#0907 Genetic screens of imaging-derived kidney volumes identify genes linked to kidney function

Le résumé fourni par la source

Abstract Background and Aims Genome-wide association studies (GWAS) of kidney function typically rely on biochemical biomarkers like serum creatinine or cystatin C to estimate the glomerular filtration rate (eGFR). These biomarkers have limitations, including varying eGFR equations, biological variation, and difficulty distinguishing loci affecting kidney function from those influencing biomarker metabolism. This study explores the genetic architecture of imaging-derived kidney volumes as alternative markers of kidney function. Method We utilized the two-point Dixon sequence from abdominal MRIs of 38,816 UK Biobank (UKB) participants of European ancestry. Kidney compartment volumes (total kidney volume (TKV), cortex, medulla, and sinus volumes) were derived from automatically segmented kidney images using Patchwork, which uses a convolutional neural network to train ground truth data established by experienced radiologists. The volumes were normalized to body surface area and an inverse normal transformation of ranks was applied. GWAS was based on imputed UKB genotypes (allele frequency > 1%) and was performed using linear regression as implemented in the regenie software (v3.2.9). Analyses were adjusted for age, age-squared, sex, assessment centre, and the first 10 genetic principal components. We conducted a parallel eGFR GWAS for the same population and calculated genetic effect size correlations between the index SNPs for kidney volumes and eGFR. Causal genes for each locus were prioritized using a developed annotation pipeline incorporating gene proximity, Ensembl Variant Effect Predictor, genetic colocalization with cis-expression and -protein quantitative trait loci (QTL), and linkage disequilibrium. We assessed locus overlap between volumes, marker-based kidney function, and clinical traits using colocalization analyses (genepicoloc package), performed gene enrichment analysis to identify tissue-specific (GTEx v8) and renal cell type-specific (KPMP) gene expression patterns, and Gene Ontology enriched terms. Associations with renal outcomes were evaluated using results from the AstraZeneca PheWAS Portal, a platform that provides phenome-wide association study data derived from UKB exome sequencing. Results GWAS identified 34 significant loci for TKV, 24 for medulla, 26 for cortex, and 71 for sinus volumes (P-value < 5e-8). Pairwise colocalization analysis identified nine loci specific to the medulla and 66 to the sinus (probability of colocalization > 0.8). We observed strong correlations between genetic effect sizes of eGFR GWAS and kidney volumes for TKV (r = 0.89), cortex (r = 0.86), and medulla (r = 0.82), but a lower correlation with sinus (r = 0.37). Enrichment analyses highlighted that genes associated with cortex volume were highly expressed in the kidney cortex, while sinus volume genes were predominantly expressed in adipose and vascular tissues, supported by cell type-specific overrepresentation analyses. Gene Ontology enrichment showed that TKV and cortex genes are involved in renal development, while medulla genes are involved in hypoxia-related processes. Additionally, colocalization analysis identified 94 shared genetic signals between kidney volumes and traditional kidney function traits, supporting a shared genetic basis. The eGFR-decreasing G allele at rs77924615 (UMOD) was also associated with lower TKV (P-value = 1.5e-21). At the same time, many kidney volume-associated loci established in this study were not previously linked to traditional markers even in large GWAS meta-analyses. Moreover, we found evidence for a shared genetic basis between kidney volumes and hypertension, cardiovascular diseases, diabetes. By mining the AstraZeneca PheWAS Portal we found links between the rare, loss-of-function variants in prioritized genes (e.g., UMOD, SLC22A7) and renal outcomes: chronic kidney disease stages and kidney function markers. Conclusion This study reveals novel genetic determinants of kidney structure unreported in previous biomarker-based studies, providing insights into the genetic architecture of kidney structure and its relationship with renal function.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.