Improve phenotyping, including methods for electronic phenotyping.
Improve the quality of genomic studies, including analytical and computational methods.
Improve data access while protecting the privacy of research participants.
Create processes for transferring genetic information obtained through clinical testing to research.
Study health-economic impacts of genetic testing in nephrology.
Establish a process for periodic reanalysis of unsolved cases with kidney disease.
Implement high-throughput techniques for in silico and in vitro variant characterization.
Identify and characterize rare variants, structural variants, and functional variants using functional genomic, epigenetic, and other multi-omic approaches.
Employ new approaches to identify more homogeneous CKD phenotypes and subclassifications for genetic studies, e.g. using non-traditional omics biomarkers, electronic health record data, imaging, or machine learning.
Assemble larger cohorts with genetically defined kidney disease for both research and clinical trials; collaborate internationally if possible.
Reduce measurement errors in eGFR and misclassification in the resulting CKD definition, e.g. reassess coefficients based on race, sex, and chronological age in eGFR equations.
Conduct large-scale genetic studies on specific kidney sub-phenotypes, e.g. CKD progression, acute kidney injury, cause-specific disease severity, and manifestations.
Integrate genetic studies with biomarker and multi-omic profiling to leverage findings and increase power for both variant and pathway identification.
Generate comprehensive molecular maps of kidney tissue/cells as well as in vitro and animal models to enable mechanistic studies of genes identified in GWAS of kidney traits.
Encourage broad data sharing (FAIR principals: Findable, Accessible, Interoperable, Reusable), transparent protocols for data generation, quality control, and analyses.
Use federated networks to standardize key data elements across platforms and countries.
Use portals (cloud-based) to “safely” share individual data and allow for democratization and broader scale of integrative in silico analyses.
Extend discovery analyses to non-additive genetic models (e.g. recessive) and include non-autosomal regions (e.g. chromosome X, mitochondrial).
Improve imputation reference panels.
Apply and develop approaches specific to admixed populations.
Conduct Mendelian randomization analysis to elucidate causal mechanisms.
