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Lorenzi A, Bauland C, Pin S, Madur D, Combes V, Palaffre C, Guillaume C, Touzy G, Mary-Huard T, Charcosset A, Moreau L. Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles. Theor Appl Genet 2024; 137:75. [PMID: 38453705 DOI: 10.1007/s00122-024-04566-4] [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: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
KEY MESSAGE We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.
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Affiliation(s)
- Alizarine Lorenzi
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Sophie Pin
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | | | - Gaëtan Touzy
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France.
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2
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Sanchez D, Allier A, Ben Sadoun S, Mary-Huard T, Bauland C, Palaffre C, Lagardère B, Madur D, Combes V, Melkior S, Bettinger L, Murigneux A, Moreau L, Charcosset A. Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize. Theor Appl Genet 2024; 137:19. [PMID: 38214870 PMCID: PMC10786986 DOI: 10.1007/s00122-023-04509-5] [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: 06/01/2023] [Accepted: 11/18/2023] [Indexed: 01/13/2024]
Abstract
KEY MESSAGE Implementing a collaborative pre-breeding multi-parental population efficiently identifies promising donor x elite pairs to enrich the flint maize elite germplasm. Genetic diversity is crucial for maintaining genetic gains and ensuring breeding programs' long-term success. In a closed breeding program, selection inevitably leads to a loss of genetic diversity. While managing diversity can delay this loss, introducing external sources of diversity is necessary to bring back favorable genetic variation. Genetic resources exhibit greater diversity than elite materials, but their lower performance levels hinder their use. This is the case for European flint maize, for which elite germplasm has incorporated only a limited portion of the diversity available in landraces. To enrich the diversity of this elite genetic pool, we established an original cooperative maize bridging population that involves crosses between private elite materials and diversity donors to create improved genotypes that will facilitate the incorporation of original favorable variations. Twenty donor × elite BC1S2 families were created and phenotyped for hybrid value for yield related traits. Crosses showed contrasted means and variances and therefore contrasted potential in terms of selection as measured by their usefulness criterion (UC). Average expected mean performance gain over the initial elite material was 5%. The most promising donor for each elite line was identified. Results also suggest that one more generation, i.e., 3 in total, of crossing to the elite is required to fully exploit the potential of a donor. Altogether, our results support the usefulness of incorporating genetic resources into elite flint maize. They call for further effort to create fixed diversity donors and identify those most suitable for each elite program.
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Affiliation(s)
- Dimitri Sanchez
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Antoine Allier
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Syngenta, 12 Chemin de L'Hobit, 31790, Saint-Sauveur, France
| | - Sarah Ben Sadoun
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Bernard Lagardère
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | | | | | - Alain Murigneux
- Limagrain Europe, 28 Route d'Ennezat, 63720, Chappes, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France.
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Beugnot A, Mary-Huard T, Bauland C, Combes V, Madur D, Lagardère B, Palaffre C, Charcosset A, Moreau L, Fievet JB. Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels. Theor Appl Genet 2023; 136:219. [PMID: 37816986 PMCID: PMC10564676 DOI: 10.1007/s00122-023-04431-w] [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: 07/08/2022] [Accepted: 07/25/2023] [Indexed: 10/12/2023]
Abstract
KEY MESSAGE An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.
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Affiliation(s)
- Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Valerie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | | | | | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Julie B Fievet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France.
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Galić V, Anđelković V, Kravić N, Grčić N, Ledenčan T, Jambrović A, Zdunić Z, Nicolas S, Charcosset A, Šatović Z, Šimić D. Genetic diversity and selection signatures in a gene bank panel of maize inbred lines from Southeast Europe compared with two West European panels. BMC Plant Biol 2023; 23:315. [PMID: 37316827 PMCID: PMC10265872 DOI: 10.1186/s12870-023-04336-2] [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] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
Southeast Europe (SEE) is a very important maize-growing region, comparable to the Corn belt region of the United States, with similar dent germplasm (dent by dent hybrids). Historically, this region has undergone several genetic material swaps, following the trends in the US, with one of the most significant swaps related to US aid programs after WWII. The imported accessions used to make double-cross hybrids were also mixed with previously adapted germplasm originating from several more distant OPVs, supporting the transition to single cross-breeding. Many of these materials were deposited at the Maize Gene Bank of the Maize Research Institute Zemun Polje (MRIZP) between the 1960s and 1980s. A part of this Gene Bank (572 inbreds) was genotyped with Affymetrix Axiom Maize Genotyping Array with 616,201 polymorphic variants. Data were merged with two other genotyping datasets with mostly European flint (TUM dataset) and dent (DROPS dataset) germplasm. The final pan-European dataset consisted of 974 inbreds and 460,243 markers. Admixture analysis showed seven ancestral populations representing European flint, B73/B14, Lancaster, B37, Wf9/Oh07, A374, and Iodent pools. Subpanel of inbreds with SEE origin showed a lack of Iodent germplasm, marking its historical context. Several signatures of selection were identified at chromosomes 1, 3, 6, 7, 8, 9, and 10. The regions under selection were mined for protein-coding genes and were used for gene ontology (GO) analysis, showing a highly significant overrepresentation of genes involved in response to stress. Our results suggest the accumulation of favorable allelic diversity, especially in the context of changing climate in the genetic resources of SEE.
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Affiliation(s)
- Vlatko Galić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia.
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia.
| | - Violeta Anđelković
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Natalija Kravić
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Nikola Grčić
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Tatjana Ledenčan
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
| | - Antun Jambrović
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Zvonimir Zdunić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Stéphane Nicolas
- GQE ‑ Le Moulon, INRAE, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif‑sur‑Yvette, 91190, France
| | - Alain Charcosset
- GQE ‑ Le Moulon, INRAE, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif‑sur‑Yvette, 91190, France
| | - Zlatko Šatović
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
- Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Domagoj Šimić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
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Arca M, Gouesnard B, Mary-Huard T, Le Paslier MC, Bauland C, Combes V, Madur D, Charcosset A, Nicolas SD. Genotyping of DNA pools identifies untapped landraces and genomic regions to develop next-generation varieties. Plant Biotechnol J 2023; 21:1123-1139. [PMID: 36740649 DOI: 10.1111/pbi.14022] [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] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 01/18/2023] [Indexed: 05/27/2023]
Abstract
Landraces, that is, traditional varieties, have a large diversity that is underexploited in modern breeding. A novel DNA pooling strategy was implemented to identify promising landraces and genomic regions to enlarge the genetic diversity of modern varieties. As proof of concept, DNA pools from 156 American and European maize landraces representing 2340 individuals were genotyped with an SNP array to assess their genome-wide diversity. They were compared to elite cultivars produced across the 20th century, represented by 327 inbred lines. Detection of selective footprints between landraces of different geographic origin identified genes involved in environmental adaptation (flowering times, growth) and tolerance to abiotic and biotic stress (drought, cold, salinity). Promising landraces were identified by developing two novel indicators that estimate their contribution to the genome of inbred lines: (i) a modified Roger's distance standardized by gene diversity and (ii) the assignation of lines to landraces using supervised analysis. It showed that most landraces do not have closely related lines and that only 10 landraces, including famous landraces as Reid's Yellow Dent, Lancaster Surecrop and Lacaune, cumulated half of the total contribution to inbred lines. Comparison of ancestral lines directly derived from landraces with lines from more advanced breeding cycles showed a decrease in the number of landraces with a large contribution. New inbred lines derived from landraces with limited contributions enriched more the haplotype diversity of reference inbred lines than those with a high contribution. Our approach opens an avenue for the identification of promising landraces for pre-breeding.
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Affiliation(s)
- Mariangela Arca
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Brigitte Gouesnard
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Cyril Bauland
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Valérie Combes
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Delphine Madur
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Stéphane D Nicolas
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
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Rio S, Charcosset A, Moreau L, Mary-Huard T. Detecting directional and non-directional epistasis in bi-parental populations using genomic data. Genetics 2023:7160556. [PMID: 37170627 DOI: 10.1093/genetics/iyad089] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/16/2023] [Accepted: 05/10/2023] [Indexed: 05/13/2023] Open
Abstract
Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred bi-parental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multi-parental populations, i.e., the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g., the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic assocations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi-/multi-parental inbred population evaluated for a trait of interest.
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Affiliation(s)
- Simon Rio
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
| | - Alain Charcosset
- Universite Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190, Gif-sur-Yvette, France
| | - Laurence Moreau
- Universite Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Universite Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190, Gif-sur-Yvette, France
- Universite Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, 91120, Palaiseau, France
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7
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Legarra A, Gonzalez-Dieguez DO, Charcosset A, Vitezica ZG. Impact of Interpopulation Distance on Dominance Variance and Average Heterosis in Hybrid Populations within Species. Genetics 2023; 224:7109763. [PMID: 37021800 DOI: 10.1093/genetics/iyad059] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/07/2023] Open
Abstract
Interpopulation improvement for crosses of close populations in crops and livestock depends on the amount of heterosis and the amount of variance of dominance deviations in the hybrids. It has been intuited that the further the distance between populations, the lower the amount of dominance variation and the higher the heterosis. Although experience in speciation and interspecific crosses shows, however, that this is not the case when populations are so distant - here we confine ourselves to the case of not-too-distant populations typical in crops and livestock. We present equations that relate the distance between two populations, expressed as Nei's genetic distance or as correlation of allele frequencies, quadratically to the amount of dominance deviations across all possible crosses and linearly to the expected heterosis averaging all possible crosses. The amount of variation of dominance deviations decreases with genetic distance until the point where allele frequencies are uncorrelated, and then increases for negatively correlated frequencies. Heterosis always increases with Nei's genetic distance. These expressions match well and complete previous theoretical and empirical findings. In practice, and for close enough populations, they mean that unless frequencies are negatively correlated, selection for hybrids will be more efficient when populations are distant.
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Affiliation(s)
- Andrés Legarra
- GenPhySE, INPT, INRAE, ENVT, F-31326, Castanet Tolosan, France
| | - David Omar Gonzalez-Dieguez
- International Maize and Wheat Improvement Center (CIMMYT), Global Wheat Program, Carretera México-Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, México
| | - Alain Charcosset
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université ́ Paris-Saclay, 91190 Gif-sur-Yvette, France
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8
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Sanchez D, Sadoun SB, Mary-Huard T, Allier A, Moreau L, Charcosset A. Improving the use of plant genetic resources to sustain breeding programs' efficiency. Proc Natl Acad Sci U S A 2023; 120:e2205780119. [PMID: 36972431 PMCID: PMC10083577 DOI: 10.1073/pnas.2205780119] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
Genetic progress of crop plants is required to face human population growth and guarantee production stability in increasingly unstable environmental conditions. Breeding is accompanied by a loss in genetic diversity, which hinders sustainable genetic gain. Methodologies based on molecular marker information have been developed to manage diversity and proved effective in increasing long-term genetic gain. However, with realistic plant breeding population sizes, diversity depletion in closed programs appears ineluctable, calling for the introduction of relevant diversity donors. Although maintained with significant efforts, genetic resource collections remain underutilized, due to a large performance gap with elite germplasm. Bridging populations created by crossing genetic resources to elite lines prior to introduction into elite programs can manage this gap efficiently. To improve this strategy, we explored with simulations different genomic prediction and genetic diversity management options for a global program involving a bridging and an elite component. We analyzed the dynamics of quantitative trait loci fixation and followed the fate of allele donors after their introduction into the breeding program. Allocating 25% of total experimental resources to create a bridging component appears highly beneficial. We showed that potential diversity donors should be selected based on their phenotype rather than genomic predictions calibrated with the ongoing breeding program. We recommend incorporating improved donors into the elite program using a global calibration of the genomic prediction model and optimal cross selection maintaining a constant diversity. These approaches use efficiently genetic resources to sustain genetic gain and maintain neutral diversity, improving the flexibility to address future breeding objectives.
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Affiliation(s)
- Dimitri Sanchez
- Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Génétique Quantitative et Evolution (GQE) Le Moulon, 91190, Gif sur Yvette, France
| | - Sarah Ben Sadoun
- Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Génétique Quantitative et Evolution (GQE) Le Moulon, 91190, Gif sur Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Génétique Quantitative et Evolution (GQE) Le Moulon, 91190, Gif sur Yvette, France
- Université Paris-Saclay, AgroParisTech, Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), UMR Mathématiques et Informatique Appliquées (MIA) Paris-Saclay, 91120, Palaiseau, France
| | | | - Laurence Moreau
- Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Génétique Quantitative et Evolution (GQE) Le Moulon, 91190, Gif sur Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Génétique Quantitative et Evolution (GQE) Le Moulon, 91190, Gif sur Yvette, France
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Lorenzi A, Bauland C, Mary-Huard T, Pin S, Palaffre C, Guillaume C, Lehermeier C, Charcosset A, Moreau L. Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage. Theor Appl Genet 2022; 135:3143-3160. [PMID: 35918515 DOI: 10.1007/s00122-022-04176-y] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Calibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and equivalent for others. In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (1) allocating each of them to a different tester for the tester design, or (2) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.
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Affiliation(s)
- Alizarine Lorenzi
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Cyril Bauland
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Sophie Pin
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | | | | | - Alain Charcosset
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Laurence Moreau
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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10
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Roth M, Beugnot A, Mary-Huard T, Moreau L, Charcosset A, Fiévet JB. Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics 2022; 220:6527635. [PMID: 35150258 PMCID: PMC8982028 DOI: 10.1093/genetics/iyac018] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, 8820 Zurich, Switzerland,Corresponding author: INRAE GAFL, 67 Allée des Chênes 84140 Montfavet, France.
| | - Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France,Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA-Paris Paris, 75005 Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Julie B Fiévet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
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11
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Laporte F, Charcosset A, Mary-Huard T. Efficient ReML inference in variance component mixed models using a Min-Max algorithm. PLoS Comput Biol 2022; 18:e1009659. [PMID: 35073307 PMCID: PMC8824334 DOI: 10.1371/journal.pcbi.1009659] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 02/08/2022] [Accepted: 11/18/2021] [Indexed: 12/04/2022] Open
Abstract
Since their introduction in the 50’s, variance component mixed models have been widely used in many application fields. In this context, ReML estimation is by far the most popular procedure to infer the variance components of the model. Although many implementations of the ReML procedure are readily available, there is still need for computational improvements due to the ever-increasing size of the datasets to be handled, and to the complexity of the models to be adjusted. In this paper, we present a Min-Max (MM) algorithm for ReML inference and combine it with several speed-up procedures. The ReML MM algorithm we present is compared to 5 state-of-the-art publicly available algorithms used in statistical genetics. The computational performance of the different algorithms are evaluated on several datasets representing different plant breeding experimental designs. The MM algorithm ranks among the top 2 methods in almost all settings and is more versatile than many of its competitors. The MM algorithm is a promising alternative to the classical AI-ReML algorithm in the context of variance component mixed models. It is available in the MM4LMM R-package. Mixed models have been a cornerstone of the quantitative genetics methodology for decades. Due to the growing size of datasets, their associated computational cost is a major burden, particularly in genome-wide association studies that routinely address Millions of markers, requiring as many mixed models to be fitted. In the particular case of a 2-variance component mixed models efficient procedures such as FaST-LMM or GEMMA have been developed to analyze panels with tens of thousands of individuals. However, there is room for improvement in cases where the computational burden is due to the number of variance components in the model rather than the panel size, a classical situation in plant genetics where several variance components are required to handle various polygenic effects. We consider a “MM” (Min-Max) algorithm as an alternative to the by-default AI (Average Information) algorithm used to perform inference in mixed models. The MM algorithm can be combined to the classical tricks used to accelerate the inference process (e.g. simultaneous orthogonalization or squared iterative acceleration methods). The MM procedure is implemented in an MM4LMM package, and is competitive compared to classical algorithms used in plant genetics. This package should help geneticists handling more complex models to analyze their data than today.
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Affiliation(s)
- Fabien Laporte
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France
- INRAE, AgroParisTech, Université Paris-Saclay, MIA-Paris, Paris, France
- * E-mail:
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12
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González-Diéguez D, Legarra A, Charcosset A, Moreau L, Lehermeier C, Teyssèdre S, Vitezica ZG. Genomic prediction of hybrid crops allows disentangling dominance and epistasis. Genetics 2021; 218:iyab026. [PMID: 33864072 PMCID: PMC8128411 DOI: 10.1093/genetics/iyab026] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [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: 12/17/2020] [Accepted: 02/06/2021] [Indexed: 12/28/2022] Open
Abstract
We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted "residual genetic" line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme-e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.
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Affiliation(s)
| | - Andrés Legarra
- INRAE, INP, UMR 1388 GenPhySE, F-31326 Castanet-Tolosan, France
| | - Alain Charcosset
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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13
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Diaw Y, Tollon-Cordet C, Charcosset A, Nicolas SD, Madur D, Ronfort J, David J, Gouesnard B. Genetic diversity of maize landraces from the South-West of France. PLoS One 2021; 16:e0238334. [PMID: 33524023 PMCID: PMC7850504 DOI: 10.1371/journal.pone.0238334] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/15/2021] [Indexed: 11/30/2022] Open
Abstract
From the 17th century until the arrival of hybrids in 1960s, maize landraces were cultivated in the South-West of France (SWF), a traditional region for maize cultivation. A set of landraces were collected in this area between the 1950s and 1980s and were then conserved ex situ in a germplam collection. Previous studies using molecular markers on approx. twenty landraces from this region suggested that they belonged to a Pyrenees-Galicia Flint genetic group and originated from hybridizations between Caribbean and Northern Flint germplasms introduced to Europe. In this study, we assessed the structure and genetic diversity of 194 SWF maize landraces to better elucidate their origin, using a 50K SNP array and a bulk DNA approach. We identified two weakly differentiated genetic groups, one in the Western part and the other in the Eastern part of the studied region. We highlighted the existence of a longitudinal gradient along the SWF area that was probably maintained through the interplay between genetic drifts and restricted gene flows. The contact zone between the two groups observed near the Garonne valley may be the result of these evolutionnary forces. We found in landraces from the East part of the region significant cases of admixture between landraces from the Northern Flint group and landraces from either the Caribbean, Andean or Italian groups. We then assumed that SWF landraces had a multiple origin with a predonderance of Northern Flint germplasm for the two SWF groups, notably for the East part.
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Affiliation(s)
- Yacine Diaw
- Institut Sénégalais de Recherches Agricoles, ISRA-CNRA de Bambey, Dakar, Sénégal
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | | | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Stéphane D. Nicolas
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Delphine Madur
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Joëlle Ronfort
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | - Jacques David
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | - Brigitte Gouesnard
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
- * E-mail:
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14
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Arca M, Mary-Huard T, Gouesnard B, Bérard A, Bauland C, Combes V, Madur D, Charcosset A, Nicolas SD. Deciphering the Genetic Diversity of Landraces With High-Throughput SNP Genotyping of DNA Bulks: Methodology and Application to the Maize 50k Array. Front Plant Sci 2021; 11:568699. [PMID: 33488638 PMCID: PMC7817617 DOI: 10.3389/fpls.2020.568699] [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: 06/01/2020] [Accepted: 11/12/2020] [Indexed: 05/13/2023]
Abstract
Genebanks harbor original landraces carrying many original favorable alleles for mitigating biotic and abiotic stresses. Their genetic diversity remains, however, poorly characterized due to their large within genetic diversity. We developed a high-throughput, cheap and labor saving DNA bulk approach based on single-nucleotide polymorphism (SNP) Illumina Infinium HD array to genotype landraces. Samples were gathered for each landrace by mixing equal weights from young leaves, from which DNA was extracted. We then estimated allelic frequencies in each DNA bulk based on fluorescent intensity ratio (FIR) between two alleles at each SNP using a two step-approach. We first tested either whether the DNA bulk was monomorphic or polymorphic according to the two FIR distributions of individuals homozygous for allele A or B, respectively. If the DNA bulk was polymorphic, we estimated its allelic frequency by using a predictive equation calibrated on FIR from DNA bulks with known allelic frequencies. Our approach: (i) gives accurate allelic frequency estimations that are highly reproducible across laboratories, (ii) protects against false detection of allele fixation within landraces. We estimated allelic frequencies of 23,412 SNPs in 156 landraces representing American and European maize diversity. Modified Roger's genetic Distance between 156 landraces estimated from 23,412 SNPs and 17 simple sequence repeats using the same DNA bulks were highly correlated, suggesting that the ascertainment bias is low. Our approach is affordable, easy to implement and does not require specific bioinformatics support and laboratory equipment, and therefore should be highly relevant for large-scale characterization of genebanks for a wide range of species.
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Affiliation(s)
- Mariangela Arca
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Brigitte Gouesnard
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Aurélie Bérard
- Université Paris-Saclay, INRAE, Etude du Polymorphisme des Génomes Végétaux, Evry-Courcouronnes, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
| | - Stéphane D. Nicolas
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, Gif-sur-Yvette, France
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15
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Rio S, Moreau L, Charcosset A, Mary-Huard T. Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize. Genetics 2020; 216:27-41. [PMID: 32680885 PMCID: PMC7463286 DOI: 10.1534/genetics.120.303278] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/10/2020] [Indexed: 02/01/2023] Open
Abstract
Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.
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Affiliation(s)
- Simon Rio
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
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16
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Seye AI, Bauland C, Charcosset A, Moreau L. Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs. Theor Appl Genet 2020; 133:1995-2010. [PMID: 32185420 DOI: 10.1007/s00122-020-03573-5] [Citation(s) in RCA: 14] [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: 08/23/2019] [Accepted: 02/28/2020] [Indexed: 06/10/2023]
Abstract
Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.
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Affiliation(s)
- A I Seye
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - C Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - A Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - L Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France.
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Allier A, Teyssèdre S, Lehermeier C, Moreau L, Charcosset A. Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genomics 2020; 21:349. [PMID: 32393177 PMCID: PMC7216646 DOI: 10.1186/s12864-020-6756-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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: 01/15/2020] [Accepted: 04/23/2020] [Indexed: 11/10/2022] Open
Abstract
Background The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. Results We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses. Conclusion Results of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.
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Affiliation(s)
- Antoine Allier
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France. .,RAGT2n, Statistical Genetics Unit, 12510, Druelle, France.
| | | | | | - Laurence Moreau
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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18
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Rio S, Mary-Huard T, Moreau L, Bauland C, Palaffre C, Madur D, Combes V, Charcosset A. Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering. PLoS Genet 2020; 16:e1008241. [PMID: 32130208 PMCID: PMC7075643 DOI: 10.1371/journal.pgen.1008241] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [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: 06/10/2019] [Revised: 03/16/2020] [Accepted: 01/29/2020] [Indexed: 12/21/2022] Open
Abstract
When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred "Flint-Dent" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.
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Affiliation(s)
- Simon Rio
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l’INRA, 40390, Saint-Martin-de-Hinx, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
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19
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Allier A, Teyssèdre S, Lehermeier C, Charcosset A, Moreau L. Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs. Theor Appl Genet 2020; 133:201-215. [PMID: 31595338 DOI: 10.1007/s00122-019-03451-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 09/28/2019] [Indexed: 05/02/2023]
Abstract
Collaborative diversity panels and genomic prediction seem relevant to identify and harness genetic resources for polygenic trait-specific enrichment of elite germplasms. In plant breeding, genetic diversity is important to maintain the pace of genetic gain and the ability to respond to new challenges in a context of climatic and social expectation changes. Many genetic resources are accessible to breeders but cannot all be considered for broadening the genetic diversity of elite germplasm. This study presents the use of genomic predictions trained on a collaborative diversity panel, which assembles genetic resources and elite lines, to identify resources to enrich an elite germplasm. A maize collaborative panel (386 lines) was considered to estimate genome-wide marker effects. Relevant predictive abilities (0.40-0.55) were observed on a large population of private elite materials, which supported the interest of such a collaborative panel for diversity management perspectives. Grain-yield estimated marker effects were used to select a donor that best complements an elite recipient at individual loci or haplotype segments, or that is expected to give the best-performing progeny with the elite. Among existing and new criteria that were compared, some gave more weight to the donor-elite complementarity than to the donor value, and appeared more adapted to long-term objective. We extended this approach to the selection of a set of donors complementing an elite population. We defined a crossing plan between identified donors and elite recipients. Our results illustrated how collaborative projects based on diversity panels including both public resources and elite germplasm can contribute to a better characterization of genetic resources in view of their use to enrich elite germplasm.
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Affiliation(s)
- Antoine Allier
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | | | | | - Alain Charcosset
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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20
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Allier A, Lehermeier C, Charcosset A, Moreau L, Teyssèdre S. Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection. Front Genet 2019; 10:1006. [PMID: 31737033 PMCID: PMC6828944 DOI: 10.3389/fgene.2019.01006] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/20/2019] [Indexed: 12/30/2022] Open
Abstract
The implementation of genomic selection in recurrent breeding programs raises the concern that a higher inbreeding rate could compromise the long-term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long-term genetic gain is therefore essential. The optimal cross-selection approach aims at identifying the optimal set of crosses that maximizes the expected genetic value in the progeny under a constraint on genetic diversity in the progeny. Optimal cross-selection usually does not account for within-family selection, i.e., the fact that only a selected fraction of each family is used as parents of the next generation. In this study, we consider within-family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the usefulness criterion parental contribution (UCPC) method. We compared UCPC-based optimal cross-selection and the optimal cross-selection approach in a long-term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC-based optimal cross-selection proved to be more efficient to convert the genetic diversity into short- and long-term genetic gains than optimal cross-selection. We also showed that, using the UCPC-based optimal cross-selection, the long-term genetic gain can be increased with only a limited reduction of the short-term commercial genetic gain.
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Affiliation(s)
- Antoine Allier
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
- Genetics and Analytics Unit, RAGT2n, Druelle, France
| | | | - Alain Charcosset
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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21
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Negro SS, Millet EJ, Madur D, Bauland C, Combes V, Welcker C, Tardieu F, Charcosset A, Nicolas SD. Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies. BMC Plant Biol 2019; 19:318. [PMID: 31311506 PMCID: PMC6636005 DOI: 10.1186/s12870-019-1926-4] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 07/05/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Single Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50 K and Affymetrix Axiom 600 K arrays). RESULTS The effects of ascertainment bias of the 50 K and 600 K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution). CONCLUSIONS Conceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.
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Affiliation(s)
- Sandra S. Negro
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Emilie J. Millet
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), UMR759, INRA, SupAgro, 34060 Montpellier, France
- Present address: Biometris, Department of Plant Science, Wageningen University and Research, 6700 AA Wageningen, The Netherlands
| | - Delphine Madur
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Cyril Bauland
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Valérie Combes
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Claude Welcker
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), UMR759, INRA, SupAgro, 34060 Montpellier, France
| | - François Tardieu
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), UMR759, INRA, SupAgro, 34060 Montpellier, France
| | - Alain Charcosset
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Stéphane D. Nicolas
- GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
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22
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Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F. Genomic prediction of maize yield across European environmental conditions. Nat Genet 2019; 51:952-956. [PMID: 31110353 DOI: 10.1038/s41588-019-0414-y] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 04/08/2019] [Indexed: 11/10/2022]
Abstract
The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3-7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
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Affiliation(s)
- Emilie J Millet
- Biometris, WUR, Wageningen, the Netherlands.,LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,Biometris, WUR, Wageningen, the Netherlands
| | | | - Aude Coupel-Ledru
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,University of Bristol, School of Biological Sciences, Bristol, UK
| | - Santiago Alvarez Prado
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.,IFEVA and CONICET, Buenos Aires, Argentina
| | | | - Sébastien Lacube
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France
| | - Alain Charcosset
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Claude Welcker
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France
| | | | - François Tardieu
- LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.
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23
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Seye AI, Bauland C, Giraud H, Mechin V, Reymond M, Charcosset A, Moreau L. Quantitative trait loci mapping in hybrids between Dent and Flint maize multiparental populations reveals group-specific QTL for silage quality traits with variable pleiotropic effects on yield. Theor Appl Genet 2019; 132:1523-1542. [PMID: 30734114 DOI: 10.1007/s00122-019-03296-2] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 01/28/2019] [Indexed: 06/09/2023]
Abstract
Silage quality traits of maize hybrids between the Dent and Flint heterotic groups mostly involved QTL specific of each parental group, some of them showing unfavorable pleiotropic effects on yield. Maize (Zea mays L.) is commonly used as silage for cattle feeding in Northern Europe. In addition to biomass production, improving whole-plant digestibility is a major breeding objective. To identify loci involved in the general (GCA, parental values) and specific combining ability (SCA, cross-specific value) components of hybrid value, we analyzed an incomplete factorial design of 951 hybrids obtained by crossing inbred lines issued from two multiparental connected populations, each specific to one of the heterotic groups used for silage in Europe ("Dent" and "Flint"). Inbred lines were genotyped for approximately 20K single nucleotide polymorphisms, and hybrids were phenotyped in eight environments for seven silage quality traits measured by near-infrared spectroscopy, biomass yield and precocity (partly analyzed in a previous study). We estimated variance components for GCA and SCA and their interaction with environment. We performed QTL detection using different models adapted to this hybrid population. Strong family effects and a predominance of GCA components compared to SCA were found for all traits. In total, 230 QTL were detected, with only two showing SCA effects significant at the whole-genome level. More than 80% of GCA QTL were specific of one heterotic group. QTL explained individually less than 5% of the phenotypic variance. QTL co-localizations and correlation between QTL effects of quality and productivity traits suggest at least partial pleiotropic effects. This work opens new prospects for improving maize hybrid performances for both biomass productivity and quality accounting for complementarities between heterotic groups.
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Affiliation(s)
- Adama I Seye
- UMR 0320, Quantitative Genetics and Evolution (GQE) - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Cyril Bauland
- UMR 0320, Quantitative Genetics and Evolution (GQE) - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Heloïse Giraud
- UMR 0320, Quantitative Genetics and Evolution (GQE) - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
- Bayer Crop Science NV, Technologiepark 38, 9052, Ghent, Belgium
| | - Valérie Mechin
- UMR 1318, Institut Jean-Pierre Bourgin, INRA-AgroParisTech, CNRS, Universite Paris-Saclay, 78026, Versailles Cedex, France
| | - Matthieu Reymond
- UMR 1318, Institut Jean-Pierre Bourgin, INRA-AgroParisTech, CNRS, Universite Paris-Saclay, 78026, Versailles Cedex, France
| | - Alain Charcosset
- UMR 0320, Quantitative Genetics and Evolution (GQE) - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- UMR 0320, Quantitative Genetics and Evolution (GQE) - Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France.
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24
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Allier A, Teyssèdre S, Lehermeier C, Claustres B, Maltese S, Melkior S, Moreau L, Charcosset A. Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program. Theor Appl Genet 2019; 132:1321-1334. [PMID: 30666392 DOI: 10.1007/s00122-019-03280-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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
We review and propose easily implemented and affordable indicators to assess the genetic diversity and the potential of a breeding population and propose solutions for its long-term management. Successful plant breeding programs rely on balanced efforts between short-term goals to develop competitive cultivars and long-term goals to improve and maintain diversity in the genetic pool. Indicators of the sustainability of response to selection in breeding pools are of key importance in this context. We reviewed and proposed sets of indicators based on temporal phenotypic and genotypic data and applied them on an early maize grain program implying two breeding pools (Dent and Flint) selected in a reciprocal manner. Both breeding populations showed a significant positive genetic gain summing up to 1.43 qx/ha/year but contrasted evolutions of genetic variance. Advances in high-throughput genotyping permitted the identification of regions of low diversity, mainly localized in pericentromeric regions. Observed changes in genetic diversity were multiple, reflecting a complex breeding system. We estimated the impact of linkage disequilibrium (LD) and of allelic diversity on the additive genetic variance at a genome-wide and chromosome-wide scale. Consistently with theoretical expectation under directional selection, we found a negative contribution of LD to genetic variance, which was unevenly distributed between chromosomes. This suggests different chromosome selection histories and underlines the interest to recombine specific chromosome regions. All three sets of indicators valorize in house data and are easy to implement in the era of genomic selection in every breeding program.
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Affiliation(s)
- Antoine Allier
- GQE ‑ Le Moulon, INRA, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | | | | | | | | | | | - Laurence Moreau
- GQE ‑ Le Moulon, INRA, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE ‑ Le Moulon, INRA, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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25
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Rio S, Mary-Huard T, Moreau L, Charcosset A. Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theor Appl Genet 2019; 132:81-96. [PMID: 30288553 DOI: 10.1007/s00122-018-3196-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 09/22/2018] [Indexed: 06/08/2023]
Abstract
Population structure affects genomic selection efficiency as well as the ability to forecast accuracy using standard GBLUP. Genomic prediction models usually assume that the individuals used for calibration belong to the same population as those to be predicted. Most of the a priori indicators of precision, such as the coefficient of determination (CD), were derived from those same models. But genetic structure is a common feature in plant species, and it may impact genomic selection efficiency and the ability to forecast prediction accuracy. We investigated the impact of genetic structure in a dent maize panel ("Amaizing Dent") using different scenarios including within- or across-group predictions. For a given training set size, the best accuracies were achieved when predicting individuals using a model calibrated on the same genetic group. Nevertheless, a diverse training set representing all the groups had a certain predictive efficiency for all the validation sets, and adding extra-group individuals was almost always beneficial. It underlines the potential of such a generic training set for dent maize genomic selection applications. Alternative prediction models, taking genetic structure explicitly into account, did not improve the prediction accuracy compared to GBLUP. We also investigated the ability of different indicators of precision to forecast accuracy in the within- or across-group scenarios. There was a global encouraging trend of the CD to differentiate scenarios, although there were specific combinations of target populations and traits where the efficiency of this indicator proved to be null. One hypothesis to explain such erratic performances is the impact of genetic structure through group-specific allele diversity at QTLs rather than group-specific allele effects.
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Affiliation(s)
- Simon Rio
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRA, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Laurence Moreau
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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26
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Darracq A, Vitte C, Nicolas S, Duarte J, Pichon JP, Mary-Huard T, Chevalier C, Bérard A, Le Paslier MC, Rogowsky P, Charcosset A, Joets J. Sequence analysis of European maize inbred line F2 provides new insights into molecular and chromosomal characteristics of presence/absence variants. BMC Genomics 2018; 19:119. [PMID: 29402214 PMCID: PMC5800051 DOI: 10.1186/s12864-018-4490-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 01/22/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Maize is well known for its exceptional structural diversity, including copy number variants (CNVs) and presence/absence variants (PAVs), and there is growing evidence for the role of structural variation in maize adaptation. While PAVs have been described in this important crop species, they have been only scarcely characterized at the sequence level and the extent of presence/absence variation and relative chromosomal landscape of inbred-specific regions remain to be elucidated. RESULTS De novo genome sequencing of the French F2 maize inbred line revealed 10,044 novel genomic regions larger than 1 kb, making up 88 Mb of DNA, that are present in F2 but not in B73 (PAV). This set of maize PAV sequences allowed us to annotate PAV content and to analyze sequence breakpoints. Using PAV genotyping on a collection of 25 temperate lines, we also analyzed Linkage Disequilibrium in PAVs and flanking regions, and PAV frequencies within maize genetic groups. CONCLUSIONS We highlight the possible role of MMEJ-type double strand break repair in maize PAV formation and discover 395 new genes with transcriptional support. Pattern of linkage disequilibrium within PAVs strikingly differs from this of flanking regions and is in accordance with the intuition that PAVs may recombine less than other genomic regions. We show that most PAVs are ancient, while some are found only in European Flint material, thus pinpointing structural features that may be at the origin of adaptive traits involved in the success of this material. Characterization of such PAVs will provide useful material for further association genetic studies in European and temperate maize.
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Affiliation(s)
- Aude Darracq
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Clémentine Vitte
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Stéphane Nicolas
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | | | - Tristan Mary-Huard
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
- MIA, INRA, AgroParisTech, Université Paris-Saclay, Paris, France
| | - Céline Chevalier
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Aurélie Bérard
- EPGV US 1279, INRA, CEA, IG-CNG, Université Paris-Saclay, Evry, France
| | | | - Peter Rogowsky
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, Lyon, France
| | - Alain Charcosset
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Johann Joets
- Genetique Quantitative et Evolution – Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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Rincent R, Charcosset A, Moreau L. Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theor Appl Genet 2017; 130:2231-2247. [PMID: 28795202 PMCID: PMC5641287 DOI: 10.1007/s00122-017-2956-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 07/26/2017] [Indexed: 05/02/2023]
Abstract
KEY MESSAGE We propose a criterion to predict genomic selection efficiency for structured populations. This criterion is useful to define optimal calibration set and to estimate prediction reliability for multiparental populations. Genomic selection refers to the use of genotypic information for predicting the performance of selection candidates. It has been shown that prediction accuracy depends on various parameters including the composition of the calibration set (CS). Assessing the level of accuracy of a given prediction scenario is of highest importance because it can be used to optimize CS sampling before collecting phenotypes, and once the breeding values are predicted it informs the breeders about the reliability of these predictions. Different criteria were proposed to optimize CS sampling in highly diverse panels, which can be useful to screen collections of genotypes. But plant breeders often work on structured material such as biparental or multiparental populations, for which these criteria are less adapted. We derived from the generalized coefficient of determination (CD) theory different criteria to optimize CS sampling and to assess the reliability associated to predictions in structured populations. These criteria were evaluated on two nested association mapping (NAM) populations and two highly diverse panels of maize. They were efficient to sample optimized CS in most situations. They could also estimate at least partly the reliability associated to predictions between NAM families, but they could not estimate differences in the reliability associated to the predictions of NAM families using the highly diverse panels as calibration sets. We illustrated that the CD criteria could be adapted to various prediction scenarios including inter and intra-family predictions, resulting in higher prediction accuracies.
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Affiliation(s)
- R Rincent
- INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 chemin de Beaulieu, 63100, Clermont-Ferrand, France.
- Université Blaise Pascal, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 63178, Aubière Cedex, France.
| | - A Charcosset
- UMR de Génétique Végétale, INRA - Université Paris-Sud - CNRS, 91190, Gif-Sur-Yvette, France
| | - L Moreau
- UMR de Génétique Végétale, INRA - Université Paris-Sud - CNRS, 91190, Gif-Sur-Yvette, France
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28
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Gouesnard B, Negro S, Laffray A, Glaubitz J, Melchinger A, Revilla P, Moreno-Gonzalez J, Madur D, Combes V, Tollon-Cordet C, Laborde J, Kermarrec D, Bauland C, Moreau L, Charcosset A, Nicolas S. Genotyping-by-sequencing highlights original diversity patterns within a European collection of 1191 maize flint lines, as compared to the maize USDA genebank. Theor Appl Genet 2017; 130:2165-2189. [PMID: 28780587 DOI: 10.1007/s00122-017-2949-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 07/08/2017] [Indexed: 06/07/2023]
Abstract
Genotyping by sequencing is suitable for analysis of global diversity in maize. We showed the distinctiveness of flint maize inbred lines of interest to enrich the diversity of breeding programs. Genotyping-by-sequencing (GBS) is a highly cost-effective procedure that permits the analysis of large collections of inbred lines. We used it to characterize diversity in 1191 maize flint inbred lines from the INRA collection, the European Cornfed association panel, and lines recently derived from landraces. We analyzed the properties of GBS data obtained with different imputation methods, through comparison with a 50 K SNP array. We identified seven ancestral groups within the Flint collection (dent, Northern flint, Italy, Pyrenees-Galicia, Argentina, Lacaune, Popcorn) in agreement with breeding knowledge. Analysis highlighted many crosses between different origins and the improvement of flint germplasm with dent germplasm. We performed association studies on different agronomic traits, revealing SNPs associated with cob color, kernel color, and male flowering time variation. We compared the diversity of both our collection and the USDA collection which has been previously analyzed by GBS. The population structure of the 4001 inbred lines confirmed the influence of the historical inbred lines (B73, A632, Oh43, Mo17, W182E, PH207, and Wf9) within the dent group. It showed distinctly different tropical and popcorn groups, a sweet-Northern flint group and a flint group sub-structured in Italian and European flint (Pyrenees-Galicia and Lacaune) groups. Interestingly, we identified several selective sweeps between dent, flint, and tropical inbred lines that co-localized with SNPs associated with flowering time variation. The joint analysis of collections by GBS offers opportunities for a global diversity analysis of maize inbred lines.
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Affiliation(s)
| | - Sandra Negro
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Amélie Laffray
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Jeff Glaubitz
- Cornell University, 135 Biotechnology Bldg, Ithaca, NY, 14853, USA
| | - Albrecht Melchinger
- University of Hohenheim, 350 Institute of Plant Breeding, Seed Science, and Population Genetics, 70593, Stuttgart, Germany
| | - Pedro Revilla
- CSIC, Misión Biológica de Galicia, Apartado 28, 36080, Pontevedra, Spain
| | - Jesus Moreno-Gonzalez
- CIAM-INGACAL, Mabegondo Agricultural Research Centre, Xunta de Galicia, Carretera AC-542 de Betanzos a Mesón do Vento, km 7, Abegondo, 15318, A Coruña, Spain
| | - Delphine Madur
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Valérie Combes
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | | | - Jacques Laborde
- INRA, Unité Expérimentale du Maïs, 40390, St Martin de Hinx, France
| | - Dominique Kermarrec
- INRA, Unité Expérimentale Ressources Génétiques Végétales en Conditions Océaniques (UERGCO), Kéraïber, 29260, Ploudaniel, France
| | - Cyril Bauland
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Laurence Moreau
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Alain Charcosset
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
| | - Stéphane Nicolas
- INRA, UMR 0320 Génétique Quantitative et Évolution, le Moulon, Ferme du Moulon, 91190, Gif/Yvette, France
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29
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Bedoya CA, Dreisigacker S, Hearne S, Franco J, Mir C, Prasanna BM, Taba S, Charcosset A, Warburton ML. Genetic diversity and population structure of native maize populations in Latin America and the Caribbean. PLoS One 2017; 12:e0173488. [PMID: 28403177 PMCID: PMC5389613 DOI: 10.1371/journal.pone.0173488] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [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: 08/12/2016] [Accepted: 02/21/2017] [Indexed: 11/19/2022] Open
Abstract
This study describes the genetic diversity and population structure of 194 native maize populations from 23 countries of Latin America and the Caribbean. The germplasm, representing 131 distinct landraces, was genetically characterized as population bulks using 28 SSR markers. Three main groups of maize germplasm were identified. The first, the Mexico and Southern Andes group, highlights the Pre-Columbian and modern exchange of germplasm between North and South America. The second group, Mesoamerica lowland, supports the hypothesis that two separate human migration events could have contributed to Caribbean maize germplasm. The third, the Andean group, displayed early introduction of maize into the Andes, with little mixing since then, other than a regional interchange zone active in the past. Events and activities in the pre- and post-Columbian Americas including the development and expansion of pre-Columbian cultures and the arrival of Europeans to the Americas are discussed in relation to the history of maize migration from its point of domestication in Mesoamerica to South America and the Caribbean through sea and land routes.
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Affiliation(s)
- Claudia A. Bedoya
- International Maize and Wheat Improvement Center, Applied Biotechnology Center, Texcoco, Mexico
- Universitat de les Illes Balears, Departament de Biologia, Illes Balears, Spain
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center, Applied Biotechnology Center, Texcoco, Mexico
| | - Sarah Hearne
- International Maize and Wheat Improvement Center, Applied Biotechnology Center, Texcoco, Mexico
| | - Jorge Franco
- Universidad de la Republica, Facultad de Agronomía, Estadística y Computación, Paysandú, Uruguay
| | - Celine Mir
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique–Université Paris Sud–Centre National de la Recherche Scientifique -AgroParisTech, Yvette, France
| | - Boddupalli M. Prasanna
- International Maize and Wheat Improvement Center, Applied Biotechnology Center, Texcoco, Mexico
| | - Suketoshi Taba
- International Maize and Wheat Improvement Center, Applied Biotechnology Center, Texcoco, Mexico
| | - Alain Charcosset
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique–Université Paris Sud–Centre National de la Recherche Scientifique -AgroParisTech, Yvette, France
| | - Marilyn L. Warburton
- United States Department of Agriculture, Corn Host Plant Research Resistance Unit, Mississippi State University, Mississippi State, MS, United States of America
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30
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Brandenburg JT, Mary-Huard T, Rigaill G, Hearne SJ, Corti H, Joets J, Vitte C, Charcosset A, Nicolas SD, Tenaillon MI. Independent introductions and admixtures have contributed to adaptation of European maize and its American counterparts. PLoS Genet 2017; 13:e1006666. [PMID: 28301472 PMCID: PMC5373671 DOI: 10.1371/journal.pgen.1006666] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [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: 07/05/2016] [Revised: 03/30/2017] [Accepted: 03/01/2017] [Indexed: 12/27/2022] Open
Abstract
Through the local selection of landraces, humans have guided the adaptation of crops to a vast range of climatic and ecological conditions. This is particularly true of maize, which was domesticated in a restricted area of Mexico but now displays one of the broadest cultivated ranges worldwide. Here, we sequenced 67 genomes with an average sequencing depth of 18x to document routes of introduction, admixture and selective history of European maize and its American counterparts. To avoid the confounding effects of recent breeding, we targeted germplasm (lines) directly derived from landraces. Among our lines, we discovered 22,294,769 SNPs and between 0.9% to 4.1% residual heterozygosity. Using a segmentation method, we identified 6,978 segments of unexpectedly high rate of heterozygosity. These segments point to genes potentially involved in inbreeding depression, and to a lesser extent to the presence of structural variants. Genetic structuring and inferences of historical splits revealed 5 genetic groups and two independent European introductions, with modest bottleneck signatures. Our results further revealed admixtures between distinct sources that have contributed to the establishment of 3 groups at intermediate latitudes in North America and Europe. We combined differentiation- and diversity-based statistics to identify both genes and gene networks displaying strong signals of selection. These include genes/gene networks involved in flowering time, drought and cold tolerance, plant defense and starch properties. Overall, our results provide novel insights into the evolutionary history of European maize and highlight a major role of admixture in environmental adaptation, paralleling recent findings in humans.
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Affiliation(s)
- Jean-Tristan Brandenburg
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Tristan Mary-Huard
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
- UMR 518 AgroParisTech/INRA, France
| | - Guillem Rigaill
- Institute of Plant Sciences Paris-Saclay, UMR 9213/UMR1403, CNRS, INRA, Université Paris-Sud, Université d’Evry, Université Paris-Diderot, Sorbonne Paris-Cité, France
| | - Sarah J. Hearne
- CIMMYT (International Maize and Wheat Improvement Centre), El Batan, Texcoco, Edo de Mexico, Mexico
| | - Hélène Corti
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Johann Joets
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Clémentine Vitte
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Alain Charcosset
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Stéphane D. Nicolas
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
| | - Maud I. Tenaillon
- Génétique Quantitative et Evolution – Le Moulon, Institut National de la Recherche agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, France
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31
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Larièpe A, Moreau L, Laborde J, Bauland C, Mezmouk S, Décousset L, Mary-Huard T, Fiévet JB, Gallais A, Dubreuil P, Charcosset A. General and specific combining abilities in a maize (Zea mays L.) test-cross hybrid panel: relative importance of population structure and genetic divergence between parents. Theor Appl Genet 2017; 130:403-417. [PMID: 27913832 DOI: 10.1007/s00122-016-2822-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 11/03/2016] [Indexed: 05/11/2023]
Abstract
General and specific combining abilities of maize hybrids between 288 inbred lines and three tester lines were highly related to population structure and genetic distance inferred from SNP data. Many studies have attempted to provide reliable and quick methods to identify promising parental lines and combinations in hybrid breeding programs. Since the 1950s, maize germplasm has been organized into heterotic groups to facilitate the exploitation of heterosis. Molecular markers have proven efficient tools to address the organization of genetic diversity and the relationship between lines or populations. The aim of the present work was to investigate to what extent marker-based evaluations of population structure and genetic distance may account for general (GCA) and specific (SCA) combining ability components in a population composed of 800 inter and intra-heterotic group hybrids obtained by crossing 288 inbred lines and three testers. Our results illustrate a strong effect of groups identified by population structure analysis on both GCA and SCA components. Including genetic distance between parental lines of hybrids in the model leads to a significant decrease of SCA variance component and an increase in GCA variance component for all the traits. The latter suggests that this approach can be efficient to better estimate the potential combining ability of inbred lines when crossed with unrelated lines, and limits the consequences of tester choice. Significant residual GCA and SCA variance components of models taking into account structure and/or genetic distance highlight the variation available for breeding programs within structure groups.
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Affiliation(s)
- A Larièpe
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
- BIOGEMMA, Genetics and Genomics in Cereals, 63720, Chappes, France
| | - L Moreau
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
| | - J Laborde
- INRA, UE 394-Unité expérimentale du maïs, 40590, St Martin De Hinx, France
| | - C Bauland
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
| | - S Mezmouk
- BIOGEMMA, Genetics and Genomics in Cereals, 63720, Chappes, France
| | - L Décousset
- BIOGEMMA, Genetics and Genomics in Cereals, 63720, Chappes, France
| | - T Mary-Huard
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
| | - J B Fiévet
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
| | - A Gallais
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
| | - P Dubreuil
- BIOGEMMA, Genetics and Genomics in Cereals, 63720, Chappes, France
| | - A Charcosset
- UMR de Génétique Végétale, INRA-Univ-Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France.
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32
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Laporte F, Charcosset A, Mary-Huard T. Estimation of the relatedness coefficients from biallelic markers, application in plant mating designs. Biometrics 2017; 73:885-894. [PMID: 28084017 DOI: 10.1111/biom.12634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 10/01/2016] [Accepted: 10/01/2016] [Indexed: 01/22/2023]
Abstract
The problem of inferring the relatedness distribution between two individuals from biallelic marker data is considered. This problem can be cast as an estimation task in a mixture model: at each marker the latent variable is the relatedness state, and the observed variable is the genotype of the two individuals. In this model, only the prior proportions are unknown, and can be obtained via ML estimation using the EM algorithm. When the markers are biallelic and the data unphased, the identifiability of the model is known not to be guaranteed. In this article, model identifiability is investigated in the case of phased data generated from a crossing design, a classical situation in plant genetics. It is shown that identifiability can be guaranteed under some conditions on the crossing design. The adapted ML estimator is implemented in an R package called Relatedness. The performance of the ML estimator is evaluated and compared to that of the benchmark moment estimator, both on simulated and real data. Compared to its competitor, the ML estimator is shown to be more robust and to provide more realistic estimates.
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Affiliation(s)
- Fabien Laporte
- INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Évolution-Le Moulon F-91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Évolution-Le Moulon F-91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Évolution-Le Moulon F-91190 Gif-sur-Yvette, France.,AgroParisTech, UMR518 MIA-Paris, F-75231 Paris Cedex 05, France INRA, UMR518 MIA-Paris F-75231 Paris Cedex 05, France
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33
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Bouchet S, Bertin P, Presterl T, Jamin P, Coubriche D, Gouesnard B, Laborde J, Charcosset A. Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits. Heredity (Edinb) 2016; 118:249-259. [PMID: 27876803 DOI: 10.1038/hdy.2016.88] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 06/28/2016] [Accepted: 07/04/2016] [Indexed: 11/10/2022] Open
Abstract
Plant architecture, phenology and yield components of cultivated plants have repeatedly been shaped by selection to meet human needs and adaptation to different environments. Here we assessed the genetic architecture of 24 correlated maize traits that interact during plant cycle. Overall, 336 lines were phenotyped in a network of 9 trials and genotyped with 50K single-nucleotide polymorphisms. Phenology was the main factor of differentiation between genetic groups. Then yield components distinguished dents from lower yielding genetic groups. However, most of trait variation occurred within group and we observed similar overall and within group correlations, suggesting a major effect of pleiotropy and/or linkage. We found 34 quantitative trait loci (QTLs) for individual traits and six for trait combinations corresponding to PCA coordinates. Among them, only five were pleiotropic. We found a cluster of QTLs in a 5 Mb region around Tb1 associated with tiller number, ear row number and the first PCA axis, the latter being positively correlated to flowering time and negatively correlated to yield. Kn1 and ZmNIP1 were candidate genes for tillering, ZCN8 for leaf number and Rubisco Activase 1 for kernel weight. Experimental repeatabilities, numbers of QTLs and proportion of explained variation were higher for traits related to plant development such as tillering, leaf number and flowering time, than for traits affected by growth such as yield components. This suggests a simpler genetic determinism with larger individual QTL effects for the first category.
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Affiliation(s)
- S Bouchet
- UMR Génétique Quantitative et Évolution-Le Moulon, INRA-Université Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, Gif-sur-Yvette, France
| | - P Bertin
- UMR Génétique Quantitative et Évolution-Le Moulon, INRA-Université Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, Gif-sur-Yvette, France
| | | | - P Jamin
- UMR Génétique Quantitative et Évolution-Le Moulon, INRA-Université Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, Gif-sur-Yvette, France
| | - D Coubriche
- UMR Génétique Quantitative et Évolution-Le Moulon, INRA-Université Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, Gif-sur-Yvette, France
| | - B Gouesnard
- INRA INRA, UMR AGAP 1334, Montpellier, France
| | - J Laborde
- INRA Stn Expt Mais, St Martin De Hinx, France
| | - A Charcosset
- UMR Génétique Quantitative et Évolution-Le Moulon, INRA-Université Paris-Sud-CNRS-AgroParisTech, Ferme du Moulon, Gif-sur-Yvette, France
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34
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Millet EJ, Welcker C, Kruijer W, Negro S, Coupel-Ledru A, Nicolas SD, Laborde J, Bauland C, Praud S, Ranc N, Presterl T, Tuberosa R, Bedo Z, Draye X, Usadel B, Charcosset A, Van Eeuwijk F, Tardieu F. Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios. Plant Physiol 2016; 172:749-764. [PMID: 27436830 PMCID: PMC5047082 DOI: 10.1104/pp.16.00621] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 07/12/2016] [Indexed: 05/18/2023]
Abstract
Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.
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Affiliation(s)
- Emilie J Millet
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Claude Welcker
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Willem Kruijer
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Sandra Negro
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Aude Coupel-Ledru
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Stéphane D Nicolas
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Jacques Laborde
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Cyril Bauland
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Sebastien Praud
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Nicolas Ranc
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Thomas Presterl
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Roberto Tuberosa
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Zoltan Bedo
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Xavier Draye
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Björn Usadel
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Alain Charcosset
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - Fred Van Eeuwijk
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
| | - François Tardieu
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)
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Fernandez O, Urrutia M, Bernillon S, Giauffret C, Tardieu F, Le Gouis J, Langlade N, Charcosset A, Moing A, Gibon Y. Fortune telling: metabolic markers of plant performance. Metabolomics 2016; 12:158. [PMID: 27729832 PMCID: PMC5025497 DOI: 10.1007/s11306-016-1099-1] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 08/16/2016] [Indexed: 02/01/2023]
Abstract
BACKGROUND In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained. AIM OF REVIEW (i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding. KEY MESSAGE Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.
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Affiliation(s)
- Olivier Fernandez
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Maria Urrutia
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Stéphane Bernillon
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | | | | | | | - Nicolas Langlade
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Alain Charcosset
- UMR GQE, INRA, CNRS, Université Paris Sud, AgroParisTech, Ferme du Moulon, 91190 Gif-Sur-Yvette, France
| | - Annick Moing
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
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36
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Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, Melchinger AE, Schön CC, Bauer E, Altmann T, Brunel D, Moreno-González J, Campo L, Ouzunova M, Álvarez Á, Ruíz de Galarreta JI, Laborde J, Malvar RA. Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biol 2016; 16:127. [PMID: 27267760 PMCID: PMC4895824 DOI: 10.1186/s12870-016-0816-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [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: 12/08/2015] [Accepted: 05/20/2016] [Indexed: 05/19/2023]
Abstract
BACKGROUND Breeding for cold tolerance in maize promises to allow increasing growth area and production in temperate zones. The objective of this research was to conduct genome-wide association analyses (GWAS) in temperate maize inbred lines and to find strategies for pyramiding genes for cold tolerance. Two panels of 306 dent and 292 European flint maize inbred lines were evaluated per se and in testcrosses under cold and control conditions in a growth chamber. We recorded indirect measures for cold tolerance as the traits number of days from sowing to emergence, relative leaf chlorophyll content or quantum efficiency of photosystem II. Association mapping for identifying genes associated to cold tolerance in both panels was based on genotyping with 49,585 genome-wide single nucleotide polymorphism (SNP) markers. RESULTS We found 275 significant associations, most of them in the inbreds evaluated per se, in the flint panel, and under control conditions. A few candidate genes coincided between the current research and previous reports. A total of 47 flint inbreds harbored the favorable alleles for six significant quantitative trait loci (QTL) detected for inbreds per se evaluated under cold conditions, four of them had also the favorable alleles for the main QTL detected from the testcrosses. Only four dent inbreds (EZ47, F924, NK807 and PHJ40) harbored the favorable alleles for three main QTL detected from the evaluation of the dent inbreds per se under cold conditions. There were more QTL in the flint panel and most of the QTL were associated with days to emergence and ΦPSII. CONCLUSIONS These results open new possibilities to genetically improve cold tolerance either with genome-wide selection or with marker assisted selection.
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Affiliation(s)
- Pedro Revilla
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), PO Box 2836080, Pontevedra, Spain.
| | - Víctor Manuel Rodríguez
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), PO Box 2836080, Pontevedra, Spain
| | - Amando Ordás
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), PO Box 2836080, Pontevedra, Spain
| | - Renaud Rincent
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS - AgroParisTech, Gif-sur-Yvette, France
| | - Alain Charcosset
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS - AgroParisTech, Gif-sur-Yvette, France
| | - Catherine Giauffret
- UMR INRA/USTL 1281 Stress Abiotiques et Différenciation des Végetaux cultivés, Péronne, France
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, Universität Hohenheim, Stuttgart, Germany
| | | | - Eva Bauer
- Plant Breeding, Technische Universität München, Freising, Germany
| | - Thomas Altmann
- Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | | | | | - Laura Campo
- Centro Investigacións Agrarias Mabegondo (CIAM), A Coruña, Spain
| | | | - Ángel Álvarez
- Estación Experimental de Aula Dei (CSIC), Saragossa, Spain
| | | | | | - Rosa Ana Malvar
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), PO Box 2836080, Pontevedra, Spain
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Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, Malvar RA, Moreno-Gonzalez J, Campo L, Melchinger AE, Schipprack W, Bauer E, Schoen CC, Meyer N, Ouzunova M, Dubreuil P, Giauffret C, Madur D, Combes V, Dumas F, Bauland C, Jamin P, Laborde J, Flament P, Moreau L, Charcosset A. Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor Appl Genet 2014; 127:2313-31. [PMID: 25301321 DOI: 10.1007/s00122-014-2379-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Accepted: 08/15/2014] [Indexed: 05/18/2023]
Abstract
Genetic and phenotypic analysis of two complementary maize panels revealed an important variation for biomass yield. Flowering and biomass QTL were discovered by association mapping in both panels. The high whole plant biomass productivity of maize makes it a potential source of energy in animal feeding and biofuel production. The variability and the genetic determinism of traits related to biomass are poorly known. We analyzed two highly diverse panels of Dent and Flint lines representing complementary heterotic groups for Northern Europe. They were genotyped with the 50 k SNP-array and phenotyped as hybrids (crossed to a tester of the complementary pool) in a western European field trial network for traits related to flowering time, plant height, and biomass. The molecular information revealed to be a powerful tool for discovering different levels of structure and relatedness in both panels. This study revealed important variation and potential genetic progress for biomass production, even at constant precocity. Association mapping was run by combining genotypes and phenotypes in a mixed model with a random polygenic effect. This permitted the detection of significant associations, confirming height and flowering time quantitative trait loci (QTL) found in literature. Biomass yield QTL were detected in both panels but were unstable across the environments. Alternative kinship estimator only based on markers unlinked to the tested SNP increased the number of significant associations by around 40% with a satisfying control of the false positive rate. This study gave insights into the variability and the genetic architectures of biomass-related traits in Flint and Dent lines and suggests important potential of these two pools for breeding high biomass yielding hybrid varieties.
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Affiliation(s)
- R Rincent
- UMR de Génétique Végétale, INRA, Université Paris-Sud, CNRS, AgroParisTech, Ferme du Moulon, 91190, Gif-Sur-Yvette, France
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38
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Bessout R, Sémont A, Demarquay C, Charcosset A, Benderitter M, Mathieu N. Mesenchymal stem cell therapy induces glucocorticoid synthesis in colonic mucosa and suppresses radiation-activated T cells: new insights into MSC immunomodulation. Mucosal Immunol 2014; 7:656-69. [PMID: 24172849 DOI: 10.1038/mi.2013.85] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Accepted: 09/16/2013] [Indexed: 02/04/2023]
Abstract
Non-neoplastic tissues around an abdomino-pelvic tumor can be damaged by the radiotherapy protocol, leading to chronic gastrointestinal complications that affect the quality of life with substantial mortality. Stem cell-based approaches using immunosuppressive bone marrow mesenchymal stem cells (MSCs) are promising cell therapy tools. In a rat model of radiation proctitis, we evidenced that a single MSC injection reduces colonic mucosa damages induced by ionizing radiation with improvement of the re-epithelization process for up to 21 days. Immune cell infiltrate and inflammatory molecule expressions in the colonic mucosa were investigated. We report that MSC therapy specifically reduces T-cell infiltration and proliferation, and increases apoptosis of radiation-activated T cells. We assessed the underlying molecular mechanisms and found that interleukin-10 and regulatory T lymphocytes are not involved in the immunosuppressive process in this model. However, an increased level of corticosterone secretion and HSD11b1 (11β-hydroxysteroid dehydrogenase type 1)-steroidogenic enzyme expression was detected in colonic mucosa 21 days after MSC treatment. Moreover, blocking the glucocorticoid (GC) receptor using the RU486 molecule statistically enhances the allogenic lymphocyte proliferation inhibited by MSCs in vitro and abrogates the mucosal protection induced by MSC treatment in vivo. Using the irradiation model, we found evidence for a new MSC immunosuppressive mechanism involving GCs.
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Affiliation(s)
- R Bessout
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
| | - A Sémont
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
| | - C Demarquay
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
| | - A Charcosset
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
| | - M Benderitter
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
| | - N Mathieu
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM, SRBE, LRTE, Fontenay-aux-Roses, France
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Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, Moreno-Gonzalez J, Nicolas S, Madur D, Combes V, Dumas F, Altmann T, Brunel D, Ouzunova M, Flament P, Dubreuil P, Charcosset A, Mary-Huard T. Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics 2014; 197:375-87. [PMID: 24532779 PMCID: PMC4012494 DOI: 10.1534/genetics.113.159731] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 02/09/2014] [Indexed: 11/18/2022] Open
Abstract
Association mapping has permitted the discovery of major QTL in many species. It can be applied to existing populations and, as a consequence, it is generally necessary to take into account structure and relatedness among individuals in the statistical model to control false positives. We analytically studied power in association studies by computing noncentrality parameter of the tests and its relationship with parameters characterizing diversity (genetic differentiation between groups and allele frequencies) and kinship between individuals. Investigation of three different maize diversity panels genotyped with the 50k SNPs array highlighted contrasted average power among panels and revealed gaps of power of classical mixed models in regions with high linkage disequilibrium (LD). These gaps could be related to the fact that markers are used for both testing association and estimating relatedness. We thus considered two alternative approaches to estimating the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers that are not located on the same chromosome than the tested SNP. In the second one, correlation between markers was taken into account to weight the contribution of each marker to the kinship. Simulations revealed that these two approaches were efficient to control false positives and were more powerful than classical models.
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Affiliation(s)
- Renaud Rincent
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
- Biogemma, Genetics and Genomics in Cereals, 63720 Chappes, France
- Kleinwanzlebener Saatzucht Saat AG, 37555 Einbeck, Germany
- Limagrain, site d’Ulice, BP173, 63204 Riom Cedex, France
| | - Laurence Moreau
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Hervé Monod
- Institut National de la Recherche Agronomique, Unité de Mathématique et Informatique Appliquées, 78352 Jouy-en-Josas, France
| | - Estelle Kuhn
- Institut National de la Recherche Agronomique, Unité de Mathématique et Informatique Appliquées, 78352 Jouy-en-Josas, France
| | - Albrecht E. Melchinger
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Rosa A. Malvar
- Misión Biológica de Galicia, Spanish National Research Council, 36080 Pontevedra, Spain
| | | | - Stéphane Nicolas
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Delphine Madur
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Valérie Combes
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Fabrice Dumas
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Thomas Altmann
- Max-Planck Institute for Molecular Plant Physiology, 14476 Potsdam-Golm and Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany
| | - Dominique Brunel
- Institut National de la Recherche Agronomique, Etude du Polymorphisme des Génomes Végétaux, Commissariat à l'Energie Atomique Institut de Génomique, Centre National de Génotypage, 91057 Evry, France
| | | | - Pascal Flament
- Limagrain, site d’Ulice, BP173, 63204 Riom Cedex, France
| | - Pierre Dubreuil
- Biogemma, Genetics and Genomics in Cereals, 63720 Chappes, France
| | - Alain Charcosset
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris-Sud, Centre National de la Recherche Scientifique, 91190 Gif-sur-Yvette, France
- Institut National de la Recherche Agronomique/AgroParisTech, Unité Mixte de Recherche 518, 75231, Paris, France
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Mir C, Zerjal T, Combes V, Dumas F, Madur D, Bedoya C, Dreisigacker S, Franco J, Grudloyma P, Hao PX, Hearne S, Jampatong C, Laloë D, Muthamia Z, Nguyen T, Prasanna BM, Taba S, Xie CX, Yunus M, Zhang S, Warburton ML, Charcosset A. Out of America: tracing the genetic footprints of the global diffusion of maize. Theor Appl Genet 2013; 126:2671-82. [PMID: 23921956 DOI: 10.1007/s00122-013-2164-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 07/12/2013] [Indexed: 05/24/2023]
Abstract
Maize was first domesticated in a restricted valley in south-central Mexico. It was diffused throughout the Americas over thousands of years, and following the discovery of the New World by Columbus, was introduced into Europe. Trade and colonization introduced it further into all parts of the world to which it could adapt. Repeated introductions, local selection and adaptation, a highly diverse gene pool and outcrossing nature, and global trade in maize led to difficulty understanding exactly where the diversity of many of the local maize landraces originated. This is particularly true in Africa and Asia, where historical accounts are scarce or contradictory. Knowledge of post-domestication movements of maize around the world would assist in germplasm conservation and plant breeding efforts. To this end, we used SSR markers to genotype multiple individuals from hundreds of representative landraces from around the world. Applying a multidisciplinary approach combining genetic, linguistic, and historical data, we reconstructed possible patterns of maize diffusion throughout the world from American "contribution" centers, which we propose reflect the origins of maize worldwide. These results shed new light on introductions of maize into Africa and Asia. By providing a first globally comprehensive genetic characterization of landraces using markers appropriate to this evolutionary time frame, we explore the post-domestication evolutionary history of maize and highlight original diversity sources that may be tapped for plant improvement in different regions of the world.
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Affiliation(s)
- C Mir
- Unité Mixte de Recherche de Génétique Végétale, Institut National de la Recherche Agronomique, Université Paris Sud, Centre National de la Recherche Scientifique (INRA), AgroParisTech, Ferme du Moulon, 91190, Gif-sur-Yvette, France
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41
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Bardol N, Ventelon M, Mangin B, Jasson S, Loywick V, Couton F, Derue C, Blanchard P, Charcosset A, Moreau L. Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theor Appl Genet 2013; 126:2717-2736. [PMID: 23975245 DOI: 10.1007/s00122-013-2167-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Accepted: 07/12/2013] [Indexed: 06/02/2023]
Abstract
Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.
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Affiliation(s)
- N Bardol
- UMR0320/UMR8120 de Génétique Végétale, INRA, Université Paris-Sud, CNRS, Ferme du Moulon, 91190, Gif-sur-Yvette, France
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42
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Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, Brunel D, Laborde J, Charcosset A, Nicolas S. Adaptation of maize to temperate climates: mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus. PLoS One 2013; 8:e71377. [PMID: 24023610 PMCID: PMC3758321 DOI: 10.1371/journal.pone.0071377] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [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: 12/07/2012] [Accepted: 07/01/2013] [Indexed: 12/22/2022] Open
Abstract
The migration of maize from tropical to temperate climates was accompanied by a dramatic evolution in flowering time. To gain insight into the genetic architecture of this adaptive trait, we conducted a 50K SNP-based genome-wide association and diversity investigation on a panel of tropical and temperate American and European representatives. Eighteen genomic regions were associated with flowering time. The number of early alleles cumulated along these regions was highly correlated with flowering time. Polymorphism in the vicinity of the ZCN8 gene, which is the closest maize homologue to Arabidopsis major flowering time (FT) gene, had the strongest effect. This polymorphism is in the vicinity of the causal factor of Vgt2 QTL. Diversity was lower, whereas differentiation and LD were higher for associated loci compared to the rest of the genome, which is consistent with selection acting on flowering time during maize migration. Selection tests also revealed supplementary loci that were highly differentiated among groups and not associated with flowering time in our panel, whereas they were in other linkage-based studies. This suggests that allele fixation led to a lack of statistical power when structure and relatedness were taken into account in a linear mixed model. Complementary designs and analysis methods are necessary to unravel the architecture of complex traits. Based on linkage disequilibrium (LD) estimates corrected for population structure, we concluded that the number of SNPs genotyped should be at least doubled to capture all QTLs contributing to the genetic architecture of polygenic traits in this panel. These results show that maize flowering time is controlled by numerous QTLs of small additive effect and that strong polygenic selection occurred under cool climatic conditions. They should contribute to more efficient genomic predictions of flowering time and facilitate the dissemination of diverse maize genetic resources under a wide range of environments.
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Affiliation(s)
- Sophie Bouchet
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
| | - Bertrand Servin
- UMR444, Laboratoire de Genetique Cellulaire, INRA, Castanet-Tolosan, France
| | - Pascal Bertin
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
| | - Delphine Madur
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
| | - Valérie Combes
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
| | - Fabrice Dumas
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
| | - Dominique Brunel
- UR1279, Etude du Polymorphisme des Génomes Végétaux, INRA, Commissariat à l'Energie Atomique (CEA) Institut de Génomique, Centre National de Génotypage, Evry, France
| | | | - Alain Charcosset
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
- * E-mail:
| | - Stéphane Nicolas
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS, Gif-sur-Yvette, France
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Valente F, Gauthier F, Bardol N, Blanc G, Joets J, Charcosset A, Moreau L. OptiMAS: a decision support tool for marker-assisted assembly of diverse alleles. ACTA ACUST UNITED AC 2013; 104:586-90. [PMID: 23576670 PMCID: PMC3678297 DOI: 10.1093/jhered/est020] [Citation(s) in RCA: 19] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Current advances in plant genotyping lead to major progress in the knowledge of genetic architecture of traits of interest. It is increasingly important to develop decision support tools to help breeders and geneticists to conduct marker-assisted selection methods to assemble favorable alleles that are discovered. Algorithms have been implemented, within an interactive graphical interface, to 1) trace parental alleles throughout generations, 2) propose strategies to select the best plants based on estimated molecular scores, and 3) efficiently intermate them depending on the expected value of their progenies. With the possibility to consider a multi-allelic context, OptiMAS opens new prospects to assemble favorable alleles issued from diverse parents and further accelerate genetic gain.
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Affiliation(s)
- Fabio Valente
- INRA, UMR 0320/UMR 8120 Génétique Végétale, F-91190 Gif-sur-Yvette, France
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Bauer E, Falque M, Walter H, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Rincent R, Schipprack W, Altmann T, Flament P, Melchinger AE, Menz M, Moreno-González J, Ouzunova M, Revilla P, Charcosset A, Martin OC, Schön CC. Intraspecific variation of recombination rate in maize. Genome Biol 2013; 14:R103. [PMID: 24050704 PMCID: PMC4053771 DOI: 10.1186/gb-2013-14-9-r103] [Citation(s) in RCA: 135] [Impact Index Per Article: 12.3] [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: 04/11/2013] [Accepted: 09/10/2013] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND In sexually reproducing organisms, meiotic crossovers ensure the proper segregation of chromosomes and contribute to genetic diversity by shuffling allelic combinations. Such genetic reassortment is exploited in breeding to combine favorable alleles, and in genetic research to identify genetic factors underlying traits of interest via linkage or association-based approaches. Crossover numbers and distributions along chromosomes vary between species, but little is known about their intraspecies variation. RESULTS Here, we report on the variation of recombination rates between 22 European maize inbred lines that belong to the Dent and Flint gene pools. We genotype 23 doubled-haploid populations derived from crosses between these lines with a 50 k-SNP array and construct high-density genetic maps, showing good correspondence with the maize B73 genome sequence assembly. By aligning each genetic map to the B73 sequence, we obtain the recombination rates along chromosomes specific to each population. We identify significant differences in recombination rates at the genome-wide, chromosome, and intrachromosomal levels between populations, as well as significant variation for genome-wide recombination rates among maize lines. Crossover interference analysis using a two-pathway modeling framework reveals a negative association between re combination rate and interference strength. CONCLUSIONS To our knowledge, the present work provides the most comprehensive study on intraspecific variation of recombination rates and crossover interference strength in eukaryotes. Differences found in recombination rates will allow for selection of high or low recombining lines in crossing programs. Our methodology should pave the way for precise identification of genes controlling recombination rates in maize and other organisms.
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Affiliation(s)
- Eva Bauer
- Plant Breeding, Technische Universität München, 85354 Freising, Germany
| | - Matthieu Falque
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS, 91190 Gif-sur-Yvette, France
| | - Hildrun Walter
- Plant Breeding, Technische Universität München, 85354 Freising, Germany
| | - Cyril Bauland
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS, 91190 Gif-sur-Yvette, France
| | | | - Laura Campo
- Centro Investigacións Agrarias Mabegondo (CIAM), 15080 La Coruña, Spain
| | | | | | - Renaud Rincent
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS, 91190 Gif-sur-Yvette, France
- Limagrain Europe, 63720 Chappes, France
- KWS SAAT AG, 37574 Einbeck, Germany
- BIOGEMMA, Genetics and Genomics in Cereals, 63720 Chappes, France
| | | | - Thomas Altmann
- Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany
| | | | | | | | | | | | - Pedro Revilla
- Misión Biológica de Galicia (CSIC), 36080 Pontevedra, Spain
| | - Alain Charcosset
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS, 91190 Gif-sur-Yvette, France
| | - Olivier C Martin
- INRA, UMR de Génétique Végétale/Université Paris-Sud - CNRS, 91190 Gif-sur-Yvette, France
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Causse M, Santoni S, Damerval C, Maurice A, Charcosset A, Deatrick J, Vienne D. A composite map of expressed sequences in maize. Genome 2012; 39:418-32. [PMID: 18469903 DOI: 10.1139/g96-053] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
A maize genetic map based mainly on expressed sequences has been constructed. The map incorporates data from four segregating populations. Three recombinant inbred line populations were derived from the nonreciprocal crosses between three inbred lines. A map derived from an independent F2 progeny from one of the crosses was also used. With a total of 521 genotyped individuals, accuracy in gene order is expected. Five sources of markers were used: (i) 109 loci corresponding to 69 genes of known function, (ii) 39 loci controlling protein position shifts revealed by two-dimensional electrophoresis, (iii) 8 isozyme loci, (iv) 17 loci corresponding to 14 sequenced cDNAs for which no homology was found in gene banks, and (v) 102 loci corresponding to 81 anonymous probes. As many loci were common to all maps, we tested heterogeneity between recombination fractions. The comparison of recombination fractions revealed: (i) a good correspondence between the maps derived from the same cross, (ii) few significant differences in interval distances, and (iii) global differences, which can reach 20% of the total map length. A composite map of 275 loci covering 1765 cM has been constructed. Key words : Zea mays L., RFLP, genetic map, molecular markers, proteins.
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Truntzler M, Ranc N, Sawkins MC, Nicolas S, Manicacci D, Lespinasse D, Ribière V, Galaup P, Servant F, Muller C, Madur D, Betran J, Charcosset A, Moreau L. Diversity and linkage disequilibrium features in a composite public/private dent maize panel: consequences for association genetics as evaluated from a case study using flowering time. Theor Appl Genet 2012; 125:731-747. [PMID: 22622520 DOI: 10.1007/s00122-012-1866-y] [Citation(s) in RCA: 9] [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: 09/27/2011] [Accepted: 04/04/2012] [Indexed: 06/01/2023]
Abstract
Recent progress in genotyping and resequencing techniques have opened new opportunities for deciphering quantitative trait variation by looking for associations between traits of interest and polymorphisms in panels of diverse inbred lines. Association mapping raises specific issues related to the choice of appropriate (i) panels and marker-densities and (ii) statistical methods to capture associations. In this study, we used a panel of 314 maize inbred lines from the dent pool, composed of inbred material from public institutes (113 inbred lines) and a private company (201 inbred lines). We showed that local LD was higher and genetic diversity lower in the material of private origin than in the public material. We compared the results obtained by different software for identifying population structure and computing relatedness among lines, and ran association tests for earliness related traits. Our results confirmed the importance of the mite polymorphism of Vgt1 on flowering time, but also showed that its effect can be captured by zmRap2.7 polymorphisms located 70 kb apart. We also highlighted associations with polymorphisms within genes putatively involved in lignin biosynthesis pathway, which deserve further investigations.
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Affiliation(s)
- M Truntzler
- INRA, UMR de Genetique Vegetale INRA/Université Paris-Sud/CNRS, Gif-sur-Yvette, France.
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Abstract
Summary: Compilation of genetic maps combined to quantitative trait loci (QTL) meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. Despite several limitations, the software is still widely used. We developed a new version proposing additional up to date methods and improving graphical representation and exploration of large datasets. Availability and implementation: BioMercator V3 is implemented in JAVA and freely available (http://moulon.inra.fr/biomercator) Contact:joets@moulon.inra.fr
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Affiliation(s)
- Olivier Sosnowski
- INRA, UMR 0320/UMR 8120 Génétique Végétale, F-91190 Gif-sur-Yvette, France
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Zerjal T, Rousselet A, Mhiri C, Combes V, Madur D, Grandbastien MA, Charcosset A, Tenaillon MI. Maize genetic diversity and association mapping using transposable element insertion polymorphisms. Theor Appl Genet 2012; 124:1521-1537. [PMID: 22350086 DOI: 10.1007/s00122-012-1807-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2011] [Accepted: 01/31/2012] [Indexed: 05/31/2023]
Abstract
Transposable elements are the major component of the maize genome and presumably highly polymorphic yet they have not been used in population genetics and association analyses. Using the Transposon Display method, we isolated and converted into PCR-based markers 33 Miniature Inverted Repeat Transposable Elements (MITE) polymorphic insertions. These polymorphisms were genotyped on a population-based sample of 26 American landraces for a total of 322 plants. Genetic diversity was high and partitioned within and among landraces. The genetic groups identified using Bayesian clustering were in agreement with published data based on SNPs and SSRs, indicating that MITE polymorphisms reflect maize genetic history. To explore the contribution of MITEs to phenotypic variation, we undertook an association mapping approach in a panel of 367 maize lines phenotyped for 26 traits. We found a highly significant association between the marker ZmV1-9, on chromosome 1, and male flowering time. The variance explained by this association is consistent with a flowering delay of +123 degree-days. This MITE insertion is located at only 289 nucleotides from the 3' end of a Cytochrome P450-like gene, a region that was never identified in previous association mapping or QTL surveys. Interestingly, we found (i) a non-synonymous mutation located in the exon 2 of the gene in strong linkage disequilibrium with the MITE polymorphism, and (ii) a perfect sequence homology between the MITE sequence and a maize siRNA that could therefore potentially interfere with the expression of the Cytochrome P450-like gene. Those two observations among others offer exciting perspectives to validate functionally the role of this region on phenotypic variation.
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Affiliation(s)
- Tatiana Zerjal
- CNRS, UMR 0320/UMR 8120 Génétique Végétale, Ferme Du Moulon, 91190 Gif sur Yvette, France.
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Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner EM, Hansen M, Joets J, Le Paslier MC, McMullen MD, Montalent P, Rose M, Schön CC, Sun Q, Walter H, Martin OC, Falque M. A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One 2011; 6:e28334. [PMID: 22174790 PMCID: PMC3234264 DOI: 10.1371/journal.pone.0028334] [Citation(s) in RCA: 378] [Impact Index Per Article: 29.1] [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: 09/14/2011] [Accepted: 11/05/2011] [Indexed: 01/05/2023] Open
Abstract
SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations - IBM (B73×Mo17) and LHRF (F2×F252) - were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.
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Affiliation(s)
| | | | | | - Aurélie Bérard
- Etude du Polymorphisme des Génomes Végétaux, INRA – CEA – Institut de Génomique – Centre National de Génotypage, Evry, France
| | | | - Alain Charcosset
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS – AgroParisTech, Gif-sur-Yvette, France
| | - Joseph D. Clarke
- Syngenta Biotechnology Inc., Research Triangle Park, North Carolina, United States of America
| | | | - Mark Hansen
- Illumina Inc., San Diego, California, United States of America
| | - Johann Joets
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS – AgroParisTech, Gif-sur-Yvette, France
| | - Marie-Christine Le Paslier
- Etude du Polymorphisme des Génomes Végétaux, INRA – CEA – Institut de Génomique – Centre National de Génotypage, Evry, France
| | - Michael D. McMullen
- Plant Genetics Research Unit, USDA-Agricultural Research Service, Columbia, Missouri, United States of America
| | - Pierre Montalent
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS – AgroParisTech, Gif-sur-Yvette, France
| | - Mark Rose
- Syngenta Biotechnology Inc., Research Triangle Park, North Carolina, United States of America
| | - Chris-Carolin Schön
- Department of Plant Breeding, Technische Universität München, Freising, Germany
| | - Qi Sun
- Cornell University, Ithaca, New York, United States of America
| | - Hildrun Walter
- Department of Plant Breeding, Technische Universität München, Freising, Germany
| | - Olivier C. Martin
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS – AgroParisTech, Gif-sur-Yvette, France
| | - Matthieu Falque
- UMR de Génétique Végétale, INRA – Université Paris-Sud – CNRS – AgroParisTech, Gif-sur-Yvette, France
- * E-mail:
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Welcker C, Sadok W, Dignat G, Renault M, Salvi S, Charcosset A, Tardieu F. A common genetic determinism for sensitivities to soil water deficit and evaporative demand: meta-analysis of quantitative trait Loci and introgression lines of maize. Plant Physiol 2011; 157:718-29. [PMID: 21795581 PMCID: PMC3192567 DOI: 10.1104/pp.111.176479] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2011] [Accepted: 07/25/2011] [Indexed: 05/19/2023]
Abstract
Evaporative demand and soil water deficit equally contribute to water stress and to its effect on plant growth. We have compared the genetic architectures of the sensitivities of maize (Zea mays) leaf elongation rate with evaporative demand and soil water deficit. The former was measured via the response to leaf-to-air vapor pressure deficit in well-watered plants, the latter via the response to soil water potential in the absence of evaporative demand. Genetic analyses of each sensitivity were performed over 21 independent experiments with (1) three mapping populations, with temperate or tropical materials, (2) one population resulting from the introgression of a tropical drought-tolerant line in a temperate line, and (3) two introgression libraries genetically independent from mapping populations. A very large genetic variability was observed for both sensitivities. Some lines maintained leaf elongation at very high evaporative demand or water deficit, while others stopped elongation in mild conditions. A complex architecture arose from analyses of mapping populations, with 19 major meta-quantitative trait loci involving strong effects and/or more than one mapping population. A total of 68% of those quantitative trait loci affected sensitivities to both evaporative demand and soil water deficit. In introgressed lines, 73% of the tested genomic regions affected both sensitivities. To our knowledge, this study is the first genetic demonstration that hydraulic processes, which drive the response to evaporative demand, also have a large contribution to the genetic variability of plant growth under water deficit in a large range of genetic material.
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