What is Pleiotropy?
The popular dogma in identifying genetic variants is that each variant has one biological effect, or that multiple variants together can create a complex disease phenotype. What is less well-understood is pleiotropy, when one gene or genetic variant produces multiple effects or serves multiple functions.
Major improvements in genome-wide association study (GWAS) interpretation including expression and protein quantitative trait loci analysis (eQTL and pQTL) and machine learning methods have allowed researchers to revisit the interactions and pleiotropic effects of human genetic variants associated with disease.
The Human “Interactome”
In a recent article published in Nature Genetics, Barrio-Hernandez et. al. used a network expansion-based approach to specifically characterize pleiotropy for over 1,000 traits identified by GWAS. Crucially, the authors’ analysis relied on human interactome databases, including IntAct, Reactome and SIGNOR, which detail the interactions between different proteins and signaling pathways.
The authors leveraged a machine learning approach by Open Targets Genetics called the locus-to-gene (L2G) score to identify GWAS variants that had more than a 50% chance of being causal for their associated trait and mapped those variants to genes in the interactome databases. By examining the resultant network of genes and traits, the authors’ approach found groups of traits under the influence of the same cellular processes and began to uncover a map of human cell pleiotropy.
Implications for Drug Targets
Identifying genes and cellular processes that are highly pleiotropic is vitally important for discovering and developing new drugs and drug targets. Targeting a gene that is highly pleiotropic can cause unwanted off-target effects, making a map of pleiotropy an important resource for early screens of potential drug targets.
However, from a basic research standpoint, pleiotropy analysis can be useful to better characterize the underlying biology of human disease. Barrio-Hernandez et. al. applied their mapping approaches to identify new disease-relevant genes for inflammatory bowel disease, potentially highlighting new research directions.
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Jane Cook, Biochemist & Content Writer, Bridge Informatics
Jane Cook, leading Content Writer for Bridge Informatics, has written over 100 articles on the latest topics and trends for the bioinformatics community. Jane’s broad and deep interdisciplinary molecular biology experience spans developing biochemistry assays to genomics. Prior to joining Bridge, Jane held research assistant roles in biochemistry research labs across a variety of therapeutic areas. While obtaining her B.A. in Biochemistry from Trinity College in Dublin, Ireland, Jane also studied journalism at New York University’s Arthur L. Carter Journalism Institute. As a native Texan, she embraces any challenge that comes her way. Jane hails from Dallas but returns to Ireland any and every chance she gets. If you’re interested in reaching out, please email jennifer.martinez@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
