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Translating Research at SingHealth: Cancer.

Translating Research at SingHealth: Cardiometabollic Diseases.

Translating Research at SingHealth: Eye Diseases.

Translating Research at SingHealth: Health Services Research.

Translating Research at SingHealth: Infectious Diseases.

Translating Research at SingHealth: Neurosciences (Stroke).

Current and Emerging Diagnostic and Therapeutic Developments in Age-Related Macular Degeneration (AMD).

International Collaborative Research Program focusing on Aging.

Anterior Segment Optical Coherence Tomography Angiography (OCTA).

Clinical Trials in our Real World.

Ophthalmology Workforce Planning and Projection – A New Integrated Approach.

With the rapid increase in the quality and quantity of data generated by modern high-throughput sequencing techniques, there has been a need for innovative methods able to convert this tremendous amount of data into more accessible forms. Networks have been a corner stone of this movement, as they are an intuitive way of representing interaction data, yet they offer a full set of sophisticated statistical tools to analyze the phenomena they model. We propose a novel approach to reveal and analyze pleiotropic and epistatic effects at the genome-wide scale using a bipartite network composed of human diseases, phenotypic traits, and several types of predictive elements (i.e. SNPs, genes, or pathways). We take advantage of publicly available GWAS data, gene and pathway databases, and more to construct networks different levels of granularity, from common genetic variants to entire biological pathways. We use the connections between the layers of the network to approximate the pleiotropy and epistasis effects taking place between the traits and the predictive elements. The global graph-theory based quantitative methods reveal that the levels of pleiotropy and epistasis are comparable for all types of predictive element. The results of the magnified “glaucoma” region of the network demonstrate the existence of well documented interactions, supported by overlapping genes and biological pathway, and more obscure associations. As the amount and complexity of genetic data increases, bipartite, and more generally multipartite networks that combine human diseases and other physical attributes with layers of genetic information, have the potential to become ubiquitous tools in the study of complex genetic and phenotypic interactions.

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease.