Caracterización genómica de un grupo de individuos con Deterioro Cognitivo Leve (DCL) en población del Atlántico, Colombia

dc.contributor.advisorPinzón Velasco, Andrés Mauriciospa
dc.contributor.advisorArboleda Bustos, Carlos Eduardospa
dc.contributor.authorLargo González, Johan Hernandospa
dc.contributor.cvlacLargo Gonzalez, Johan Hernando [0000149121]spa
dc.contributor.orcidLargo Gonzalez, Johan Hernando [0000000247872960]spa
dc.contributor.researchgateLargo Gonzalez, Johan Hernando [Johan-Largo-Gonzalez]spa
dc.contributor.researchgroupGrupo de Investigación en Bioinformática y Biología de Sistemasspa
dc.coverage.countryColombiaspa
dc.coverage.regionAtlánticospa
dc.date.accessioned2025-07-14T13:46:18Z
dc.date.available2025-07-14T13:46:18Z
dc.date.issued2025
dc.descriptionilustraciones a color, diagramasspa
dc.description.abstractEl deterioro cognitivo leve (DCL) es una etapa temprana de la enfermedad de Alzheimer (EA), caracterizada por una disminución en la cognición sin llegar a ser demencia. Factores genéticos influyen en su desarrollo, y se han identificado variantes asociadas a su progresión, destacando el alelo APOE-ϵ4 como un marcador clave en la transición de DCL a EA. La prevalencia de DCL varía entre 5,0% y 36,7% a nivel mundial, mientras que en Colombia afecta al 17,5% de la población, con mayor incidencia en la región del Atlántico. A pesar de las herramientas diagnósticas actuales, es necesario un enfoque multiómico para una comprensión más profunda de la enfermedad. Los avances en secuenciación de nueva generación (NGS) permiten identificar factores genéticos clave en enfermedades neurodegenerativas, facilitando el desarrollo de estrategias preventivas y terapéuticas. En este contexto, surge la oportunidad de realizar un estudio de asociación y caracterización genómica en pacientes con DCL y controles de la población del Atlántico, Colombia, con el objetivo de profundizar en los mecanismos genéticos involucrados en esta región, que presenta una alta prevalencia de enfermedades neurodegenerativas (Texto tomado de la fuente).spa
dc.description.abstractMild Cognitive Impairment (MCI) is an early stage of Alzheimer’s disease (AD), characterized by a decline in cognition without reaching the threshold for dementia. Genetic factors influence its development, and several variants associated with its progression have been identified, with the APOE-ϵ4 allele standing out as a key marker in the transition from MCI to AD. The prevalence of MCI ranges from 5.0% to 36.7% worldwide, while in Colombia, it affects 17.5% of the population, with the highest incidence in the Atlántico region. Despite the availability of current diagnostic tools, a multi-omics approach is necessary for a deeper understanding of the disease. Advances in next-generation sequencing (NGS) enable the identification of key genetic factors in neurodegenerative diseases, facilitating the development of preventive and therapeutic strategies. In this context, the opportunity arises to conduct an association study and genomic characterization of MCI patients and controls from the Atlántico region, Colombia, aiming to gain deeper insights into the genetic mechanisms involved in this population, which exhibits a high prevalence of neurodegenerative diseases.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Bioinformáticaspa
dc.description.methodsPara llevar a cabo el objetivo general del trabajo de grado se determinó un estudio observacional de tipo caso control. Para tal fin se establecieron puntos críticos para el desarrollo del mismo.spa
dc.description.researchareaBiología de Sistemasspa
dc.format.extent78 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/88331
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Bioinformáticaspa
dc.relation.referencesAarsland, D., Batzu, L., Halliday, G. M., Geurtsen, G. J., Ballard, C., Ray Chaudhuri, K., & Weintraub, D. (2021). Parkinson disease-associated cognitive impairment. Nature Reviews Disease Primers, 7(1). https://doi.org/10.1038/s41572-021-00280-3spa
dc.relation.referencesAbdul Rehman, S. A., Armstrong, L. A., Lange, S. M., Kristariyanto, Y. A., Gräwert, T. W., Knebel, A., Svergun, D. I., & Kulathu, Y. (2021). Mechanism of activation and regulation of deubiquitinase activity in MINDY1 and MINDY2. Molecular Cell, 81(20), 4176-4190.e6. https://doi.org/10.1016/j.molcel.2021.08.024spa
dc.relation.referencesAdam, F., & Nathan, W. (2020). Best Practices for De Novo Transcriptome Assembly with Trinity. Harvard FAS Informatics. https://informatics.fas.harvard.edu/best-practices-for-de-novo-transcriptome-assembly-with-trinity.htmlspa
dc.relation.referencesAlvarado, C., Gómez, J. F., Etayo, E., Giraldo, C. E., Pineda, A., & Toro, E. (2014). Estudio EDECO (Estudio poblacional de deterioro cognitivo en población colombiana). Acta Médica Colombiana, 264–271. https://doi.org/10.36104/amc.2014.196spa
dc.relation.referencesAnderson, N. D. (2019). State of the science on mild cognitive impairment (MCI). In CNS Spectrums (Vol. 24, Issue 1). https://doi.org/10.1017/S1092852918001347spa
dc.relation.referencesAndrews, S. (2010). FastQC. Babraham Bioinformatics. http://www.bioinformatics.babraham.ac.uk/projects/spa
dc.relation.referencesAngela M. Sanford. (2017). Mild Cognitive Impairment. Clinics in Geriatric Medicine, 33(3), 325–337. http://dx.doi.org/10.1016/j.cger.2017.02.005spa
dc.relation.referencesBai, W., Chen, P., Cai, H., Zhang, Q., Su, Z., Cheung, T., Jackson, T., Sha, S., & Xiang, Y. T. (2022). Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies. Age and Ageing, 51(8). https://doi.org/10.1093/ageing/afac173spa
dc.relation.referencesBaltira, C., Aronica, E., Elmquist, W. F., Langer, O., Löscher, W., Sarkaria, J. N., Wesseling, P., de Gooijer, M. C., & van Tellingen, O. (2024). The impact of ATP-binding cassette transporters in the diseased brain: Context matters. Cell Reports Medicine, 5(6), 101609. https://doi.org/10.1016/j.xcrm.2024.101609spa
dc.relation.referencesBellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., Holmans, P. A., Boland, A., Damotte, V., van der Lee, S. J., Costa, M. R., Kuulasmaa, T., Yang, Q., de Rojas, I., Bis, J. C., … Lambert, J.-C. (2022). New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature Genetics, 54(4), 412–436. https://doi.org/10.1038/s41588-022-01024-zspa
dc.relation.referencesBelsare, S., Levy-Sakin, M., Mostovoy, Y., Durinck, S., Chaudhuri, S., Xiao, M., Peterson, A. S., Kwok, P.-Y., Seshagiri, S., & Wall, J. D. (2019). Evaluating the quality of the 1000 genomes project data. BMC Genomics, 20(1), 620. https://doi.org/10.1186/s12864-019-5957-xspa
dc.relation.referencesBenavides-Caro, C. . (2017). Deterioro cognitivo en el adulto mayor. Revista Mexicana de Anestesiología, 40(2).spa
dc.relation.referencesBlennow, K., de Leon, M. J., & Zetterberg, H. (2006). Alzheimer’s disease. The Lancet, 368(9533), 387–403. https://doi.org/10.1016/S0140-6736(06)69113-7spa
dc.relation.referencesBorrás Blasco, C., & Viña Ribes, J. (2016). Neurofisiología y envejecimiento. Concepto y bases fisiopatológicas del deterioro cognitivo. Revista Espanola de Geriatria y Gerontologia, 51. https://doi.org/10.1016/S0211-139X(16)30136-6spa
dc.relation.referencesBrabec, J. L., Lara, M. K., Tyler, A. L., & Mahoney, J. M. (2021). System-Level Analysis of Alzheimer’s Disease Prioritizes Candidate Genes for Neurodegeneration. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.625246spa
dc.relation.referencesBroad Institute. (2019). Picard toolkit. In Broad Institute, GitHub repository.spa
dc.relation.referencesBu, S., Lv, Y., Liu, Y., Qiao, S., & Wang, H. (2021). Zinc Finger Proteins in Neuro-Related Diseases Progression. Frontiers in Neuroscience, 15. https://doi.org/10.3389/fnins.2021.760567spa
dc.relation.referencesCalderari, S., Ria, M., Gérard, C., Nogueira, T. C., Villate, O., Collins, S. C., Neil, H., Gervasi, N., Hue, C., Suarez-Zamorano, N., Prado, C., Cnop, M., Bihoreau, M.-T., Kaisaki, P. J., Cazier, J.-B., Julier, C., Lathrop, M., Werner, M., Eizirik, D. L., & Gauguier, D. (2018). Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons. Genomics, 110(2), 98–111. https://doi.org/10.1016/j.ygeno.2017.09.001spa
dc.relation.referencesCampbell, N. L., Unverzagt, F., LaMantia, M. A., Khan, B. A., & Boustani, M. A. (2013). Risk factors for the progression of mild cognitive impairment to dementia. In Clinics in Geriatric Medicine (Vol. 29, Issue 4). https://doi.org/10.1016/j.cger.2013.07.009spa
dc.relation.referencesCarrasquillo, M. M., Crook, J. E., Pedraza, O., Thomas, C. S., Pankratz, V. S., Allen, M., Nguyen, T., Malphrus, K. G., Ma, L., Bisceglio, G. D., Roberts, R. O., Lucas, J. A., Smith, G. E., Ivnik, R. J., Machulda, M. M., Graff-Radford, N. R., Petersen, R. C., Younkin, S. G., & Ertekin-Taner, N. (2015). Late-onset Alzheimer’s risk variants in memory decline, incident mild cognitive impairment, and Alzheimer’s disease. Neurobiology of Aging, 36(1), 60–67. https://doi.org/10.1016/j.neurobiolaging.2014.07.042spa
dc.relation.referencesChen, Z., Simmons, M. S., Perry, R. T., Wiener, H. W., Harrell, L. E., & Go, R. C. P. (2008). Genetic Association of Neurotrophic Tyrosine Kinase Receptor Type 2 ( NTRK2 ) With Alzheimer’s Disease. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 147B(3), 363–369. https://doi.org/10.1002/ajmg.b.30607spa
dc.relation.referencesDanecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2). https://doi.org/10.1093/gigascience/giab008spa
dc.relation.referencesde Mendonça, A., Ribeiro, F., Guerreiro, M., & Garcia, C. (2004). Frontotemporal mild cognitive impairment. Journal of Alzheimer’s Disease, 6(1), 1–9. https://doi.org/10.3233/JAD-2004-6101spa
dc.relation.referencesDean, M., Moitra, K., & Allikmets, R. (2022). The human ATP‐binding cassette (ABC) transporter superfamily. Human Mutation, 43(9), 1162–1182. https://doi.org/10.1002/humu.24418spa
dc.relation.referencesdel Carmen Díaz-Mardomingo, M., García-Herranz, S., Rodríguez-Fernández, R., Venero, C., & Peraita, H. (2017). Problems in classifying mild cognitive impairment (MCI): One or multiple syndromes? Brain Sciences, 7(9). https://doi.org/10.3390/brainsci7090111spa
dc.relation.referencesDelcheva, G., Stefanova, K., & Stankova, T. (2024). Ceramides—Emerging Biomarkers of Lipotoxicity in Obesity, Diabetes, Cardiovascular Diseases, and Inflammation. Diseases, 12(9), 195. https://doi.org/10.3390/diseases12090195spa
dc.relation.referencesDePristo, M. A., Banks, E., Poplin, R., Garimella, K. V, Maguire, J. R., Hartl, C., Philippakis, A. A., del Angel, G., Rivas, M. A., Hanna, M., McKenna, A., Fennell, T. J., Kernytsky, A. M., Sivachenko, A. Y., Cibulskis, K., Gabriel, S. B., Altshuler, D., & 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.806spa
dc.relation.referencesDíaz Cabezas, R., Marulanda Mejía, F., & Martínez Arias, M. H. (2013). Prevalencia de deterioro cognitivo y demencia en mayores de 65 años en una población urbana colombiana. Acta Neurológica Colombiana, 29(3). https://actaneurologica.com/index.php/anc/article/view/1376spa
dc.relation.referencesDuff, K., Paulsen, J., Mills, J., Beglinger, L. J., Moser, D. J., Smith, M. M., Langbehn, D., Stout, J., Queller, S., & Harrington, D. L. (2010). Mild cognitive impairment in prediagnosed Huntington disease. Neurology, 75(6). https://doi.org/10.1212/WNL.0b013e3181eccfa2spa
dc.relation.referencesElman, J. A., Panizzon, M. S., Logue, M. W., Gillespie, N. A., Neale, M. C., Reynolds, C. A., Gustavson, D. E., Rana, B. K., Andreassen, O. A., Dale, A. M., Franz, C. E., Lyons, M. J., & Kremen, W. S. (2019). Genetic risk for coronary heart disease alters the influence of Alzheimer’s genetic risk on mild cognitive impairment. Neurobiology of Aging, 84, 237.e5-237.e12. https://doi.org/10.1016/j.neurobiolaging.2019.06.001spa
dc.relation.referencesEspinosa, A., Hernández-Olasagarre, B., Moreno-Grau, S., Kleineidam, L., Heilmann-Heimbach, S., Hernández, I., Wolfsgruber, S., Wagner, H., Rosende-Roca, M., Mauleón, A., Vargas, L., Lafuente, A., Rodríguez-Gómez, O., Abdelnour, C., Gil, S., Marquié, M., Santos-Santos, M. A., Sanabria, Á., Ortega, G., … Ruiz, A. (2018). Exploring Genetic Associations of Alzheimer’s Disease Loci With Mild Cognitive Impairment Neurocognitive Endophenotypes. Frontiers in Aging Neuroscience, 10. https://doi.org/10.3389/fnagi.2018.00340spa
dc.relation.referencesEwels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19). https://doi.org/10.1093/bioinformatics/btw354spa
dc.relation.referencesFolstein, M. F., Folstein, S. E., & McHugh, P. R. (2014). Mini-Mental State Examination. In PsycTESTS Dataset. https://doi.org/10.1037/t07757-000spa
dc.relation.referencesFortes Marin, E., Carrera Marcolin, L., Martí Melero, L., Tintoré Gazulla, M., & Beltran Porres, M. (2025). The Prevalence of Single Nucleotide Polymorphisms of the AOC1 Gene Associated with Diamine Oxidase (DAO) Enzyme Deficiency in Healthy Newborns: A Prospective Population-Based Cohort Study. Genes, 16(2). https://doi.org/10.3390/genes16020141spa
dc.relation.referencesFu, X., Eikelboom, R. H., Tian, R., Liu, B., Wang, S., & Jayakody, D. M. P. (2023). The Relationship of Age-Related Hearing Loss with Cognitive Decline and Dementia in a Sinitic Language-Speaking Adult Population: A Systematic Review and Meta-Analysis. Innovation in Aging, 7(1). https://doi.org/10.1093/geroni/igac078spa
dc.relation.referencesFukui, H., Rünker, A., Fabel, K., Buchholz, F., & Kempermann, G. (2018). Transcription factor Runx1 is pro-neurogenic in adult hippocampal precursor cells. PLOS ONE, 13(1), e0190789. https://doi.org/10.1371/journal.pone.0190789spa
dc.relation.referencesFurney, S. J., Simmons, A., Breen, G., Pedroso, I., Lunnon, K., Proitsi, P., Hodges, A., Powell, J., Wahlund, L.-O., Kloszewska, I., Mecocci, P., Soininen, H., Tsolaki, M., Vellas, B., Spenger, C., Lathrop, M., Shen, L., Kim, S., Saykin, A. J., … Lovestone, S. (2011). Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer’s disease. Molecular Psychiatry, 16(11), 1130–1138. https://doi.org/10.1038/mp.2010.123spa
dc.relation.referencesGallione, C. J., Detter, M. R., Sheline, A., Christmas, H. M., Lee, C., & Marchuk, D. A. (2022). Genetic genealogy uncovers a founder deletion mutation in the cerebral cavernous malformations 2 gene. Human Genetics, 141(11), 1761–1769. https://doi.org/10.1007/s00439-022-02458-5spa
dc.relation.referencesGarcia-Cifuentes, E., Jaramillo-Jimenez, A., Aguillon, D., Gómez-Vega, M., Velez-Hernandez, J. E., Cano Gutiérrez, C., & Lopera, F. (2019). Prevenir la demencia: un reto para la salud pública en Colombia. Acta Neurológica Colombiana, 35(4), 208–210. https://doi.org/10.22379/24224022269spa
dc.relation.referencesGenis-Mendoza, A., Martínez-Magaña, J., Téllez Martínez, J. A., Jiménez-Guenchi, J., Roche Bergua, A., Castañeda, C., Tovilla-Zarate, C. A., & Nicolini, H. (2020). Identification of high impact variants in TREM2 and ABCA7 in Mexican individuals diagnosed with Alzheimer’s disease. Revista Mexicana de Psiquiatría y Salud Mental, 1(8), 224–229. https://goo.su/L1u26spa
dc.relation.referencesGenome Reference Consortium. (2019). GRCh38.p13 Genome Reference Assembly. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39/spa
dc.relation.referencesGil, L., Ruiz De Sánchez, C., Gil, F., Romero, S. J., & Pretelt Burgos, F. (2015). Validation of the Montreal Cognitive Assessment (MoCA) in Spanish as a screening tool for mild cognitive impairment and mild dementia in patients over 65 years old in Bogotá, Colombia. International Journal of Geriatric Psychiatry, 30(6). https://doi.org/10.1002/gps.4199spa
dc.relation.referencesGjøra, L., Strand, B. H., Bergh, S., Borza, T., Brækhus, A., Engedal, K., Johannessen, A., Kvello-Alme, M., Krokstad, S., Livingston, G., Matthews, F. E., Myrstad, C., Skjellegrind, H., Thingstad, P., Aakhus, E., Aam, S., & Selbæk, G. (2021). Current and future prevalence estimates of mild cognitive impairment, dementia, and its subtypes in a population-based sample of people 70 years and older in Norway: The HUNT study. Journal of Alzheimer’s Disease, 79(3). https://doi.org/10.3233/JAD-201275spa
dc.relation.referencesGomar, J. J. (2011). Utility of Combinations of Biomarkers, Cognitive Markers, and Risk Factors to Predict Conversion From Mild Cognitive Impairment to Alzheimer Disease in Patients in the Alzheimer’s Disease Neuroimaging Initiative. Archives of General Psychiatry, 68(9), 961. https://doi.org/10.1001/archgenpsychiatry.2011.96spa
dc.relation.referencesGranot-Hershkovitz, E., Xia, R., Yang, Y., Spitzer, B., Tarraf, W., Vásquez, P. M., Lipton, R. B., Daviglus, M., Argos, M., Cai, J., Kaplan, R., Fornage, M., DeCarli, C., Gonzalez, H. M., & Sofer, T. (2023). Interaction analysis of ancestry-enriched variants with APOE-ɛ4 on MCI in the Study of Latinos-Investigation of Neurocognitive Aging. Scientific Reports, 13(1), 5114. https://doi.org/10.1038/s41598-023-32028-2spa
dc.relation.referencesGutiérrez Rodríguez, J., & Guzmán Gutiérrez, G. (2017). Definición y prevalencia del deterioro cognitivo leve. Revista Española de Geriatría y Gerontología, 52. https://doi.org/10.1016/s0211-139x(18)30072-6spa
dc.relation.referencesHan, M.-R., Schellenberg, G. D., & Wang, L.-S. (2010). Genome-wide association reveals genetic effects on human Aβ 42 and τ protein levels in cerebrospinal fluids: a case control study. BMC Neurology, 10(1), 90. https://doi.org/10.1186/1471-2377-10-90spa
dc.relation.referencesHaridy, S. F. A., Shahin, N. N., Shabayek, M. I., Selim, M. M., Abdelhafez, M. A., & Motawi, T. K. (2023). Diagnostic and prognostic value of the RUNXOR/RUNX1 axis in multiple sclerosis. Neurobiology of Disease, 178, 106032. https://doi.org/10.1016/j.nbd.2023.106032spa
dc.relation.referencesHenao-Arboleda, E., Moreno- Carrillo, C., Ramos, V., Aguirre-Acevedo, D. C., Pineda, D., & Lopera, F. (2010). Caracterización de síntomas neuropsiquiátricos en pacientes con DCL de tipo amnésico en una población colombiana. Revista Chilena de Neuropsicología, 5(2), 153–159.spa
dc.relation.referencesHenao Arboleda, E., Aguirre Acevedo, D. C., Muñoz, C., Pineda Salazar, D. A., & Lopera Restrepo, F. (2008). Prevalencia de deterioro cognitivo leve de tipo amnésico en una población colombiana. Revista de Neurología, 46(12), 709. https://doi.org/10.33588/rn.4612.2007569spa
dc.relation.referencesHorgusluoglu-Moloch, E., Nho, K., Risacher, S. L., Kim, S., Foroud, T., Shaw, L. M., Trojanowski, J. Q., Aisen, P. S., Petersen, R. C., Jack, C. R., Lovestone, S., Simmons, A., Weiner, M. W., & Saykin, A. J. (2017). Targeted neurogenesis pathway-based gene analysis identifies ADORA2A associated with hippocampal volume in mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 60, 92–103. https://doi.org/10.1016/j.neurobiolaging.2017.08.010spa
dc.relation.referencesHostage, C. A., Roy Choudhury, K., Doraiswamy, P. M., & Petrella, J. R. (2013). Dissecting the Gene Dose-Effects of the APOE ε4 and ε2 Alleles on Hippocampal Volumes in Aging and Alzheimer’s Disease. PLoS ONE, 8(2), e54483. https://doi.org/10.1371/journal.pone.0054483spa
dc.relation.referencesHu, T., Chen, J., Lin, X., He, W., Liang, H., Wang, M., Li, W., Wu, Z., Han, M., Jin, X., Kristiansen, K., Xiao, L., & Zou, Y. (2024). Comparison of the DNBSEQ platform and Illumina HiSeq 2000 for bacterial genome assembly. Scientific Reports, 14(1), 1292. https://doi.org/10.1038/s41598-024-51725-0spa
dc.relation.referencesHu, X., Pickering, E. H., Hall, S. K., Naik, S., Liu, Y. C., Soares, H., Katz, E., Paciga, S. A., Liu, W., Aisen, P. S., Bales, K. R., Samad, T. A., & John, S. L. (2011). Genome-wide association study identifies multiple novel loci associated with disease progression in subjects with mild cognitive impairment. Translational Psychiatry, 1(11), e54–e54. https://doi.org/10.1038/tp.2011.50spa
dc.relation.referencesHughes, C. P., Berg, L., Danziger, W. L., Coben, L. A., & Martin, R. L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140(6). https://doi.org/10.1192/bjp.140.6.566spa
dc.relation.referencesHussenoeder, F. S., Conrad, I., Roehr, S., Fuchs, A., Pentzek, M., Bickel, H., Moesch, E., Weyerer, S., Werle, J., Wiese, B., Mamone, S., Brettschneider, C., Heser, K., Kleineidam, L., Kaduszkiewicz, H., Eisele, M., Maier, W., Wagner, M., Scherer, M., … Riedel-Heller, S. G. (2020). Mild cognitive impairment and quality of life in the oldest old: a closer look. Quality of Life Research, 29(6). https://doi.org/10.1007/s11136-020-02425-5spa
dc.relation.referencesKelley, B. J., & Petersen, R. C. (2007). Alzheimer’s Disease and Mild Cognitive Impairment. In Neurologic Clinics (Vol. 25, Issue 3, pp. 577–609). https://doi.org/10.1016/j.ncl.2007.03.008spa
dc.relation.referencesKim, H.-M., Jeon, S., Chung, O., Jun, J. H., Kim, H.-S., Blazyte, A., Lee, H.-Y., Yu, Y., Cho, Y. S., Bolser, D. M., & Bhak, J. (2021). Comparative analysis of 7 short-read sequencing platforms using the Korean Reference Genome: MGI and Illumina sequencing benchmark for whole-genome sequencing. GigaScience, 10(3). https://doi.org/10.1093/gigascience/giab014spa
dc.relation.referencesKong, X., Liu, Z., Huang, L., Wang, X., Yang, Z., Zhou, G., Zhen, Z., & Liu, J. (2015). Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI. PLOS ONE, 10(11), e0141840. https://doi.org/10.1371/journal.pone.0141840spa
dc.relation.referencesKorte, A., & Farlow, A. (2013). The advantages and limitations of trait analysis with GWAS: a review. Plant Methods, 9(1), 29. https://doi.org/10.1186/1746-4811-9-29spa
dc.relation.referencesKubo, S., Yamamoto, H., Kajimura, N., Omori, Y., Maeda, Y., Chaya, T., & Furukawa, T. (2021). Functional analysis of Samd11, a retinal photoreceptor PRC1 component, in establishing rod photoreceptor identity. Scientific Reports, 11(1), 4180. https://doi.org/10.1038/s41598-021-83781-1spa
dc.relation.referencesLacour, A., Espinosa, A., Louwersheimer, E., Heilmann, S., Hernández, I., Wolfsgruber, S., Fernández, V., Wagner, H., Rosende-Roca, M., Mauleón, A., Moreno-Grau, S., Vargas, L., Pijnenburg, Y. A. L., Koene, T., Rodríguez-Gómez, O., Ortega, G., Ruiz, S., Holstege, H., Sotolongo-Grau, O., … Ruiz, A. (2017). Genome-wide significant risk factors for Alzheimer’s disease: role in progression to dementia due to Alzheimer’s disease among subjects with mild cognitive impairment. Molecular Psychiatry, 22(1), 153–160. https://doi.org/10.1038/mp.2016.18spa
dc.relation.referencesLee, E., Giovanello, K. S., Saykin, A. J., Xie, F., Kong, D., Wang, Y., Yang, L., Ibrahim, J. G., Doraiswamy, P. M., & Zhu, H. (2017). Single‐nucleotide polymorphisms are associated with cognitive decline at Alzheimer’s disease conversion within mild cognitive impairment patients. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 8(1), 86–95. https://doi.org/10.1016/j.dadm.2017.04.004spa
dc.relation.referencesLi, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics (Oxford, England), 27(21), 2987–2993. https://doi.org/10.1093/bioinformatics/btr509spa
dc.relation.referencesLi, 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/btp324spa
dc.relation.referencesLi, J. Q., Tan, L., Wang, H. F., Tan, M. S., Tan, L., Xu, W., Zhao, Q. F., Wang, J., Jiang, T., & Yu, J. T. (2016). Risk factors for predicting progression from mild cognitive impairment to Alzheimer’s disease: A systematic review and meta-analysis of cohort studies. Journal of Neurology, Neurosurgery and Psychiatry, 87(5). https://doi.org/10.1136/jnnp-2014-310095spa
dc.relation.referencesLi, L., Yang, Y., Zhang, Q., Wang, J., Jiang, J., & Neuroimaging Initiative, A. D. (2021). Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment. Behavioural Neurology, 2021, 1–15. https://doi.org/10.1155/2021/3359103spa
dc.relation.referencesLin, F., Marchetti, S., Pluim, D., Iusuf, D., Mazzanti, R., Schellens, J. H. M., Beijnen, J. H., & van Tellingen, O. (2013). Abcc4 Together with Abcb1 and Abcg2 Form a Robust Cooperative Drug Efflux System That Restricts the Brain Entry of Camptothecin Analogues. Clinical Cancer Research, 19(8), 2084–2095. https://doi.org/10.1158/1078-0432.CCR-12-3105spa
dc.relation.referencesLiu, B., Ruan, J., Chen, M., Li, Z., Manjengwa, G., Schlüter, D., Song, W., & Wang, X. (2022). Deubiquitinating enzymes (DUBs): decipher underlying basis of neurodegenerative diseases. Molecular Psychiatry, 27(1), 259–268. https://doi.org/10.1038/s41380-021-01233-8spa
dc.relation.referencesLiu, L., Zhang, D., Liu, H., & Arendt, C. (2013). Robust methods for population stratification in genome wide association studies. BMC Bioinformatics, 14(1), 132. https://doi.org/10.1186/1471-2105-14-132spa
dc.relation.referencesLiu, P., Liu, S., Zhu, C., Li, Y., Li, Y., Fei, X., Hou, J., Wang, X., & Pan, Y. (2023). The deubiquitinating enzyme MINDY2 promotes pancreatic cancer proliferation and metastasis by stabilizing ACTN4 expression and activating the PI3K/AKT/mTOR signaling pathway. Frontiers in Oncology, 13, 1169833. https://doi.org/10.3389/fonc.2023.1169833spa
dc.relation.referencesLv, N., Wang, Y., Zhao, M., Dong, L., & Wei, H. (2021). The Role of PAX2 in Neurodevelopment and Disease. Neuropsychiatric Disease and Treatment, Volume 17, 3559–3567. https://doi.org/10.2147/NDT.S332747spa
dc.relation.referencesMak, S. S. T., Gopalakrishnan, S., Carøe, C., Geng, C., Liu, S., Sinding, M.-H. S., Kuderna, L. F. K., Zhang, W., Fu, S., Vieira, F. G., Germonpré, M., Bocherens, H., Fedorov, S., Petersen, B., Sicheritz-Pontén, T., Marques-Bonet, T., Zhang, G., Jiang, H., & Gilbert, M. T. P. (2017). Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing. GigaScience, 6(8). https://doi.org/10.1093/gigascience/gix049spa
dc.relation.referencesMarees, A. T., de Kluiver, H., Stringer, S., Vorspan, F., Curis, E., Marie-Claire, C., & Derks, E. M. (2018). A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. International Journal of Methods in Psychiatric Research, 27(2), e1608. https://doi.org/10.1002/mpr.1608spa
dc.relation.referencesMaulik, U., Sen, S., Mallik, S., & Bandyopadhyay, S. (2018). Detecting TF-miRNA-gene network based modules for 5hmC and 5mC brain samples: a intra- and inter-species case-study between human and rhesus. BMC Genetics, 19(1), 9. https://doi.org/10.1186/s12863-017-0574-7spa
dc.relation.referencesMayeux, R., Saunders, A. M., Shea, S., Mirra, S., Evans, D., Roses, A. D., Hyman, B. T., Crain, B., Tang, M.-X., & Phelps, C. H. (1998). Utility of the Apolipoprotein E Genotype in the Diagnosis of Alzheimer’s Disease. New England Journal of Medicine, 338(8), 506–511. https://doi.org/10.1056/NEJM199802193380804spa
dc.relation.referencesMcInnis, J. J., Sood, D., Guo, L., Dufault, M. R., Garcia, M., Passaro, R., Gao, G., Zhang, B., & Dodge, J. C. (2024). Unravelling neuronal and glial differences in ceramide composition, synthesis, and sensitivity to toxicity. Communications Biology, 7(1), 1597. https://doi.org/10.1038/s42003-024-07231-0spa
dc.relation.referencesMcKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303. https://doi.org/10.1101/gr.107524.110spa
dc.relation.referencesMcLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 122. https://doi.org/10.1186/s13059-016-0974-4spa
dc.relation.referencesMesa Interinstitucional de Población. (2023). 3.3. ENVEJECIMIENTO Y DERECHOS DE LAS PERSONAS MAYORES. In Fondo de Población de las Naciones Unidas(UNFPA) (Ed.), Análisis de Situación de Población (ASP Colombia 2023) Presentación y Capítulo dinámica demográfica (p. 28). Departamento Nacional de Planeación. https://colombia.unfpa.org/sites/default/files/pub-pdf/3.3_envejecimiento.pdfspa
dc.relation.referencesMinisterio de Salud y Protección Social. (2013). Plan Decenal de Salud Pública 2012-2021. Ministerio de Salud y Protección Social. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/ED/PSP/PDSP.pdfspa
dc.relation.referencesMinisterio de Salud y Protección Social. (2017). Boletín de salud mental Demencia. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/PP/ENT/Boletin-demencia-salud-mental.pdfspa
dc.relation.referencesMinisterio de Salud y Protección Social, & Colciencias. (2015). Política Colombiana de Envejecimiento Humano y Vejez 2015-2024 (p. 54). Ministerio de Salud y Protección Social. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PS/Política-colombiana-envejecimiento-humano-vejez-2015-2024.pdfspa
dc.relation.referencesMoreira, T., & Bond, J. (2008). Does the prevention of brain ageing constitute anti-ageing medicine? Outline of a new space of representation for Alzheimer’s Disease. Journal of Aging Studies, 22(4), 356–365. https://doi.org/10.1016/j.jaging.2008.05.008spa
dc.relation.referencesMorozova, A., Zorkina, Y., Abramova, O., Pavlova, O., Pavlov, K., Solovevа, K., Volkova, M., Alekseeva, P., Andryshchenko, A., Kostyuk, G., Gurina, O., & Chekhonin, V. (2022). Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders. International Journal of Molecular Sciences, 23(3). https://doi.org/10.3390/ijms23031217spa
dc.relation.referencesNasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2014). Montreal Cognitive Assessment. In PsycTESTS Dataset. https://doi.org/10.1037/t27279-000spa
dc.relation.referencesNg, P. C., & Kirkness, E. F. (2010). Whole genome sequencing. In Methods in Molecular Biology (Vol. 628). https://doi.org/10.1007/978-1-60327-367-1_12spa
dc.relation.referencesOphey, A., Wolfsgruber, S., Roeske, S., Polcher, A., Spottke, A., Frölich, L., Hüll, M., Jessen, F., Kornhuber, J., Maier, W., Peters, O., Ramirez, A., Wiltfang, J., Liepelt‐Scarfone, I., Becker, S., Berg, D., Schulz, J. B., Reetz, K., Wojtala, J., … Kalbe, E. (2021). Cognitive profiles of patients with mild cognitive impairment due to Alzheimer’s versus Parkinson’s disease defined using a base rate approach: Implications for neuropsychological assessments. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1). https://doi.org/10.1002/dad2.12223spa
dc.relation.referencesOspina García, N. (2015). Adaptación y validación en Colombia del addenbrooke’s cognitive examination-revisado (ACE-R) en pacientes con deterioro cognoscitivo leve y demencia. [Universidad Nacional de Colombia]. https://repositorio.unal.edu.co/handle/unal/52292spa
dc.relation.referencesPathak, G. A., Silzer, T. K., Sun, J., Zhou, Z., Daniel, A. A., Johnson, L., O’Bryant, S., Phillips, N. R., & Barber, R. C. (2019). Genome-Wide Methylation of Mild Cognitive Impairment in Mexican Americans Highlights Genes Involved in Synaptic Transport, Alzheimer’s Disease-Precursor Phenotypes, and Metabolic Morbidities. Journal of Alzheimer’s Disease, 72(3), 733–749. https://doi.org/10.3233/JAD-190634spa
dc.relation.referencesPedraza L, O. L., Sánchez, E., Plata, S. J., Montalvo, C., Galvis, P., Chiquillo, A., & Arévalo-Rodríguez, I. (2014). Puntuaciones del MoCA y el MMSE en pacientes con deterioro cognitivo leve y demencia en una clínica de memoria en Bogotá. Acta Neurológica Colombiana, 30(1).spa
dc.relation.referencesPeriñán, M. T., Macías‐García, D., Labrador‐Espinosa, M. Á., Jesús, S., Buiza‐Rueda, D., Adarmes‐Gómez, A. D., Muñoz‐Delgado, L., Gómez‐Garre, P., & Mir, P. (2021). Association of PICALM with Cognitive Impairment in Parkinson’s Disease. Movement Disorders, 36(1), 118–123. https://doi.org/10.1002/mds.28283spa
dc.relation.referencesPetersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3). https://doi.org/10.1111/j.1365-2796.2004.01388.xspa
dc.relation.referencesPetersen, R. C. (2016). Mild Cognitive Impairment. CONTINUUM: Lifelong Learning in Neurology, 22(2, Dementia), 404–418. https://doi.org/10.1212/CON.0000000000000313spa
dc.relation.referencesPoplin, R., Ruano-Rubio, V., DePristo, M. A., Fennell, T. J., Carneiro, M. O., Van der Auwera, G. A., Kling, D. E., Gauthier, L. D., Levy-Moonshine, A., Roazen, D., Shakir, K., Thibault, J., Chandran, S., Whelan, C., Lek, M., Gabriel, S., Daly, M. J., Neale, B., MacArthur, D. G., & Banks, E. (2017). Scaling accurate genetic variant discovery to tens of thousands of samples. https://doi.org/10.1101/201178spa
dc.relation.referencesPorreca, G. J. (2010). Genome sequencing on nanoballs. Nature Biotechnology, 28(1), 43–44. https://doi.org/10.1038/nbt0110-43spa
dc.relation.referencesPradhan, J., Noakes, P. G., & Bellingham, M. C. (2019). The Role of Altered BDNF/TrkB Signaling in Amyotrophic Lateral Sclerosis. Frontiers in Cellular Neuroscience, 13. https://doi.org/10.3389/fncel.2019.00368spa
dc.relation.referencesPradilla A., G., Vesga A., B. E., & León-Sarmiento, F. E. (2003). Estudio neuroepidemiológico nacional (EPINEURO) colombiano. Revista Panamericana de Salud Pública, 14(2). https://doi.org/10.1590/s1020-49892003000700005spa
dc.relation.referencesPrince, M., Wimo, A., Guerchet, M., Gemma-Claire, A., Wu, Y.-T., & Prina, M. (2015). World Alzheimer Report 2015: The Global Impact of Dementia - An analysis of prevalence, incidence, cost and trends. Alzheimer’s Disease International. https://doi.org/10.1111/j.0963-7214.2004.00293.xspa
dc.relation.referencesPurcell, 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., & Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559–575. https://doi.org/10.1086/519795spa
dc.relation.referencesRaghavan, N. S., Dumitrescu, L., Mormino, E., Mahoney, E. R., Lee, A. J., Gao, Y., Bilgel, M., Goldstein, D., Harrison, T., Engelman, C. D., Saykin, A. J., Whelan, C. D., Liu, J. Z., Jagust, W., Albert, M., Johnson, S. C., Yang, H.-S., Johnson, K., Aisen, P., … Mayeux, R. (2020). Association Between Common Variants in RBFOX1 , an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer Disease. JAMA Neurology, 77(10), 1288. https://doi.org/10.1001/jamaneurol.2020.1760spa
dc.relation.referencesRaudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., & Vilo, J. (2019). g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Research, 47(W1), W191–W198. https://doi.org/10.1093/nar/gkz369spa
dc.relation.referencesReitz, C., & Mayeux, R. (2010). Use of Genetic Variation as Biomarkers for Mild Cognitive Impairment and Progression of Mild Cognitive Impairment to Dementia. Journal of Alzheimer’s Disease, 19(1), 229–251. https://doi.org/10.3233/JAD-2010-1255spa
dc.relation.referencesRiess, O., Thies, U., Siedlaczck, I., Potisek, S., Graham, R., Theilmann, J., Grimm, T., Epplen, J. T., & Hayden, M. R. (1994). Precise Mapping of the Brain α2-Adrenergic Receptor Gene within Chromosome 4p16. Genomics, 19(2), 298–302. https://doi.org/10.1006/geno.1994.1061spa
dc.relation.referencesRíos-Gallardo, Á. M., Muñoz-Bernal, L. F., Aldana-Camacho, L. V., Santamaría-Íñiguez, M. F., & Villanueva-Bonilla, C. (2017). Perfil neuropsicológico de un grupo de adultos mayores diagnosticados con deterioro cognitivo leve. Revista Mexicana de Neurociencia, 18(5).spa
dc.relation.referencesRoberts, R., & Knopman, D. S. (2013). Classification and Epidemiology of MCI. Clinics in Geriatric Medicine, 29(4), 753–772. https://doi.org/10.1016/j.cger.2013.07.003spa
dc.relation.referencesRobins, C., Liu, Y., Fan, W., Duong, D. M., Meigs, J., Harerimana, N. V., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Beach, T. G., Reiman, E. M., De Jager, P. L., Bennett, D. A., Lah, J. J., Wingo, A. P., Levey, A. I., Seyfried, N. T., & Wingo, T. S. (2021). Genetic control of the human brain proteome. The American Journal of Human Genetics, 108(3), 400–410. https://doi.org/10.1016/j.ajhg.2021.01.012spa
dc.relation.referencesRollano, O. M., & Mollinedo, P. (2017). Análisis Bioinformático De Arn-Seq Con Una Perspectiva Para Bolivia. Revista Boliviana de Química, 34(2). https://bit.ly/3vk7vX5spa
dc.relation.referencesSachdev, P. S., Lipnicki, D. M., Kochan, N. A., Crawford, J. D., Thalamuthu, A., Andrews, G., Brayne, C., Matthews, F. E., Stephan, B. C. M., Lipton, R. B., Katz, M. J., Ritchie, K., Carrière, I., Ancelin, M. L., Lam, L. C. W., Wong, C. H. Y., Fung, A. W. T., Guaita, A., Vaccaro, R., … Lobo, E. (2015). The prevalence of mild cognitive impairment in diverse geographical and ethnocultural regions: The COSMIC Collaboration. PLoS ONE, 10(11). https://doi.org/10.1371/journal.pone.0142388spa
dc.relation.referencesSager, K. L., Wuu, J., Leurgans, S. E., Rees, H. D., Gearing, M., Mufson, E. J., Levey, A. I., & Lah, J. J. (2007). Neuronal LR11/sorLA expression is reduced in mild cognitive impairment. Annals of Neurology, 62(6), 640–647. https://doi.org/10.1002/ana.21190spa
dc.relation.referencesSarmiento Buitrago, A. F., Cerón Perdomo, D., & Mayorga Bogota, M. A. (2024). Asociación entre el deterioro cognitivo y factores socioeconómicos y sociodemográficos en adultos mayores colombianos. Revista Colombiana de Psiquiatría, 53(2), 134–141. https://doi.org/10.1016/j.rcp.2022.02.005spa
dc.relation.referencesSaykin, A. J., Shen, L., Yao, X., Kim, S., Nho, K., Risacher, S. L., Ramanan, V. K., Foroud, T. M., Faber, K. M., Sarwar, N., Munsie, L. M., Hu, X., Soares, H. D., Potkin, S. G., Thompson, P. M., Kauwe, J. S. K., Kaddurah‐Daouk, R., Green, R. C., Toga, A. W., & Weiner, M. W. (2015). Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans. Alzheimer’s & Dementia, 11(7), 792–814. https://doi.org/10.1016/j.jalz.2015.05.009spa
dc.relation.referencesShen, L., Kim, S., Risacher, S. L., Nho, K., Swaminathan, S., West, J. D., Foroud, T., Pankratz, N., Moore, J. H., Sloan, C. D., Weiner, M. W., & Saykin, A. J. (2010). Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage, 53(3), 1051–1063. https://doi.org/10.1016/j.neuroimage.2010.01.042spa
dc.relation.referencesShen, Y., Wang, H., Sun, Q., Yao, H., Keegan, A. P., Mullan, M., Wilson, J., Lista, S., Leyhe, T., Laske, C., Rujescu, D., Levey, A., Wallin, A., Blennow, K., Li, R., & Hampel, H. (2018). Increased Plasma Beta-Secretase 1 May Predict Conversion to Alzheimer’s Disease Dementia in Individuals With Mild Cognitive Impairment. Biological Psychiatry, 83(5). https://doi.org/10.1016/j.biopsych.2017.02.007spa
dc.relation.referencesSmith, A. D., & de Sena Brandine, G. (2021). Falco: High-speed FastQC emulation for quality control of sequencing data. F1000Research, 8. https://doi.org/10.12688/f1000research.21142.2spa
dc.relation.referencesStein, J. L., Hua, X., Morra, J. H., Lee, S., Hibar, D. P., Ho, A. J., Leow, A. D., Toga, A. W., Sul, J. H., Kang, H. M., Eskin, E., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., Huentelman, M. J., Craig, D. W., Gerber, J. D., Allen, A. N., … Thompson, P. M. (2010). Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer’s disease. NeuroImage, 51(2), 542–554. https://doi.org/10.1016/j.neuroimage.2010.02.068spa
dc.relation.referencesStites, S. D., Harkins, K., Rubright, J. D., & Karlawish, J. (2018). Relationships between cognitive complaints and quality of life in older adults with mild cognitive impairment, mild Alzheimer disease dementia, and normal cognition. Alzheimer Disease and Associated Disorders, 32(4). https://doi.org/10.1097/WAD.0000000000000262spa
dc.relation.referencesSzklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & von Mering, C. (2023). The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, 51(D1), D638–D646. https://doi.org/10.1093/nar/gkac1000spa
dc.relation.referencesSzot, P., White, S. S., Greenup, J. L., Leverenz, J. B., Peskind, E. R., & Raskind, M. A. (2006). Compensatory Changes in the Noradrenergic Nervous System in the Locus Ceruleus and Hippocampus of Postmortem Subjects with Alzheimer’s Disease and Dementia with Lewy Bodies. The Journal of Neuroscience, 26(2), 467–478. https://doi.org/10.1523/JNEUROSCI.4265-05.2006spa
dc.relation.referencesTam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019). Benefits and limitations of genome-wide association studies. In Nature Reviews Genetics (Vol. 20, Issue 8). https://doi.org/10.1038/s41576-019-0127-1spa
dc.relation.referencesUffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1), 59. https://doi.org/10.1038/s43586-021-00056-9spa
dc.relation.referencesvan der Auwera, G., & O’Connor, B. D. (2020). Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O’Reilly Media, Incorporated. https://books.google.com.co/books?id=wwiCswEACAAJspa
dc.relation.referencesWang, K., Lu, Y., Morrow, D. F., Xiao, D., & Xu, C. (2022). Associations of ARHGAP26 Polymorphisms with Alzheimer’s Disease and Cardiovascular Disease. Journal of Molecular Neuroscience, 72(5). https://doi.org/10.1007/s12031-022-01972-5spa
dc.relation.referencesWang, M. H., Cordell, H. J., & Van Steen, K. (2019). Statistical methods for genome-wide association studies. Seminars in Cancer Biology, 55, 53–60. https://doi.org/10.1016/j.semcancer.2018.04.008spa
dc.relation.referencesWang, Y., Wang, Y., Tang, J., Li, R., Jia, Y., Yang, H., & Wei, H. (2024). Impaired neural circuitry of hippocampus in Pax2 nervous system‐specific knockout mice leads to restricted repetitive behaviors. CNS Neuroscience & Therapeutics, 30(4). https://doi.org/10.1111/cns.14482spa
dc.relation.referencesWechsler, D. (2012). Test de inteligencia de Wechsler para adultos-IV (WAIS-IV). Explicación Del Test.spa
dc.relation.referencesWen, J., Cui, Y., Yang, Z., Bao, J., Chen, J., Erus, G., Abdulkadir, A., Mamourian, E., Singh, A., Yang, S., Fan, Y., Saykin, A. J., Thompson, P. M., Jun, G. R., Ritchie, M. D., Shen, L., Wolk, D. A., Shou, H., Nasrallah, I. M., & Davatzikos, C. (2022). Genetic heterogeneity of four MCI/AD neuroanatomical dimensions discovered via deep learning. Alzheimer’s & Dementia, 18(S6). https://doi.org/10.1002/alz.065223spa
dc.relation.referencesWhitley, E., Deary, I. J., Ritchie, S. J., Batty, G. D., Kumari, M., & Benzeval, M. (2016). Variations in cognitive abilities across the life course: Cross-sectional evidence from Understanding Society: The UK Household Longitudinal Study. Intelligence, 59. https://doi.org/10.1016/j.intell.2016.07.001spa
dc.relation.referencesWickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. In Springer-Verlag New York. https://ggplot2.tidyverse.orgspa
dc.relation.referencesXi, J., Ding, D., Zhao, Q., Liang, X., Zheng, L., Guo, Q., Hong, Z., Fu, H., Xu, J., & Xiao, Q. (2020). Joint Effect of ABCA7 rs4147929 and Body Mass Index on Progression from Mild Cognitive Impairment to Alzheimer’s Disease: The Shanghai Aging Study. Current Alzheimer Research, 17(2), 185–195. https://doi.org/10.2174/1567205017666200317095608spa
dc.relation.referencesXiang, J., Wang, X., Gao, Y., Li, T., Cao, R., Yan, T., Ma, Y., Niu, Y., Xue, J., & Wang, B. (2020). Phosphodiesterase 4D Gene Modifies the Functional Network of Patients With Mild Cognitive Impairment and Alzheimer’s Disease. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.00890spa
dc.relation.referencesXu, S., Duan, P., Li, J., Senkowski, T., Guo, F., Chen, H., Romero, A., Cui, Y., Liu, J., & Jiang, S.-W. (2016). Zinc Finger and X-Linked Factor (ZFX) Binds to Human SET Transcript 2 Promoter and Transactivates SET Expression. International Journal of Molecular Sciences, 17(10). https://doi.org/10.3390/ijms17101737spa
dc.relation.referencesXu, Y., Lin, Z., Tang, C., Tang, Y., Cai, Y., Zhong, H., Wang, X., Zhang, W., Xu, C., Wang, J., Wang, J., Yang, H., Yang, L., & Gao, Q. (2019). A new massively parallel nanoball sequencing platform for whole exome research. BMC Bioinformatics, 20(1), 153. https://doi.org/10.1186/s12859-019-2751-3spa
dc.relation.referencesYadav, S. K., Bhat, A. A., Hashem, S., Nisar, S., Kamal, M., Syed, N., Temanni, M.-R., Gupta, R. K., Kamran, S., Azeem, M. W., Srivastava, A. K., Bagga, P., Chawla, S., Reddy, R., Frenneaux, M. P., Fakhro, K., & Haris, M. (2021). Genetic variations influence brain changes in patients with attention-deficit hyperactivity disorder. Translational Psychiatry, 11(1), 349. https://doi.org/10.1038/s41398-021-01473-wspa
dc.relation.referencesYoganathan, S., Arunachal, G., Gowda, V. K., Vinayan, K. P., Thomas, M., Whitney, R., & Jain, P. (2021). NTRK2-related developmental and epileptic encephalopathy: Report of 5 new cases. Seizure, 92, 52–55. https://doi.org/10.1016/j.seizure.2021.08.008spa
dc.relation.referencesZhang, J., Wang, X., Duan, H., Chen, C., Lu, Z., Zhang, D., & Li, S. (2023). The Association of Calcium Signaling Pathway Gene Variants, Bone Mineral Density and Mild Cognitive Impairment in Elderly People. Genes, 14(4), 828. https://doi.org/10.3390/genes14040828spa
dc.relation.referencesZhang, X. (2020). Review of genome-wide association study. In Kexue Tongbao/Chinese Science Bulletin (Vol. 65, Issue 8, pp. 671–683). Chinese Academy of Sciences. https://doi.org/10.1360/TB-2019-0063spa
dc.relation.referencesZhang, Y., Elgart, M., Granot-Hershkovitz, E., Wang, H., Tarraf, W., Ramos, A. R., Stickel, A. M., Zeng, D., Garcia, T. P., Testai, F. D., Wassertheil-Smoller, S., Isasi, C. R., Daviglus, M. L., Kaplan, R., Fornage, M., DeCarli, C., Redline, S., González, H. M., & Sofer, T. (2023). Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos. EBioMedicine, 87, 104393. https://doi.org/10.1016/j.ebiom.2022.104393spa
dc.relation.referencesZhao, Y., Hu, D., Wang, R., Sun, X., Ropelewski, P., Hubler, Z., Lundberg, K., Wang, Q., Adams, D. J., Xu, R., & Qi, X. (2022). ATAD3A oligomerization promotes neuropathology and cognitive deficits in Alzheimer’s disease models. Nature Communications, 13(1), 1121. https://doi.org/10.1038/s41467-022-28769-9spa
dc.relation.referencesZhao, Y., Sun, X., Hu, D., Prosdocimo, D. A., Hoppel, C., Jain, M. K., Ramachandran, R., & Qi, X. (2019). ATAD3A oligomerization causes neurodegeneration by coupling mitochondrial fragmentation and bioenergetics defects. Nature Communications, 10(1), 1371. https://doi.org/10.1038/s41467-019-09291-xspa
dc.relation.referencesZhou, Y., Hao, N., Sander, J. W., Lin, X., Xiong, W., & Zhou, D. (2023). KCNH2 variants in a family with epilepsy and long QT syndrome: A case report and literature review. Epileptic Disorders, 25(4), 492–499. https://doi.org/10.1002/epd2.20046spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc570 - Biología::576 - Genética y evoluciónspa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresspa
dc.subject.ddc610 - Medicina y salud::616 - Enfermedadesspa
dc.subject.decsDisfunción Cognitivaspa
dc.subject.decsCognitive Dysfunctioneng
dc.subject.decsEnfermedad de Alzheimerspa
dc.subject.decsAlzheimer Diseaseeng
dc.subject.decsEnfermedades Neurodegenerativasspa
dc.subject.decsNeurodegenerative Diseaseseng
dc.subject.decsInestabilidad Genómicaspa
dc.subject.decsGenomic Instabilityeng
dc.subject.proposalDeterioro Cognitivo Levespa
dc.subject.proposalAtlánticospa
dc.subject.proposalGWASspa
dc.subject.proposalGenómicaspa
dc.subject.proposalMild Cognitive Impairmenteng
dc.subject.proposalGenomicseng
dc.subject.proposalGWASeng
dc.titleCaracterización genómica de un grupo de individuos con Deterioro Cognitivo Leve (DCL) en población del Atlántico, Colombiaspa
dc.title.translatedGenomic characterization of a cohort with Mild Cognitive Impairment (MCI) in the population of Atlántico, Colombiaeng
dc.typeTrabajo de grado - Maestríaspa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
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dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
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