Show simple item record

dc.contributor.authorMartinez-Velasco, Antonieta
dc.contributor.authorPerez-Ortiz, Andric C.
dc.contributor.authorAntonio-Aguirre, Bani
dc.contributor.authorMartinez-Villaseñor, Lourdes
dc.contributor.authorLira, Esmeralda
dc.contributor.authorRamírez-Sánchez, Israel
dc.contributor.authorOrtiz Bibriesca, Daniela
dc.contributor.authorEstrada, Francisco Javier
dc.contributor.otherCampus Ciudad de Méxicoes
dc.date.accessioned2020-09-14T19:29:44Z
dc.date.available2020-09-14T19:29:44Z
dc.date.issued2020-08-24
dc.identifier.citationMartínez Velasco, A., Pérez Ortíz, A. C. ; Antonio Aguirre, B., Martinez-Villaseñor, L., Lira, E., Ramírez Sánchez, I. … y Estrada, F. J. (2020). Assessment of CFH and HTRA1 polymorphisms in age-related macular degeneration using classic and machine-learning approaches. Ophthalmic Genetics. DOI: http://dx.doi.org/10.1080/13816810.2020.1804945en
dc.identifier.issn1381-6810
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5311
dc.identifier.urihttp://dx.doi.org/10.1080/13816810.2020.1804945
dc.description.abstractCFH: and HTRA1 are pivotal genes driving increased risk for age-related macular degeneration (AMD) among several populations. Here, we performed a hospital-based case-control study to evaluate the effects of three single nucleotide polymorphisms (SNPs) among Hispanics from Mexico. Materials and methods: 122 cases and 249 controls were genotyped using Taqman probes. Experienced ophthalmologists diagnosed AMD following the American Association of Ophthalmology guidelines. We studied CFH (rs1329428, rs203687) and HTRA1 (rs11200638) SNPs thoroughly by logistic regression models (assuming different modes of inheritance) and machine learning-based methods (ML). HTRA1: rs11200638 is the most significant polymorphism associated with AMD in our studied population. In a multivariate regression model adjusted for clinically and statistically meaningful covariates, the A/G and A/A genotypes increased the odds of disease by a factor of 2.32 and 7.81, respectively (P < .05) suggesting a multiplicative effect of the polymorphic A allele. Furthermore, this observation remains statistically meaningful in the allelic, dominant, and recessive models, and ML algorithms. When stratifying by phenotype, this polymorphism was significantly associated with increased odds for geographic atrophy (GA) in a recessive mode of inheritance (12.4, p < .05). Conclusions: In sum, this work supports a strong association between HTRA1 genetic variants and AMD in Hispanics from Mexico, especially with GA. Moreover, ML was able to replicate the results of conventional biostatistics methods unbiasedly. © 2020 Taylor & Francis Group, LLC.en
dc.language.isoeng
dc.publisherTaylor and Francis Ltd.en
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofOPENAIREes
dc.rightsAcceso embargadoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0*
dc.rights.urihttps://v2.sherpa.ac.uk/id/publication/968
dc.sourceOphthalmic Geneticsen
dc.subjectAge-related macular degenerationen
dc.subjectComplement factor Hen
dc.subjectGenetic association studyen
dc.subjectHigh-temperature requirement A serine peptidase 1en
dc.subjectMachine-learningen
dc.subject.classificationMEDICINA Y CIENCIAS DE LA SALUDes
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.subject.classificationCiencias de la Saludes
dc.titleAssessment of CFH and HTRA1 polymorphisms in age-related macular degeneration using classic and machine-learning approachesen
dc.typeArtículoes
dcterms.audienceInvestigadoreses
dcterms.audienceEstudianteses
dcterms.audienceMaestroses
dcterms.bibliographicCitationWong WL, Su X, Li X, Cheung CMG, Klein R, Cheng C-Y, Wong TY. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health. 2014;2(2):e106–e116. doi:10.1016/S2214-109X(13)70145-1.en
dcterms.bibliographicCitationKlein R, Chou C-F, Klein BEK, Zhang X, Meuer SM, Saaddine JB. 2011. Prevalence of age-related macular degeneration in the US population. Arch Ophthalmol. 129(1):75–80. doi:10.1001/archophthalmol.2010.318en
dcterms.bibliographicCitationPennington KL, DeAngelis MM. 2016. Epidemiology of age-related macular degeneration (AMD): associations with cardiovascular disease phenotypes and lipid factors. Eye Vis. 3(1):34. doi:10.1186/s40662-016-0063-5en
dcterms.bibliographicCitationCheung CMG, Tai ES, Kawasaki R, Tay WT, Lee JL, Hamzah H, Wong TY. Prevalence of and risk factors for age-related macular degeneration in a multiethnic Asian cohort. Arch Ophthalmol. 2012;130(4):480–86. doi:10.1001/archophthalmol.2011.376.en
dcterms.bibliographicCitationChopdar A, Chakravarthy U, Verma D. 2003. Age related macular degeneration. BMJ. 326(7387):485–88. doi:10.1136/bmj.326.7387.485en
dcterms.bibliographicCitationSpencer KL, Olson LM, Schnetz-Boutaud N, Gallins P, Agarwal A, Iannaccone A, Kritchevsky SB, Garcia M, Nalls MA, Newman AB, et al. Using genetic variation and environmental risk factor data to identify individuals at high risk for age-related macular degeneration. PLoS ONE. 2011;6(3):1–9. doi:10.1371/journal.pone.0017784.en
dcterms.bibliographicCitationDing X, Patel M, Chan -C-C-C. 2009. Molecular pathology of age-related macular degeneration. Prog Retin Eye Res. 28 (1):1–18. doi:10.1016/j.preteyeres.2008.10.001en
dcterms.bibliographicCitationPerez-Ortiz AC, Luna-Angulo A, Zenteno JC, Rendon A, CortesBallinas LG, Jimenez-Collado D, Antonio Aguirre B, PeraltaIldefonso MJ, Ramírez I, Jacob-Kuttothara S, et al. Significant association between variant in SGCD and age-related macular degeneration. Genes. 2018;9(10):467. doi:10.3390/genes9100467.en
dcterms.bibliographicCitationMartínez-Velasco A, Zenteno JC, Martínez-Villaseñor L, MirallesPechúan L, Pérez-Ortiz A, Estrada-Mena FJ. Machine learning method to establish the connection between age related macular degeneration and some genetic variations. In: García CR, Caballero-Gil P, Burmester M, Quesada-Arencibia A, editors. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Vol. 10070. LNCS: Springer, Cham; 2016, 28–39. doi: 10.1007/978-3-319-48799-1_4.en
dcterms.bibliographicCitationContreras AV, Zenteno JC, Fernández-López JC, RodríguezCorona U, Falfán-Valencia R, Sebastian L, Morales F, OchoaContreras D, Carnevale A, et al. CFH haplotypes and ARMS2, C2, C3, and CFB alleles show association with susceptibility to age-related macular degeneration in Mexicans. Mol Vis. 2014;20:105–116.en
dcterms.bibliographicCitationVan M, Campagne L, Lecouter J, Yaspan BL, Ye W. Mechanisms of age-related macular degeneration and therapeutic opportunities. J Pathol J Pathol. 2014;232:151–64. doi: 10.1002/path.4266.en
dcterms.bibliographicCitationChakravarthy U, Wong TY, Fletcher A, Piault E, Evans C, Zlateva G, Buggage R, Pleil A, Mitchell P. Clinical risk factors for age-related macular degeneration: a systematic review and meta-analysis. BMC Ophthalmol. 2010;10(1):31. doi:10.1186/1471-2415-10-31.en
dcterms.bibliographicCitationTong Y, Liao J, Zhang Y, Zhou J, Zhang H, Mao M. LOC387715/HTRA1 gene polymorphisms and susceptibility to age- related macular degeneration: a huge review and meta-analysis. Mol Vis. 2010;16:1958–1981.en
dcterms.bibliographicCitationLu F, Liu S, Hao Q, Liu L, Zhang J, Chen X, Hu W, Huang P. Association between complement factor C2/C3/CFB/CFH polymorphisms and age-related macular degeneration: a meta-analysis. Genet Test Mol Biomark;2018. gtmb.2018.0110. doi: 10.1089/gtmb.2018.0110.en
dcterms.bibliographicCitationPerez-Ortiz AC, Peralta-Ildefonso MJ, Lira-Romero E, MoyaAlbor E, Brieva J, Ramirez-Sanchez I, Clapp C, Luna-Angulo A, Rendon A, Adan-Castro E, et al. Lack of delta-sarcoglycan (Sgcd) results in retinal degeneration. Int J Mol Sci. 2019;20(21):5480. doi:10.3390/ijms20215480.en
dcterms.bibliographicCitationSivakumaran TA, Igo RP, Kidd JM, Itsara A, Kopplin LJ, Chen W, Hagstrom SA, Peachey NS, Francis PJ, Klein ML, et al. A 32 kb critical region excluding Y402H in CFH mediates risk for age-related macular degeneration. PloS One. 2011;6(10):e25598. doi:10.1371/journal.pone.0025598.en
dcterms.bibliographicCitationFourgeux C, Dugas B, Richard F, Björkhem I, Acar N, Bron AM, Korobelnik J-F, Leveziel N, Zerbib J, Puche N, et al. Single nucleotide polymorphism in the Cholesterol-24S-hydroxylase (CYP46A1) gene and its association with CFH and LOC387715 gene polymorphisms in age-related macular degeneration. Invest Ophthalmol Vis Sci. 2012;53(11):7026–33. doi:10.1167/iovs.12-9652.en
dcterms.bibliographicCitationLorés-Motta L, Paun CC, Corominas J, Pauper M, Geerlings MJ, Altay L, Schick T, Daha MR, Fauser S, Hoyng CB, et al. Genomewide association study reveals variants in CFH and CFHR4 associated with systemic complement activation: implications in age-related macular degeneration. Ophthalmology. 2018;125 (7):1064–74. doi:10.1016/j.ophtha.2017.12.023.en
dcterms.bibliographicCitationKlein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385–89. doi:10.1126/science.1109557.en
dcterms.bibliographicCitationRyu E, Friedly B, Tosakulwong N, Bailey KR, Edwards AO. Genome-wide association analyses of genetic, phenotypic, and environmental risks in the age-related eye disease study. Mol Vis. 2010;16:2811–21.en
dcterms.bibliographicCitationKanda A, Chen W, Othman M, Branham KEH, Brooks M, Khanna R, He S, Lyons R, Abecasis GR, Swaroop A, et al. A variant of mitochondrial protein LOC387715/ARMS2, not HTRA1, is strongly associated with age-related macular degeneration. Proc Natl Acad Sci U S A. 2007;104(41):16227–32. doi:10.1073/pnas.0703933104.en
dcterms.bibliographicCitationDeWan A, Liu M, Hartman S, Zhang SS-M, Liu DTL, Zhao C, Tam POS, Chan WM, Lam DSC, Snyder M, et al. HTRA1 promoter polymorphism in wet age-related macular degeneration. Science. 2006;314(5801):989–92. doi:10.1126/science.1133807.en
dcterms.bibliographicCitationYang Z, Camp NJ, Sun H, Tong Z, Gibbs D, Cameron DJ, Chen H, Zhao Y, Pearson E, Li X, et al. A variant of the HTRA1 gene increases susceptibility to age-related macular degeneration. Science. 2006;314(5801):992–93. doi:10.1126/science.1133811.en
dcterms.bibliographicCitationYang Z, Tong Z, Chen Y, Zeng J, Lu F, Sun X, Zhao C, Wang K, Davey L, Chen H, et al. Genetic and functional dissection of HTRA1 and LOC387715 in age-related macular degeneration. PLoS Genet. 2010;6(2):1–9. doi:10.1371/journal.pgen.1000836.en
dcterms.bibliographicCitationFraccaro P, Nicolo M, Bonetto M, Giacomini M, Weller P, Traverso CE, Prosperi M, OSullivan D. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach. BMC Ophthalmol. 2015;15(1):10. doi:10.1186/1471-2415-15-10.en
dcterms.bibliographicCitationR Core Team. R: A language and environment for statistical computing. R foundation for statistical computing; 2016. https://www.R-project.org/en
dcterms.bibliographicCitationWarnes G, Duffy D, Man M, Weiliang Qiu RL GeneticsDesign: functions for designing genetics studies. The R Genetics Project; 2010. doi:10.18129/B9.bioc.GeneticsDesignen
dcterms.bibliographicCitationPurcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81 (3):559–75. doi:10.1086/519795.en
dcterms.bibliographicCitationSAS institute inc. SAS software 9.4.; 2014. http://search.ebscohost.com/login.aspx?direct=true&db=plh&AN=101476231&site=eds-liveen
dcterms.bibliographicCitationAmerican academy of ophthalmology - Retina/vitreous panel. age-related macular degeneration. Prefer Pract Pattern Guidel. Published online 2015:12–16. doi:10.1002/14651858.CD005137.pub3en
dcterms.bibliographicCitationLeisch F, Man M, Warnes MG R-Package ’ genetics ’ Ver.1.3.8.1. Published online 2013:43.en
dcterms.bibliographicCitationWojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, Highland HM, Patel YM, Sorokin EP, Avery CL, et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature. 2019;570(7762):514–18. doi:10.1038/s41586-019-1310-4.en
dcterms.bibliographicCitationThe 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi:10.1038/nature15393.en
dcterms.bibliographicCitationGrassmann F, Heid IM, International AMD Genomics Consortium, Weber BHF. Recombinant haplotypes narrow the ARMS2/HTRA1 association signal for age-related macular degeneration. Genetics. 2017;205(2):919–24. doi:10.1534/genetics.116.195966.en
dcterms.bibliographicCitationQi Y, Yale J, Heng H, Huang H, Swaroop A, Chew EY, Weeks DE, Chen W, Ding Y. GWAS-based machine learning for prediction of age-related macular degeneration Risk. MedRxiv Prepr. Published online 2019. doi:10.1101/19006155en
dcterms.bibliographicCitationKourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17. doi:10.1016/j.csbj.2014.11.005.en
dcterms.bibliographicCitationMing C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. 2019. Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Res. 21(1):1–11. doi:10.1186/s13058-019-1158-4en
dcterms.bibliographicCitationTaninaga J, Nishiyama Y, Fujibayashi K, Gunji T, Sasabe N, Iijima K, Naito T. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Sci Rep. 2019;9(1):1–9. doi:10.1038/s41598-019-48769-y.en
dcterms.bibliographicCitationGil Y, Honaker J, Gupta S, Ma Y, D'Orazio V, Garijo D, Gadewar S, Yang Q, Jahanshad N. Towards human-guided machine learning. In: proceedings of the 24th International Conference on Intelligent User Interfaces - IUI ’19. New York (NY): ACM Press; 2019, 614–24. doi: 10.1145/3301275.3302324.en
dcterms.bibliographicCitationThakkinstian A, Han P, McEvoy M, Smith W, Hoh J, Magnusson K, Zhang K, Attia J. Systematic review and metaanalysis of the association between complementary factor H Y402H polymorphisms and age-related macular degeneration. Hum Mol Genet. 2006;15(18):2784–90. doi:10.1093/hmg/ddl220.en
dcterms.bibliographicCitationSepp T, Khan JC, Thurlby DA, Shahid H, Clayton DG, Moore AT, Bird AC, Yates JRW, and the Genetic Factors in AMD Study Group. Complement factor H variant Y402H is a major risk determinant for geographic atrophy and choroidal neovascularization in smokers and nonsmokers. Investig Ophthalmol Vis Sci. 2006;47(2):536–40. doi:10.1167/iovs.05-1143.en
dc.description.versionVersión aceptadaes
dc.identifier.doi10.1080/13816810.2020.1804945


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Acceso embargado
Except where otherwise noted, this item's license is described as Acceso embargado