Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-

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2021
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Universidad de Caldas
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Repositorio Institucional U. Caldas
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spa
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https://repositorio.ucaldas.edu.co
Palabra clave:
Genómica
Atención médica
Enfermedades crónicas
Sistema de Salud
Colombia
Ciencias médicas
Biología molecular
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id REPOUCALDA_60f931e16be3ae7589f8930db51a9bde
oai_identifier_str oai:repositorio.ucaldas.edu.co:ucaldas/16878
network_acronym_str REPOUCALDA
network_name_str Repositorio Institucional U. Caldas
repository_id_str
dc.title.none.fl_str_mv Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
title Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
spellingShingle Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
Genómica
Atención médica
Enfermedades crónicas
Sistema de Salud
Colombia
Ciencias médicas
Biología molecular
title_short Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
title_full Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
title_fullStr Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
title_full_unstemmed Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
title_sort Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-
dc.contributor.none.fl_str_mv Arboleda Valencia, Jorge William
dc.subject.none.fl_str_mv Genómica
Atención médica
Enfermedades crónicas
Sistema de Salud
Colombia
Ciencias médicas
Biología molecular
topic Genómica
Atención médica
Enfermedades crónicas
Sistema de Salud
Colombia
Ciencias médicas
Biología molecular
description Ilustraciones
publishDate 2021
dc.date.none.fl_str_mv 2021-07-16T17:01:59Z
2021-07-16T17:01:59Z
2021-05-13
dc.type.none.fl_str_mv Informe de pasantía
http://purl.org/coar/resource_type/c_18ws
Text
info:eu-repo/semantics/other
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.identifier.none.fl_str_mv https://repositorio.ucaldas.edu.co/handle/ucaldas/16878
Universidad de Caldas
Repositorio institucional Universidad de Caldas
https://repositorio.ucaldas.edu.co
url https://repositorio.ucaldas.edu.co/handle/ucaldas/16878
https://repositorio.ucaldas.edu.co
identifier_str_mv Universidad de Caldas
Repositorio institucional Universidad de Caldas
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv Abecasis, G. R., Auton, A., Brooks, L. D., DePristo, M. A., Durbin, R. M., Handsaker, R. E., McVean, G. A. (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56–65. https://doi.org/10.1038/nature11632
Adhikari, K., Chacón-Duque, J. C., Mendoza-Revilla, J., Fuentes-Guajardo, M., & Ruiz-Linares, A. (2017). The Genetic Diversity of the Americas. Annual Review of Genomics and Human Genetics, 18(1), 277–296. https://doi.org/10.1146/annurev-genom-083115-022331
Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655–1664. https://doi.org/10.1101/gr.094052.109
Altshuler, D. M., Gibbs, R. A., Peltonen, L., Schaffner, S. F., Yu, F., Dermitzakis, E., McEwen, J. E. (2010). Integrating common and rare genetic variation in diverse human populations. Nature, 467(7311), 52–58. https://doi.org/10.1038/nature09298
Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Retrieved from http://www.bioinformatics.babraham.ac.uk/projects/fastqc
Ardila, E. (2018). Las enfermedades crónicas. Biomédica, 38(Supp 1), 5–6.
Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522. https://doi.org/10.1038/nrg.2016.86
Auton, A., Abecasis, G. R., Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Abecasis, G. R. (2015). A global reference for human genetic variation. Nature, 526(7571), 68– 74. https://doi.org/10.1038/nature15393
Belbin, G. M., Nieves-Colón, M. A., Kenny, E. E., Moreno-Estrada, A., & Gignoux, C. R. (2018). Genetic diversity in populations across Latin America: implications for population and medical genetic studies. Current Opinion in Genetics and Development, Vol. 53, pp. 98–104. https://doi.org/10.1016/j.gde.2018.07.006
Bergonzoli, G., & Rodríguez, A. (2013). Lineamientos técnicos y operativos para el análisis de la situación de las enfermedades crónicas no transmisibles en Colombia. Bogotá.
Bradley, P., Shiekh, M., Mehra, V., Vrbicky, K., Layle, S., Olson, M. C., Lukowiak, A. A. (2018). Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: A randomized clinical trial demonstrating clinical utility. Journal of Psychiatric Research, 96, 100–107. https://doi.org/10.1016/j.jpsychires.2017.09.024
Broad Institute (s.f) Picard. http://broadinstitute.github.io/picard/
Browning, S. R., & Browning, B. L. (2007). Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. American Journal of Human Genetics, 81(5), 1084–1097. https://doi.org/10.1086/521987
Bryc, K., Velez, C., Karafet, T., Moreno-Estrada, A., Reynolds, A., Auton, A., Ostrer, H. (2010). Colloquium paper: genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proceedings of the National Academy of Sciences of the United States of America, 107 Suppl, 8954–8961. https://doi.org/10.1073/pnas.0914618107
Camacho, S., Maldonado, N., Bustamante, J., Llorente, B., Cueto, E., Cardona, F., & Arango, C. (2018). How much for a broken heart? Costs of cardiovascular disease in Colombia using a personbased approach. PLoS ONE, 13(12). https://doi.org/10.1371/journal.pone.0208513
Cann, H. M., de Toma, C., Cazes, L., Legrand, M.-F., Morel, V., Piouffre, L., Cavalli-Sforza, L. L. (2002). A human genome diversity cell line panel. Science (New York, N.Y.), 296(5566), 261– 262. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11954565
Chacón-Duque, J.-C., Adhikari, K., Fuentes-Guajardo, M., Mendoza-Revilla, J., Acuña-Alonzo, V., Barquera, R., Ruiz-Linares, A. (2018). Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance. Nature Communications, 9(1), 5388. https://doi.org/10.1038/s41467-018-07748-z
Chang, W. L., Grady, N., & NBD-PWG NIST Big Data Public Working Group. (2015). NIST Big Data Interoperability Framework: Volume. No. Special Publication (NIST SP)-1500-1.
Choudhury, A., Ramsay, M., Hazelhurst, S., Aron, S., Bardien, S., Botha, G., Pepper, M. S. (2017). Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans. Nature Communications, 8(1), 2062. https://doi.org/10.1038/s41467-017-00663-9
Cock-Rada, A. M. & Gómez Ossa, C. A. (2018). Leveraging International Collaborations to Advance Genomic Medicine in Colombia. In Genomic Medicine in Emerging Economies 46–69 15. World Health Organization. Noncommunicable diseases country profiles 2018.
Cohen, J., Pertsemlidis, A., Kotowski, I. K., Graham, R., Garcia, C. K., & Hobbs, H. H. (2005). Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nature Genetics, 37(2), 161–165. https://doi.org/10.1038/ng1509
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z
Bedoya, G., Montoya, P., García, J., Soto, I., Bourgeois, S., Carvajal, L., Ruiz-Linares, A. (2006). Admixture dynamics in Hispanics: a shift in the nuclear genetic ancestry of a South American population isolate. Proceedings of the National Academy of Sciences of the United States of America, 103(19), 7234–7239. https://doi.org/10.1073/pnas.0508716103
Collins, Francis S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523
Collins, Francis S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523
De Castro, M., & Restrepo, C. M. (2015). Genetics and Genomic Medicine in Colombia. Molecular Genetics & Genomic Medicine, 3(2), 84–91. https://doi.org/10.1002/mgg3.139
Delaneau, O., Zagury, J.-F., & Marchini, J. (2013). Improved whole-chromosome phasing for disease and population genetic studies. Nature Methods, 10(1), 5–6. https://doi.org/10.1038/nmeth.2307
Departamento Administrativo Nacional de Estadística- DANE. (2009). Metodología Encuesta Nacional de Calidad de Vida. Edición, 2009. https://www.dane.gov.co/files/investigaciones/fichas/ECV.pdf
DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V, Maguire, J. R., Hartl, C., Daly, M. J. (2011). A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491–498. https://doi.org/10.1038/ng.806
Euesden, J., Lewis, C. M., & O’Reilly, P. F. (2015). PRSice: Polygenic Risk Score software. Bioinformatics, 31(9), 1466–1468. https://doi.org/10.1093/bioinformatics/btu848
Fumagalli, M. (2013). Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS ONE, 8(11). https://doi.org/10.1371/journal.pone.0079667
Gallardo-Solarte K., K., Benavides-Acosta F.P., F. P., & Rosales-Jiménez R., R. (2016). Costos de la enfermedad crónica no transmisible: la realidad colombiana. Revista Ciencias de La Salud, 14(1), 103–114. https://doi.org/10.12804/revsalud14.01.2016.09
Ginsburg, G. S., & Phillips, K. A. (2018). Precision Medicine: From Science To Value. Health Affairs, 37(5), 694–701. https://doi.org/10.1377/hlthaff.2017.1624
Guglielmi, G. (2019). Facing up to injustice in genome science. Nature 568, 290–293 https://doi.org/10.1038/d41586-019-01166-x.
Guio, H., Poterico, J. A., Levano, K. S., Cornejo-Olivas, M., Mazzetti, P., Manassero-Morales, G., Abarca-Barriga, H. (2018). Genetics and genomics in Peru: Clinical and research perspective. Molecular Genetics & Genomic Medicine, 6(6), 873–886. https://doi.org/10.1002/mgg3.533
Hamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., & Haines, J. (2011). The PhenX Toolkit: get the most from your measures. American journal of epidemiology, 174(3), 253-260.
Harati, M. D., Williams, R. R., Movassaghi, M., Hojat, A., Lucey, G. M., & Yong, W. H. (2019). An Introduction to Starting a Biobank. In Methods in molecular biology (Clifton, N.J.) (Vol. 1897, pp. 7–16). https://doi.org/10.1007/978-1-4939-8935-5_2
Harper, A. R., Nayee, S. & Topol, E. J. (2015). Protective alleles and modifier variants in human health and disease. Nat. Rev. Genet. 16, 689–701
Harris, D. N., Song, W., Shetty, A. C., Levano, K. S., Cáceres, O., Padilla, C., Guio, H. (2018b). Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire. Proceedings of the National Academy of Sciences of the United States of America, 115(28), E6526–E6535. https://doi.org/10.1073/pnas.1720798115
Hill, I. D. (1973). Algorithm AS 66: The normal integral. Journal of the Royal Statistical Society. Series C (Applied Statistics), 22(3), 424-427. https://doi.org/10.2307/2346800
Jimenez-Sanchez, G. (2015). Genomics innovation: Transforming healthcare, business, and the global economy. Genome 58, 511–517.
Johnson, J. L., & Abecasis, G. R. (2017). GAS Power Calculator: web-based power calculator for genetic association studies. BioRxiv, 164343. https://doi.org/10.1101/164343
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., MacArthur, D. G. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581(7809), 434–443. https://doi.org/10.1038/s41586-020-2308-7
Kelley, E. F., Snyder, E. M., Alkhatib, N. S., Snyder, S. C., Sprissler, R., Olson, T. P., Abraham, I. (2018). Economic evaluation of a pharmacogenomic multi-gene panel test to optimize antihypertension therapy: simulation study. Journal of Medical Economics, 21(12), 1246–1253. https://doi.org/10.1080/13696998.2018.1531011
Köhler, S., Doelken, S. C., Mungall, C. J., Bauer, S., Firth, H. V, Bailleul-Forestier, I., Robinson, P. N. (2014). The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Research, 42(Database issue), D966-74. https://doi.org/10.1093/nar/gkt1026
Le, V. S., Tran, K. T., Bui, H. T. P., Le, H. T. T., Nguyen, C. D., Do, D. H., Nguyen, L. T. (2019). A Vietnamese human genetic variation database. Human Mutation, 40(10), 1664–1675. https://doi.org/10.1002/humu.23835
Lee, C. R., Sriramoju, V. B., Cervantes, A., Howell, L. A., Varunok, N., Madan, S., Stouffer, G. A. (2018). Clinical Outcomes and Sustainability of Using CYP2C19 Genotype–Guided Antiplatelet Therapy After Percutaneous Coronary Intervention. Circulation: Genomic and Precision Medicine, 11(4), e002069. https://doi.org/10.1161/CIRCGEN.117.002069
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324
López, B., Torrent-Fontbona, F., Viñas, R., & Fernández-Real, J. M. (2018). Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction. Artificial Intelligence in Medicine, 85, 43–49. https://doi.org/10.1016/j.artmed.2017.09.005
López-Correa, C. & Patrinos, G. P. (2018). Genomic Medicine in Developing and Emerging Economies: State-of-the-Art and Future Trends. In Genomic Medicine in Emerging Economies 1– 12.
Lorente-Galdos, B., Lao, O., Serra-Vidal, G., Santpere, G., Kuderna, L. F. K., Arauna, L. R., Comas, D. (2019). Whole-genome sequence analysis of a Pan African set of samples reveals archaic gene flow from an extinct basal population of modern humans into sub-Saharan populations. Genome Biology, 20(1), 77. https://doi.org/10.1186/s13059-019-1684-5
Luft, F. C. (2004). Geneticism of essential hypertension. Hypertension, 43(6), 1155–1159. https://doi.org/10.1161/01.HYP.0000128242.41442.71
Maas, P., Barrdahl, M., Joshi, A. D., Auer, P. L., Gaudet, M. M., Milne, R. L., Chatterjee, N. (2016). Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States. JAMA Oncology, 2(10), 1295–1302. https://doi.org/10.1001/jamaoncol.2016.1025
Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D. (2013). RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. American Journal of Human Genetics, 93(2), 278–288. https://doi.org/10.1016/j.ajhg.2013.06.020
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Kenny, E. E. (2017). Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. The American Journal of Human Genetics, 100(4), 635–649. https://doi.org/10.1016/j.ajhg.2017.03.004
Martínez‐Cruzado, J. C., Toro‐Labrador, G., Viera‐Vera, J., Rivera‐Vega, M. Y., Startek, J., Latorre‐ Esteves, M., & Valencia‐Rivera, P. (2005). Reconstructing the population history of Puerto Rico by means of mtDNA phylogeographic analysis. American journal of physical anthropology, 128(1), 131-155.
Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341
Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341
Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341
Mendy, M., Lawlor, R. T., van Kappel, A. L., Riegman, P. H. J., Betsou, F., Cohen, O. D., & Henderson, M. K. (2018). Biospecimens and Biobanking in Global Health. Clinics in Laboratory Medicine, 38(1), 183–207. https://doi.org/10.1016/j.cll.2017.10.015
Mensah, G. A., Peprah, E. K., Sampson, U. K. A., & Cooper, R. S. (2015). H3Africa comes of age. Cardiovascular Journal of Africa, 26(2 Suppl 1), S3-5. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25962946
Ministerio de Salud y Protección Social República de Colombia. (Octubre 4, 1993). Resolución 8430 de 1993. Por la cual se establecen las normas científicas, técnicas y administrativas para la investigación en salud. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/DIJ/RESOLUCION8430-DE-1993.PDF
Morales, J., Welter, D., Bowler, E. H., Cerezo, M., Harris, L. W., McMahon, A. C., MacArthur, J. A. L. (2018). A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biology, 19(1), 21. https://doi.org/10.1186/s13059-018-1396-2
Moreno-Estrada, A., Gravel, S., Zakharia, F., McCauley, J. L., Byrnes, J. K., Gignoux, C. R., Bustamante, C. D. (2013). Reconstructing the population genetic history of the Caribbean. PLoS Genetics, 9(11), e1003925. https://doi.org/10.1371/journal.pgen.1003925
Mulder, N., Abimiku, A., Adebamowo, S. N., de Vries, J., Matimba, A., Olowoyo, P., Stein, D. J. (2018). H3Africa: current perspectives. Pharmacogenomics and Personalized Medicine, Volume 11, 59–66. https://doi.org/10.2147/PGPM.S141546
National Institutes of Health (2015). All of Us Research Program. https://allofus.nih.gov/
Norris, E. T., Wang, L., Conley, A. B., Rishishwar, L., Mariño-Ramírez, L., Valderrama-Aguirre, A., & Jordan, I. K. (2018). Genetic ancestry, admixture and health determinants in Latin America. BMC Genomics, 19(Suppl 8), 861. https://doi.org/10.1186/s12864-018-5195-7
Pfeifer, S. P. (2017). From next-generation resequencing reads to a high-quality variant data set. Heredity, Vol. 118, pp. 111–124. https://doi.org/10.1038/hdy.2016.102
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Sham, P. C. (2007a). 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
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Sham, P. C. (2007b). 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
Ramsay, M., Sankoh, O., & as members of the AWI-Gen study and the H3Africa Consortium. (2016). African partnerships through the H3Africa Consortium bring a genomic dimension to longitudinal population studies on the continent. International Journal of Epidemiology, 45(2), 305–308. https://doi.org/10.1093/ije/dyv187
Rashkin, S., Jun, G., Chen, S., & Abecasis, G. R. (2017). Optimal sequencing strategies for identifying disease-associated singletons. PLoS Genetics, 13(6). https://doi.org/10.1371/journal.pgen.1006811
Reed, E., Nunez, S., Kulp, D., Qian, J., Reilly, M. P., & Foulkes, A. S. (2015). A guide to genomewide association analysis and post-analytic interrogation. Statistics in Medicine, 34(28), 3769– 3792. https://doi.org/10.1002/sim.6605
Rishishwar, L., Conley, A. B., Wigington, C. H., Wang, L., Valderrama-Aguirre, A., & King Jordan, I. (2015). Ancestry, admixture and fitness in Colombian genomes. Scientific Reports, 5(1), 12376. https://doi.org/10.1038/srep12376
Rodríguez-García, J., Peñaloza-Quintero, R. E., y Amaya-Lara, J. L. (2017). Estimación de la carga global de enfermedad en Colombia 2012: nuevos aspectos metodológicos. Revista de Salud Pública, 19(2), 235–240. https://doi.org/10.15446/rsap.v19n2.66179
Rojas, W., Parra, M. V., Campo, O., Caro, M. A., Lopera, J. G., Arias, W., Bedoya, G. (2010). Genetic make up and structure of Colombian populations by means of uniparental and biparental DNA markers. American Journal of Physical Anthropology, 143(1), 13–20. https://doi.org/10.1002/ajpa.21270
Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias, W., Gonzalez-José, R. (2014). Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals. PLoS Genetics, 10(9), e1004572. https://doi.org/10.1371/journal.pgen.1004572
Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Research, 29(1), 308–311. https://doi.org/10.1093/nar/29.1.308
Sirugo, G., Williams, S. M. & Tishkoff, S. A. (2019). The Missing Diversity in Human Genetic Studies. Cell 177, 26–31 https://doi.org/10.1016/j.cell.2019.02.048.
Skol, A. D., Scott, L. J., Abecasis, G. R., & Boehnke, M. (2006). Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nature genetics, 38(2), 209-213. https://doi.org/10.1038/ng1706
The 1000 Genomes Project Consortium. (2010). A map of human genome variation from populationscale sequencing. Nature, 467(7319), 1061–1073. https://doi.org/10.1038/nature09534
The Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678. https://doi.org/10.1038/nature05911
Tsui, N. B. Y., Cheng, G., Chung, T., Lam, C. W. K., Yee, A., Chung, P. K. C., Fok, M. (2018). Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning. Scientific Reports, 8(1), 1853. https://doi.org/10.1038/s41598-017-19017-y
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American Journal of Human Genetics, Vol. 101, pp. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005
Wall, J. D., Stawiski, E. W., Ratan, A., Kim, H. L., Kim, C., Gupta, R., Peterson, A. S. (2019). The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature, 576(7785), 106–111. https://doi.org/10.1038/s41586-019-1793-z
Wang, S., Ray, N., Rojas, W., Parra, M. V, Bedoya, G., Gallo, C., Ruiz-Linares, A. (2008). Geographic patterns of genome admixture in Latin American Mestizos. PLoS Genetics, 4(3), e1000037. https://doi.org/10.1371/journal.pgen.1000037
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., & Parkinson, H. (2014). The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic acids research, 42(D1), D1001-D1006. https://doi.org/10.1093/nar/gkt1229
Wichura, M. J. (1988). Algorithm AS 241: The percentage points of the normal distribution. Journal of the Royal Statistical Society. Series C (Applied Statistics), 37(3), 477-484. https://doi.org/10.2307/2347330
Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Flicek, P. (2018). Ensembl 2018. Nucleic Acids Research, 46(D1), D754–D761. https://doi.org/10.1093/nar/gkx1098
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spelling Incremento del uso de la información genómica en la atención médica en Colombia - Programa ORIGEN-GenómicaAtención médicaEnfermedades crónicasSistema de SaludColombiaCiencias médicasBiología molecularIlustracionesspa: En los últimos años, las enfermedades crónicas no transmisibles (ECNT) han desplazado en Colombia a las enfermedades infecciosas como principales causas de muerte; como es la hipertensión arterial, las enfermedades cardiovasculares, el cáncer y la diabetes, las cuales representan el 75% del total de las muertes en el país. Estas enfermedades son prevenibles en cierta medida, sin embargo, generalmente reciben diagnóstico cuando ya llevan años de evolución y sólo resta tratar la enfermedad y prevenir sus complicaciones y/o la aparición de otras comorbilidades. La práctica de no prevenir, genera unos costos asociados altos y para el 2016 se estimó que la atención global de las ECNT se encontraba en 13,2 billones de pesos, valor que corresponde acerca del 40% del presupuesto destinado para la salud en Colombia. Esta carga podría reducirse en parte, con la adopción de estrategias eficaces de prevención y en aquellos que ya presentan la enfermedad, tratamientos más adecuados al perfil de riesgo de cada paciente o de la subpoblación a la cual pertenece. Este tipo de atención médica se conoce como medicina de precisión. Es por ello, que con base a esta situación, se presenta el siguiente proyecto, el cual busca promover el uso de la información genómica en la atención medica de los colombianos, por medio de una mejora en el conocimiento de la diversidad genética de la población colombiana y los determinantes genéticos de las enfermedades, fortaleciendo el conocimiento sobre genómica y bioinformática entre profesionales de la salud, tomadores de decisiones del sector salud, político y público general. Por medio de la construcción de bases, a través de técnicas moleculares que permitan la secuenciación y genotipificación del genoma humano colombiano. Esto apoyado de Machine Learning y manejo de Big Data, necesarias para la implementación de la medicina de precisión y el desarrollo de la genómica en el país, a través de la caracterización de la diversidad genética de nuestra población y la identificación de los determinantes genéticos de las ECNT de mayor impacto para el país.eng: In recent years, chronic non-communicable diseases (CNCDs) have displaced infectious diseases as the main causes of death in Colombia; such as high blood pressure, cardiovascular diseases, cancer and diabetes, which They represent 75% of the total deaths in the country. These diseases are preventable to a certain extent, however, they generally receive a diagnosis after years of evolution and it only remains to treat the disease and prevent its complications and / or the appearance of other comorbidities. The practice of not preventing generates high associated costs and for 2016 it was estimated that the global attention of CNCDs was at 13.2 trillion pesos, a value that corresponds to about 40% of the budget allocated for health in Colombia. This burden could be partially reduced with the adoption of effective prevention strategies and, in those who already have the disease, treatments that are more appropriate to the risk profile of each patient or the subpopulation to which they belong. This type of medical care is known as precision medicine. That is why, based on this situation, the following project is presented, which seeks to promote the use of genomic information in the medical care of Colombians, through an improvement in the knowledge of the genetic diversity of the population Colombia and the genetic determinants of diseases, strengthening the knowledge about genomics and bioinformatics among health professionals, decision makers in the health sector, politicians and the general public. Through the construction of bases, through molecular techniques that allow the sequencing and genotyping of the Colombian human genome. This supported by Machine Learning and Big Data management, necessary for the implementation of precision medicine and the development of genomics in the country, through the characterization of the genetic diversity of our population and the identification of the genetic determinants of the CNCD with the greatest impact for the country.Este documento no está autorizado para publicación, debido a que es producto de pasantía académica y no es copia completa del documento original, debido a su extensión.ManizalesArboleda Valencia, Jorge WilliamLópez Ceferino, Manuela2021-07-16T17:01:59Z2021-07-16T17:01:59Z2021-05-13Informe de pasantíahttp://purl.org/coar/resource_type/c_18wsTextinfo:eu-repo/semantics/otherhttp://purl.org/coar/version/c_970fb48d4fbd8a85application/pdfapplication/pdfapplication/pdfapplication/pdfhttps://repositorio.ucaldas.edu.co/handle/ucaldas/16878Universidad de CaldasRepositorio institucional Universidad de Caldashttps://repositorio.ucaldas.edu.cospaAbecasis, G. R., Auton, A., Brooks, L. D., DePristo, M. A., Durbin, R. M., Handsaker, R. E., McVean, G. A. (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56–65. https://doi.org/10.1038/nature11632Adhikari, K., Chacón-Duque, J. C., Mendoza-Revilla, J., Fuentes-Guajardo, M., & Ruiz-Linares, A. (2017). The Genetic Diversity of the Americas. Annual Review of Genomics and Human Genetics, 18(1), 277–296. https://doi.org/10.1146/annurev-genom-083115-022331Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655–1664. https://doi.org/10.1101/gr.094052.109Altshuler, D. M., Gibbs, R. A., Peltonen, L., Schaffner, S. F., Yu, F., Dermitzakis, E., McEwen, J. E. (2010). Integrating common and rare genetic variation in diverse human populations. Nature, 467(7311), 52–58. https://doi.org/10.1038/nature09298Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Retrieved from http://www.bioinformatics.babraham.ac.uk/projects/fastqcArdila, E. (2018). Las enfermedades crónicas. Biomédica, 38(Supp 1), 5–6.Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522. https://doi.org/10.1038/nrg.2016.86Auton, A., Abecasis, G. R., Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Abecasis, G. R. (2015). A global reference for human genetic variation. Nature, 526(7571), 68– 74. https://doi.org/10.1038/nature15393Belbin, G. M., Nieves-Colón, M. A., Kenny, E. E., Moreno-Estrada, A., & Gignoux, C. R. (2018). Genetic diversity in populations across Latin America: implications for population and medical genetic studies. Current Opinion in Genetics and Development, Vol. 53, pp. 98–104. https://doi.org/10.1016/j.gde.2018.07.006Bergonzoli, G., & Rodríguez, A. (2013). Lineamientos técnicos y operativos para el análisis de la situación de las enfermedades crónicas no transmisibles en Colombia. Bogotá.Bradley, P., Shiekh, M., Mehra, V., Vrbicky, K., Layle, S., Olson, M. C., Lukowiak, A. A. (2018). Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: A randomized clinical trial demonstrating clinical utility. Journal of Psychiatric Research, 96, 100–107. https://doi.org/10.1016/j.jpsychires.2017.09.024Broad Institute (s.f) Picard. http://broadinstitute.github.io/picard/Browning, S. R., & Browning, B. L. (2007). Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. American Journal of Human Genetics, 81(5), 1084–1097. https://doi.org/10.1086/521987Bryc, K., Velez, C., Karafet, T., Moreno-Estrada, A., Reynolds, A., Auton, A., Ostrer, H. (2010). Colloquium paper: genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proceedings of the National Academy of Sciences of the United States of America, 107 Suppl, 8954–8961. https://doi.org/10.1073/pnas.0914618107Camacho, S., Maldonado, N., Bustamante, J., Llorente, B., Cueto, E., Cardona, F., & Arango, C. (2018). How much for a broken heart? Costs of cardiovascular disease in Colombia using a personbased approach. PLoS ONE, 13(12). https://doi.org/10.1371/journal.pone.0208513Cann, H. M., de Toma, C., Cazes, L., Legrand, M.-F., Morel, V., Piouffre, L., Cavalli-Sforza, L. L. (2002). A human genome diversity cell line panel. Science (New York, N.Y.), 296(5566), 261– 262. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11954565Chacón-Duque, J.-C., Adhikari, K., Fuentes-Guajardo, M., Mendoza-Revilla, J., Acuña-Alonzo, V., Barquera, R., Ruiz-Linares, A. (2018). Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance. Nature Communications, 9(1), 5388. https://doi.org/10.1038/s41467-018-07748-zChang, W. L., Grady, N., & NBD-PWG NIST Big Data Public Working Group. (2015). NIST Big Data Interoperability Framework: Volume. No. Special Publication (NIST SP)-1500-1.Choudhury, A., Ramsay, M., Hazelhurst, S., Aron, S., Bardien, S., Botha, G., Pepper, M. S. (2017). Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans. Nature Communications, 8(1), 2062. https://doi.org/10.1038/s41467-017-00663-9Cock-Rada, A. M. & Gómez Ossa, C. A. (2018). Leveraging International Collaborations to Advance Genomic Medicine in Colombia. In Genomic Medicine in Emerging Economies 46–69 15. World Health Organization. Noncommunicable diseases country profiles 2018.Cohen, J., Pertsemlidis, A., Kotowski, I. K., Graham, R., Garcia, C. K., & Hobbs, H. H. (2005). Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nature Genetics, 37(2), 161–165. https://doi.org/10.1038/ng1509Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-zBedoya, G., Montoya, P., García, J., Soto, I., Bourgeois, S., Carvajal, L., Ruiz-Linares, A. (2006). Admixture dynamics in Hispanics: a shift in the nuclear genetic ancestry of a South American population isolate. Proceedings of the National Academy of Sciences of the United States of America, 103(19), 7234–7239. https://doi.org/10.1073/pnas.0508716103Collins, Francis S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523Collins, Francis S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523De Castro, M., & Restrepo, C. M. (2015). Genetics and Genomic Medicine in Colombia. Molecular Genetics & Genomic Medicine, 3(2), 84–91. https://doi.org/10.1002/mgg3.139Delaneau, O., Zagury, J.-F., & Marchini, J. (2013). Improved whole-chromosome phasing for disease and population genetic studies. Nature Methods, 10(1), 5–6. https://doi.org/10.1038/nmeth.2307Departamento Administrativo Nacional de Estadística- DANE. (2009). Metodología Encuesta Nacional de Calidad de Vida. Edición, 2009. https://www.dane.gov.co/files/investigaciones/fichas/ECV.pdfDePristo, M. A., Banks, E., Poplin, R., Garimella, K. V, Maguire, J. R., Hartl, C., Daly, M. J. (2011). A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491–498. https://doi.org/10.1038/ng.806Euesden, J., Lewis, C. M., & O’Reilly, P. F. (2015). PRSice: Polygenic Risk Score software. Bioinformatics, 31(9), 1466–1468. https://doi.org/10.1093/bioinformatics/btu848Fumagalli, M. (2013). Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS ONE, 8(11). https://doi.org/10.1371/journal.pone.0079667Gallardo-Solarte K., K., Benavides-Acosta F.P., F. P., & Rosales-Jiménez R., R. (2016). Costos de la enfermedad crónica no transmisible: la realidad colombiana. Revista Ciencias de La Salud, 14(1), 103–114. https://doi.org/10.12804/revsalud14.01.2016.09Ginsburg, G. S., & Phillips, K. A. (2018). Precision Medicine: From Science To Value. Health Affairs, 37(5), 694–701. https://doi.org/10.1377/hlthaff.2017.1624Guglielmi, G. (2019). Facing up to injustice in genome science. Nature 568, 290–293 https://doi.org/10.1038/d41586-019-01166-x.Guio, H., Poterico, J. A., Levano, K. S., Cornejo-Olivas, M., Mazzetti, P., Manassero-Morales, G., Abarca-Barriga, H. (2018). Genetics and genomics in Peru: Clinical and research perspective. Molecular Genetics & Genomic Medicine, 6(6), 873–886. https://doi.org/10.1002/mgg3.533Hamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., & Haines, J. (2011). The PhenX Toolkit: get the most from your measures. American journal of epidemiology, 174(3), 253-260.Harati, M. D., Williams, R. R., Movassaghi, M., Hojat, A., Lucey, G. M., & Yong, W. H. (2019). An Introduction to Starting a Biobank. In Methods in molecular biology (Clifton, N.J.) (Vol. 1897, pp. 7–16). https://doi.org/10.1007/978-1-4939-8935-5_2Harper, A. R., Nayee, S. & Topol, E. J. (2015). Protective alleles and modifier variants in human health and disease. Nat. Rev. Genet. 16, 689–701Harris, D. N., Song, W., Shetty, A. C., Levano, K. S., Cáceres, O., Padilla, C., Guio, H. (2018b). Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire. Proceedings of the National Academy of Sciences of the United States of America, 115(28), E6526–E6535. https://doi.org/10.1073/pnas.1720798115Hill, I. D. (1973). Algorithm AS 66: The normal integral. Journal of the Royal Statistical Society. Series C (Applied Statistics), 22(3), 424-427. https://doi.org/10.2307/2346800Jimenez-Sanchez, G. (2015). Genomics innovation: Transforming healthcare, business, and the global economy. Genome 58, 511–517.Johnson, J. L., & Abecasis, G. R. (2017). GAS Power Calculator: web-based power calculator for genetic association studies. BioRxiv, 164343. https://doi.org/10.1101/164343Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., MacArthur, D. G. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581(7809), 434–443. https://doi.org/10.1038/s41586-020-2308-7Kelley, E. F., Snyder, E. M., Alkhatib, N. S., Snyder, S. C., Sprissler, R., Olson, T. P., Abraham, I. (2018). Economic evaluation of a pharmacogenomic multi-gene panel test to optimize antihypertension therapy: simulation study. Journal of Medical Economics, 21(12), 1246–1253. https://doi.org/10.1080/13696998.2018.1531011Köhler, S., Doelken, S. C., Mungall, C. J., Bauer, S., Firth, H. V, Bailleul-Forestier, I., Robinson, P. N. (2014). The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Research, 42(Database issue), D966-74. https://doi.org/10.1093/nar/gkt1026Le, V. S., Tran, K. T., Bui, H. T. P., Le, H. T. T., Nguyen, C. D., Do, D. H., Nguyen, L. T. (2019). A Vietnamese human genetic variation database. Human Mutation, 40(10), 1664–1675. https://doi.org/10.1002/humu.23835Lee, C. R., Sriramoju, V. B., Cervantes, A., Howell, L. A., Varunok, N., Madan, S., Stouffer, G. A. (2018). Clinical Outcomes and Sustainability of Using CYP2C19 Genotype–Guided Antiplatelet Therapy After Percutaneous Coronary Intervention. Circulation: Genomic and Precision Medicine, 11(4), e002069. https://doi.org/10.1161/CIRCGEN.117.002069Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324López, B., Torrent-Fontbona, F., Viñas, R., & Fernández-Real, J. M. (2018). Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction. Artificial Intelligence in Medicine, 85, 43–49. https://doi.org/10.1016/j.artmed.2017.09.005López-Correa, C. & Patrinos, G. P. (2018). Genomic Medicine in Developing and Emerging Economies: State-of-the-Art and Future Trends. In Genomic Medicine in Emerging Economies 1– 12.Lorente-Galdos, B., Lao, O., Serra-Vidal, G., Santpere, G., Kuderna, L. F. K., Arauna, L. R., Comas, D. (2019). Whole-genome sequence analysis of a Pan African set of samples reveals archaic gene flow from an extinct basal population of modern humans into sub-Saharan populations. Genome Biology, 20(1), 77. https://doi.org/10.1186/s13059-019-1684-5Luft, F. C. (2004). Geneticism of essential hypertension. Hypertension, 43(6), 1155–1159. https://doi.org/10.1161/01.HYP.0000128242.41442.71Maas, P., Barrdahl, M., Joshi, A. D., Auer, P. L., Gaudet, M. M., Milne, R. L., Chatterjee, N. (2016). Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States. JAMA Oncology, 2(10), 1295–1302. https://doi.org/10.1001/jamaoncol.2016.1025Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D. (2013). RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. American Journal of Human Genetics, 93(2), 278–288. https://doi.org/10.1016/j.ajhg.2013.06.020Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Kenny, E. E. (2017). Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. The American Journal of Human Genetics, 100(4), 635–649. https://doi.org/10.1016/j.ajhg.2017.03.004Martínez‐Cruzado, J. C., Toro‐Labrador, G., Viera‐Vera, J., Rivera‐Vega, M. Y., Startek, J., Latorre‐ Esteves, M., & Valencia‐Rivera, P. (2005). Reconstructing the population history of Puerto Rico by means of mtDNA phylogeographic analysis. American journal of physical anthropology, 128(1), 131-155.Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341Medina Rivas, M. A., Norris, E. T., Rishishwar, L., Conley, A. B., Medina Trochez, C., ValderramaAguirre, A., Jordan, I. K. (2016). El Chocó, Colombia: un hotspot de la biodiversidad humana. Revista Biodiversidad Neotropical, 6(1), 45. https://doi.org/10.18636/bioneotropical.v6i1.341Mendy, M., Lawlor, R. T., van Kappel, A. L., Riegman, P. H. J., Betsou, F., Cohen, O. D., & Henderson, M. K. (2018). Biospecimens and Biobanking in Global Health. Clinics in Laboratory Medicine, 38(1), 183–207. https://doi.org/10.1016/j.cll.2017.10.015Mensah, G. A., Peprah, E. K., Sampson, U. K. A., & Cooper, R. S. (2015). H3Africa comes of age. Cardiovascular Journal of Africa, 26(2 Suppl 1), S3-5. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25962946Ministerio de Salud y Protección Social República de Colombia. (Octubre 4, 1993). Resolución 8430 de 1993. Por la cual se establecen las normas científicas, técnicas y administrativas para la investigación en salud. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/DIJ/RESOLUCION8430-DE-1993.PDFMorales, J., Welter, D., Bowler, E. H., Cerezo, M., Harris, L. W., McMahon, A. C., MacArthur, J. A. L. (2018). A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biology, 19(1), 21. https://doi.org/10.1186/s13059-018-1396-2Moreno-Estrada, A., Gravel, S., Zakharia, F., McCauley, J. L., Byrnes, J. K., Gignoux, C. R., Bustamante, C. D. (2013). Reconstructing the population genetic history of the Caribbean. PLoS Genetics, 9(11), e1003925. https://doi.org/10.1371/journal.pgen.1003925Mulder, N., Abimiku, A., Adebamowo, S. N., de Vries, J., Matimba, A., Olowoyo, P., Stein, D. J. (2018). H3Africa: current perspectives. Pharmacogenomics and Personalized Medicine, Volume 11, 59–66. https://doi.org/10.2147/PGPM.S141546National Institutes of Health (2015). All of Us Research Program. https://allofus.nih.gov/Norris, E. T., Wang, L., Conley, A. B., Rishishwar, L., Mariño-Ramírez, L., Valderrama-Aguirre, A., & Jordan, I. K. (2018). Genetic ancestry, admixture and health determinants in Latin America. BMC Genomics, 19(Suppl 8), 861. https://doi.org/10.1186/s12864-018-5195-7Pfeifer, S. P. (2017). From next-generation resequencing reads to a high-quality variant data set. Heredity, Vol. 118, pp. 111–124. https://doi.org/10.1038/hdy.2016.102Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Sham, P. C. (2007a). 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/10.1086/519795Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Sham, P. C. (2007b). 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/10.1086/519795Ramsay, M., Sankoh, O., & as members of the AWI-Gen study and the H3Africa Consortium. (2016). African partnerships through the H3Africa Consortium bring a genomic dimension to longitudinal population studies on the continent. International Journal of Epidemiology, 45(2), 305–308. https://doi.org/10.1093/ije/dyv187Rashkin, S., Jun, G., Chen, S., & Abecasis, G. R. (2017). Optimal sequencing strategies for identifying disease-associated singletons. PLoS Genetics, 13(6). https://doi.org/10.1371/journal.pgen.1006811Reed, E., Nunez, S., Kulp, D., Qian, J., Reilly, M. P., & Foulkes, A. S. (2015). A guide to genomewide association analysis and post-analytic interrogation. Statistics in Medicine, 34(28), 3769– 3792. https://doi.org/10.1002/sim.6605Rishishwar, L., Conley, A. B., Wigington, C. H., Wang, L., Valderrama-Aguirre, A., & King Jordan, I. (2015). Ancestry, admixture and fitness in Colombian genomes. Scientific Reports, 5(1), 12376. https://doi.org/10.1038/srep12376Rodríguez-García, J., Peñaloza-Quintero, R. E., y Amaya-Lara, J. L. (2017). Estimación de la carga global de enfermedad en Colombia 2012: nuevos aspectos metodológicos. Revista de Salud Pública, 19(2), 235–240. https://doi.org/10.15446/rsap.v19n2.66179Rojas, W., Parra, M. V., Campo, O., Caro, M. A., Lopera, J. G., Arias, W., Bedoya, G. (2010). Genetic make up and structure of Colombian populations by means of uniparental and biparental DNA markers. American Journal of Physical Anthropology, 143(1), 13–20. https://doi.org/10.1002/ajpa.21270Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias, W., Gonzalez-José, R. (2014). Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals. PLoS Genetics, 10(9), e1004572. https://doi.org/10.1371/journal.pgen.1004572Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Research, 29(1), 308–311. https://doi.org/10.1093/nar/29.1.308Sirugo, G., Williams, S. M. & Tishkoff, S. A. (2019). The Missing Diversity in Human Genetic Studies. Cell 177, 26–31 https://doi.org/10.1016/j.cell.2019.02.048.Skol, A. D., Scott, L. J., Abecasis, G. R., & Boehnke, M. (2006). Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nature genetics, 38(2), 209-213. https://doi.org/10.1038/ng1706The 1000 Genomes Project Consortium. (2010). A map of human genome variation from populationscale sequencing. Nature, 467(7319), 1061–1073. https://doi.org/10.1038/nature09534The Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678. https://doi.org/10.1038/nature05911Tsui, N. B. Y., Cheng, G., Chung, T., Lam, C. W. K., Yee, A., Chung, P. K. C., Fok, M. (2018). Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning. Scientific Reports, 8(1), 1853. https://doi.org/10.1038/s41598-017-19017-yVisscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American Journal of Human Genetics, Vol. 101, pp. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005Wall, J. D., Stawiski, E. W., Ratan, A., Kim, H. L., Kim, C., Gupta, R., Peterson, A. S. (2019). The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature, 576(7785), 106–111. https://doi.org/10.1038/s41586-019-1793-zWang, S., Ray, N., Rojas, W., Parra, M. V, Bedoya, G., Gallo, C., Ruiz-Linares, A. (2008). Geographic patterns of genome admixture in Latin American Mestizos. PLoS Genetics, 4(3), e1000037. https://doi.org/10.1371/journal.pgen.1000037Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., & Parkinson, H. (2014). The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic acids research, 42(D1), D1001-D1006. https://doi.org/10.1093/nar/gkt1229Wichura, M. J. (1988). Algorithm AS 241: The percentage points of the normal distribution. Journal of the Royal Statistical Society. Series C (Applied Statistics), 37(3), 477-484. https://doi.org/10.2307/2347330Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Flicek, P. (2018). Ensembl 2018. Nucleic Acids Research, 46(D1), D754–D761. https://doi.org/10.1093/nar/gkx1098http://purl.org/coar/access_right/c_14cboai:repositorio.ucaldas.edu.co:ucaldas/168782024-07-16T21:51:48Z