Discovery of Novel Genetic Risk Loci for Acute Central Serous Chorioretinopathy and Genetic Pleiotropic Effect With Age-Related Macular Degeneration

Clinical and genetic characteristics of simple central serous chorioretinopathy according to age

PURPOSE

To investigate whether genetic and clinical characteristics differ depending on generations using 326 patients (male/female, 259/67; mean age, 55.4 ± 12.5 years) with simple CSC.

METHODS

All patients were diagnosed with simple CSC, defined as a retinal pigment epithelium alteration area equal to or smaller than 2-disc areas based on multimodal imaging at the initial presentation. We cross-sectionally evaluated clinical characteristics at the initial visit and genotyped CFH rs800292 and rs1329428 for all patients using TaqMan technology.

RESULTS

As generations decreased, the proportion of males, subfoveal choroidal thickness, and prevalence of fibrin significantly increased (p < 0.001, p < 0.001, and p = 0.012, respectively), and the best-corrected visual acuity improved (p < 0.001); in contrast, the prevalence of pachydrusen significantly decreased (p < 0.001). The younger presentation was significantly associated with male and risk variants (T allele) of CFH rs1329428 (p = 9.1 × 10-7 and p = 0.042, respectively), and patients were estimated to present 2 years younger per one T allele of CFH rs1329428 (p = 0.042, β = -1.95, stepwise regression analysis).

CONCLUSION

Clinical and genetic characteristics differed significantly among patients with simple CSC, depending on their generation.

Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection

In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection.