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

Genome 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....

Descripción completa

Detalles Bibliográficos
Autores principales: López Hernández, Felipe, Burbano Erazo, Esteban, León Pacheco, Rommel Igor, Cordero Cordero, Carina Cecilia, Villanueva Mejía, Diego F., Tofiño Rivera, Adriana Patricia, Cortés, Andrés J.
Formato: article
Lenguaje:Inglés
Publicado: Cold Sprimg Harbor Laboratory (CSH) 2024
Materias:
Acceso en línea:https://www.biorxiv.org/content/10.1101/2022.08.03.502649v1.full
http://hdl.handle.net/20.500.12324/40335
https://doi.org/10.1101/2022.08.03.502649
id RepoAGROSAVIA40335
record_format dspace
institution Corporación Colombiana de Investigación Agropecuaria
collection Repositorio AGROSAVIA
language Inglés
topic Cultivo - F01
Genoma
Fríjol
Rendimiento
Hortalizas y plantas aromáticas
http://aims.fao.org/aos/agrovoc/c_3224
http://aims.fao.org/aos/agrovoc/c_331566
http://aims.fao.org/aos/agrovoc/c_8488
spellingShingle Cultivo - F01
Genoma
Fríjol
Rendimiento
Hortalizas y plantas aromáticas
http://aims.fao.org/aos/agrovoc/c_3224
http://aims.fao.org/aos/agrovoc/c_331566
http://aims.fao.org/aos/agrovoc/c_8488
López Hernández, Felipe
Burbano Erazo, Esteban
León Pacheco, Rommel Igor
Cordero Cordero, Carina Cecilia
Villanueva Mejía, Diego F.
Tofiño Rivera, Adriana Patricia
Cortés, Andrés J.
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
description Genome 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.
format article
author López Hernández, Felipe
Burbano Erazo, Esteban
León Pacheco, Rommel Igor
Cordero Cordero, Carina Cecilia
Villanueva Mejía, Diego F.
Tofiño Rivera, Adriana Patricia
Cortés, Andrés J.
author_facet López Hernández, Felipe
Burbano Erazo, Esteban
León Pacheco, Rommel Igor
Cordero Cordero, Carina Cecilia
Villanueva Mejía, Diego F.
Tofiño Rivera, Adriana Patricia
Cortés, Andrés J.
author_sort López Hernández, Felipe
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher Cold Sprimg Harbor Laboratory (CSH)
publishDate 2024
url https://www.biorxiv.org/content/10.1101/2022.08.03.502649v1.full
http://hdl.handle.net/20.500.12324/40335
https://doi.org/10.1101/2022.08.03.502649
work_keys_str_mv AT lopezhernandezfelipe multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT burbanoerazoesteban multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT leonpachecorommeligor multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT corderocorderocarinacecilia multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT villanuevamejiadiegof multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT tofinoriveraadrianapatricia multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
AT cortesandresj multienvironmentgenomewideassociationstudiesofyieldtraitsincommonbeanphaseolusvulgarislteparybeanpacutifoliusagrayinterspecificadvancedlinesatthehumidanddrycolombiancaribbeansubregions
_version_ 1842255631149432832
spelling RepoAGROSAVIA403352024-10-30T03:00:45Z 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 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 López Hernández, Felipe Burbano Erazo, Esteban León Pacheco, Rommel Igor Cordero Cordero, Carina Cecilia Villanueva Mejía, Diego F. Tofiño Rivera, Adriana Patricia Cortés, Andrés J. Cultivo - F01 Genoma Fríjol Rendimiento Hortalizas y plantas aromáticas http://aims.fao.org/aos/agrovoc/c_3224 http://aims.fao.org/aos/agrovoc/c_331566 http://aims.fao.org/aos/agrovoc/c_8488 Genome 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. Korea-Latin America Food and Agriculture Cooperation Initiative - KoLFACI Corporación Colombiana de Investigación Agropecuaria - (AGROSAVIA) Fríjol-Phaseolus vulgaris 2024-10-29T20:13:35Z 2024-10-29T20:13:35Z 2022-08-05 2022 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 https://www.biorxiv.org/content/10.1101/2022.08.03.502649v1.full http://hdl.handle.net/20.500.12324/40335 https://doi.org/10.1101/2022.08.03.502649 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA eng BioRxiv 1 1 1 38 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵Patil, I. (2021). Visualizations with statistical details: The “ggstatsplot” approach. J. Open Source Softw. 6, 3167. doi: 10.21105/joss.03167.CrossRefGoogle Scholar ↵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 ↵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 ↵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 ↵Quirino, B. F., Noh, Y., Himelblau, E., and Amasino, R. M. (2000). Molecular aspects of leaf senescence. 1385.Google Scholar ↵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 ↵ Salvatore Mangiafico (2022). Package ‘rcompanion.’ Funct. to Support Ext. Educ. Progr. Eval., 1–132. Available at: http://rcompanion.org/.Google Scholar ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 Tofiño 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 ↵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 ↵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 ↵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 ↵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 ↵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 ↵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 Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf application/pdf Colombia Cold Sprimg Harbor Laboratory (CSH) BioRxiv; (2022): BioRxiv (Agu.);p. 1 - 38.