Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
UNRAVELING THE GENETIC ARCHITECTURE OF COMPLEX TRAITS IN PLANTS
Descifrando la arquitectura genética de rasgos complejos en plantas
DOI:
https://doi.org/10.15446/abc.v28n3.98891Palabras clave:
Genomic selection, linkage disequilibrium, polygenic trait, quantitative trait loci (en)Desequilibrio de ligamiento, loci de rasgos cuantitativos, rasgo poligénico, selección genómica (es)
Descargas
Complex traits are those whose inheritance does not follow simple and predictable patterns. They are not governed by a single locus, instead, they are determined by several loci and are influenced by the environment. Most of the traits with agronomic interest and economic importance such as resistance to biotic and abiotic stress, and yield, among others, are quantitative traits and their study is based on dissecting the underlying genetic architecture, the number of loci responsible for the variance of a quantitative trait, the relevant contribution made by each locus and their interaction with the environment. This review provides the most relevant conceptual bases for the study of the genetic architecture of complex quantitative traits in plants. The methodologies that allow identifying the loci and candidate genes that govern this type of traits are described, such as QTL mapping by linkage and association mapping. In addition, the incorporation of these loci in phenotype prediction strategies such as marker-assisted selection and genomic selection, exhibits the benefits and limitations of these approaches. Finally, the challenges and perspectives facing the study of the genetic architecture of complex traits in plants are discussed.
Los rasgos complejos son aquellos cuya herencia no sigue patrones simples y predecibles. No están gobernados por un solo locus, sino que están determinados por varios loci y, además, están influenciados por el entorno. La mayoría de los rasgos de interés agronómico como la resistencia al estrés biótico y abiótico, el rendimiento, entre otros, son rasgos complejos, gobernados por múltiples genes a lo largo del genoma. El estudio de la arquitectura genética de rasgos complejos se basa en la identificación del número de loci asociados a un rasgo, la contribución individual de cada loci al rasgo, la heredabilidad y el grado de influencia que del ambiente en el fenotipo. Esta revisión proporciona los conceptos más relevantes para el estudio de la arquitectura genética de rasgos complejos en plantas. Se describen las metodologías que permiten identificar los loci y genes candidatos, que gobiernan este tipo de rasgos como el mapeo QTL por ligamiento y el mapeo por asociación. Además, la incorporación de estos loci en estrategias de predicción del fenotipo como la selección asistida por marcadores y la selección genómica, presentando los beneficios y limitaciones de estos enfoques. Finalmente, se presentan los desafíos y perspectivas que enfrenta el estudio de la arquitectura genética de rasgos complejos en plantas.
Referencias
Abdurakhmonov, I. Y., and Abdukarimov, A. (2008). Application of association mapping to understanding the genetic diversity of plant germplasm resources. International Journal of Plant Genomics, 2008. https://doi.org/10.1155/2008/574927
Albert, F. W., and Kruglyak, L. (2015). The role of regulatory variation in complex traits and disease. Nature Reviews Genetics, 16(4), 197–212. https://doi.org/10.1038/nrg3891
Alqudah, A. M., Sallam, A., Baenziger, P. S., and Börner, A. (2020). GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley – A review. Journal of Advanced Research, 22, 119–135. https://doi.org/10.1016/j.jare.2019.10.013
Anderson, R., Edwards, D., Batley, J., and Bayer, P. E. (2019). Genome-Wide Association Studies in Plants. ELS, 1–7. https://doi.org/10.1002/9780470015902.a0027950
Assefa, T., Zhang, J., Chowda-Reddy, R. V., Moran Lauter, A. N., Singh, A., O’Rourke, J. A., Graham, M. A., and Singh, A. K. (2020). Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches. BMC Plant Biology, 20(42), 1–13. https://doi.org/10.1186/s12870-020-2237-5
Bartkiewicz, A., Chilla, F., Terefe-Ayana, D., Lübeck, J., Strahwald, J., Tacke, E., Hofferbert, H. R., Flath, K., Linde, M., and Debener, T. (2018). Improved genetic resolution for linkage mapping of resistance to potato wart in monoparental dihaploids with potential diagnostic value in tetraploid potato varieties. Theoretical and Applied Genetics, 131(12), 2555–2566. https://doi.org/10.1007/s00122-018-3172-9
Bazakos, C., Hanemian, M., Trontin, C., Jimenéz-Gómez, J. M., and Loudet, O. (2017). New Strategies and Tools in Quantitative Genetics : How to Go from the Phenotype to the Genotype. Annual Review of Plant Biology, 68, 1–21. https://doi.org/10.1146/annurevarplant-042916-040820
Bernardo, R. N. (2010). Breeding for Quantitative Traits in Plants (Stemma Press (ed.); Second Edi). Cavanagh, C., Morell, M., Mackay, I., and Powell, W. (2008). From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Current Opinion in Plant Biology, 11(2), 215–221. https://doi.org/10.1016/j.pbi.2008.01.002
Bontpart, T., Concha, C., Giuffrida, M. V., Robertson, I., Admkie, K., Degefu, T., Girma, N., Tesfaye, K., Haileselassie, T., Fikre, A., Fetene, M., Tsaftaris, S. A., and Doerner, P. (2020). Affordable and robust phenotyping framework to analyse root system architecture of soilgrown plants. Plant Journal, 103(6), 2330–2343. https://doi.org/10.1111/tpj.14877
Boopathi, N. M. (2020). Genetic Mapping and Marker Assisted Selection. In Springer. https://doi.org/10.1007/978-981-15-2949-8
Boyle, E. A., Li, Y. I., and Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 169(7), 1177–1186. https://doi.org/10.1016/j.cell.2017.05.038
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(19). https://doi.org/10.1093/bioinformatics/btm308
Breseghello, F., and Sorrells, M. E. (2006). Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) Cultivars. Genetics, 172(2), 1165–1177. https://doi.org/10.1534/genetics.105.044586
Bucksch, A., Burridge, J., York, L. M., Das, A., Nord, E., Weitz, J. S., and Lynch, J. P. (2014). Image-based high-throughput field phenotyping of crop roots. Plant Physiology, 166(2), 470–486. https://doi.org/10.1104/pp.114.243519
Burghardt, L. T., Young, N. D., and Tiffin, P. (2020). A Guide to Genome-Wide Association Mapping in Plants. Current Protocols in Plant Biology, 2(1), 22–38. https://doi.org/10.1002/cppb.20041
Cai, G., Yang, Q., Chen, H., Yang, Q., Zhang, C., Fan, C., and Zhou, Y. (2016). Genetic dissection of plant architecture and yield-related traits in Brassica napus. Scientific Reports, 6(21625), 1–16. https://doi.org/10.1038/srep21625
Collard, B. C. Y., Jahufer, M. Z. Z., Brouwer, J. B., and Pang, E. C. K. (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica, 142(1–2), 169–196. https://doi.org/10.1007/s10681-005-1681-5
Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., Dreisigacker, S., Singh, R., Zhang, X., Gowda, M., Roorkiwal, M., Rutkoski, J., and Varshney, R. K. (2017). Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science, 22(11), 961–975. https://doi.org/10.1016/j.tplants.2017.08.011
Silva, L., Wang, S., and Zeng, Z. B. (2012). Composite interval mapping and multiple interval mapping: Procedures and guidelines for using windows QTL cartographer. In Methods in Molecular Biology (Vol. 871). https://doi.org/10.1007/978-1-61779-785-9_6
Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M., and Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using nextgeneration sequencing. Nature Reviews Genetics, 12(7), 499–510. https://doi.org/10.1038/nrg3012
de Oliveira Silva, F. M., Lichtenstein, G., Alseekh, S., Rosado-Souza, L., Conte, M., Fuentes V., Silvestre B., Fanourakis, D., Usadel, B., Lopes L., DaMatta, F. M., Sulpice, R., Araújo, W. L., Rossi, M., de Setta, N., Fernie, A. R., Carrari, F., and Nunes-Nesi, A. (2018). The genetic architecture of photosynthesis and plant growth-related traits in tomato. Plant Cell and Environment, 41(2), 327–341. https://doi.org/10.1111/pce.13084
Diers, B. W., Specht, J., Rainey, K. M., Cregan, P., Song, Q., Ramasubramanian, V., Graef, G., Nelson, R., Schapaugh, W., Wang, D., Shannon, G., Mchale, L., Kantartzi, S. K., Xavier, A., Mian, R., Stupar, R. M., Michno, J. M., An, Y. Q. C., Goettel, W., Ward, R., Fox, C., Lipka, A. E., Hyten, D., Cary, T., and Beavis, W. D. (2018). Genetic architecture of soybean yield and agronomic traits. G3: Genes, Genomes, Genetics, 8(10), 3367–3375. https://doi.org/10.1534/g3.118.200332
Diouf, I., Derivot, L., Koussevitzky, S., Carretero, Y., Bitton, F., Moreau, L., and Causse, M. (2020). Genetic basis of phenotypic plasticity and genotype × environment interactions in a multi-parental tomato population. Journal of Experimental Botany, 71, 5365–5376. https://doi.org/10.1101/2020.02.07.938456
Edwards, M. D., Helentjaris, T., Wright, S., and Stuber, C. W. (1992). Molecular-marker-facilitated investigations of quantitative trait loci in maize. Theoretical and Applied Genetics, 83 (6–7), 765–774. https://doi.org/10.1007/bf00226696
Edwards, M. D., Stuber, C. W., and Wendel, J. F. (1987). Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics, 116(1), 113–125.
Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., and Mitchell, S. E. (2011). A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE, 6(5), 1–10. https://doi.org/10.1371/journal.pone.0019379
Ersoz, E. S., Yu, J., and Buckler, E. S. (2007). Applications of linkage disequilibrium and association mapping in crop plants. In Genomics-Assisted Crop Improvement (Vol. 1). https://doi.org/10.1007/978-1-4020-6295-7_5
Fiorani, F., and Schurr, U. (2013). Future scenarios for plant phenotyping. Annual Review of Plant Biology, 64, 267–291. https://doi.org/10.1146/annurevarplant-050312-120137
Fisher, R. A. (1919). XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399–433. https://doi.org/10.1017/S0080456800012163
Flint‐Garcia, S. A., Thuillet, A. C., Yu, J., Pressoir, G., Romero, S. M., Mitchell, S. E., Doebley, J., Kresovich, S., Goodman, M. M., and Buckler, E. S. (2005). Maize association population: a high-resolution platform for quantitative trait locus dissection. The Plant Journal, 44(6), 1054–1064. https://doi.org/10.1111/j.1365-313X.2005.02591.x
Flint, J., and Mott, R. (2001). Finding the molecular basis of quantitative traits: Successes and pitfalls. Nature Reviews Genetics, 2(6), 437–445. https://doi.org/10.1038/35076585
Foolad, M. R., and Panthee, D. R. (2012). Marker-Assisted Selection in Tomato Breeding. Critical Reviews in Plant Sciences, 31(2), 93–123. https://doi.org/10.1080/07352689.2011.616057
Francia, E., Tacconi, G., Crosatti, C., Barabaschi, D., Bulgarelli, D., Dall’Aglio, E., and Valè, G. (2005). Marker assisted selection in crop plants. Plant Cell, Tissue and Organ Culture, 82(3), 317–342. https://doi.org/10.1007/s11240-005-2387-z
Gage, J. L., Monier, B., Giri, A., and Buckler, E. S. (2020). Ten years of the maize nested association mapping population: Impact, limitations, and future directions. Plant Cell, 32(7), 2083–2093. https://doi.org/10.1105/tpc.19.00951
Gallagher, M. D., and Chen-Plotkin, A. S. (2018). The Post-GWAS Era: From Association to Function. American Journal of Human Genetics, 102(5), 717–730. https://doi.org/10.1016/j.ajhg.2018.04.002
Giri, P., Yadav, M. L., and Mohapatra, B. (2018). QTL Linkage Analysis. Springer International Publishing. https://doi.org/10.1007/978-3-319-47829-6_161-1
Giri, P., and Mohapatra, B. (2017). Candidate Gene. Encyclopedia of Animal Cognition and Behavior, 1–4. https://doi.org/10.1007/978-3-319-47829-6_1-1
Goddard, M. E., Kemper, K. E., MacLeod, I. M., Chamberlain, A. J., and Hayes, B. J. (2016). Genetics of complex traits: Prediction of phenotype, identification of causal polymorphisms and genetic architecture. Proceedings of the Royal Society B: Biological Sciences, 283(1835). https://doi.org/10.1098/rspb.2016.0569
Guo, Z., Yang, W., Chang, Y., Ma, X., Tu, H., Xiong, F., Jiang, N., Feng, H., Huang, C., Yang, P., Zhao, H., Chen, G., Liu, H., Luo, L., Hu, H., Liu, Q., and Xiong, L. (2018). Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Molecular Plant, 11(6), 789–805. https://doi.org/10.1016/j.molp.2018.03.018
Gupta, P. K., Rustgi, S., and Kulwal, P. L. (2005). Linkage disequilibrium and association studies in higher plants: Present status and future prospects. Plant Molecular Biology, 57(4), 461–485. https://doi.org/10.1007/s11103-005-0257-z
Hansen, T. E. (2006). The evolution of genetic architecture. Annual Review of Ecology, Evolution, and Systematics, 37(8), 123–157. https://doi.org/10.1146/annurev.ecolsys.37.091305.110224
Hayward, A. C., Tollenaere, R., Dalton-Morgan, J., and Batley, J. (2015). Molecular marker applications in plants. Methods in Molecular Biology, (pp. 13–27). https://doi.org/10.1007/978-1-4939-1966-6_2
Hill, W. G. (2010). Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1537), 73–85. https://doi.org/10.1098/rstb.2009.0203
Ibrahim, A. K., Zhang, L., Niyitanga, S., Afzal, M. Z., Xu, Y., Zhang, L., Zhang, L., and Qi, J. (2020). Principles and approaches of association mapping in plant breeding. Tropical Plant Biology, 13(3), 212–224. https://doi.org/10.1007/s12042-020-09261-4
Jaganathan, D., Bohra, A., Thudi, M., and Varshney, R. K. (2020). Fine mapping and gene cloning in the post-NGS era: advances and prospects. Theoretical and Applied Genetics, 133(5), 1791–1810. https://doi.org/10.1007/s00122-020-03560-w
Jannink, J. L., Lorenz, A. J., and Iwata, H. (2010). Genomic selection in plant breeding: From theory to practice. Briefings in Functional Genomics and Proteomics, 9(2), 166–177. https://doi.org/10.1093/bfgp/elq001
Jansen, R. C. (1993). Interval mapping of multiple quantitative trait loci. Genetics, 135(1), 205–211. https://doi.org/10.1093/genetics/135.1.205
Joo, J. W. J., Hormozdiari, F., Han, B., and Eskin, E. (2016). Multiple testing correction in linear mixed models. Genome Biology, 17, 62. https://doi.org/10.1186/s13059-016-0903-6
Ju, M., Zhou, Z., Mu, C., Zhang, X., Gao, J., Liang, Y., Chen, J., Wu, Y., Li, X., Wang, S., Wen, J., Yang, L., and Wu, J. (2017). Dissecting the genetic architecture of Fusarium verticillioides seed rot resistance in maize by combining QTL mapping and genome-wide association analysis. Scientific Reports, 7, 1–11. https://doi.org/10.1038/srep46446
Juyo Rojas, D. K., Soto Sedano, J. C., Ballvora, A., Léon, J., and Mosquera Vásquez, T. (2019). Novel Organ-Specific Genetic Factors for Quantitative Resistance to Late Blight in Potato. PLoS ONE, 14, 1–15. https://doi.org/10.1371/journal.pone.0213818
Kaler, A. S., and Purcell, L. C. (2019). Estimation of a significance threshold for genome-wide association studies. BMC Genomics, 20(1), 1–8. https://doi.org/10.1186/s12864-019-5992-7
Khan, R., Ma, X., Shah, S., Wu, X., Shaheen, A., Xiao, L., Wu, Y., and Wang, S. (2020). Drought-hardening improves drought tolerance in Nicotiana tabacum at physiological, biochemical, and molecular levels. BMC Plant Biology, 20(1), 1–19. https://doi.org/10.1186/s12870-020-02688-7
Kirchgessner, N., Liebisch, F., Yu, K., Pfeifer, J., Friedli, M., Hund, A., and Walter, A. (2016). The ETH field phenotyping platform FIP: A cable-suspended multisensor system. Functional Plant Biology, 44(1), 154–168. https://doi.org/10.1071/FP16165
Korte, A., and Farlow, A. (2013). The advantages and limitations of trait analysis with GWAS: A review. Plant Methods, 9(1), 1. https://doi.org/10.1186/1746-4811-9-29
Krajewski, P., Chen, D., Ćwiek, H., Van Dijk, A. D. J., Fiorani, F., Kersey, P., Klukas, C., Lange, M., Markiewicz, A., Nap, J. P., Van Oeveren, J., Pommier, C., Scholz, U., Van Schriek, M., Usadel, B., and Weise, S. (2015). Towards recommendations for metadata and data handling in plant phenotyping. Journal of Experimental Botany, 66(18), 5417–5427. https://doi.org/10.1093/jxb/erv271
Lander, E. S., and Botstein, D. (1989). Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps. Genetics, 121(1), 185–199. https://doi.org/10.1093/genetics/121.1.185
Li, F., Wen, W., Liu, J., Zhang, Y., Cao, S., He, Z., Rasheed, A., Jin, H., Zhang, C., Yan, J., Zhang, P., Wan, Y., and Xia, X. (2019). Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC Plant Biology, 19(168), 1–19. https://doi.org/10.1186/s12870-019-1781-3
Li, H., Hearne, S., Bänziger, M., Li, Z., and Wang, J. (2010). Statistical properties of QTL linkage mapping in biparental genetic populations. Heredity, 105(3), 257–267. https://doi.org/10.1038/hdy.2010.56
Lipka, A. E., Tian, F., Wang, Q., Peiffer, J., Li, M., Bradbury, P. J., Gore, M. A., Buckler, E. S., and Zhang, Z. (2012). GAPIT: Genome association and prediction integrated tool. Bioinformatics, 28(18), 2397–2399. https://doi.org/10.1093/bioinformatics/bts444
Liu, B. H. (1998). Statistical Genomics: Linkage, Mapping and QTL Analysis. CRC Press.
Liu, N., Xue, Y., Guo, Z., Li, W., and Tang, J. (2016). Genome-wide association study identifies candidate genes for starch content regulation in maize kernels. Frontiers in Plant Science, 7, 1046. https://doi.org/10.3389/fpls.2016.01046
Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S., and Hirschhorn, J. N. (2003). Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nature Genetics, 33, 177–182. https://doi.org/10.1038 / ng1071
Lynch, J. P. (2011). Root phenes for enhanced soil exploration and phosphorus acquisition: Tools for future crops. Plant Physiology, 156(3), 1041–1049. https://doi.org/10.1104/pp.111.175414
Lynch, M., and Walsh, B. (1998). Genetics and Analysis of Quantitative Traits. Sinauer Associates, Inc. Mackay, T. F. C. (2001). The genetic architecture of quantitative traits. Annual Review of Genetics, 35(1), 303–339. https://doi.org/10.1146/annurev.genet.35.102401.090633
Marla, S. R., Burow, G., Chopra, R., Hayes, C., Olatoye, M. O., Felderhoff, T., Hu, Z., Raymundo, R., Perumal, R., and Morris, G. P. (2019). Genetic Architecture of Chilling Tolerance in Sorghum Dissected with a Nested Association Mapping Population. Genetics, 9(12), 4045–4057. https://doi.org/https://doi.org/10.1534/g3.119.400353
Meunier, R. (2016). The many lives of experiments: Wilhelm Johannsen, selection, hybridization, and the complex relations of genes and characters. History and Philosophy of the Life Sciences, 38(1), 42–64. https://doi.org/10.1007/s40656-015-0093-7
Morris, J., Navarro, N., Rastas, P., Rawlins, L. D., Sammy, J., Mallet, J., and Dasmahapatra, K. K. (2019). The genetic architecture of adaptation: convergence and pleiotropy in Heliconius wing pattern evolution. Heredity, 123(2), 138–152. https://doi.org/10.1038/s41437-018-0180-0
Nienhuis, J., Helentjaris, T., Slocum, M., Ruggero, B., and Schaefer, A. (1987). Restriction Fragment Length Polymorphism Analysis of Loci Associated with Insect Resistance in Tomato1 . Crop Science, 27(4), 797–803. https://doi.org/10.2135/cropsci1987.0011183x002700040039x
Nilsson-Ehle, H. (1908). Einige Ergebnisse von Kreuzungen bei Hafer und Weizen. Botaniska Notiser, 301–350.
Ordas, B., Malvar, R. A., Santiago, R., and Butron, A. (2010). QTL mapping for Mediterranean corn borer resistance in European flint germplasm using recombinant inbred lines. BMC Genomics, 11(174). https://doi.org/10.1186/1471-2164-11-174
Osborn, T. C., Alexander, D. C., and Fobes, J. F. (1987). Identification of restriction fragment length polymorphisms linked to genes controlling soluble solids content in tomato fruit. Theoretical and Applied Genetics, 73(3), 350–356. https://doi.org/10.1007/BF00262500
Pan, Y., Zhang, H., Zhang, D., Li, J., Xiong, H., Yu, J., Li, J., Rashid, M. A. R., Li, G., Ma, X., Cao, G., Han, L., and Li, Z. (2015). Genetic analysis of cold tolerance at the germination and booting stages in rice by association mapping. PLoS ONE, 10(3). https://doi.org/10.1371/journal.pone.0120590
Patnala, R., Clements, J., and Batra, J. (2013). Candidate gene association studies: a comprehensive guide to useful in silicotools. Molecular Breeding, 14(39). https://doi.org/10.1186/1471-2156-14-39
Pavan, S., Delvento, C., Ricciardi, L., Lotti, C., Ciani, E., and D’Agostino, N. (2020). Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies. Frontiers in Genetics, 11(). https://doi.org/10.3389/fgene.2020.00447
Pflieger, S., Lefebvre, V., and Causse, M. (2001). The candidate gene approach in plant genetics: a review. Molecular Breeding, 7(4), 275–291. https://doi.org/10.1023/A:1011605013259
Pieruschka, R., and Schurr, U. (2019). Plant Phenotyping: Past, Present, and Future. Plant Phenomics, 2019, 1–6. https://doi.org/10.34133/2019/7507131
Platt, A., Vilhjálmsson, B. J., and Nordborg, M. (2010). Conditions under which genome-wide association studies will be positively misleading. Genetics, 186(3), 1045–1052. https://doi.org/10.1534/genetics.110.121665
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38(8), 904–909. https://doi.org/10.1038/ng1847
Pritchard, J. K., Stephens, M., Rosenberg, N. A., and Donnelly, P. (2000). Association mapping in structured populations. The American Journal of Human Genetics, 67(1), 170–181. https://doi.org/10.1086 / 302959
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., De Bakker, P. I. W., Daly, M. J., and Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559–575. https://doi.org/10.1086/519795
Rajarammohan, S., Kumar, A., Gupta, V., Pental, D., Pradhan, A. K., and Kaur, J. (2017). Genetic architecture of resistance to alternaria brassicae in Arabidopsis thaliana: QTL mapping reveals two major resistance conferring loci. Frontiers in Plant Science, 8, 1–9. https://doi.org/10.3389/fpls.2017.00260
Rajpal, V. R., Dreisigacke, S., Sukumaran, S., Guzmán, C., He, X., Lan, C., Bonnett, D., and Crossa, J. (2016). Molecular Breeding for Sustainable Crop Improvement. In Springer (Vol. 11, Issue ii). https://doi.org/10.1007/978-3-319-27090-6
Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., and Zhang, F. (2013). Genome engineering using the CRISPR-Cas9 system. Nature Protocols, 8(11), 2281–2308. https://doi.org/10.1038/nprot.2013.143
Rasheed, A., Hao, Y., Xia, X., Khan, A., Xu, Y., Varshney, R. K., and He, Z. (2017). Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives. Molecular Plant, 10(8), 1047–1064. https://doi.org/10.1016/j.molp.2017.06.008
Remington, D. L. (2015). Alleles versus mutations: Understanding the evolution of genetic architecture requires a molecular perspective on allelic origins. Evolution, 69(12), 3025–3038. https://doi.org/10.1111/evo.12775
Risch, N., and Merikangas, K. (1996). The future of genetic studies of complex human diseases. Science, 273(5281), 1516–1517. https://doi.org/10.1126/science.273.5281.1516
Robertson, D. (2004). VIGS Vectors for Gene Silencing: Many targets, many tools. Annual Review of Plant Biology, 55, 495–519. https://doi.org/10.1146/annurev.arplant.55.031903.141803
Robinson, R. A. (1996). Return to resistance. Breeding crops to Reduce Pesticide Dependence. Australasian Plant Pathology, 216-217. https://doi.org/10.1007/BF03213684
Sax, K. (1923). The Association of Size Differences with Seed-Coat Pattern and Pigmentation in Phaseolus Vulgaris. Genetics, 8(6), 552–560. https://doi.org/10.1093/genetics/8.6.552
Sella, G., and Barton, N. H. (2019). Thinking about the Evolution of Complex Traits in the Era of Genome-Wide Association Studies. Annual Review of Genomics and Human Genetics, 20, 461–493. https://doi.org/10.1146/annurevgenom-083115-022316
Semagn, K., Bjørnstad, Å., and Xu, Y. (2010). The genetic dissection of quantitative traits in crops. Electronic Journal of Biotechnology, 13(5). https://doi.org/10.2225/vol13-issue5-fulltext-14
Soto, J. C., Ortiz, J. F., Perlaza-Jiménez, L., Vásquez, A. X., Lopez-Lavalle, L. A. B., Mathew, B., Léon, J., Bernal, A. J., Ballvora, A., and López, C. E. (2015). A genetic map of cassava (Manihot esculenta Crantz) with integrated physical mapping of immunity-related genes. BMC Genomics, 16(1), 1–16. https://doi.org/10.1186/s12864-015-1397-4
Soto , J. C., Mora , R. E., Mathew, B., Léon, J., Gómez , F. A., Ballvora, A., and López , C. E. (2017). Major novel QTL for resistance to Cassava bacterial blight identified through a multi-environmental analysis. Frontiers in Plant Science, 8(), 1–13. https://doi.org/10.3389/fpls.2017.01169
St.clair, D. A. (2010). Quantitative disease resistance and quantitative resistance loci in breeding. Annual Review of Phytopathology, 48, 247–268. https://doi.org/10.1146/annurev-phyto-080508-081904
Szalma, S. J., Hostert, B. M., LeDeaux, J. R., Stuber, C. W., and Holland, J. B. (2007). QTL mapping with nearisogenic lines in maize. Theoretical and Applied Genetics, 114(7), 1211–1228. https://doi.org/10.1007/s00122-007-0512-6
Tam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., and Meyre, D. (2019). Benefits and limitations of genomewide association studies. Nature Reviews Genetics, 20(8), 467–484. https://doi.org/10.1038/s41576-019-0127-1
Tanksley, S. D. (1993). Mapping polygenes. Annual Review of Genetics, 27(1), 205–233. https://doi.org/10.1146/annurev.ge.27.120193.001225
Thoen, M. P. M., Davila, N. H., Kloth, K. J., Coolen, S., Huang, P. P., Aarts, M. G. M., Bac-Molenaar, J. A., Bakker, J., Bouwmeester, H. J., Broekgaarden, C., Bucher, J., Busscher-Lange, J., Cheng, X., Fradin, E. F., Jongsma, M. A., Julkowska, M. M., Keurentjes, J. J. B., Ligterink, W., Pieterse, C. M. J., Ruyter-Spira, C., Smant, G., Testerink, C., Usadel, B., van Loon, J. J. A., van Pelt, J. A., van Schaik, C. C., van Wees, S. C. M., Visser, R. G. F., Voorrips, R., Vosman, B., Vreugdenhil, D., Warmerdam, S., Wiegers, G. J., van Heerwaarden, J., Kruijer, W., van Eeuwijk, F. A., andDicke, M. (2017). Genetic architecture of plant stress resistance: multitrait genome-wide association mapping. New Phytologist, 213(3), 1346–1362. https://doi.org/10.1111/nph.14220
Unver, T., and Budak, H. (2009). Virus-induced gene silencing, a post transcriptional gene silencing method. International Journal of Plant Genomics, 2009. https://doi.org/10.1155/2009/198680
Villanueva, B., Fernández, A., Saura, M., Caballero, A., Fernández, J., Morales-González, E., Toro, M. A., and Pong-Wong, R. (2021). The value of genomic relationship matrices to estimate levels of inbreeding. Genetics Selection Evolution, 53(42), 1–17. https://doi.org/10.1186/s12711-021-00635-0
Waghmode, B. D., Sabnis, G. R., Navhale, V. C., and Thaware, B. L. (2017). Inheritance studies in red kernel rice (Oryza sativa L.). Electronic Journal of Plant Breeding, 8(2), 521–527. https://doi.org/10.5958/0975-928X.2017.00078.3
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., and Mons, B. (2016). Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 1–9. https://doi.org/10.1038/sdata.2016.18
Williams, J. G. K., Kubelik, A. R., Livak, K. J., and Rafalski, J. A., T. S. (1990). DNA polymorphisms amplified methods to plant systematic and evolutionary biology. In Molecular systematic and evolutionary biology (pp. 43–86).
Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16(3). https://doi.org/10.1093/genetics/16.3.290
Yadav, A., and Sinha, H. (2018). Gene–gene and gene–environment interactions in complex traits in yeast. Yeast, 35(6), 403–416. https://doi.org/10.1002/yea.3304
Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J. H., Batchelor, W. D., Xiong, L., and Yan, J. (2020). Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant, 13(2), 187–214. https://doi.org/10.1016/j.molp.2020.01.008
Yano, K., Morinaka, Y., Wang, F., Huang, P., Takehara, S., Hirai, T., Ito, A., Koketsu, E., Kawamura, M., Kotake, K., Yoshida, S., Endo, M., Tamiya, G., Kitano, H., Ueguchi-Tanaka, M., Hirano, K., and Matsuoka, M. (2019). GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture. Proceedings of the National Academy of Sciences of the United States of America, 116(42), 2162–21267. https://doi.org/10.1073/pnas.1904964116
York, L. M. (2019). Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. Journal of Experimental Botany, 70(2), 379–386. https://doi.org/10.1093/jxb/ery379
Yu, X., Li, X., Guo, T., Zhu, C., Wu, Y., Mitchell, S. E., Roozeboom, K. L., Wang, D., Wang, M. L., Pederson, G. A., Tesso, T. T., Schnable, P. S., Bernardo, R., and Yu, J. (2016). Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nature Plants, 2(10), 1–7. https://doi.org/10.1038/nplants.2016.150
Zakir, M. (2018). Review on Genotype X Environment Interaction in Plant Breeding and Agronomic Stability of Crops. Journal of Biology, Agriculture and Healthcare, 8(12), 14–21. https://www.iiste.org/Journals/index.php/JBAH/article/view/43065
Zan, Y., and Carlborg, Ö. (2018). A multilocus association analysis method integrating phenotype and expression data reveals multiple novel associations to flowering time variation in wild‐collected Arabidopsis thaliana. Molecular Ecology Resources, 18(4), 798–808. https://doi.org/10.1111/1755-0998.12757
Zaw, H., Raghavan, C., Pocsedio, A., Swamy, B. P. M., Jubay, M. L., Singh, R. K., Bonifacio, J., Mauleon, R., Hernandez, J. E., Mendioro, M. S., Gregorio, G. B., and Hei, L. (2019). Exploring genetic architecture of grain yield and quality traits in a 16- way indica by japonica rice MAGIC global population. Scientific Reports, 9, 1–11. https://doi.org/10.1038/s41598-019-55357-7
Zhao, Y., Mette, M. F., Gowda, M., Longin, C. F. H., and Reif, J. C. (2014). Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity, 112(6), 638–645. https://doi.org/10.1038/hdy.2014.1
Zhu, C., Gore, M., Buckler, E. S., and Yu, J. (2008). Status and Prospects of Association Mapping in Plants. The Plant Genome, 1(1), 5–20. https://doi.org/10.3835/plantgenome2008.02.0089
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
Dimensions
PlumX
- Captures
- Mendeley - Readers: 4
- Mentions
- News: 1
Visitas a la página del resumen del artículo
Descargas
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
1. La aceptación de manuscritos por parte de la revista implicará, además de su edición electrónica de acceso abierto bajo licencia Attribution-NonCommercial-ShareAlike 4.0 (CC BY NC SA), la inclusión y difusión del texto completo a través del repositorio institucional de la Universidad Nacional de Colombia y en todas aquellas bases de datos especializadas que el editor considere adecuadas para su indización con miras a incrementar la visibilidad de la revista.
2. Acta Biológica Colombiana permite a los autores archivar, descargar y compartir, la versión final publicada, así como las versiones pre-print y post-print incluyendo un encabezado con la referencia bibliográfica del articulo publicado.
3. Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista.
4. Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos institucionales, en su página web o en redes sociales cientificas como Academia, Researchgate; Mendelay) lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).
