Abstract: TH-PO0124
Identification of 15 Independent Loci Associated with the Variance of eGFR in 359,677 Adults
Session Information
- Integrative Genomics and Multi-Omics in Noncystic Genetic Kidney Disease
October 22, 2026 | Location: Exhibit Hall A, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1202 Genetic Diseases of the Kidneys: Non-Cystic (Complex and Non-Cystic Monogenic)
Authors
- Giannini, Courtney, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Corcoran, Riley M., University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Westerman, Kenneth E., Massachusetts General Hospital, Boston, Massachusetts, United States
- Sampson, Matt G., Boston Children's Hospital, Boston, Massachusetts, United States
- Cole, Joanne B., University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
Background
Chronic Kidney Disease is a complex multifactorial disease influenced by both genetic and environmental factors. However, neither fully explains inter-individual differences in susceptibility. Gene-environment interactions (GEIs) may explain this additional variance. Although GEI discovery has been historically met by analytical challenges, recent advances in statistical genetics methods, computational models, and the emergence of large biobanks now enable more powerful and systematic GEI investigation. One recently developed approach scans the genome for loci associated with the variance (rather than the mean) of a quantitative trait, termed variance quantitative trait loci (vQTLs). vQTL are enriched for GEIs and can prioritize loci for gene-environment interaction testing. However, no prior studies have applied these methods to CKD-related traits.
Methods
To identify loci associated with differential variance in estimated glomerular filtration rage (eGFR), we performed a quantile regression genome-wide scan for vQTLs in 359,677 adults (ages 40 – 69) of European ancestry from the UK Biobank. eGFR was calculated using the 2021 CKD-EPI formula and adjusted for age, sex, age*sex and genetic ancestry. To identify the underlying environmental or biological exposures that are contributing to differential variance at each lead vQTL, we are conducting ongoing SNP-by-exposure interaction testing with >2000 exposures in UK Boibank, including both environmental factors, such as diet, physical activity, and socioeconomic, as well as other biological constructs, such as body weight and biomarker levels.
Results
We identified 15 independent vQTLs associated with eGFR (P<5.0x10-8), Preliminarily GEI analysis confirm known interactions, including an interaction between an intronic variant for UMOD on chromosome 16 and hemoglobin A1c, a locus known to have differential effects on kidney function in diabetes vs. the general population.
Conclusion
This study identified 15 independent vQTLs associated with eGFR, representing loci that likely harbor an underlying interaction contributing to the differential variance. Next, we will continue an exposure-wide scan across a broad range of exposures (diet, physical activity, health conditions, socio-economic status, etc.) to identify gene-environment interactions. Discovery of GEIs can inform precision prevention strategies for CKD.
Funding
- Other NIH Support