A method for estimating coherence of molecular mechanisms in major human disease and traits
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METHODOLOGY ARTICLE
Open Access
A method for estimating coherence of molecular mechanisms in major human disease and traits Mikhail G. Dozmorov1,2*† , Kellen G. Cresswell1†, Silviu‑Alin Bacanu3, Carl Craver6, Mark Reimers4,5 and Kenneth S. Kendler3* *Correspondence: mikhail. [email protected]; kenneth.kendler@vcuhealth. org † Mikhail G. Dozmorov and Kellen G. Cresswell contributed equally to this work 1 Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA 3 Virginia Institute for Psychiatric and Behavior Genetics and the Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA Full list of author information is available at the end of the article
Abstract Background: Phenotypes such as height and intelligence, are thought to be a prod‑ uct of the collective effects of multiple phenotype-associated genes and interactions among their protein products. High/low degree of interactions is suggestive of coher‑ ent/random molecular mechanisms, respectively. Comparing the degree of interac‑ tions may help to better understand the coherence of phenotype-specific molecular mechanisms and the potential for therapeutic intervention.However, direct compari‑ son of the degree of interactions is difficult due to different sizes and configurations of phenotype-associated gene networks. Methods: We introduce a metric for measuring coherence of molecular-interaction networks as a slope of internal versus external distributions of the degree of inter‑ actions. The internal degree distribution is defined by interaction counts within a phenotype-specific gene network, while the external degree distribution counts interactions with other genes in the whole protein–protein interaction (PPI) network. We present a novel method for normalizing the coherence estimates, making them directly comparable. Results: Using STRING and BioGrid PPI databases, we compared the coherence of 116 phenotype-associated gene sets from GWAScatalog against size-matched KEGG pathways (the reference for high coherence) and random networks (the lower limit of coherence). We observed a range of coherence estimates for each category of pheno‑ types. Metabolic traits and diseases were the most coherent, while psychiatric disorders and intelligence-related traits were the least coherent. We demonstrate that coherence and modularity measures capture distinct network properties. Conclusions: We present a general-purpose method for estimating and comparing the coherence of molecular-interaction gene networks that accounts for the network size and shape differences. Our results highlight gaps in our current knowledge of genetics and molecular mechanisms of complex phenotypes and suggest priorities for future GWASs. Keywords: GWAS, Network, Degree, Coherence
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit
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