Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration.

Autor:	Waksmunski AR; Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States.; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States.; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States., Grunin M; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States.; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States., Kinzy TG; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States., Igo RP Jr; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States., Haines JL; Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States.; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States.; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States., Cooke Bailey JN; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States.; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States.
Jazyk:	angličtina
Zdroj:	Investigative ophthalmology & visual science [Invest Ophthalmol Vis Sci] 2019 Sep 03; Vol. 60 (12), pp. 4041-4051.
DOI:	10.1167/iovs.19-27827
Abstrakt:	Purpose: Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. Methods: We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. Results: We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways. Conclusions: We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.
Databáze:	MEDLINE
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