Objectives: Heterogenous feature of chronic kidney disease (CKD) and fragmentary analysis method may hinder precise identification of its novel biomarkers. Herein, we addressed this issue using independent two cohorts to integrate genomics and metabolomics and to identify cause-specific biomarkers of CKD progression in Korean population. Methods: For discovering the biomarker related with the risk of CKD, longitudinal study using the genomic data of KoGES cohort was conducted. To validate the genomic biomarkers resulted in KoGES cohort and identify the correlations of genomic markers and metabolites, Genome Wide Association Study (GWAS) and metabolomics study were conducted using Seoul National University Hospital (SNUH) cohort consisting of biopsy-confirmed patients. The SNUH cohort was divided into normal and subgroups of CKD, including diabetic nephropathy (DMN), hypertensive nephropathy (HN), IgA nephropathy (IgAN) and membranous nephropathy (MN), using propensity score matching. Results: From the genomic data of the KoGES cohort, 7,943,674 SNPs were analyzed, and a total of 5,969 single nucleotide polymorphisms (SNPs) were statistically significant for CKD progression in both the discovery and replication sets. Among the 515,990 SNPs in the genomic data of the biopsy cohort, 325 SNPs overlapped with the results of the KoGES cohort. Compared with healthy individuals, 8, 6, 5, and 15 SNPs were selected as being significant in T2DN, HN, IgAN, and MN, respectively. Regarding plasma metabolomics, 51, 28, 29, and 71 metabolites in T2DN, HN, IgAN, and MN, respectively, were different from those from healthy individuals. Accordingly, 32, 16, 7, and 103 pairs of SNPs and metabolites had correlations in T2DN, HN, IgAN, and MN, respectively. Conclusions: Integrating both GWAS and metabolomics with independent CKD cohorts enables to discover cause-specific biomarkers of CKD in Korean population.