Abstract: FR-PO648
Multiancestry Genome-Wide Association Meta-Analyses of Kidney Function Traits: The CKDGen Consortium R5 Project
Session Information
- Genetic Kidney Diseases: Cohort Studies - Genetic Associations and Diagnoses
October 25, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1202 Genetic Diseases of the Kidneys: Non-Cystic
Authors
- Ghasemi, Sahar, Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- König, Eva, Institute for Biomedicine, Eurac Research, Bolzano, Italy
- Gorski, Mathias, Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Schlosser, Pascal, Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Wuttke, Matthias, Institute of Genetic Epidemiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
Group or Team Name
- CKDGen Consortium.
Background
Advancing Chronic Kidney Disease (CKD) genetics research involves aggregating genome-wide association studies (GWAS) from multiple ancestries on several kidney function phenotypes including estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR). Multi-lateral collaborations and rigorous, centralized data quality control (QC) are crucial to obtain reliable results.
Methods
We used our newly developed "metaGWASmanager" package for streamlined high-quality QC workflow. Studies imputed genotypes using state-of-the-art reference panels including low-frequency variants (SNPs/InDels), and implemented the same distributed set of scripts for standardized phenotyping and GWAS (regenie). Following ancestry alignment based on principal component analysis of allele frequencies, GWAS were combined in a hierarchical fixed-effects meta-analysis (METAL): within-ancestry meta-analysis (Step1) followed by between-ancestry meta-analyses (Step2). , GWAS were combined in a trans-ethnic meta-regression analysis (MrMega).
Results
In an ongoing effort, we already aggregated >1,500 GWAS from >100 studies, with improved representation of global diversity: 60% European (EUR), 20% East Asian (EAS), 12% African (AFR), 4% Hispanic/Latino (HIS), 3% South Asian (SAS), and 1% Middle Eastern (MID) ancestries. Employed imputation panels include 69% TopMed, 20% 1000Genomes, and 9% Haplotype Reference Consortium. Preliminary meta-analysis (METAL-Step2) verified known positive controls like UMOD (rs13329952, p=2.1e-269, n=2,264,920) and CUBN (rs1801239, p=6.0e-130, n=625,976) for eGFR and UACR, respectively. Both METAL and MrMega showed UMOD being heterogeneous across ancestries, while CUBN was homogeneous (Table).
Conclusion
By leveraging the large sample size, multi-ancestry representation through >100 studies, and the assessment of non-SNP genetic variations, the CKDGen Round 5 promises to substantially enhance our understanding of the genetic basis of CKD.
Hierarchical meta-analysis and trans-ethnic meta-regression results for UMOD and CUBN
Funding
- Government Support – Non-U.S.