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Updated: May 22, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
Pathway analysis of genome-wide association studies on uric acid concentrations
1Division of Rheumatology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 126-1, Anam-dong 5-ga, Seongbuk-gu, Seoul 136-705, Republic of Korea. lyhcgh@korea.ac.kr
Objective:
The aims of this study were to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms influencing uric acid level and to generate hypotheses for the SNP to gene to pathways that influence uric acid concentrations.
Methods:
Meta-analysis data of 954 SNPs with genome-wide significance in 14 genome-wide association studies (GWASs) comprising 28,141 individuals of European ancestry was subjected to ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis to establish associations between pathways and uric acid concentrations.
Results:
ICSNPathway analysis identified 14 candidate causal SNPs, five genes, and two candidate causal pathways, which provided two hypothetical biologic mechanisms: (1) rs2728121 (regulatory region) to polycystic kidney disease 2 (PKD2) to ion transmembrane transporter activity; (2) rs942377, rs3799346, rs3799344, rs2762353, rs13197601, rs3757131, rs1165215, rs1165196 to SLC17A1 to ion transmembrane transporter activity and secondary active transmembrane transporter activity. SLC17A1, SLC17A3, SLC17A4, SLC22A11, and SLC2A9 were involved in both pathways, and PKD2 and SLC16A9 in one pathway.
Conclusion:
By applying ICSNPathway analysis to GWAS data on uric acid levels, 14 SNPs, five genes, and two pathways involving the PKD2, SLC17A1, SLC17A3, SLC17A4, and SLC2A9 genes were identified that might contribute to the condition in Europeans.
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