AI Article Synopsis
- Prenatal and perinatal exposures to factors like sunlight and allergens vary by season and are linked to certain diseases, with birth month often used as an indicator of these seasonal changes.
- The research introduces an algorithm that connects epidemiological data (birth month studies) with genetic data (gene-disease associations) to illuminate the relationships between seasonally varying factors and diseases.
- The study explores seven birth month dependent diseases and highlights three significant biological networks, shedding light on possible genetic mechanisms behind their seasonal influences.
Article Abstract
Prenatal and perinatal exposures vary seasonally (e.g., sunlight, allergens) and many diseases are linked with variance in exposure. Epidemiologists often measure these changes using birth month as a proxy for seasonal variance. Likewise, Genome-Wide Association Studies have associated or implicated these same diseases with many genes. Both disparate data types (epidemiological and genetic) can provide key insights into the underlying disease biology. We developed an algorithm that links 1) epidemiological data from birth month studies with 2) genetic data from published gene-disease association studies. Our framework uses existing data repositories - PubMed, DisGeNET and Gene Ontology - to produce a bipartite network that connects enriched seasonally varying biofactorss with birth month dependent diseases (BMDDs) through their overlapping developmental gene sets. As a proof-of-concept, we investigate 7 known BMDDs and highlight three important biological networks revealed by our algorithm and explore some interesting genetic mechanisms potentially responsible for the seasonal contribution to BMDDs.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001771 | PMC |
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