A framework for genomics-informed ecophysiological modeling in plants. J Exp Bot 2019; 70:2561-2574. [PMID: 30825375 PMCID: PMC6487588 DOI: 10.1093/jxb/erz090] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 02/18/2019] [Indexed: 05/06/2023]
Abstract
Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios.
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Affiliation(s)
- Diane R Wang
- Geography Department, University at Buffalo, Buffalo, NY, USA
| | | | - Xiaowei Mao
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, USA
| | - D Scott Mackay
- Geography Department, University at Buffalo, Buffalo, NY, USA
| | | | | | - Cynthia Weinig
- Botany Department, University of Wyoming, Laramie, WY, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, USA
- USDA-ARS, Ithaca, NY, USA
| | - Brent E Ewers
- Botany Department, University of Wyoming, Laramie, WY, USA
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Ozimati A, Kawuki R, Esuma W, Kayondo SI, Pariyo A, Wolfe M, Jannink JL. Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection. Crop Sci 2019; 59:460-473. [PMID: 33343017 PMCID: PMC7680944 DOI: 10.2135/cropsci2018.01.0060] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 09/27/2018] [Indexed: 05/14/2023]
Abstract
Cassava (Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population (Cycle 0, C0) and the progeny (Cycle 1, C1) derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad-sense heritability (H 2 = 0.95) and narrow-sense heritability (h 2 = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H 2 = 0.06 and h 2 = 0.00). We observed the highest genetic correlation (r g= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (r g= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (r g= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C0 and C1 was not well defined, implying that the 100 selected progenitors of C1 captured the diversity in the C0. Overall, genetic gain for most traits were observed from C0 to C1.
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Affiliation(s)
- Alfred Ozimati
- A. Ozimati, R. Kawuki, W. Esuma, S.I. Kayondo, and A. Pariyo, National Crops Resources Research Institute (NaCRRI), PO Box, 7084 Kampala, Uganda
- A. Ozimati, M. Wolfe, and J.-L. Jannink, School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell Univ., Ithaca, NY, 14853
- Corresponding author (). Assigned to Associate Editor Manjit Kang
| | - Robert Kawuki
- A. Ozimati, R. Kawuki, W. Esuma, S.I. Kayondo, and A. Pariyo, National Crops Resources Research Institute (NaCRRI), PO Box, 7084 Kampala, Uganda
| | - Williams Esuma
- A. Ozimati, R. Kawuki, W. Esuma, S.I. Kayondo, and A. Pariyo, National Crops Resources Research Institute (NaCRRI), PO Box, 7084 Kampala, Uganda
| | - Siraj I Kayondo
- A. Ozimati, R. Kawuki, W. Esuma, S.I. Kayondo, and A. Pariyo, National Crops Resources Research Institute (NaCRRI), PO Box, 7084 Kampala, Uganda
| | - Anthony Pariyo
- A. Ozimati, R. Kawuki, W. Esuma, S.I. Kayondo, and A. Pariyo, National Crops Resources Research Institute (NaCRRI), PO Box, 7084 Kampala, Uganda
| | - Marnin Wolfe
- A. Ozimati, M. Wolfe, and J.-L. Jannink, School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell Univ., Ithaca, NY, 14853
- M. Wolfe and J.-L. Jannink, USDA-ARS, R.W. Holley Center for Agriculture and Health, Ithaca, NY 14853
| | - Jean-Luc Jannink
- A. Ozimati, M. Wolfe, and J.-L. Jannink, School of Integrative Plant Science, Plant Breeding and Genetics Section, Cornell Univ., Ithaca, NY, 14853
- M. Wolfe and J.-L. Jannink, USDA-ARS, R.W. Holley Center for Agriculture and Health, Ithaca, NY 14853
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Abstract
Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions.
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Affiliation(s)
- Nicholas Santantonio
- Cornell University, Plant Breeding and Genetics Section, School of Integrated Plant Sciences, College of Agriculture and Life Sciences, Ithaca, New York 14853
| | - Jean-Luc Jannink
- Cornell University, Plant Breeding and Genetics Section, School of Integrated Plant Sciences, College of Agriculture and Life Sciences, Ithaca, New York 14853
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - Mark Sorrells
- Cornell University, Plant Breeding and Genetics Section, School of Integrated Plant Sciences, College of Agriculture and Life Sciences, Ithaca, New York 14853
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Ikeogu UN, Akdemir D, Wolfe MD, Okeke UG, Chinedozi A, Jannink JL, Egesi CN. Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots. Front Plant Sci 2019; 10:1570. [PMID: 31867030 PMCID: PMC6904298 DOI: 10.3389/fpls.2019.01570] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 11/08/2019] [Indexed: 05/21/2023]
Abstract
Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans β-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P < 0.001) with high positive values (r > 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ∼0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement.
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Affiliation(s)
- Ugochukwu N. Ikeogu
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
- *Correspondence: Ugochukwu N. Ikeogu,
| | - Deniz Akdemir
- Cornell University Statistical Consulting Unit (CSCU), Cornell University, Ithaca, NY, United States
| | - Marnin D. Wolfe
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Uche G. Okeke
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Amaefula Chinedozi
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Plant, Soil and Nutrition Research, Robert W. Holley Center for Agriculture & Health, Agricultural Research Service, United States Department of Agriculture (USDA), Ithaca, NY, United States
| | - Chiedozie N. Egesi
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
- Biotechnology Department, National Root Crops Research Institute, Umudike, Nigeria
- Cassava Breeding Department, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
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Chan AW, Williams AL, Jannink JL. A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data. BMC Bioinformatics 2018; 19:478. [PMID: 30541436 PMCID: PMC6292093 DOI: 10.1186/s12859-018-2512-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 11/19/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Researchers typically sequence a given individual multiple times, either re-sequencing the same DNA sample (technical replication) or sequencing different DNA samples collected on the same individual (biological replication) or both. Before merging the data from these replicate sequence runs, it is important to verify that no errors, such as DNA contamination or mix-ups, occurred during the data collection pipeline. Methods to detect such errors exist but are often ad hoc, cannot handle missing data and several require phased data. Because they require some combination of genotype calling, imputation, and haplotype phasing, these methods are unsuitable for error detection in low- to moderate-depth sequence data where such tasks are difficult to perform accurately. Additionally, because most existing methods employ a pairwise-comparison approach for error detection rather than joint analysis of the putative replicates, results may be difficult to interpret. RESULTS We introduce a new method for error detection suitable for shallow-, moderate-, and high-depth sequence data. Using Bayes Theorem, we calculate the posterior probability distribution over the set of relations describing the putative replicates and infer which of the samples originated from an identical genotypic source. CONCLUSIONS Our method addresses key limitations of existing approaches and produced highly accurate results in simulation experiments. Our method is implemented as an R package called BIGRED (Bayes Inferred Genotype Replicate Error Detector), which is freely available for download: https://github.com/ac2278/BIGRED .
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Affiliation(s)
- Ariel W Chan
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, 407 Bradfield Hall, Ithaca, NY, 14853, USA.
| | - Amy L Williams
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- RW Holley Center for Agriculture and Health, United States Department of Agriculture -- Agricultural Research Service, School of Integrative Plant Sciences, Cornell University, 258 Emerson Hall, Ithaca, NY, 14853, USA
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Ozimati A, Kawuki R, Esuma W, Kayondo IS, Wolfe M, Lozano R, Rabbi I, Kulakow P, Jannink JL. Training Population Optimization for Prediction of Cassava Brown Streak Disease Resistance in West African Clones. G3 (Bethesda) 2018; 8:3903-3913. [PMID: 30373913 PMCID: PMC6288821 DOI: 10.1534/g3.118.200710] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 10/13/2018] [Indexed: 02/06/2023]
Abstract
Cassava production in the central, southern and eastern parts of Africa is under threat by cassava brown streak virus (CBSV). Yield losses of up to 100% occur in cases of severe infections of edible roots. Easy illegal movement of planting materials across African countries, and long-range movement of the virus vector (Bemisia tabaci) may facilitate spread of CBSV to West Africa. Thus, effort to pre-emptively breed for CBSD resistance in W. Africa is critical. Genomic selection (GS) has become the main approach for cassava breeding, as costs of genotyping per sample have declined. Using phenotypic and genotypic data (genotyping-by-sequencing), followed by imputation to whole genome sequence (WGS) for 922 clones from National Crops Resources Research Institute, Namulonge, Uganda as a training population (TP), we predicted CBSD symptoms for 35 genotyped W. African clones, evaluated in Uganda. The highest prediction accuracy (r = 0.44) was observed for cassava brown streak disease severity scored at three months (CBSD3s) in the W. African clones using WGS-imputed markers. Optimized TPs gave higher prediction accuracies for CBSD3s and CBSD6s than random TPs of the same size. Inclusion of CBSD QTL chromosome markers as kernels, increased prediction accuracies for CBSD3s and CBSD6s. Similarly, WGS imputation of markers increased prediction accuracies for CBSD3s and for cassava brown streak disease root severity (CBSDRs), but not for CBSD6s. Based on these results we recommend TP optimization, inclusion of CBSD QTL markers in genomic prediction models, and the use of high-density (WGS-imputed) markers for CBSD predictions across population.
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Affiliation(s)
- Alfred Ozimati
- National Crops Resources Research Institute (NaCRRI), P.O. Box, 7084 Kampala, Uganda
- School of Integrative Plant Science, Plant breeding and Genetics Section, Cornell University, Ithaca, New York
| | - Robert Kawuki
- National Crops Resources Research Institute (NaCRRI), P.O. Box, 7084 Kampala, Uganda
| | - Williams Esuma
- National Crops Resources Research Institute (NaCRRI), P.O. Box, 7084 Kampala, Uganda
| | - Ismail Siraj Kayondo
- National Crops Resources Research Institute (NaCRRI), P.O. Box, 7084 Kampala, Uganda
| | - Marnin Wolfe
- School of Integrative Plant Science, Plant breeding and Genetics Section, Cornell University, Ithaca, New York
| | - Roberto Lozano
- School of Integrative Plant Science, Plant breeding and Genetics Section, Cornell University, Ithaca, New York
| | - Ismail Rabbi
- International Institute for Tropical Agriculture (IITA), Ibadan, Oyo, Nigeria
| | - Peter Kulakow
- International Institute for Tropical Agriculture (IITA), Ibadan, Oyo, Nigeria
| | - Jean-Luc Jannink
- School of Integrative Plant Science, Plant breeding and Genetics Section, Cornell University, Ithaca, New York
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca 14853, NY
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Fritsche-Neto R, Akdemir D, Jannink JL. Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theor Appl Genet 2018; 131:1603. [PMID: 29796770 DOI: 10.1007/s00122-018-3118-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Unfortunately, the first author name of the above-mentioned article was incorrectly published in the original publication. The complete correct name should read as follows.
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Affiliation(s)
- Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | | | - Jean-Luc Jannink
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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Yabe S, Iwata H, Jannink JL. Impact of Mislabeling on Genomic Selection in Cassava Breeding. Crop Sci 2018; 58:1470-1480. [PMID: 33343009 PMCID: PMC7680938 DOI: 10.2135/cropsci2017.07.0442] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 12/19/2017] [Indexed: 05/20/2023]
Abstract
In plant breeding, humans occasionally make mistakes. Genomic selection is particularly prone to human error because it involves more steps than conventional phenotypic selection. The impact of human mistakes should be determined to evaluate the cost effectiveness of controlling human error in plant breeding. We used simulation to evaluate the impact of mislabeling, where marker scores from one plant are associated with the performance records of another plant in cassava (Manihot esculenta Crantz) breeding. Results showed that, although selection with mislabeling reduced genetic gains, scenarios including six levels of mislabeling (from 5 to 50%) persisted in achieving gain because mislabeling decreased the genetic variance lost from the population. Breeding populations with higher rates of mislabeling experienced lower selection intensity, resulting in higher genetic variance, which partially compensated for the mislabeling. For low mislabeling rates (10% or less), the increased genetic variance observed under mislabeling led to improved accuracy of the prediction model in later selection cycles. Large-scale mislabeling should therefore be prevented, but the value of preventing small-scale mislabeling depends on the effort already being invested in preventing the loss of genetic variance during the course of selection. In a program, such as the one we simulated, that makes no effort to avoid loss of genetic variance, small-scale mislabeling has a less negative effect than expected. We assume that negative effects would be greater if best practices to avoid genetic variance loss were already implemented.
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Affiliation(s)
- Shiori Yabe
- Dep. of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Science, The Univ. of Tokyo, Bunkyo, Tokyo 113-8657, Japan
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, and Cornell Univ. Section of Plant Breeding and Genetics, Ithaca, NY 14853
| | - Hiroyoshi Iwata
- Dep. of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Science, The Univ. of Tokyo, Bunkyo, Tokyo 113-8657, Japan
| | - Jean-Luc Jannink
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, and Cornell Univ. Section of Plant Breeding and Genetics, Ithaca, NY 14853
- Corresponding author (). Assigned to Associate Editor Vasu Kraparthy
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Fritsche-Neto R, Akdemir D, Jannink JL. Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theor Appl Genet 2018; 131:1153-1162. [PMID: 29445844 DOI: 10.1007/s00122-018-3068-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Accepted: 02/08/2018] [Indexed: 05/02/2023]
Abstract
Testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.
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Affiliation(s)
- Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | | | - Jean-Luc Jannink
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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Elias AA, Rabbi I, Kulakow P, Jannink JL. Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition. G3 (Bethesda) 2018; 8:933-944. [PMID: 29358232 PMCID: PMC5844313 DOI: 10.1534/g3.117.300354] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 01/09/2018] [Indexed: 11/24/2022]
Abstract
Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance.
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Affiliation(s)
- Ani A Elias
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853
| | - Ismail Rabbi
- International Institute of Tropical Agriculture, Ibadan 200001, Nigeria and
| | - Peter Kulakow
- International Institute of Tropical Agriculture, Ibadan 200001, Nigeria and
| | - Jean-Luc Jannink
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853
- United States Department of Agriculture-Agricultural Research Station (USDA-ARS), Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853-2901
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44
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Okeke UG, Akdemir D, Rabbi I, Kulakow P, Jannink JL. Regional Heritability Mapping Provides Insights into Dry Matter Content in African White and Yellow Cassava Populations. Plant Genome 2018; 11:10.3835/plantgenome2017.06.0050. [PMID: 29505634 PMCID: PMC7822058 DOI: 10.3835/plantgenome2017.06.0050] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 06/20/2017] [Indexed: 05/21/2023]
Abstract
The HarvestPlus program for cassava ( Crantz) fortifies cassava with β-carotene by breeding for carotene-rich tubers (yellow cassava). However, a negative correlation between yellowness and dry matter (DM) content has been identified. We investigated the genetic control of DM in white and yellow cassava. We used regional heritability mapping (RHM) to associate DM with genomic segments in both subpopulations. Significant segments were subjected to candidate gene analysis and candidates were validated with prediction accuracies. The RHM procedure was validated via a simulation approach and revealed significant hits for white cassava on chromosomes 1, 4, 5, 10, 17, and 18, whereas hits for the yellow were on chromosome 1. Candidate gene analysis revealed genes in the carbohydrate biosynthesis pathway including plant serine-threonine protein kinases (SnRKs), UDP (uridine diphosphate)-glycosyltransferases, UDP-sugar transporters, invertases, pectinases, and regulons. Validation using 1252 unique identifiers from the SnRK gene family genome-wide recovered 50% of the predictive accuracy of whole-genome single nucleotide polymorphisms for DM, whereas validation using 53 likely genes (extracted from the literature) from significant segments recovered 32%. Genes including an acid invertase, a neutral or alkaline invertase, and a glucose-6-phosphate isomerase were validated on the basis of an a priori list for the cassava starch pathway, and also a fructose-biphosphate aldolase from the Calvin cycle pathway. The power of the RHM procedure was estimated as 47% when the causal quantitative trait loci generated 10% of the phenotypic variance (sample size = 451). Cassava DM genetics are complex and RHM may be useful for complex traits.
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Affiliation(s)
- Uche Godfrey Okeke
- Section of Plant Breeding and Genetics, School of Integrative
Plant Sci., College of Agriculture and Life Sci., Cornell Univ., 14853, Ithaca,
NY
| | - Deniz Akdemir
- Section of Plant Breeding and Genetics, School of Integrative
Plant Sci., College of Agriculture and Life Sci., Cornell Univ., 14853, Ithaca,
NY
- current address, Statgen Consulting, Ithaca, NY 14850
| | | | | | - Jean-Luc Jannink
- Section of Plant Breeding and Genetics, School of Integrative
Plant Sci., College of Agriculture and Life Sci., Cornell Univ., 14853, Ithaca,
NY
- USDAARS, Robert W. Holley Centre for Agriculture and Health, Tower
Road, Ithaca, NY 14853
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Rosas JE, Martínez S, Blanco P, Pérez de Vida F, Bonnecarrère V, Mosquera G, Cruz M, Garaycochea S, Monteverde E, McCouch S, Germán S, Jannink JL, Gutiérrez L. Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice. Plant Genome 2018; 11:170029. [PMID: 29505639 DOI: 10.3835/plantgenome2017.03.0029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Stem rot and aggregated sheath spot are the two major stem and sheath diseases affecting rice (Oryza sativa L.) in temperate areas. A third fungal disease, sheath blight, is a major disease in tropical areas. Resistance to these diseases is a key objective in rice breeding programs but phenotyping is challenged by the confounding effects of phenological and morphological traits such as flowering time (FT) and plant height (PH). This study sought to identify quantitative trait loci (QTL) for resistance to these three diseases after removing the confounding effects of FT and PH. Two populations of advanced breeding germplasm, one with 316 tropical japonica and the other with 325 indica genotypes, were evaluated in field and greenhouse trials for resistance to the diseases. Phenotypic means for field and greenhouse disease resistance, adjusted by FT and PH, were analyzed for associations with 29,000 single nucleotide polymorphisms (SNPs) in tropical japonica and 50,000 SNPs in indica. A total of 29 QTL were found for resistance that were not associated with FT or PH. Multilocus models with selected resistance-associated SNPs were fitted for each disease to estimate their effects on the other diseases. A QTL on chromosome 9 accounted for more than 15% of the phenotypic variance for the three diseases. When resistance-associated SNPs at this locus from both the tropical japonica and indica populations were incorporated into the model, resistance was improved for all three diseases with little impact on FT and PH.
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Fritsche-Neto R, Akdemir D, Jannink JL. Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theor Appl Genet 2018. [PMID: 29445844 DOI: 10.1007/s00122‐018‐3068‐8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
Abstract
KEY MESSAGE Testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.
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Affiliation(s)
- Roberto Fritsche-Neto
- Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | | | - Jean-Luc Jannink
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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Kayondo SI, Pino Del Carpio D, Lozano R, Ozimati A, Wolfe M, Baguma Y, Gracen V, Offei S, Ferguson M, Kawuki R, Jannink JL. Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta. Sci Rep 2018; 8:1549. [PMID: 29367617 PMCID: PMC5784162 DOI: 10.1038/s41598-018-19696-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 01/08/2018] [Indexed: 12/04/2022] Open
Abstract
Cassava (Manihot esculenta Crantz) is an important security crop that faces severe yield loses due to cassava brown streak disease (CBSD). Motivated by the slow progress of conventional breeding, genetic improvement of cassava is undergoing rapid change due to the implementation of quantitative trait loci mapping, Genome-wide association mapping (GWAS), and genomic selection (GS). In this study, two breeding panels were genotyped for SNP markers using genotyping by sequencing and phenotyped for foliar and CBSD root symptoms at five locations in Uganda. Our GWAS study found two regions associated to CBSD, one on chromosome 4 which co-localizes with a Manihot glaziovii introgression segment and one on chromosome 11, which contains a cluster of nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. We evaluated the potential of GS to improve CBSD resistance by assessing the accuracy of seven prediction models. Predictive accuracy values varied between CBSD foliar severity traits at 3 months after planting (MAP) (0.27-0.32), 6 MAP (0.40-0.42) and root severity (0.31-0.42). For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. Our results provide an insight into the genetics of CBSD resistance to guide CBSD marker-assisted breeding and highlight the potential of GS to improve cassava breeding.
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Affiliation(s)
- Siraj Ismail Kayondo
- National Crop Resources Research Institute, NaCRRI, P.O. Box, 7084, Kampala, Uganda.
- West Africa Center for Crop Improvement, , (WACCI), University of Ghana, Accra, Ghana.
| | - Dunia Pino Del Carpio
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
| | - Roberto Lozano
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
| | - Alfred Ozimati
- National Crop Resources Research Institute, NaCRRI, P.O. Box, 7084, Kampala, Uganda
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
| | - Marnin Wolfe
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
| | - Yona Baguma
- National Crop Resources Research Institute, NaCRRI, P.O. Box, 7084, Kampala, Uganda
| | - Vernon Gracen
- West Africa Center for Crop Improvement, , (WACCI), University of Ghana, Accra, Ghana
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
| | - Samuel Offei
- West Africa Center for Crop Improvement, , (WACCI), University of Ghana, Accra, Ghana
| | - Morag Ferguson
- International Institute for Tropical Agriculture (IITA), Nairobi, Kenya
| | - Robert Kawuki
- National Crop Resources Research Institute, NaCRRI, P.O. Box, 7084, Kampala, Uganda
| | - Jean-Luc Jannink
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA
- US Department of Agriculture, Agricultural Research Service (USDA-ARS), Ithaca, New York, USA
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48
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Ikeogu UN, Davrieux F, Dufour D, Ceballos H, Egesi CN, Jannink JL. Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS). PLoS One 2017; 12:e0188918. [PMID: 29228026 PMCID: PMC5724885 DOI: 10.1371/journal.pone.0188918] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 11/15/2017] [Indexed: 01/01/2023] Open
Abstract
Portable Vis/NIRS are flexible tools for fast and unbiased analyses of constituents with minimal sample preparation. This study developed calibration models for dry matter content (DMC) and carotenoids in fresh cassava roots using a portable Vis/NIRS system. We examined the effects of eight data pre-treatment combinations on calibration models and assessed calibrations on processed and intact root samples. We compared Vis/NIRS derived-DMC to other phenotyping methods. The results of the study showed that the combination of standard normal variate and de-trend (SNVD) with first derivative calculated on two data points and no smoothing (SNVD+1111) was adequate for a robust model. Calibration performance was higher with processed than the intact root samples for all the traits although intact root models for some traits especially total carotenoid content (TCC) (R2c = 96%, R2cv = 90%, RPD = 3.6 and SECV = 0.63) were sufficient for screening purposes. Using three key quality traits as templates, we developed models with processed fresh root samples. Robust calibrations were established for DMC (R2c = 99%, R2cv = 95%, RPD = 4.5 and SECV = 0.9), TCC (R2c = 99%, R2cv = 91%, RPD = 3.5 and SECV = 2.1) and all Trans β-carotene (ATBC) (R2c = 98%, R2cv = 91%, RPD = 3.5 and SECV = 1.6). Coefficient of determination on independent validation set (R2p) for these traits were also satisfactory for ATBC (91%), TCC (88%) and DMC (80%). Compared to other methods, Vis/NIRS-derived DMC from both intact and processed roots had very high correlation (>0.95) with the ideal oven-drying than from specific gravity method (0.49). There was equally a high correlation (0.94) between the intact and processed Vis/NIRS DMC. Therefore, the portable Vis/NIRS could be employed for the rapid analyses of DMC and quantification of carotenoids in cassava for nutritional and breeding purposes.
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Affiliation(s)
- Ugochukwu N. Ikeogu
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States of America
- National Root Crops Research Institute, Umudike, Nigeria
| | - Fabrice Davrieux
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR Qualisud, St. Pierre, Reunion Island, France
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR Qualisud, Montpellier, France
| | - Dominique Dufour
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR Qualisud, Montpellier, France
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR Qualisud, Cali, Colombia
- Centro Internacional de Agricultura Tropical (CIAT), Apartado Aéreo 6713, Cali, Colombia
| | - Hernan Ceballos
- Centro Internacional de Agricultura Tropical (CIAT), Apartado Aéreo 6713, Cali, Colombia
| | - Chiedozie N. Egesi
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States of America
- National Root Crops Research Institute, Umudike, Nigeria
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States of America
- United States Department of Agriculture (USDA), Ithaca, NY, United States of America
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49
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Rabbi IY, Udoh LI, Wolfe M, Parkes EY, Gedil MA, Dixon A, Ramu P, Jannink JL, Kulakow P. Genome-Wide Association Mapping of Correlated Traits in Cassava: Dry Matter and Total Carotenoid Content. Plant Genome 2017; 10:10.3835/plantgenome2016.09.0094. [PMID: 29293815 PMCID: PMC7822061 DOI: 10.3835/plantgenome2016.09.0094] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 06/12/2017] [Indexed: 05/19/2023]
Abstract
Cassava is a starchy root crop cultivated in the tropics for fresh consumption and commercial processing. Primary selection objectives in cassava breeding include dry matter content and micronutrient density, particularly provitamin A carotenoids. These traits are negatively correlated in the African germplasm. This study aimed at identifying genetic markers associated with these traits and uncovering whether linkage and/or pleiotropy were responsible for observed negative correlation. A genome-wide association mapping using 672 clones genotyped at 72,279 single nucleotide polymorphism (SNP) loci was performed. Root yellowness was used indirectly to assess variation in carotenoid content. Two major loci for root yellowness were identified on chromosome 1 at positions 24.1 and 30.5 Mbp. A single locus for dry matter content that colocated with the 24.1 Mbp peak for carotenoids was identified. Haplotypes at these loci explained 70 and 37% of the phenotypic variability for root yellowness and dry matter content, respectively. Evidence of megabase-scale linkage disequilibrium (LD) around the major loci of the two traits and detection of the major dry matter locus in independent analysis for the white- and yellow-root subpopulations suggests that physical linkage rather that pleiotropy is more likely to be the cause of the negative correlation between the target traits. Moreover, candidate genes for carotenoid () and starch biosynthesis ( and ) occurred in the vicinity of the identified locus at 24.1 Mbp. These findings elucidate the genetic architecture of carotenoids and dry matter in cassava and provide an opportunity to accelerate breeding of these traits.
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Affiliation(s)
- Ismail Y. Rabbi
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
- Corresponding author ()
| | - Lovina I. Udoh
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Marnin Wolfe
- Dep. of Plant Breeding and Genetics, Cornell Univ., Ithaca, NY 14853
| | - Elizabeth Y. Parkes
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Melaku A. Gedil
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Alfred Dixon
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Punna Ramu
- Institute of Genomic Diversity, Cornell Univ., Ithaca, NY 14853
| | - Jean-Luc Jannink
- Dep. of Plant Breeding and Genetics, Cornell Univ., Ithaca, NY 14853
- USDA-ARS, R.W. Holley Center for Agriculture and Health, Ithaca, NY 14853
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
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50
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Wolfe MD, Carpio DPD, Alabi O, Ezenwaka LC, Ikeogu UN, Kayondo IS, Lozano R, Okeke UG, Ozimati AA, Williams E, Egesi C, Kawuki RS, Kulakow P, Rabbi IY, Jannink JL. Prospects for Genomic Selection in Cassava Breeding. Plant Genome 2017; 10:10.3835/plantgenome2017.03.0015. [PMID: 29293806 PMCID: PMC7822052 DOI: 10.3835/plantgenome2017.03.0015] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Cassava ( Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.
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Affiliation(s)
- Marnin D. Wolfe
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- Corresponding authors (, )
| | - Dunia Pino Del Carpio
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- Corresponding authors (, )
| | - Olumide Alabi
- International Inst. for Tropical Agriculture, Ibadan, Oyo, Nigeria
| | | | - Ugochukwu N. Ikeogu
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- National Root Crops Research Inst., Umudike, Umuahia, Nigeria
| | | | - Roberto Lozano
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
| | - Uche G. Okeke
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- International Inst. for Tropical Agriculture, Ibadan, Oyo, Nigeria
| | - Alfred A. Ozimati
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- National Crops Resources Research Inst., Namulonge, Uganda
| | - Esuma Williams
- National Crops Resources Research Inst., Namulonge, Uganda
| | - Chiedozie Egesi
- International Inst. for Tropical Agriculture, Ibadan, Oyo, Nigeria
- National Root Crops Research Inst., Umudike, Umuahia, Nigeria
- International Programs, College of Agriculture and Life Sciences, Cornell Univ., Ithaca, NY
| | | | - Peter Kulakow
- International Inst. for Tropical Agriculture, Ibadan, Oyo, Nigeria
| | - Ismail Y. Rabbi
- International Inst. for Tropical Agriculture, Ibadan, Oyo, Nigeria
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, Cornell Univ., Ithaca, NY
- USDA-ARS, R.W. Holley Center for Agriculture and Health, Ithaca, NY
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