Kidney Function and Your Genetics

By the ExomeDNA Research Team

This page contains general information only. For personal health decisions, consult a qualified clinician.

Kidney function — measured as estimated glomerular filtration rate (eGFR) — reflects how efficiently your kidneys filter waste and excess fluid from your blood, and large-scale genome-wide association studies spanning hundreds of thousands of participants have now identified dozens of genetic loci that influence this filtration rate. Among the genes associated with eGFR, variants near ABO, A4GALT, ABCC1, ABCG5, ABLIM3, and A1CF have emerged from multi-ancestry analyses as contributors to kidney filtration capacity. Below: what eGFR measures, the biology of these genes, and evidence-based lifestyle factors that support kidney health.

What is kidney function?

Kidney function describes the kidneys' capacity to perform their core job: filtering roughly 180 liters of blood plasma per day, removing metabolic waste products, regulating electrolyte balance, controlling blood pressure, and activating vitamin D. The standard clinical metric for this capacity is the estimated glomerular filtration rate (eGFR) — an estimate of how many milliliters of blood the kidneys filter per minute, normalized to 1.73 m² of body surface area.

A normal adult eGFR is generally 60 mL/min/1.73m² or above. eGFR naturally declines modestly with age — roughly 1 mL/min/1.73m² per year after age 40 — so the baseline reference range accounts for age. Clinicians use eGFR to stage chronic kidney disease (CKD): values between 30–59 indicate moderate CKD, values between 15–29 indicate severe CKD, and values below 15 indicate kidney failure. Conversely, higher eGFR values reflect better filtration reserve and are considered beneficial for long-term kidney health.

The filtration itself takes place in roughly one million microscopic units called glomeruli — tiny knots of capillaries where pressure forces water and small molecules out of the blood while retaining larger proteins and blood cells. The health of the glomerular capillary wall, the renal tubule cells that reabsorb essential molecules, and the endothelial lining of the renal vasculature all contribute to measured eGFR. Genetic variants that influence any of these components — sphingolipid accumulation in glomerular cells, transporter activity in tubule epithelium, or glycosylation patterns on endothelial surfaces — can shift eGFR in ways detectable at the population level.

The genetics behind kidney function

Genome-wide association studies of eGFR have expanded rapidly since the formation of the CKDGen Consortium, which pooled data across European, East Asian, South Asian, African, and Hispanic/Latino populations. Multi-ancestry analyses have now fine-mapped loci with increasing precision, revealing several biologically coherent gene candidates near eGFR-associated signals.

ABO is among the most pleiotropic genes in the human genome. It encodes the glycosyltransferase enzyme that determines ABO blood group by adding sugar residues to antigens on red blood cells and in plasma. Beyond blood typing, ABO variants influence von Willebrand factor levels, P-selectin expression, glycosylation patterns on plasma proteins, and endothelial function — all of which have downstream effects on glomerular filtration. The ABO locus has appeared in multiple large eGFR association analyses, illustrating how blood-group biochemistry extends to kidney physiology (Wuttke et al., 2019; Sinnott-Armstrong et al., 2021).

A4GALT encodes alpha-1,4-galactosyltransferase, the enzyme that synthesizes globotriaosylceramide (Gb3) — a glycosphingolipid that is the hallmark substrate of Fabry disease. In Fabry disease, a rare X-linked disorder, deficiency of alpha-galactosidase A causes Gb3 to accumulate in kidney glomeruli and tubules, progressively impairing filtration. Common variants near A4GALT influence Gb3 levels and kidney sphingolipid metabolism without causing full Fabry disease, providing a mechanistic link between sphingolipid pathway genetics and population-level eGFR variation. Studies identifying A4GALT-region signals in kidney function GWAS highlight the broader role of sphingolipid balance in maintaining glomerular integrity (Wuttke et al., 2019).

ABCC1 encodes multidrug resistance protein 1 (MRP1), an ATP-binding cassette (ABC) transporter expressed in kidney tubule epithelial cells. MRP1 mediates the efflux of organic anions, oxidized glutathione, drugs, and xenobiotic conjugates from tubule cells into the urinary space — a key step in the kidneys' filtration and excretion of waste. Genetic variation in ABCC1 could modulate how efficiently the tubular epithelium clears organic solutes, contributing to measured eGFR variation across individuals.

ABCG5 encodes an ABC half-transporter that dimerizes with ABCG8 to mediate the excretion of cholesterol and plant sterols from liver into bile. Variants in ABCG5 affect circulating sterol levels, with downstream consequences for endothelial health and vascular function. Because glomerular filtration depends critically on the integrity of the renal microvasculature, the same sterol-handling genetics that influence cardiovascular disease risk also appear in kidney function GWAS signals. The ABCG5/G8 locus exemplifies the convergence of lipid metabolism and kidney filtration physiology (Graham et al., 2019).

ABLIM3 encodes actin-binding LIM domain protein 3, a cytoskeletal scaffolding protein that links actin filaments to cell signaling pathways. ABLIM3 is expressed in kidney glomeruli, where podocytes — the specialized cells that form the filtration barrier — depend on a precisely organized actin cytoskeleton to maintain their foot process architecture. Variants that alter podocyte cytoskeletal dynamics could affect glomerular permselectivity and measured filtration rate.

A1CF encodes APOBEC complementation factor, an RNA-binding protein that forms the core of the apolipoprotein B mRNA editing complex, catalyzing cytidine-to-uridine deamination in APOB transcripts. A1CF is expressed in the kidney, and its role in RNA editing and post-transcriptional regulation may influence renal lipid handling and tubular function. Its appearance in eGFR GWAS signals points to RNA editing as an underappreciated layer of kidney biology.

What the research says

Research base: Robust.

The genetics of kidney function has been one of the most productive areas of complex-trait GWAS in the past decade. The CKDGen Consortium's landmark 2019 catalog meta-analysis (Wuttke et al., 2019; PMID 31152163) analyzed eGFR across populations and identified over 260 loci — the largest eGFR genetic architecture map at the time. A parallel trans-ethnic analysis by Morris et al. (2019; PMID 30604766) integrated European and East Asian cohorts to fine-map putative causal genes, demonstrating that multi-ancestry designs resolve association signals that remain ambiguous in single-population studies.

Key findings across the authorized evidence base:

• Wuttke et al. (2019) identified 264 genome-wide-significant loci for kidney function traits through a meta-analysis spanning hundreds of thousands of individuals, substantially expanding the known genetic architecture of eGFR and providing the gene-set enrichment context for tubule-expressed transporters and glomerular structural proteins (PMID 31152163).
• Morris et al. (2019) conducted a trans-ethnic kidney function GWAS and identified putative causal genes by integrating association signals across European and East Asian ancestries, with renal transcriptome mapping supporting biological prioritization of candidates near identified loci (PMID 30604766).
• Graham et al. (2019) characterized sex-specific and pleiotropic genetic effects on kidney function, revealing that several eGFR loci show differential effect sizes between males and females — a finding with implications for interpreting population-level GWAS signals (PMID 31015462).
• Wojcik et al. (2019) demonstrated that including genetically diverse populations in GWAS improves discovery power and fine-mapping resolution for complex traits including kidney function, with signals identified only in non-European populations clarifying the causal variant landscape (PMID 31217584).
• Hellwege et al. (2019) mapped eGFR loci to the renal transcriptome using the VA Million Veteran Program cohort, linking GWAS signals to kidney-expressed genes and providing tissue-specific functional context for the eGFR genetic architecture (PMID 31451708).
• Sinnott-Armstrong et al. (2021) analyzed 35 blood and urine biomarkers in the UK Biobank using whole-genome data, confirming eGFR genetic signals and extending the catalog to biomarker combinations that jointly reflect kidney filtration capacity (PMID 33462484).
• Lin et al. (2021) performed whole-genome sequence analyses of eGFR in over 23,000 individuals representing multiple ancestries, identifying low-frequency and rare variant contributions to kidney function beyond what common-variant GWAS captures (PMID 33418499).

Across these studies, eGFR genetics shows polygenic architecture: many common variants of small effect, distributed across genes involved in glomerular structure, tubular transport, vascular endothelial function, and lipid metabolism. No single gene explains large fractions of population variance; rather, genetic predisposition to higher or lower eGFR reflects the cumulative influence of dozens of loci.

How kidney function affects you

Kidney function sits at the intersection of multiple organ systems and metabolic processes. Because the kidneys regulate blood pressure through the renin-angiotensin-aldosterone system, clear metabolic byproducts including creatinine and urea, handle drug excretion, and produce erythropoietin for red blood cell production, sustained high eGFR supports a wide range of downstream health outcomes.

Population-level studies consistently show that individuals with higher eGFR — all else equal — maintain better metabolic waste clearance, more stable blood pressure regulation, and lower concentrations of uremic solutes that affect cardiovascular and neurological function. The genetic contributors to eGFR identified by GWAS act through several distinct biological pathways:

Vascular and endothelial pathways. Genes like ABO and ABCG5 influence eGFR through mechanisms that also affect cardiovascular health — glycosylation of endothelial surface proteins, sterol handling, and vascular tone. This biological overlap means that genetic variants supporting higher eGFR through endothelial mechanisms may also support cardiometabolic health more broadly.

Sphingolipid and glomerular integrity pathways. The A4GALT-Gb3 axis illustrates how sphingolipid balance in kidney tissue affects filtration. Gb3 accumulation in glomerular cells disrupts the filtration barrier; genetic variation that modulates Gb3 synthesis affects the resilience of this barrier over decades.

Tubular transport pathways. Transporters like ABCC1 determine how efficiently the proximal tubule clears organic solutes from filtered plasma. Variation in transporter activity contributes to individual differences in baseline eGFR independent of glomerular filtration rate per se.

Cytoskeletal integrity. Podocyte health — maintained by proteins like ABLIM3 — is increasingly recognized as a central determinant of long-term glomerular function. Podocytes are terminally differentiated and do not regenerate; cytoskeletal stability determines how well they maintain the filtration slit over a lifetime.

Because eGFR declines with age, the genetic baseline an individual carries interacts with cumulative environmental exposures — blood pressure history, glycemic control, medication use, hydration habits — to determine the trajectory of kidney function over decades.

Working with your kidney function result

Genetic data on eGFR describes a constitutional tendency — the baseline kidney filtration capacity your biology predisposes you toward. Environmental, behavioral, and medical factors have substantial influence on where eGFR actually lands and how it changes over time. The following evidence-based strategies support kidney function regardless of genetic background:

1. Maintain adequate hydration. Adequate fluid intake supports glomerular perfusion pressure. Most adults benefit from 2–3 liters of water per day under normal conditions, with higher needs during heat exposure or physical activity. Chronic mild dehydration repeatedly stresses glomerular hemodynamics.

2. Manage blood pressure to target. Hypertension is the leading environmental driver of declining eGFR — elevated pressure damages glomerular capillaries over years. Current evidence supports a blood pressure target below 130/80 mmHg for kidney protection, particularly in individuals with existing risk factors. Regular monitoring and lifestyle or pharmacologic management are first-line interventions.

3. Maintain blood glucose control. Diabetes is the other primary driver of CKD globally. Hyperglycemia damages glomerular endothelium through advanced glycation end-products and oxidative stress. HbA1c management — targeting below 7% in most guidelines — is one of the most powerful kidney-protective interventions available.

4. Use NSAIDs cautiously. Non-steroidal anti-inflammatory drugs (ibuprofen, naproxen, and similar agents) reduce renal prostaglandin synthesis, which normally maintains glomerular arteriolar dilation. Frequent or high-dose NSAID use — especially during dehydration or illness — can transiently but repeatedly reduce eGFR. Individuals with lower baseline eGFR or existing kidney risk factors should use NSAIDs sparingly and discuss alternatives with a clinician.

5. Calibrate protein intake. Higher dietary protein increases glomerular hyperfiltration — the kidneys work harder to clear the additional nitrogen load. In individuals with robust eGFR this is typically well-tolerated; in those with lower baseline eGFR or progressive CKD, evidence supports moderating dietary protein under clinical supervision to reduce filtration workload.

6. Schedule regular eGFR monitoring. For individuals with hypertension, diabetes, family history of kidney disease, or other risk factors, eGFR testing every one to two years provides early detection of declining function when interventions are most effective. A single eGFR reading has limited meaning; trends over time are the clinically actionable signal.

The genetic variants influencing eGFR act primarily through biological mechanisms — glomerular architecture, transporter activity, sphingolipid balance — that unfold over years and decades. This makes them relevant background for understanding your kidney health trajectory rather than deterministic predictors of any specific outcome.

Related traits and genes

Kidney function intersects with several other traits in the ExomeDNA panel. Blood pressure shares genetic architecture with eGFR through vascular endothelial pathways, and the ABO locus influences both. Cholesterol metabolism overlaps via ABCG5/G8, where sterol handling genetics affect both cardiovascular and renal vascular health. Uric acid levels — elevated in hyperuricemia and gout — are closely linked to kidney filtration because the kidneys are the primary route for uric acid excretion; reduced eGFR and elevated uric acid frequently co-occur (Yasukochi et al., 2018; PMID 29124443). Type 2 diabetes genetic risk is relevant context because diabetic nephropathy is a leading cause of eGFR decline; understanding both traits together gives a fuller picture of metabolic kidney risk factors.

Among the genes implicated in eGFR, several appear in other trait contexts. ABO variants influence multiple cardiovascular and hematologic traits. ABCG5 appears in cholesterol and lipid trait analyses. The breadth of these pleiotropic effects reinforces that kidney function is embedded in systemic metabolic physiology rather than being an isolated organ-level trait.

Frequently asked questions

See below.

References

Yasukochi Y, et al. (2018). Identification of CDC42BPG as a novel susceptibility locus for hyperuricemia. PMID 29124443.

Hishida A, et al. (2018). Genome-wide association study of renal function traits: results from the Japan Multi-Institutional Collaborative Cohort Study. PMID 29779033.

Morris AP, et al. (2019). Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies. PMID 30604766.

Graham SE, et al. (2019). Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis. PMID 31015462.

Wuttke M, et al. (2019). A catalog of genetic loci associated with kidney function from analyses of a large meta-analysis of GWAS studies of eGFR and kidney disease. PMID 31152163.

Wojcik GL, et al. (2019). Genetic analyses of diverse populations improves discovery for complex traits. PMID 31217584.

Hellwege JN, et al. (2019). Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. PMID 31451708.

Qian H, et al. (2020). Genome-wide association of kidney traits in Hispanics/Latinos using dense imputed whole-genome sequencing data. PMID 32600054.

Lin BM, et al. (2021). Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries. PMID 33418499.

Sinnott-Armstrong N, et al. (2021). Genetics of 35 blood and urine biomarkers in the UK Biobank. PMID 33462484.

Data sources: GWAS Catalog, Open Targets, ClinVar, ClinGen.

ExomeDNA genetic results are for wellness and educational purposes only. Consult a clinician for personalized health guidance.