A polygenic score for reduced kidney function and adverse outcomes in a cohort with chronic kidney disease

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations

Key Points

The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits. However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations. Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.

Background

CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.

Methods

This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.

Results

The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.

Conclusions

In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

Exploring the impact and utility of genomic sequencing in established CKD

Clinical genetics is increasingly recognized as an important area within nephrology care. Clinicians require awareness of genetic kidney disease to recognize clinical phenotypes, consider use of genomics to aid diagnosis, and inform treatment decisions. Understanding the broad spectrum of clinical phenotypes and principles of genomic sequencing is becoming increasingly required in clinical nephrology, with nephrologists requiring education and support to achieve meaningful patient outcomes. Establishment of effective clinical resources, multi-disciplinary teams and education is important to increase application of genomics in clinical care, for the benefit of patients and their families. Novel applications of genomics in chronic kidney disease include pharmacogenomics and clinical translation of polygenic risk scores. This review explores established and emerging impacts and utility of genomics in kidney disease.

Monogenic and polygenic concepts in chronic kidney disease (CKD)

Kidney function is strongly influenced by genetic factors with both monogenic and polygenic factors contributing to kidney function. Monogenic disorders with primarily autosomal dominant inheritance patterns account for 10% of adult and 50% of paediatric kidney diseases. However, kidney function is also a complex trait with polygenic architecture, where genetic factors interact with environment and lifestyle factors. Family studies suggest that kidney function has significant heritability at 35-69%, capturing complexities of the genome with shared environmental factors. Genome-wide association studies estimate the single nucleotide polymorphism-based heritability of kidney function between 7.1 and 20.3%. These heritability estimates, measuring the extent to which genetic variation contributes to CKD risk, indicate a strong genetic contribution. Polygenic Risk Scores have recently been developed for chronic kidney disease and kidney function, and validated in large populations. Polygenic Risk Scores show correlation with kidney function but lack the specificity to predict individual-level changes in kidney function. Certain kidney diseases, such as membranous nephropathy and IgA nephropathy that have significant genetic components, may benefit most from polygenic risk scores for improved risk stratification. Genetic studies of kidney function also provide a potential avenue for the development of more targeted therapies and interventions. Understanding the development and validation of genomic scores is required to guide their implementation and identify the most appropriate potential implications in clinical practice. In this review, we provide an overview of the heritability of kidney function traits in population studies, explore both monogenic and polygenic concepts in kidney disease, with a focus on recently developed polygenic risk scores in kidney function and chronic kidney disease, and review specific diseases which are most amenable to incorporation of genomic scores.