African ancestry meta-analysis of genome-wide association studies identifies a novel locus and validates multiple loci associated with kidney function

doi:10.21203/rs.3.rs-2576509/v1

Despite recent efforts to increase diversity in genome-wide association studies (GWASs), most loci currently associated with kidney function are still limited to European ancestry due to the underlying sample selection bias in available GWASs. We set out to identify susceptibility loci associated with estimated glomerular filtration rate (eGFRcrea) in 80027 individuals of African-ancestry from the UK Biobank (UKBB), Million Veteran Program (MVP), and Chronic Kidney Disease genetics (CKDGen) consortia.

We identified 8 lead loci, 7 of which were previously associated with eGFR in other populations. We identified one novel variant, rs77408001 which is an intronic variant mapped to the ELN gene. We validated seven previously reported loci at GATM-SPATA5L1 and AGPAT3. Fine-mapping analysis identified variants rs77121243 and rs201602445 as having a 99.9% posterior probability of being causal. Our results warrant designing bigger studies within individuals of African ancestry to gain new insights into the pathogenesis of Chronic Kidney Disease (CKD) and identify genomic variants unique to this ancestry that may influence renal function and disease.

Chronic kidney disease (CKD) is defined by an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m² for three or more months[1]. The global prevalence of CKD is estimated to be ~ 10% in adults [2], and sub-Saharan Africa has an estimated prevalence of 13.1%[3]. Care and management of CKD impact a significant financial burden on healthcare systems worldwide, even more so in developing economies, majorly in Africa[4].

The genetic risk factors associated with CKD are ethnically driven including but not limited to telomere length, copy number variation (CNVs), and mitochondrial DNA [5]. Numerous loci related to estimated glomerular filtration rate (eGFR), which is a measure of kidney function for defining CKD, have mainly been discovered using Genome-wide association studies (GWAS) of European and Asian ancestry[6–8]. Owing to the systematic sample selection bias in global GWASs, many of the significant loci discovered in Eurocentric populations can hardly be replicated in African ancestry groups. This is attributed to differences in linkage disequilibrium (LD), differences in heritability, and differences in allele frequencies across ancestries[5, 9–12]. Polymorphisms in two critical genes, the plasma kallikrein gene (KLKB1), and the human homolog of the rodent renal failure gene, have been reported to be more associated with End-stage renal disease (ESRD) in the American black population than in whites[13].

In the past 15 years, efforts have been made to increase diversity in cohorts that perform GWASs of complex traits by increasing the number of previously under-represented ancestries, like the African-ancestry[14–17]. However, these efforts are still limited by various logistical challenges. Thus, low power due to the underlying sampling bias towards European ancestry individuals in most global consortia is still eminent[18, 19]. Most such studies have included the African-ancestry population as a small validation cohort in trans-ethnic analyses, mainly for comparison to a much larger European-ancestry cohort. This has not yielded much ancestry-specific discovery of loci to CKD. As a result, such study designs could have missed some of the loci unique to African-ancestry individuals within these global consortia.

Therefore, our study leveraged eGFRcrea GWAS summary data of only African ancestry individuals within three global consortia: Million Veteran Program (MVP), UK Biobank (UKBB), and Chronic Kidney Disease Consortium (CKDGen consortium), with a combined sample size of over 80k African-ancestry participants. We aimed to uncover loci associated with CKD in these individuals, further characterize their functionality and phenotype association by performing a Phenome-wide association analysis, and determine the causality of variants using Bayesian Fine-mapping methods.

Study samples

GWAS summary statistics of eGFR for 80,027 individuals of African ancestry from two global population-based consortia: UKBB [20] and CKDGen , and one Hospital-based cohort, MVP [21] were meta-analyzed. A summary of study sample characteristics is shown in Table 1. Further details of the cohorts contributing to this study are in the Methods section.

Genome-wide Associated SNPs with eGFR

We analyzed ~ 22.6M SNPs and 403 SNPs from those attained genome-wide significance (P < 5 X 10E-8). We identified lead SNPs by clumping at ±500kb window around the significant SNPs and identified 8 lead variants. Seven of them had previously been reported in association with eGFRcrea. Figure 1 summarizes the meta-analysis results of African-ancestry eGFRcrea for the three cohorts.

SNP rs201602445, located on chromosome 15, at base position 45592887, had the strongest association (p-value = 1.326e-38, figure 1, supplementary Table s1). This SNP is located near the spermatogenesis-associated 5-like 1 (SPATA5L1) at the GATM-SPATA5L1 locus. We identified one intronic variant, rs77408001, mapping to the ELN gene (Table 2, figure 3), which had not previously been associated with eGFRcrea, and we considered this as a novel SNP henceforth. Lead SNP rs10084572 is located near 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3), found on chromosome 21, and lead SNP rs200950799 is located near Solute Carrier Family 15, Member 5 gene (SLC15A5) on chromosome 12 have been previously reported in association with eGFRcrea in non-Hispanic blacks. Further information about the genetic architecture of the lead SNPs is illustrated using regional association plots in supplementary figure s1.

We applied genomic control correction to the meta-analysis results to account for inflation. We found no evidence of significant variation in expected allele frequencies across individuals in each cohort due to residual population structure as shown in figure 2. The inflation lambda was 1.107, and it did not indicate substantial bias in the test statistics.

Bayesian Fine-mapping

Bayesian fine-mapping was performed based on a ±500kb window on each of the lead SNPs from the meta-analysis. Two variants, rs77121243 and rs201602445, each had one credible set accounting for 99% of the posterior probability (PP) of driving the eGFR association in African-ancestry individuals. SNPs 12:17157119 and 21:45412872, each had 2 credible sets accounting for 99% of the PP of being causal SNPs. The three loci around SNPs (7:73443012, 12:17157119, and 21:45412872) associated with eGFR had 5, 2 and 2 SNPs in their 99% credible sets as shown in supplementary Table s2. We then proceeded to compare the 99% credible set sizes of these loci in EUR GWAS. They were smaller 99% credible set sizes in AFR compare to EUR GWAS except for 7:1286192 loci (supplementary Table s3). This indicated that AFR GWAS enhanced the fine mapping resolution compared to the EUR GWAS.

Colocalization with gene expression

We conducted kidney expression quantitative trait locus (eQTL) analysis in Genotype-Tissue Expression (GTEx), NephQTL, the Human Kidney eQTL Atlas, and RegulomeDB. We found significant eQTLs for 3/8 of the lead SNPs; rs9908131, rs10945657, and rs13230509 (supplementary tables) at (P < 5E-11). Variants: rs9908131 and rs10945657 had significant eQTLs in both GTEx and NephQTL resources. Variant rs13230509 was not significantly associated with any eQTLs in NephQTLs. For the three variants with significant eQTLs, we concluded that there is evidence of colocalization between eGFR association and eQTLs. All eight lead SNPs were not significant eQTLs in the Human Kidney Atlas. All the lead SNPs showing significant eQTLs were mapped to non-kidney tissues. Using RegulomeDB, we found no evidence of the regulatory impact of the lead SNPs and any of those in high LD.

SNP association with related phenotypes: PheWAS and MAGMA analysis

Of all the lead SNPs, 7 showed significant association with other phenotypes. SNP rs9908131 had 517 associated traits, which was the highest (supplementary Table s4). Variants; rs1464559, rs9908131, and rs2279463 were most significantly associated with eGFR at P< 0.05 (supplementary Table s4). Variants rs77121243 and rs13230509 were most significantly associated with metabolic and haematological traits, respectively. We further performed MAGMA tissue expression analysis, but none of the variants had significant expression in the kidney.

Meta-analysis

We aggregated summary statistics by performing the Stouffer’s method implemented in METAL due to the different GWAS trait transformations that had been applied by the individual studies [22]. Briefly, this method converted the two-sided p-value to a z-statistic which reflected the direction of association based on the reference allele. Each z-score was then weighted; the squared weights were chosen to sum to 1, and each cohort-specific weight was proportional to the square root of the adequate number of individuals in that cohort. The weighted z-statistics were then summed across the three cohorts, and the summary z-score was converted to a two-sided p-value. We performed double GC correction once during the study-specific analyses and repeated the statistics from the meta-analysis. QQ-plots for the individual datasets and the meta-analysis are presented in the supplementary data.

Fine-mapping

We used a Bayesian method to fine-map all SNPs within 500kb of the eight lead SNPs[23]. We used the Z-score for the ith SNP (Zi) to calculate the Baye’s factor in favour of the association, denoted BFi, using equation (i) below:

where K is the number of studies.

The posterior probability (PP) for the ith SNP was calculated using equation (ii) below;

where ∑BFi denotes the total sum of SNPs at that locus. We obtained the 99% credible sets using Bayes’ factors of the SNPs after rearranging them in descending order. We only counted those SNPs that had a posterior probability of ≥ 0.99.

Exploring Functional Mapping And Annotation

FUMA analysis

To perform gene annotation and functional mapping for each lead SNP, we used a web-based platform called FUMA. This resource uses information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization, and interactive visualization[24].

Phewas Analysis

We characterized the functionality of the lead SNPs using the global GWAS ATLAS resource[25]. This resource compiled a catalogue of > 4k GWAS results across over 3000 unique traits in 28 domains. Leveraging this resource, for each lead SNP, we assessed the genetic nature of these variants in effect size distribution, minor allele frequency, and biological functionality of the traits associated with the lead variants. We also characterized the extent of pleiotropy at a trait-associated locus and associated genes and investigated trait polygenicity and SNP heritability.

Magma Gene Set Analysis

Gene set analysis based on P-values was performed using MAGMA[26], a tool embroiled within FUMA. The method performs gene-based P-value analysis for protein-coding genes by mapping SNPs to genes if they are located within the same genes. The default MAGMA setting used an SNP-wise model for gene analysis and a competitive model for gene set analysis. Multiple testing was corrected using the Bonferroni correction for gene analysis and FDR for gene-set research.

Eqtl Analysis

We leveraged four publicly available kidney eQTL resources: GTEx[27], NephQTL[28], the Human Kidney eQTL Atlas, and RegulomeDB (finish sentence..). For each lead SNP, we searched eQTLs associated with each lead SNP in the kidney cortex, glomerular and tubulointerstitial kidney tissue, whole kidney, glomerulus and tubules in GTEx, NephQTL, and Human kidney eQTL Atlas, respectively. We used RegulomeDB to determine the regulatory impact of each lead SNP.

We present the results from a meta-analysis of GWAS summary statistics of eGFR based on serum creatinine, in individuals of African ancestry. To the best of our knowledge, this is one of few studies to combine summary statistics of eGFRcrea from strictly African-ancestry individuals within these 3-major global GWAS consortia (UKBB, CKDGen, and MVP).

None of the lead SNPs identified in our analysis had previously been reported in European ancestry as associated with eGFRcrea. We validated seven variants as mapped to previously reported eGFR loci in individuals of African Ancestry; GATM/SPATA5L1, SLC15A5, and AGPAT3 [7, 8]. The lead SNP with the most significant association is located near the GATM/SPATA5L1 locus, previously mapped to eGFRcrea in continental Africans[29]. This lead SNP is different from those similarly mapped to the GATM locus in individuals of European ancestry[6, 30]. GATM is an enzyme involved in creatinine biosynthesis by encoding glycine amidinotransferase. SNPs at the GATM locus have been reported to have a role in serum creatinine levels without influencing susceptibility to kidney disease in individuals of European ancestry [7, 30]. A similar association has been recently reported in continental Africans[29]. Variant rs13230509 has previously been associated with eGFRcrea in both non-Hispanic blacks and non-Hispanic whites[30].

Loci SPATA5L1 is a known locus for eGFR[6]. This locus was previously reported to be associated with eGFR in individuals of European ancestry [8]. The mechanism of action of the SPATA5L1 encoded protein affects kidney function, and how abundant it is in kidney tissues is still not yet clear. Similarly, the SLC15A5 has previously been associated with eGFR in Europeans [30]. SLC15A5 encodes a protein that enables symporter activity. It is an integral component of the membrane and is predicted to be involved in peptide transport, protein transport, and transmembrane transport.

One lead SNP, rs77408001, has not yet been mapped to any eGFR loci and is located in the ELN gene. This gene encodes a protein, one of two elastic fibre components. The ELN gene, also known as the elastin gene, codes for a protein called tropoelastin from which Elastin is synthesized. Elastin is the major component of elastic fibres, which support the body's connective tissues[31]. A deletion mutation in this gene at 7q11.23 has been linked to Williams syndrome (WS), characterized by cardiovascular disease including elastin arteriopathy, peripheral pulmonary stenosis, supravalvar aortic stenosis, and hypertension[32].

The bayesian fine-mapping analysis identified two lead SNPs, rs77121243 and rs201602445, with a single credible set each showing a 99.9% posterior probability of driving the association loci at respective loci. The lead SNP; rs201602445, located near the more characterized locus; GATM, is significantly causally associated with eGFR in this African-ancestry population. Overall, the aggregation of African Ancestry GWAS enhanced the fine mapping resolution compared to European GWAS.

Most of the lead variants were not significant expression quantitative trait loci (eQTLs) in publicly available kidney gene expression resources, and they did not co-localize to any kidney tissues. This is unsurprising considering the lack of generalizability across populations in these resources. For example, over 80% of the expression quantitative trait locus (eQTL) is from individuals of European ancestry[27]. The most significant PheWAS association was with eGFR, and two SNPs, rs10084572 and rs77408001, were associated with Height and Total cholesterol in large VLDL, respectively, but these were not significant associations.

Our study had some limitations, for example, diabetes and hypertension are common risk factors for kidney damage[33], however, we were not able to perform a stratified analysis based on underlying patient kidney disease comorbidities like diabetes and hypertension within these cohorts. This would enable us to identify which comorbidity groups contributed the most toward observed associations. Our largest contributing cohort, MVP, had previously reported diabetes-stratified GWAS of over 9k individuals with 32 genome-wide significant variants[30]. They said that one of the loci, SPATA5L1, identified in this study had the strongest association in the non-diabetics group. In this study, we only had access to GWAS summary statistics from these cohorts with no socio-demographics information, and thus couldn’t conduct this analysis.

Our findings uncover a potential new locus for eGFR in African-ancestry individuals and further affirm those previously reported in other ancestries. Some of the variants reported here are unique to the African ancestry, and therefore this warrants larger ancestry-specific meta-analyses to decipher more loci associated with CKD in Africans. Furthermore, we justify here that even African-ancestry individuals outside Africa have genetic differences from continental Africans, and caution should be taken not to generalize individuals within this ancestry while conducting GWASs.

Ethics approval and consent to participate

Not applicable. This study used publicly available GWAS summary statistics. All participants in respective studies provided informed consent. Each study had its research protocol approved by the local ethics committee.

Consent for publication

Not applicable.

Availability of data and materials

All materials and related data in this study are available upon request directed to [email protected]

Conflict of Interests

None declared

Funding

This work was supported by the Wellcome Trust [grant number: 220740/Z/20/Z] awarded to Segun Fatumo. This work was supported by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement, through core funding to the MRC/UVRI and LSHTM Uganda Research Unit. TC is an international training fellow supported by the Wellcome Trust grant (214205/Z/18/Z).

Authors' contributions

SF conceptualised the study. CK performed the main analyses with support of OS, TM, TC and SF. CK wrote the first draft of the manuscript. SF and TC supervised the project. All authors read and approved the final version of the manuscript.

Acknowledgements

The authors thank Million Veteran Program (MVP) staff, researchers, and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now generously agreed to enroll in the study. (See https://www.research.va.gov/mvp/ for more details). The citation for MVP is Gaziano, J.M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 70, 214-23 (2016). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by the Veterans Administration (VA) Cooperative Studies Program (CSP) award #G002

Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group, KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease, Kidney Int. 2013. 3: p. 1-150.
Eckardt, K.-U., et al., Evolving importance of kidney disease: from subspecialty to global health burden. The Lancet, 2013. 382(9887): p. 158-169.
Ajayi, S.O., et al., Prevalence of Chronic Kidney Disease as a Marker of Hypertension Target Organ Damage in Africa: A Systematic Review and Meta-Analysis. Int J Hypertens, 2021. 2021: p. 7243523.
Xie, Y., et al., Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int, 2018. 94(3): p. 567-581.
Canadas-Garre, M., et al., Genetic Susceptibility to Chronic Kidney Disease - Some More Pieces for the Heritability Puzzle. Front Genet, 2019. 10: p. 453.
Wuttke, M., et al., A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet, 2019. 51(6): p. 957-972.
Kottgen, A., et al., Multiple loci associated with indices of renal function and chronic kidney disease. Nat Genet, 2009. 41(6): p. 712-7.
Pattaro, C., et al., Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun, 2016. 7: p. 10023.
Fatumo, S., The opportunity in African genome resource for precision medicine. EBioMedicine, 2020. 54: p. 102721.
Deepti Gurdasani, T.C., Fasil Tekola-Ayele, Luca Pagani, Ioanna Tachmazidou,, et al., The African Genome Variation Project shapes medical genetics in Africa. Nature, 2015. 517: p. 328.
Gurdasani, D., et al., Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa. Cell, 2019. 179(4): p. 984-1002 e36.
Udler, M.S., et al., Effect of Genetic African Ancestry on eGFR and Kidney Disease. J Am Soc Nephrol, 2015. 26(7): p. 1682-92.
Hsu, C.Y., et al., Racial differences in the progression from chronic renal insufficiency to end-stage renal disease in the United States. J Am Soc Nephrol, 2003. 14(11): p. 2902-7.
Barroso, I., The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits. Diabetologia, 2021. 64(12): p. 2653-2664.
Kelleher, J., et al., 2018.
Mulder, N., et al., H3Africa: current perspectives. Pharmgenomics Pers Med, 2018. 11: p. 59-66.
Ramirez, A.H., et al., The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y), 2022. 3(8): p. 100570.
Improving equity in human genomics research. Commun Biol, 2022. 5(1): p. 281.
Peterson, R.E., et al., Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell, 2019. 179(3): p. 589-603.
Allen, N., et al., UK Biobank: Current status and what it means for epidemiology. Health Policy and Technology, 2012. 1(3): p. 123-126.
Gaziano, J.M., et al., Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol, 2016. 70: p. 214-23.
Willer, C.J., Y. Li, and G.R. Abecasis, METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 2010. 26(17): p. 2190-1.
Wellcome Trust Case Control, C., et al., Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet, 2012. 44(12): p. 1294-301.
Watanabe, K., et al., Functional mapping and annotation of genetic associations with FUMA. Nat Commun, 2017. 8(1): p. 1826.
Watanabe, K., et al., A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet, 2019. 51(9): p. 1339-1348.
de Leeuw, C.A., et al., MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol, 2015. 11(4): p. e1004219.
Consortium, G.T., The Genotype-Tissue Expression (GTEx) project. Nat Genet, 2013. 45(6): p. 580-5.
Gillies, C.E., et al., An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. Am J Hum Genet, 2018. 103(2): p. 232-244.
Fatumo, S., et al., Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans. Hum Mol Genet, 2021. 30(16): p. 1559-1568.
Hellwege, J.N., et al., Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. Nat Commun, 2019. 10(1): p. 3842.
Brooke, B., New Insights into Elastin and Vascular Disease. Trends in Cardiovascular Medicine, 2003. 13(5): p. 176-181.
Schubert, C., The genomic basis of the Williams-Beuren syndrome. Cell Mol Life Sci, 2009. 66(7): p. 1178-97.
Kazancioglu, R., Risk factors for chronic kidney disease: an update. Kidney Int Suppl (2011), 2013. 3(4): p. 368-371.

Table 1

Characteristics of the study cohorts
Study	Study full name	Ancestry	Study design	Sample size
UKBB	UK Biobank	African ancestry	Population based	6217
CKDGen consortium	Chronic Kidney Disease Genetics Consortium	African ancestry	Population based	16474
MVP	Million Veteran Program	African ancestry	Hospital based (Out-patient clinics)	57336

Table 2

Genome-wide significant locus (P < 5 x 10^− 8) associated with eGFR after meta-analysis
											UKBB								MVP									Meta-analysis
Locus	Lead SNP		Chr		BP(b37)		EA		NEA		Beta		SE	MAF			P-value		Beta	SE		MAF		P-value		P-value		N
ELN	rs77408001		7		73443012		C		A		0.3869		0.1804			NA	0.03198		-23.2432			4.3171		0.0135		7.283e-08		7.264e-09		63553

Snp, Single Nucleotide Polymorphism; Char, Chromosome; Bp, Base Position; Nea, Non-effect Allele; Se, Standard Error

No competing interests reported.

supplementaryKC.pdf

African ancestry meta-analysis of genome-wide association studies identifies a novel locus and validates multiple loci associated with kidney function

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Methods

Meta-analysis

Fine-mapping

Exploring Functional Mapping And Annotation

FUMA analysis

Phewas Analysis

Magma Gene Set Analysis

Eqtl Analysis

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1