Genetic Associations and Survival in AMD

SNP genotypes were denoted as 0/0 for homozygous reference alleles, 0/1 for heterozygous alleles, and 1/1 for homozygous alternate alleles (0: reference allele; 1: alternate allele). Association analysis of logistic regression was performed using the Python package statsmodels (Seabold et al., 2010 ). An additive model was used for the association between the SNPs and AMD. For additive logistic regression analysis, homozygous reference alleles, heterozygous alleles, and homozygous alternate alleles were respectively defined as the values 0, 1, and 2. The clinical data mining and management of the SQL server in TCVGH was conducted using Microsoft Azure Data Studio. Patient comorbidities included hypertension (ICD-9-CM codes 401.xx—405.xx), coronary artery disease (410.xx—414.xx), cardiac dysrhythmias (427.xx, 785.0, and 785.1), cerebrovascular diseases (433.xx—438.xx), chronic respiratory diseases (490—496), and hyperlipidemia (272.x). Individuals with any comorbidity were identified through diagnoses performed during at least two ambulatory visits to TCVGH. Statistical significance was defined as a p-value < 0.05.
Survival analysis was assessed by the Kaplan–Meier estimate using the R package survival (Therneau and Grambsch, 2000 ). Observation time was defined as the period of duration from the first outpatient visit for a comorbidity to the first time receiving a diagnosis for AMD. The survival curve was plotted by the R package survminer (https://CRAN.R-project.org/package=survminer). Log-rank tests for significant differences in survival time between the two groups were performed using the survdiff function in the survival package. A Cox proportional hazard (PH) model was used to estimate the hazard ratio (HR) using the coxph function in the survival package. For Cox PH model, homozygous reference alleles (0/0), heterozygous alleles (0/1), and homozygous alternate alleles (1/1) were respectively defined as the values 0, 1, and 2.