medRxivGenetic & Genomic MedicinePreprint
Human Kidney Alternative Splicing Information Illuminates Cardiovascular-Kidney-Metabolic Syndrome Risk
Samer MohandesiD, Daigoro HirohamaiD, Eunji HaiD, Bernhard DumouliniD, Siyu PaniD, Andrea Bergeson, Amin AbediniiD, Thao NguyeniD, Fiona Elizabeth McAllisteriD, Katalin SusztákiD
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Abstract
Alternative splicing generates multiple transcripts from a single gene, contributing to functional protein diversity. However, short-read RNA sequencing often aggregates isoforms, limiting transcript-level resolution. By analyzing splicing variation in 404 human kidney cortical samples across a spectrum of disease severity, we define the splicing landscape of the human kidney in health and disease. To pinpoint likely disease-causing events, we performed splicing and expression quantitative trait locus (sQTL and eQTL) analyses and identified 1,948 genes with significant sQTLs. Integration with genome-wide association study (GWAS) data for kidney function and cardiovascular-kidney-metabolic (CKM) syndrome traits revealed 30 splice variants with evidence of shared genetic regulation via Bayesian colocalization and summary-based Mendelian randomization. Six genes--including MANBA, CHMP1A, NT5DC2, GSTA1, GSTA2, and MGMT--demonstrated concordant sQTL, eQTL, and protein QTL (pQTL) signals, implicating transcript-level regulation as a key mediator of kidney function-associated variation. Similarly, multi-layer QTL evidence implicated a CLCNK splice variant in blood pressure regulation. To resolve full transcript structures, we applied long-read RNA sequencing, identifying novel isoforms and validating short-read quantifications. These analyses nominate specific isoforms, including those of MANBA and GSTA2, as candidate mediators of kidney disease risk. Our findings highlight transcript-specific genetic regulation in the human kidney and underscore the value of isoform-resolved analysis for understanding complex trait biology.
- organism: human
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: Bayesian inference
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