Background and Aims: The multiple renal cysts (MRC) occur in some patients with noncirrhotic portal hypertension (NCPH) could be a subset of ciliopathy. However, the potential genetic influencers and/or determinants in NCPH with MRC are largely unknown. The aim of this study was to explore the potential candidate variants/genes associated with those patients.Methods: 8,295 cirrhotic patients with portal hypertension were enrolled in cohort 1 and 267 patients affected with NCPH were included in cohort 2. MRC was defined as at least two cysts in both kidneys within a patient detected by ultrasonography or computed tomography. Whole-genome sequencing (WGS) was performed in nine patients (four from cohort 1 and five from cohort 2). Then we integrated WGS and publicly available single-cell RNA sequencing (scRNA-seq) to prioritize potential candidate genes. Genes co-expressed with known pathogenic genes within same cell types were likely associated NCPH with MRC.Results: The prevalence of MRC in NCPH patients (19.5%, 52/267) was significantly higher than cirrhotic patients (6.2%, 513/8,295). Further, the clinical characteristics of NCPH patients with MRC were distinguishable from cirrhotic patients, including late-onset, more prominent portal hypertension however having preserved liver functions. In the nine whole genome sequenced patients, we identified three patients with early onset harboring compound rare putative pathogenic variants in the known disease gene PKHD1. For the remaining patients, by assessing cilia genes profile in kidney and liver scRNA-seq data, we identified CRB3 was the most co-expressed gene with PKHD1 that highly expressed in ureteric bud cell, kidney stromal cell and hepatoblasts. Moreover, we found a homozygous variant, CRB3 p.P114L, that caused conformational changes in the evolutional conserved domain, which may associate with NCPH with MRC.Conclusion: ScRNA-seq enables unravelling cell heterogeneity with cell specific gene expression across multiple tissues. With the boosting public accessible scRNA-seq data, we believe our proposed analytical strategy would effectively help disease risk gene identification.