Multi-environment Genome Wide Association Studies of Yield Traits in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Interspecific Advanced Lines at the Humid and Dry Colombian Caribbean Subregions

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dc.audienceInvestigador
dc.coverageColombia
dc.creatorLópez Hernández, Felipe
dc.creatorBurbano Erazo, Esteban
dc.creatorLeón Pacheco, Rommel Igor
dc.creatorCordero Cordero, Carina Cecilia
dc.creatorVillanueva Mejía, Diego F.
dc.creatorTofiño Rivera, Adriana Patricia
dc.creatorCortés, Andrés J.
dc.date2024-10-29T20:13:35Z
dc.date2024-10-29T20:13:35Z
dc.date2022-08-05
dc.date2022
dc.date.accessioned2026-06-27T04:31:23Z
dc.descriptionGenome Wide Associations Studies GWAS are a powerful strategy for the exploration adaptive genetic variation to drought stress in advanced lines in common bean with interspecific genotypes, yet they still lack behind in the use of arid multi-environments as the subregions of the Colombian Caribbean. In order to bridge this gap, we couple an advanced genotypes panel integrated with Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) interspecific lines with GWAS algorithms to identify novel sources of drought tolerance across the subregions of Colombian Caribbean. One of the most important challenges in agriculture is to achieve food security in environments vulnerable to climate change which worsens with the passing of the years. The common bean, a key product of the food basket of vulnerable regions of the Caribbean is affected by the reduction in yield under drought stress. A total of 87 advanced accessions with interspecific lines were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Five yield traits were developed for each accession and inputted in GWAS algorithms (i.e. FarmCPU, and BLINK) to identify putative associated loci in drought stress. Best-fit models revealed 47 significantly associated alleles distributed in all 11 common bean chromosomes. Flanking candidate genes were identified using 1-kb genomic windows centered in each associated SNP marker. A pathways enriched analysis was carried out using the mapped output in the GWAS step for each yield traits indices. Some of these genes were directly linked to response mechanisms of drought stress to level morphological, physiological, metabolic, signal transduction, and fatty acid and phospholipid metabolism. This work offers putative associated loci for marker-assisted and genomic selection for drought tolerance in common bean. It also demonstrates that it is feasible to identify genome-wide associations with an interspecific panel of genotypes and modern GWAS algorithms in multiples environments.
dc.descriptionKorea-Latin America Food and Agriculture Cooperation Initiative - KoLFACI
dc.descriptionCorporación Colombiana de Investigación Agropecuaria - (AGROSAVIA)
dc.descriptionFríjol
dc.descriptionPhaseolus vulgaris
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.identifierhttps://www.biorxiv.org/content/10.1101/2022.08.03.502649v1.full
dc.identifierhttp://hdl.handle.net/20.500.12324/40335
dc.identifierhttps://doi.org/10.1101/2022.08.03.502649
dc.identifierreponame:Biblioteca Digital Agropecuaria de Colombia
dc.identifierinstname:Corporación colombiana de investigación agropecuaria AGROSAVIA
dc.identifier.urihttp://hdl.handle.net/123456789/30251
dc.languageeng
dc.publisherCold Sprimg Harbor Laboratory (CSH)
dc.relationBioRxiv
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dc.relation↵Afgan, E., Baker, D., Batut, B., Van Den Beek, M., Bouvier, D., Ech, M., et al. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544. doi: 10.1093/nar/gky379.CrossRefPubMedGoogle Scholar
dc.relation↵Ambachew, D., and Blair, M. W. (2021). Genome Wide Association Mapping of Root Traits in the Andean Genepool of Common Bean (Phaseolus vulgaris L.) Grown With and Without Aluminum Toxicity. Front. Plant Sci. 12, 1–14. doi: 10.3389/fpls.2021.628687.CrossRefGoogle Scholar
dc.relation↵Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.Google Scholar
dc.relation↵Asakura, H., Yamakawa, T., Tamura, T., Ueda, R., Taira, S., Saito, Y., et al. (2021). Transcriptomic and Metabolomic Analyses Provide Insights into the Upregulation of Fatty Acid and Phospholipid Metabolism in Tomato Fruit under Drought Stress. J. Agric. Food Chem. 69, 2894–2905. doi: 10.1021/acs.jafc.0c06168.CrossRefGoogle Scholar
dc.relation↵Assefa, T., Assibi Mahama, A., Brown, A. V., Cannon, E. K. S., Rubyogo, J. C., Rao, I. M., et al. (2019). A review of breeding objectives, genomic resources, and marker-assisted methods in common bean (Phaseolus vulgaris L.). Mol. Breed. 39. doi: 10.1007/s11032-018-0920-0.CrossRefGoogle Scholar
dc.relation↵Barbulescu, D. M., et al. (2018). Imputation to Whole-Genome Sequence Increases the Power of Genome Wide Association Studies for Blackleg Resistance in Canola. AusCanola 2018 Co-hosts, 29. Available at: https://espace.library.uq.edu.au/view/UQ:2b40e3a/AusCanola_2018_Proceedings_E-book.pdf#page=29 [Accessed May 22, 2019].Google Scholar
dc.relation↵Beebe, S. E., Rao, I. M., Blair, M. W., and Acosta-Gallegos, J. A. (2013). Phenotyping common beans for adaptation to drought. Front. Physiol. 4 MAR, 1–20. doi: 10.3389/fphys.2013.00035.CrossRefPubMedGoogle Scholar
dc.relation↵Belivanis, and Doré (1986). lnterspecific hybridization of Phaseolus vulgaris L. and Phaseolus angustissimus A. Gray using in vitro embryo culture. Plant Cell Rep. 5, 329–331. Available at: https://europepmc.org/article/med/24248290.Google Scholar
dc.relation↵Bhatta, M., Morgounov, A., Belamkar, V., and Baenziger, P. S. (2018). Genome-wide association study reveals novel genomic regions for grain yield and yield-related traits in drought-stressed synthetic hexaploid wheat. Int. J. Mol. Sci. 19. doi: 10.3390/ijms19103011.CrossRefGoogle Scholar
dc.relation↵Blair, M. W., Soler, A., and Cortés, A. J. (2012). Diversification and Population Structure in Common Beans (Phaseolus vulgaris L.). PLoS One 7, e49488. doi: 10.1371/journal.pone.0049488.CrossRefPubMedGoogle Scholar
dc.relation↵Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/bioinformatics/btu170.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., and Buckler, E. S. (2007). TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635. doi: 10.1093/bioinformatics/btm308.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Bresadola, L., Caseys, C., Castiglione, S., Buerkle, C. A., Wegmann, D., and Lexer, C. (2019). Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits. New Phytol. 223, 2076–2089. doi: 10.1111/nph.15930.CrossRefGoogle Scholar
dc.relation↵Buitrago-Bitar, M. A., Cortés, A. J., López-Hernández, F., Londoño-Caicedo, J. M., Muñoz-Florez, J. E., Carmenza Muñoz, L., et al. (2021). Allelic diversity at abiotic stress responsive genes in relationship to ecological drought indices for cultivated tepary bean, phaseolus acutifolius a. Gray, and its wild relatives. Genes (Basel). 12, 1–17. doi: 10.3390/genes12040556.CrossRefGoogle Scholar
dc.relation↵Burbano-Erazo, E., León-Pacheco, R. I., Cordero-Cordero, C. C., López-Hernández, F., Cortés, A. J., and Tofiño-Rivera, A. P. (2021). Multi-environment yield components in advanced common bean (Phaseolus vulgaris l.) × tepary bean (p. acutifolius a. gray) interspecific lines for heat and drought tolerance. Agronomy 11. doi: 10.3390/agronomy11101978.CrossRefGoogle Scholar
dc.relation↵Caspi, R., Billington, R., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., et al. (2018). The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639. doi: 10.1093/nar/gkx935.CrossRefPubMedGoogle Scholar
dc.relation↵Chen, J., Li, N., Wang, X., Meng, X., Cui, X., Chen, Z., et al. (2021). Late embryogenesis abundant (LEA) gene family in Salvia miltiorrhiza: identification, expression analysis, and response to drought stress. Plant Signal. Behav. 16. doi: 10.1080/15592324.2021.1891769.CrossRefGoogle Scholar
dc.relation↵Chen, M., Zhu, X., Zhang, Y., Du, Z., Chen, X., Kong, X., et al. (2020). Drought stress modify cuticle of tender tea leaf and mature leaf for transpiration barrier enhancement through common and distinct modes. Sci. Rep. 10, 1–12. doi: 10.1038/s41598-020-63683-4.CrossRefPubMedGoogle Scholar
dc.relation↵Cheng, M. C., Liao, P. M., Kuo, W. W., and Lin, T. P. (2013). The arabidopsis ETHYLENE RESPONSE FACTOR1 Regulates abiotic stress-responsive gene expression by binding to different cis-acting elements in response to different stress signals. Plant Physiol. 162, 1566–1582. doi: 10.1104/pp.113.221911.Abstract/FREE Full TextGoogle Scholar
dc.relation↵Cortés, A. J., and Blair, M. W. (2018). Genotyping by Sequencing and Genome–Environment Associations in Wild Common Bean Predict Widespread Divergent Adaptation to Drought. Front. Plant Sci. 9, 128. doi: 10.3389/fpls.2018.00128.CrossRefGoogle Scholar
dc.relation↵Deng, Y., and Lu, S. (2017). Biosynthesis and Regulation of Phenylpropanoids in Plants. CRC. Crit. Rev. Plant Sci. 36, 257–290. doi: 10.1080/07352689.2017.1402852.CrossRefGoogle Scholar
dc.relation↵Diaz, S., Ariza-Suarez, D., Izquierdo, P., Lobaton, J. D., de la Hoz, J. F., Acevedo, F., et al. (2020). Genetic mapping for agronomic traits in a MAGIC population of common bean (Phaseolus vulgaris L.). BMC Genomics 21, 799. Available at: https://doi.org/10.7910/DVN/JR4X4C.Google Scholar
dc.relation↵Dornbos, D. L., and Mullen, R. E. (1992). Soybean seed protein and oil contents and fatty acid composition adjustments by drought and temperature. J. Am. Oil Chem. Soc. 69, 228–231. doi: 10.1007/BF02635891.CrossRefWeb of ScienceGoogle Scholar
dc.relation↵Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., et al. (2011). A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS One 6, e19379. doi: 10.1371/journal.pone.0019379.CrossRefPubMedGoogle Scholar
dc.relation↵FAO (2020). Panorama de la seguridad alimentaria y nutricional. Available at: https://www.paho.org/ecu/index.php?option=com_content&view=article&id=1864:panorama-de-la-seguridad-alimentaria-y-nutricional&Itemid=360.Google Scholar
dc.relation↵Frank, A., Oddou-Muratorio, S., Lalagüe, H., Pluess, A. R., Heiri, C., and Vendramin, G. G. (2016). Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica. New Phytol. 210, 589–601. doi: 10.1111/nph.13809.CrossRefGoogle Scholar
dc.relation↵Frichot, E., and François, O. (2015). LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929. doi: 10.1111/2041-210X.12382.CrossRefGoogle Scholar
dc.relation↵Gill, S. S., and Tuteja, N. (2010). Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol. Biochem. 48, 909–930. doi: 10.1016/j.plaphy.2010.08.016.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Goh, L., and Yap, V. B. (2009). Effects of normalization on quantitative traits in association test. BMC Bioinformatics 10. doi: 10.1186/1471-2105-10-415.CrossRefPubMedGoogle Scholar
dc.relation↵Gross, J., and Ligges, U. (2015). Package ‘nortest.’ 1–10. Available at: http://www.linux.ps.pl/dsk0/CRAN/web/packages/nortest/nortest.pdf?cjeknglnohdbaiec.Google Scholar
dc.relation↵Hao, Z., Lv, D., Ge, Y., Shi, J., Weijers, D., Yu, G., et al. (2020). RIdeogram: Drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, 1–11. doi: 10.7717/peerj-cs.251.CrossRefGoogle Scholar
dc.relation↵Jiri, O., Mafongoya, P. L., and Chivenge, P. (2017). Climate smart crops for food and nutritional security for semi-arid zones of Zimbabwe. African J. Food, Agric. Nutr. Dev. 17, 12280–12294. doi: 10.18697/ajfand.79.16285.CrossRefGoogle Scholar
dc.relation↵Joo, J. W. J., Hormozdiari, F., Han, B., and Eskin, E. (2016). Multiple testing correction in linear mixed models. Genome Biol., 29–33. doi: 10.1186/s13059-016-0903-6.CrossRefPubMedGoogle Scholar
dc.relation↵Kannangara, R., Branigan, C., Liu, Y., Penfield, T., Rao, V., Mouille, G., et al. (2007). The transcription factor WIN1/SHN1 regulates cutin biosynthesis in Arabidopsis thaliana. Plant Cell 19, 1278–1294. doi: 10.1105/tpc.106.047076.Abstract/FREE Full TextGoogle Scholar
dc.relation↵Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E., and Jetz, W. (2021). Global daily 1 km land surface precipitation based on cloud cover-informed downscaling. Sci. Data 8, 1–18. doi: 10.1038/s41597-021-01084-6.CrossRefGoogle Scholar
dc.relation↵Kazan, K. (2015). Diverse roles of jasmonates and ethylene in abiotic stress tolerance. Trends Plant Sci. 20, 219–229. doi: 10.1016/j.tplants.2015.02.001.CrossRefPubMedGoogle Scholar
dc.relation↵Lang-Mladek, C., Popova, O., Kiok, K., Berlinger, M., Rakic, B., Aufsatz, W., et al. (2010). Transgenerational Inheritance and Resetting of Stress-Induced Loss of Epigenetic Gene Silencing in Arabidopsis. Mol. Plant 3, 594–602. doi: 10.1093/MP/SSQ014.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵LeBlanc, C., Zhang, F., Mendez, J., Lozano, Y., Chatpar, K., Irish, V. F., et al. (2018). Increased efficiency of targeted mutagenesis by CRISPR/Cas9 in plants using heat stress. Plant J. 93, 377–386. doi: 10.1111/tpj.13782.CrossRefGoogle Scholar
dc.relation↵Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 00, 1–3. Available at: http://arxiv.org/abs/1303.3997.CrossRefGoogle Scholar
dc.relation↵Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. doi: 10.1093/bioinformatics/btp352.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Lin, Y. P., Wu, T. H., Chan, Y. K., van Zonneveld, M., and Schafleitner, R. (2022). De novo SNP calling reveals the genetic differentiation and morphological divergence in genus Amaranthus. Plant Genome, 1–19. doi: 10.1002/tpg2.20206.CrossRefGoogle Scholar
dc.relation↵Liu, N., Chen, J., Wang, T., Li, Q., Cui, P., Jia, C., et al. (2019). Overexpression of WAX INDUCER1 / SHINE1 Gene Enhances Wax Accumulation under Osmotic Stress and Oil Synthesis in Brassica napus. 1–16.Google Scholar
dc.relation↵Liu, X., Huang, M., Fan, B., Buckler, E. S., and Zhang, Z. (2016). Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLOS Genet. 12, e1005767. doi: 10.1371/journal.pgen.1005767.CrossRefGoogle Scholar
dc.relation↵López-Hernández, F., and Cortés, A. J. (2019). Last-Generation Genome–Environment Associations Reveal the Genetic Basis of Heat Tolerance in Common Bean (Phaseolus vulgaris L.). Front. Genet. 10, 1–22. doi: 10.3389/fgene.2019.00954.CrossRefGoogle Scholar
dc.relation↵McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., et al. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303. doi: 10.1101/gr.107524.110.Abstract/FREE Full TextGoogle Scholar
dc.relation↵Mhlaba, Z. B., Mashilo, J., Shimelis, H., Assefa, A. B., and Modi, A. T. (2018). Progress in genetic analysis and breeding of tepary bean (Phaseolus acutifolius A. Gray): A review. Sci. Hortic. (Amsterdam). 237, 112–119. doi: 10.1016/j.scienta.2018.04.012.CrossRefGoogle Scholar
dc.relation↵Moghaddam, S. M., Oladzad, A., Koh, C., Ramsay, L., Hart, J. P., Mamidi, S., et al. (2021). The tepary bean genome provides insight into evolution and domestication under heat stress. Nat. Commun. 12, 1–14. doi: 10.1038/s41467-021-22858-x.CrossRefPubMedGoogle Scholar
dc.relation↵Muñoz, L. C., Duque, M. C., Debouck, D. G., and Blair, M. W. (2006). Taxonomy of tepary bean and wild relatives as determined by amplified fragment length polymorphism (AFLP) markers. Crop Sci. 46, 1744–1754. doi: 10.2135/cropsci2005-12-0475.CrossRefWeb of ScienceGoogle Scholar
dc.relation↵Mwale, S. E., Shimelis, H., Mafongoya, P., and Mashilo, J. (2020). Breeding tepary bean (Phaseolus acutifolius) for drought adaptation: A review. Plant Breed. 139, 821–833. doi: 10.1111/pbr.12806.CrossRefGoogle Scholar
dc.relation↵Nagasaka, K., Nishiyama, S., Fujikawa, M., Yamane, H., Shirasawa, K., Babiker, E., et al. (2022). Genome-Wide Identification of Loci Associated With Phenology-Related Traits and Their Adaptive Variations in a Highbush Blueberry Collection. Front. Plant Sci. 12, 1–18. doi: 10.3389/fpls.2021.793679.CrossRefGoogle Scholar
dc.relation↵Oladzad, A., Porch, T., Rosas, J. C., Ma, S., Beaver, J., Beebe, S. E., et al. (2019). Single and Multi-trait GWAS Identify Genetic Factors Associated with Production Traits in Common Bean Under Abiotic Stress Environments. 9, 1881–1892. doi: 10.1534/g3.119.400072.Abstract/FREE Full TextGoogle Scholar
dc.relation↵Osorio-Guarín, J. A., Garzón-Martínez, G. A., Delgadillo-Duran, P., Bastidas, S., Moreno, L. P., Enciso-Rodríguez, F. E., et al. (2019). Genome-wide association study (GWAS) for morphological and yield-related traits in an oil palm hybrid (Elaeis oleifera x Elaeis guineensis) population. BMC Plant Biol. 19, 1–11. doi: 10.1186/s12870-019-2153-8.CrossRefGoogle Scholar
dc.relation↵Pasam, R. K., Sharma, R., Malosetti, M., van Eeuwijk, F. A., Haseneyer, G., Kilian, B., et al. (2012). Genome-wide association studies for agronomical traits in a world wide spring barley collection. BMC Plant Biol. 12, 16. doi: 10.1186/1471-2229-12-16.CrossRefPubMedGoogle Scholar
dc.relation↵Patil, I. (2021). Visualizations with statistical details: The “ggstatsplot” approach. J. Open Source Softw. 6, 3167. doi: 10.21105/joss.03167.CrossRefGoogle Scholar
dc.relation↵Patwari, P., Salewski, V., Gutbrod, K., Kreszies, T., Dresen-Scholz, B., Peisker, H., et al. (2019). Surface wax esters contribute to drought tolerance in Arabidopsis. Plant J. 98, 727–744. doi: 10.1111/tpj.14269.CrossRefGoogle Scholar
dc.relation↵Pecinka, A., Dinh, H. Q., Baubec, T., Rosa, M., Lettner, N., and Mittelsten Scheid, O. (2010). Epigenetic regulation of repetitive elements is attenuated by prolonged heat stress in Arabidopsis. Plant Cell 22, 3118–29. doi: 10.1105/tpc.110.078493.Abstract/FREE Full TextGoogle Scholar
dc.relation↵Pollard, M., Beisson, F., Li, Y., and Ohlrogge, J. B. (2008). Building lipid barriers: biosynthesis of cutin and suberin. Trends Plant Sci. 13, 236–246. doi: 10.1016/j.tplants.2008.03.003.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Quirino, B. F., Noh, Y., Himelblau, E., and Amasino, R. M. (2000). Molecular aspects of leaf senescence. 1385.Google Scholar
dc.relation↵Reimand, J., Isser, R., Voisin, V., Kucera, M., Tannus-lopes, C., Rostamianfar, A., et al. (2019). Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap-ZLO FAJN ČLANEK, TUDI RAZLAGE POSAMEZNIH TERMINOV. Nat Protoc 14, 1–8. doi: 10.1038/s41596-018-0103-9.Pathway.CrossRefPubMedGoogle Scholar
dc.relation↵ Salvatore Mangiafico (2022). Package ‘rcompanion.’ Funct. to Support Ext. Educ. Progr. Eval., 1–132. Available at: http://rcompanion.org/.Google Scholar
dc.relation↵Sánchez, E., Solman, S., Remedio, A. R. C., Berbery, H., Samuelsson, P., Da Rocha, R. P., et al. (2015). Regional climate modelling in CLARIS-LPB: a concerted approach towards twentyfirst century projections of regional temperature and precipitation over South America. Clim. Dyn. 45, 2193–2212. doi: 10.1007/s00382-014-2466-0.CrossRefGoogle Scholar
dc.relation↵Schmutz, J., Mcclean, P. E., Mamidi, S., Wu, G. A., Cannon, S. B., Grimwood, J., et al. (2014a). A reference genome for common bean and genome-wide analysis of dual domestications. Nat. Publ. Gr. 46. doi: 10.1038/ng.3008.CrossRefPubMedGoogle Scholar
dc.relation↵Schmutz, J., McClean, P. E., Mamidi, S., Wu, G. A., Cannon, S. B., Grimwood, J., et al. (2014b). A reference genome for common bean and genome-wide analysis of dual domestications. Nat. Genet. 46, 707–713. doi: 10.1038/ng.3008.CrossRefPubMedGoogle Scholar
dc.relation↵Sekula, M., Datta, S., and Datta, S. (2017). optCluster: An R Package for Determining the Optimal Clustering Algorithm. Bioinformation 13, 101–103. doi: 10.6026/97320630013101.CrossRefGoogle Scholar
dc.relation↵Sgarbieri, V. C., and Whitaker, J. R. (1982). Physical, Chemical, and Nutritional Properties of Common Bean (Phaseolus) Proteins. Adv. Food Res. 28, 93–166. doi: 10.1016/S0065-2628(08)60111-1.CrossRefPubMedWeb of ScienceGoogle Scholar
dc.relation↵Shukla, P. R., Skea, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., et al. (2019). Climate Change and Land: an IPCC special report. Clim. Chang. L. an IPCC Spec. Rep. Clim. Chang. Desertif. L. Degrad. Sustain. L. Manag. food Secur. Greenh. gas fluxes Terr. Ecosyst., 1–864. Available at: https://www.ipcc.ch/srccl/.Google Scholar
dc.relation↵Singh, C. M., Kumar, M., Pratap, A., and Tripathi, A. (2022). Genome-Wide Analysis of Late Embryogenesis Abundant Protein Gene Family in Vigna Species and Expression of VrLEA Encoding Genes in Vigna glabrescens Reveal Its Role in Heat Tolerance. 13, 1–17. doi: 10.3389/fpls.2022.843107.CrossRefGoogle Scholar
dc.relation↵Souter, J. R., Gurusamy, V., Porch, T. G., and Bett, K. E. (2017). Successful introgression of abiotic stress tolerance from wild tepary bean to common bean. Crop Sci. 57, 1160–1171. doi: 10.2135/cropsci2016.10.0851.CrossRefGoogle Scholar
dc.relation↵Teichmann, C., Eggert, B., Elizalde, A., Haensler, A., Jacob, D., Kumar, P., et al. (2013). How does a regional climate model modify the projected climate change signal of the driving GCM: A study over different CORDEX regions using REMO. Atmosphere (Basel). 4, 214–236. doi: 10.3390/atmos4020214.CrossRefGoogle Scholar
dc.relationTofiño Rivera, A., Ospina Cortés, D. A., and Rozo Leguizamón, Y. Compatibility of Ancestral and Innovative Agricultural Practices in the Kankuamo People of Colombia. 24.Google Scholar
dc.relation↵Tukey, J. W. (1977). Exploratory Data Analysis by John W. Tukey., ed. F. Mosteller Addison-Wesley Publishing Company Available at: http://www.jstor.org/stable/2529486.Google Scholar
dc.relation↵Vasylyk, I., Gorislavets, S., Matveikina, E., Lushchay, E., Lytkin, K., Grigoreva, E., et al. (2022). SNPs associated with foliar phylloxera tolerance in hybrid grape populations carrying introgression from muscadinia. Horticulturae 8, 1–14. doi: 10.3390/horticulturae8010016.CrossRefGoogle Scholar
dc.relation↵Wang, J., and Zhang, Z. (2021). GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction. Genomics, Proteomics Bioinforma. 19, 629–640. doi: 10.1016/j.gpb.2021.08.005.CrossRefGoogle Scholar
dc.relation↵Wu, X., Islam, A. S. M. F., Limpot, N., Mackasmiel, L., Mierzwa, J., Cortés, A. J., et al. (2020). Genome-Wide SNP Identification and Association Mapping for Seed Mineral Concentration in Mung Bean (Vigna radiata L.). Front. Genet. 11, 1–17. doi: 10.3389/fgene.2020.00656.CrossRefGoogle Scholar
dc.relation↵Yang, X., Lu, M., Wang, Y., Wang, Y., Liu, Z., and Chen, S. (2021). Response mechanism of plants to drought stress. Horticulturae 7. doi: 10.3390/horticulturae7030050.CrossRefGoogle Scholar
dc.relation↵Zhang, Z., Liu, X., Zhou, Y., and Summers, R. M. (2018). BLINK: a package for the next level of genome-wide association studies with both individuals and markers Meng Huang. 1–12. doi: 10.1093/gigascience/giy154.CrossRefGoogle Scholar
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceBioRxiv; (2022): BioRxiv (Agu.);p. 1 - 38.
dc.subjectCultivo - F01
dc.subjectGenoma
dc.subjectFríjol
dc.subjectRendimiento
dc.subjectHortalizas y plantas aromáticas
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_3224
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_331566
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_8488
dc.thumbnailhttps://repository.agrosavia.co/bitstreams/2ea31518-819e-4a05-b039-82268457b255/download
dc.titleMulti-environment Genome Wide Association Studies of Yield Traits in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Interspecific Advanced Lines at the Humid and Dry Colombian Caribbean Subregions
dc.titleMulti-environment Genome Wide Association Studies of Yield Traits in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Interspecific Advanced Lines at the Humid and Dry Colombian Caribbean Subregions
dc.typeArtículo científico

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