Background: Genetic distance metrics are crucial for understanding the evolutionary relationships and population structure of organisms. Progress in next-generation sequencing technology has given rise of genotyping data of thousands of individuals. The standard Variant Call Format (VCF) is widely used to store genomic variation information, but calculating genetic distance and constructing population phylogeny directly from large VCF files can be challenging. Moreover, the existing tools that implement such functions remain limited and have low performance in processing large-scale genotype data, especially in the area of memory efficiency. Findings: To address these challenges, we introduce VCF2Dis, an ultra-fast and efficient tool that calculates pairwise genetic distance directly from large VCF files and then constructs distance-based population phylogeny using the ape package. Benchmarking results demonstrate the tool’s efficiency, with rapid processing times, minimal memory usage (e.g., 0.37 GB for the complete analysis of 2,504 samples with 81.2 million variants), and high accuracy, even when handling datasets with millions of variants from thousands of individuals. Its straightforward command-line interface, compatibility with downstream phylogenetic analysis tools (e.g., MEGA, Phylip, and FastTree), and support for multithreading make it a valuable tool for researchers studying population relationships. These advantages meaning VCF2Dis has already been widely utilized in many published genomic studies. Conclusion: We present VCF2Dis, a straightforward and efficient tool for calculating genetic distance and constructing population phylogeny directly from large-scale genotype data. VCF2Dis has been widely applied, facilitating the exploration of population relationship in extensive genome sequencing studies.

Introduction: Diabetic nephropathy (DN) is a leading cause of end-stage kidney disease (ESKD), with renal fibrosis as a key pathological hallmark. However, the cellular and molecular drivers of fibrosis remain incompletely defined. Here, we employed singlecell RNA sequencing (scRNA-seq) to delineate pro-fibrotic cell subsets and their key regulatory factors in human DN kidneys, providing a higher-resolution view compared to previous fibrosis-related scRNA-seq studies. Methods: Publicly available scRNA-seq datasets from human DN and control kidneys were analyzed to identify fibrosis-associated fibroblast subsets. A Tmsb10-high fibroblast population was prioritized. Functional validation was performed through Tmsb10 knockdown in NIH-3T3 fibroblasts and in a diabetic mouse model, followed by assessment of fibrosis markers, extracellular matrix (ECM) deposition, and TGF-β/SMAD signaling. Results: scRNA-seq revealed a significant expansion of Tmsb10-high fibroblasts in DN kidneys, exhibiting strong enrichment of ECM-related and TGF-β/SMAD-responsive genes. Tmsb10 knockdown reduced Fn1, Col1a1, and α-Sma expression by approximately 50-70% and markedly attenuated ECM accumulation in vivo. Mechanistically, TMSB10 deficiency suppressed phosphorylation of SMAD2/3, mitigating fibroblast activation and matrix deposition. Discussion: This study identifies TMSB10 as a novel fibroblast-specific regulator of renal fibrosis in DN, acting through the TGF-β/ SMAD pathway. These findings expand current understanding of fibroblast heterogeneity and highlight TMSB10 as a potential therapeutic target for DN and other fibrotic diseases. Limitations include validation in a limited sample size and the use of murine fibroblast models, warranting further confirmation in human primary cells.