Adaptive gene loss in the common bean pan-genome during range expansion and domestication.

Phaseolus / genetics Genome, Plant Domestication Adaptation, Physiological / genetics Genotype Genetic Variation Crops, Agricultural / genetics Selection, Genetic Evolution, Molecular Mutation Plant Breeding / methods

Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
07 Aug 2024
Historique:
received: 15 12 2023
accepted: 28 07 2024
medline: 7 8 2024
pubmed: 7 8 2024
entrez: 6 8 2024
Statut: epublish

Résumé

The common bean (Phaseolus vulgaris L.) is a crucial legume crop and an ideal evolutionary model to study adaptive diversity in wild and domesticated populations. Here, we present a common bean pan-genome based on five high-quality genomes and whole-genome reads representing 339 genotypes. It reveals ~234 Mb of additional sequences containing 6,905 protein-coding genes missing from the reference, constituting 49% of all presence/absence variants (PAVs). More non-synonymous mutations are found in PAVs than core genes, probably reflecting the lower effective population size of PAVs and fitness advantages due to the purging effect of gene loss. Our results suggest pan-genome shrinkage occurred during wild range expansion. Selection signatures provide evidence that partial or complete gene loss was a key adaptive genetic change in common bean populations with major implications for plant adaptation. The pan-genome is a valuable resource for food legume research and breeding for climate change mitigation and sustainable agriculture.

Identifiants

DOI: 10.1038/s41467-024-51032-2 PMID: 39107305
pubmed: 39107305
doi: 10.1038/s41467-024-51032-2
pii: 10.1038/s41467-024-51032-2
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6698

Subventions

Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 862862

Informations de copyright

© 2024. The Author(s).

Références

Intergovernmental Panel on Climate Change (IPCC). Climate change and land. https://www.ipcc.ch/srccl/ (2019).
Gerten, D. et al. Feeding ten billion people is possible within four terrestrial planetary boundaries. Nat. Sustain. 3, 200–208 (2020).
doi: 10.1038/s41893-019-0465-1
Bellucci, E. et al. The INCREASE project: intelligent collections of food-legume genetic resources for European agrofood systems. Plant J. 108, 646–660 (2021).
pubmed: 34427014 pmcid: 9293105 doi: 10.1111/tpj.15472
Broughton, W. J. et al. Beans (Phaseolus spp.)—model food legumes. Plant Soil 252, 55–128 (2003).
doi: 10.1023/A:1024146710611
Cortinovis, G. et al. Towards the development, maintenance, and standardized phenotypic characterization of single-seed-descent genetic resources for common bean. Curr. Protoc. 1, e133 (2021).
pubmed: 34004060 doi: 10.1002/cpz1.133
Myers, J. R. & Kmiecik, K. Common bean: economic importance and relevance to biological science research. in The Common Bean Genome. Compendium of Plant Genomes (eds Pérez de la Vega, M., Santalla, M. & Marsolais, F.) (Springer, 2017).
Bitocchi, E. et al. Beans (Phaseolus ssp.) as a model for understanding crop evolution. Front. Plant Sci. 8, 722 (2017).
pubmed: 28533789 pmcid: 5420584 doi: 10.3389/fpls.2017.00722
Schmutz, J. et al. A reference genome for common bean and genome-wide analysis of dual domestications. Nat. Genet. 46, 707–713 (2014).
pubmed: 24908249 pmcid: 7048698 doi: 10.1038/ng.3008
Bitocchi, E. et al. Mesoamerican origin of the common bean (Phaseolus vulgaris L.) is revealed by sequence data. Proc. Natl Acad. Sci. 109, E788–E796 (2012).
pubmed: 22393017 pmcid: 3325731 doi: 10.1073/pnas.1108973109
Bitocchi, E. et al. Molecular analysis of the parallel domestication of the common bean (Phaseolus vulgaris) in Mesoamerica and the Andes. N. Phytologist 197, 300–313 (2013).
doi: 10.1111/j.1469-8137.2012.04377.x
Frascarelli, G. et al. The evolutionary history of the common bean (Phaseolus vulgaris) revealed by chloroplast and nuclear genomes. Preprint at bioRxiv https://doi.org/10.1101/2023.06.09.544374 (2023).
Cortinovis, G. et al. Current state and perspectives in population genomics of the common bean. Plants 9, 330 (2020).
pubmed: 32150958 pmcid: 7154925 doi: 10.3390/plants9030330
Bellucci, E. et al. Selection and adaptive introgression guided the complex evolutionary history of the European common bean. Nat. Commun. 14, 1908 (2023).
pubmed: 37019898 pmcid: 10076260 doi: 10.1038/s41467-023-37332-z
Golicz, A. A. et al. Towards plant pangenomics. Plant Biotechnol. J. 14, 1099–1105 (2016).
pubmed: 26593040 doi: 10.1111/pbi.12499
Tranchant-Dubreuil, C. et al. Plant pangenome: impacts on phenotypes and evolution. Annu. Plant Rev. 2, 453–478 (2019).
Furaste Danilevicz, M. et al. Plant pangenomics: approaches, applications and advancements. Curr. Opin. Plant Biol. 54, 18–25 (2020).
doi: 10.1016/j.pbi.2019.12.005
Khan, A. W. et al. Super-pangenome by integrating the wild side of a species for accelerated crop improvement. Trends Plant Sci. 25, 148–158 (2019).
pubmed: 31787539 doi: 10.1016/j.tplants.2019.10.012
Nei, M. et al. The bottleneck effect and genetic variability in populations. Evolution 29, 1–10 (1975).
pubmed: 28563291 doi: 10.2307/2407137
Bellucci, E. et al. Decreased nucleotide and expression diversity and modified coexpression patterns characterize domestication in the common bean. Plant Cell 26, 1901–1912 (2014).
pubmed: 24850850 pmcid: 4079357 doi: 10.1105/tpc.114.124040
Angioi, S. A. et al. Beans in Europe: origin and structure of the European landraces of Phaseolus vulgaris L. Theor. Appl. Genet. 121, 829–843 (2010).
pubmed: 20490446 doi: 10.1007/s00122-010-1353-2
Luo, M. et al. Regulation of flowering time by the histone deacetylase HDA5 in Arabidopsis. Plant J. 82, 925–936 (2015).
pubmed: 25922987 doi: 10.1111/tpj.12868
Blanco, F. A. et al. A small GTPase of the Rab family is required for root hair formation and preinfection stages of the common bean–rhizobium symbiotic association. Plant Cell 21, 2797–2810 (2009).
pubmed: 19749154 pmcid: 2768941 doi: 10.1105/tpc.108.063420
Dalla Via, V. et al. The monomeric GTPase RabA2 is required for progression and maintenance of membrane integrity of infection threads during root nodule symbiosis. Plant Mol. Biol. 93, 549–562 (2017).
pubmed: 28074430 doi: 10.1007/s11103-016-0581-5
Oladzad, A. et al. Genetic factors associated with nodulation and nitrogen derived from atmosphere in a middle American common bean panel. Front. Plant Sci. 11, 576078 (2020).
pubmed: 33384700 pmcid: 7769817 doi: 10.3389/fpls.2020.576078
Ke, D. et al. The small GTPase ROP6 interacts with NFR5 and is involved in nodule formation in Lotus japonicus. Plant Physiol. 159, 131–143 (2012).
pubmed: 22434040 pmcid: 3375957 doi: 10.1104/pp.112.197269
Corpas, F. J. et al. Tryptophan: a precursor of signaling molecules in higher plants. in Hormones and Plant Response. Plant in Challenging Environments, Vol 2 (eds Gupta, D. K. & Corpas, F. J.) (Springer, 2021).
Hottes, A. K. et al. Bacterial adaptation through loss of function. PLoS Genet. 9, e1003617 (2013).
pubmed: 23874220 pmcid: 3708842 doi: 10.1371/journal.pgen.1003617
Albalat, R. & Cañestro, C. Evolution by gene loss. Nat. Rev. Genet. 17, 379–391 (2016).
pubmed: 27087500 doi: 10.1038/nrg.2016.39
Murray, A. W. Can gene-inactivating mutations lead to evolutionary novelty? Curr. Biol. 30, R465–R471 (2020).
pubmed: 32428483 doi: 10.1016/j.cub.2020.03.072
Monroe, J. G. et al. The population genomics of adaptive loss of function. Heredity 126, 383–395 (2021).
pubmed: 33574599 doi: 10.1038/s41437-021-00403-2
Shimizu, K. K. et al. Independent origins of self-compatibility in Arabidopsis thaliana. Mol. Ecol. 17, 704–714 (2008).
pubmed: 18179433 doi: 10.1111/j.1365-294X.2007.03605.x
Olson, M. V. When less is more: gene loss as an engine of evolutionary change. Am. J. Hum. Genet. 64, 18–23 (1999).
pubmed: 9915938 pmcid: 1377697 doi: 10.1086/302219
Morris, J. J. et al. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036–12 (2012).
pubmed: 22448042 pmcid: 3315703 doi: 10.1128/mBio.00036-12
Wolf, Y. I. & Koonin, E. V. Genome reduction as the dominant mode of evolution. Bioessays 35, 829–837 (2013).
pubmed: 23801028 pmcid: 3840695 doi: 10.1002/bies.201300037
Suda, J. et al. The hidden side of plant invasions: the role of genome size. N. Phytologist 205, 994–1007 (2014).
doi: 10.1111/nph.13107
Lavergne, S. et al. Genome size reduction can trigger rapid phenotypic evolution in invasive plants. Ann. Bot. 105, 109–116 (2010).
pubmed: 19887472 doi: 10.1093/aob/mcp271
Díez, C. M. et al. Genome size variation in wild and cultivated maize along altitudinal gradients. N. Phytologist 199, 264–276 (2013).
doi: 10.1111/nph.12247
Goodstein, D. M. et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178–D1186 (2012).
pubmed: 22110026 doi: 10.1093/nar/gkr944
Lutz, K. A. et al. Isolation and analysis of high-quality nuclear DNA with reduced organellar DNA for plant genome sequencing and resequencing. BMC Biotechnol. 11, 54 (2011).
pubmed: 21599914 pmcid: 3131251 doi: 10.1186/1472-6750-11-54
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).
pubmed: 28298431 pmcid: 5411767 doi: 10.1101/gr.215087.116
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2019).
pubmed: 31819265 pmcid: 7004874 doi: 10.1038/s41592-019-0669-3
Vaser, R. et al. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).
pubmed: 28100585 pmcid: 5411768 doi: 10.1101/gr.214270.116
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).
pubmed: 25409509 pmcid: 4237348 doi: 10.1371/journal.pone.0112963
Simão, F. A. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
pubmed: 26059717 doi: 10.1093/bioinformatics/btv351
Cheng, H. et al. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).
pubmed: 33526886 pmcid: 7961889 doi: 10.1038/s41592-020-01056-5
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
pubmed: 29750242 pmcid: 6137996 doi: 10.1093/bioinformatics/bty191
Jayakodi, M. et al. The barley pan-genome reveals the hidden legacy of mutation breeding. Nature 588, 284–289 (2020).
pubmed: 33239781 pmcid: 7759462 doi: 10.1038/s41586-020-2947-8
Nattestad, M. & Schatz, M. C. Assemblytics: a web analytics tool for the detection of variants from an assembly. Bioinformatics 32, 3021–3023 (2016). 201.
pubmed: 27318204 pmcid: 6191160 doi: 10.1093/bioinformatics/btw369
Li, H. et al. The sequence alignment/map format and SAMTools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943 pmcid: 2723002 doi: 10.1093/bioinformatics/btp352
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
pubmed: 16731699 doi: 10.1093/bioinformatics/btl158
Chen, S. et al. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
pubmed: 30423086 pmcid: 6129281 doi: 10.1093/bioinformatics/bty560
Langmead, B. & Salzberg, S. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286 pmcid: 3322381 doi: 10.1038/nmeth.1923
Zimin, A. V. et al. The MaSuRCA genome assembler. Bioinformatics 29, 2669–2677 (2013).
pubmed: 23990416 pmcid: 3799473 doi: 10.1093/bioinformatics/btt476
Flynn, J. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. 117, 9451–9457 (2020).
pubmed: 32300014 pmcid: 7196820 doi: 10.1073/pnas.1921046117
Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinforma. 4, 4.10.1–4.10.14 (2009).
Hoff, K. J. & Stanke, M. Predicting genes in single genomes with AUGUSTUS. Curr. Protoc. Bioinforma. 65, e57 (2019).
doi: 10.1002/cpbi.57
Kim, D. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).
pubmed: 31375807 pmcid: 7605509 doi: 10.1038/s41587-019-0201-4
Gremme, G. et al. Engineering a software tool for gene structure prediction in higher organisms. Inf. Softw. Technol. 47, 965–978 (2005).
doi: 10.1016/j.infsof.2005.09.005
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
pubmed: 24451626 pmcid: 3998142 doi: 10.1093/bioinformatics/btu031
Altschul, S. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
pubmed: 2231712 doi: 10.1016/S0022-2836(05)80360-2
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
pubmed: 31727128 pmcid: 6857279 doi: 10.1186/s13059-019-1832-y
Golicz, A. A. et al. The pangenome of an agronomically important crop plant Brassica oleracea. Nat. Commun. 7, 13390 (2016).
pubmed: 27834372 pmcid: 5114598 doi: 10.1038/ncomms13390
Montenegro, J. D. et al. The pangenome of hexaploid bread wheat. Plant J. 90, 1007–1013 (2017).
pubmed: 28231383 doi: 10.1111/tpj.13515
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
pubmed: 21903627 pmcid: 3198575 doi: 10.1093/bioinformatics/btr509
Yu, G. et al. ClusterProfiler: an R package for comparing biological themes among gene clusters OMICS: A. J. Integr. Biol. 16, 284–287 (2012).
Wu, T. et al. ClusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
pubmed: 34557778 pmcid: 8454663
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
pubmed: 30944313 pmcid: 6447622 doi: 10.1038/s41467-019-09234-6
Landgraf, A. J. & Lee, Y. Dimensionality reduction for binary data through the projection of natural parameters. J. Multivar. Anal. 180, 104668 (2020).
doi: 10.1016/j.jmva.2020.104668
Wang, D. et al. KaKs Calculator 2.0: a toolkit incorporating gamma series methods and sliding window strategies. Genom. Proteom. Bioinforma. 8, 77–80 (2010).
doi: 10.1016/S1672-0229(10)60008-3
Wright, S. The genetical structure of populations. Ann. Eugen. 15, 323–354 (1951).
pubmed: 24540312 doi: 10.1111/j.1469-1809.1949.tb02451.x
Tamura, K. et al. MEGA11: molecular evolutionary genetics analysis. Mol. Biol. Evol. 38, 3022–3027 (2021).
pubmed: 33892491 pmcid: 8233496 doi: 10.1093/molbev/msab120
Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).
pubmed: 16380716 doi: 10.1038/ng1702
Liu, X. et al. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 12, e1005767 (2016).
pubmed: 26828793 pmcid: 4734661 doi: 10.1371/journal.pgen.1005767
Wang, J. & Zhang, Z. GAPIT Version 3: boosting power and accuracy for genomic association and prediction. Genom. Proteom. Bioinforma. 19, 629–640 (2021).
doi: 10.1016/j.gpb.2021.08.005
Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).
pubmed: 17586829 doi: 10.1093/bioinformatics/btm308

Auteurs

Gaia Cortinovis (G)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.

Leonardo Vincenzi (L)

Department of Biotechnology, University of Verona, 37134, Verona, Italy.
ORCID: 0009-0009-9116-349X

Robyn Anderson (R)

Centre for Applied Bioinformatics and School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia.
ORCID: 0000-0002-4872-3932

Giovanni Marturano (G)

Department of Biotechnology, University of Verona, 37134, Verona, Italy.

Jacob Ian Marsh (JI)

Centre for Applied Bioinformatics and School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia.

Philipp Emanuel Bayer (PE)

Centre for Applied Bioinformatics and School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia.
ORCID: 0000-0001-8530-3067

Lorenzo Rocchetti (L)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.

Giulia Frascarelli (G)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.

Giovanna Lanzavecchia (G)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.

Alice Pieri (A)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.

Andrea Benazzo (A)

Department of Life Sciences and Biotechnology, University of Ferrara, 44100, Ferrara, Italy.

Elisa Bellucci (E)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.
ORCID: 0000-0002-1636-0719

Valerio Di Vittori (V)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.
ORCID: 0000-0003-3796-8587

Laura Nanni (L)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.
ORCID: 0000-0002-1249-5165

Juan José Ferreira Fernández (JJ)

Regional Service for Agrofood Research and Development (SERIDA), Ctra AS-267 PK 19, 33300, Asturias, Spain.
ORCID: 0000-0002-8782-8868

Marzia Rossato (M)

Department of Biotechnology, University of Verona, 37134, Verona, Italy.
Genartis s.r.l, 37126, Verona, Italy.
ORCID: 0000-0002-6101-1550

Orlando Mario Aguilar (OM)

Institute of Biotechnology and Molecular Biology, UNLP-CONICET, CCT La Plata, La Plata, Argentina.

Peter Laurent Morrell (PL)

Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108-6026, USA.
ORCID: 0000-0001-6282-1582

Monica Rodriguez (M)

Department of Agriculture, University of Sassari, 07100, Sassari, Italy.
CBV-Centro per la Conservazione e Valorizzazione della Biodiversità Vegetale, University of Sassari, 07041, Alghero, Italy.
ORCID: 0000-0001-8926-2987

Tania Gioia (T)

School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, 85100, Potenza, Italy.
ORCID: 0000-0001-8980-3034

Kerstin Neumann (K)

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Seeland, Germany.

Juan Camilo Alvarez Diaz (JC)

CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), University of Evry, University Paris-Saclay, 91405, Orsay, France.

Ariane Gratias (A)

CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), University of Evry, University Paris-Saclay, 91405, Orsay, France.

Christophe Klopp (C)

INRAE, Genotoul Bioinformatics Platform, Applied Mathematics and Informatics of Toulouse, Sigenae, MIAT, UR875, Castanet Tolosan, France.
ORCID: 0000-0001-7126-5477

Elena Bitocchi (E)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy.
ORCID: 0000-0002-2152-4634

Valérie Geffroy (V)

CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), University of Evry, University Paris-Saclay, 91405, Orsay, France.

Massimo Delledonne (M)

Department of Biotechnology, University of Verona, 37134, Verona, Italy.
Genartis s.r.l, 37126, Verona, Italy.
ORCID: 0000-0002-7100-4581

David Edwards (D)

Centre for Applied Bioinformatics and School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia.
ORCID: 0000-0001-7599-6760

Roberto Papa (R)

Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy. r.papa@univpm.it.
ORCID: 0000-0001-9598-3131

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Viridiplantae › Streptophyta › Embryophyta › Tracheobionta › Magnoliopsida › Fabaceae › Phaseolus › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Génome végétal › Adaptive gene loss in the common bean...
questionsmedicales.fr › Technologie, industrie et agriculture › Agriculture › Domestication › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes biologiques › Adaptation biologique › Adaptation physiologique › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes génétiques › Génotype › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Adaptive gene loss in the common bean...
questionsmedicales.fr › Aliments et boissons › Aliments › Produits agricoles › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes génétiques › Sélection génétique › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes biologiques › Évolution biologique › Évolution moléculaire › Adaptive gene loss in the common bean...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Mutation › Adaptive gene loss in the common bean...
questionsmedicales.fr › Technologie, industrie et agriculture › Agriculture › Production végétale › Amélioration des plantes › Adaptive gene loss in the common bean...