GWAS for systemic sclerosis identifies six novel susceptibility loci including one in the Fcγ receptor region

Pathogenesis of systemic sclerosis: an integrative review of recent advances

Systemic sclerosis (SSc), or scleroderma, is a complex autoimmune connective tissue disease characterized by autoimmunity, vasculopathy, and progressive organ fibrosis, leading to severe organ dysfunction. The disease begins with a vascular injury triggered by autoimmune responses and environmental factors against a backdrop of genetic predisposition. This injury impairs angiogenesis and vasculogenesis, resulting in capillary loss and arteriolar constriction, which promotes immune cell infiltration and sustained inflammation within affected tissues. These vascular anomalies cause severe complications, including pulmonary artery hypertension, scleroderma renal crisis, and skin ulcers. Chronic inflammation fosters persistent fibroblast activation, resulting in extensive fibrosis that defines SSc. This review synthesizes the latest research on pathogenesis of SSc, highlighting the shift from fundamental research to a precision therapeutic approach. It explores the potential of technologies like flow cytometry and single-cell RNA sequencing to investigate pathogenic cell subtypes. These platforms integrate transcriptomic, genomic, proteomic, and epigenomic data to uncover insights into the underlying mechanisms of SSc pathogenesis. This review advocates for a multidisciplinary, patient-centric approach that harnesses recent scientific advances, directing future SSc research toward personalized and precise interventions.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

Insights into the genetic landscape of systemic sclerosis

Systemic sclerosis (SSc) is a complex autoimmune disease that clinically manifests as progressive fibrosis of the skin and internal organs. Autoimmunity and endothelial dysfunction play important roles in the development of SSc but the causes of SSc remain unknown. Accumulating evidence, first from familial aggregation studies and subsequently from candidate gene association studies and genome wide association studies underscore the crucial contributions of genetics to the development of SSc. The identification of polymorphisms in the HLA region as well as non-HLA loci is important for understanding the risks of developing SSc but can also provide important pathogenic insight in SSc. While not translating into clinic practice yet, understanding the genetic landscape of SSc will hopefully assist in the diagnosis and management of patients with and/or at risk of developing SSc in the future. Herein we review the studies that investigate genetic risks of SSc susceptibility.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BACKGROUND

Chronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.

METHODS

GWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.

RESULTS

A total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.

CONCLUSIONS

Our integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing

We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.