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Antonieta Martínez-Velasco, Andric Christopher Perez-Ortiz, Juan Carlos Zenteno, ALEXA BERENICE LUNA-ANGULO, Antonio Rafael Villa-Romero, Lourdes Martínez-Villaseñor, Alvaro Rendon, Francisco Javier Estrada; CFH and HTRA1 genes associated with AMD in Mexican population. Invest. Ophthalmol. Vis. Sci. 2017;58(8):2268.
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Genetic contribution of age-related macular degeneration (AMD) is well known from association studies. CFH and HTRA1 are relevant genes reported in Caucasian populations. Here, we performed a hospital-based case-control study to evaluate the effects of these genes on Mexican population.
119 cases and 137 controls were genotyped using Taqman probes. Experienced ophthalmologists defined AMD phenotypes following the American Association of Ophthalmology (AAO) guidelines. Cases were 60 years or older with CARMS grade 4 or 5, with no other retinal disease or history of vitreous-retinal surgery. Controls were outpatients aged 60 or older, with no drusen of RPE changes on fundus exam and negative family history of AMD. 30 clinical variables were recorded from electronic medical records. We looked for the following single-nucleotide polymorphisms (SNPs) in these genes: rs1329428 and rs203687 in CFH, and rs11200638 in HTRA1. Genotyping quality check (QC) and univariate analyses were performed with plink v. 1.9. Furthermore, logistic regression models and machine learning based methods were done in R v. 3.3.2 and corroborated with SAS v. 9.4.
Assuming a dominant mode of inheritance, CFH and HTRA1 alleles are significantly associated with AMD (Table 1). By means of machine learning based methods, we identified 10 important variables to include in a logistic regression model. HTRA1 significantly decreases the odds of disease [OR 0.10, 95CI 0.02 – 0.46, p = 3.5e-4] even after holding variables such as age and hypertension status constant. Conversely, CFH (rs203687) significantly increases the odds of AMD [OR 11.8, 95CI 2.72 – 51.22, p = 4.8 x 10 -5].
This work shows a strong association between CFH and HTRA1 genetic variants with AMD in Mexican population. We determined the odds of disease which are remarkable if we compare those OR values with other studies. Models generated with machine learning techniques support the diagnosis of AMD disease.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.
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