African ancestry-derived APOL1 risk genotypes show proximal epigenetic associations

Apolipoprotein L1 (APOL1) coding variants, termed G1 and G2, are established genetic risk factors for a growing spectrum of diseases, including kidney disease, in individuals of African ancestry. Evidence suggests that the risk variants, which show a recessive mode of inheritance, lead to toxic gain-of-function changes of the APOL1 protein. Disease occurrence and presentation vary, likely due to modifiers or second hits. To understand the role of the epigenetic landscape in relation to APOL1 risk variants, we performed methylation quantitative trait locus (meQTL) analysis to identify differentially methylated CpGs influenced by APOL1 risk variants in 611 African American individuals. We identified five CpGs that were significantly associated with APOL1 risk alleles in discovery and replication studies, and one CpG-APOL1 association was independent of other genomic variants. Our study highlights proximal DNA methylation alterations that may help explain the variable disease risk and clinical manifestation of APOL1 variants.

X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements

X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.

Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.

Association between acetaminophen use and vitiligo in US women and men

BACKGROUND/OBJECTIVES

Exposure to chemical phenols, which can act as tyrosine analogues and result in anti-melanocyte autoimmunity, has been associated with vitiligo. Acetaminophen (N-acetyl-p-aminophenol) is an over-the-counter analgesic of phenolic origin. The risk of vitiligo with systemic exposure to acetaminophen has not yet been evaluated.

METHODS

We examined the risk of vitiligo with regular use acetaminophen in women, the Nurses' Health Study (NHS) and in men, the Health Professionals Follow-up Study (HPFS). Regular acetaminophen use was asked biennially from 1990 in NHS and from 1986 in HPFS, and the year of clinician-diagnosed vitiligo was asked retrospectively in 2012 in the cohorts.

RESULTS

In NHS, a total of 161 vitiligo cases were identified during a follow-up of 571,724 person-years; in HPFS, a total of 183 vitiligo cases were identified during a follow-up of 680,313 person-years. Regular use of acetaminophen was associated with an increased vitiligo risk in NHS but not HPFS. The multivariable relative risk (RR) was 1.52 (95% confidence interval [CI] 1.03-2.25) in NHS and 1.09 (95% CI 0.76-1.55) in HPFS. The higher risk of vitiligo was similar by duration of acetaminophen use in women; the multivariable RRs were 1.47 (95% CI 0.98-2.21) for acetaminophen use under 5 years, and 1.78 (95% CI 1.11-2.84) for acetaminophen use over 5 years.

CONCLUSIONS

Acetaminophen may be associated with a higher risk of vitiligo in women.

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Ancestry-driven metabolite variation provides insights into disease states in admixed populations

BACKGROUND

Metabolic pathways are related to physiological functions and disease states and are influenced by genetic variation and environmental factors. Hispanics/Latino individuals have ancestry-derived genomic regions (local ancestry) from their recent admixture that have been less characterized for associations with metabolite abundance and disease risk.

METHODS

We performed admixture mapping of 640 circulating metabolites in 3887 Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Metabolites were quantified in fasting serum through non-targeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Replication was performed in 1856 nonoverlapping HCHS/SOL participants with metabolomic data.

RESULTS

By leveraging local ancestry, this study identified significant ancestry-enriched associations for 78 circulating metabolites at 484 independent regions, including 116 novel metabolite-genomic region associations that replicated in an independent sample. Among the main findings, we identified Native American enriched genomic regions at chromosomes 11 and 15, mapping to FADS1/FADS2 and LIPC, respectively, associated with reduced long-chain polyunsaturated fatty acid metabolites implicated in metabolic and inflammatory pathways. An African-derived genomic region at chromosome 2 was associated with N-acetylated amino acid metabolites. This region, mapped to ALMS1, is associated with chronic kidney disease, a disease that disproportionately burdens individuals of African descent.

CONCLUSIONS

Our findings provide important insights into differences in metabolite quantities related to ancestry in admixed populations including metabolites related to regulation of lipid polyunsaturated fatty acids and N-acetylated amino acids, which may have implications for common diseases in populations.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Metabolome-wide association study of estimated glomerular filtration rates in Hispanics

Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m². We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

Metabolomics Insights in Early Childhood Caries

Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log₂-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10^-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10^-6) and N-acetylneuraminate (p = 6.8 × 10^-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10^-6) and epicatechin (P = 2.9 × 10^-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry^1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific^4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations^11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions¹³-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study

Background

Chronic kidney disease (CKD) is common and disproportionally burdens United States ethnic minorities. Its genetic determinants may differ by disease severity and clinical stages. To uncover genetic factors associated CKD severity among high-risk ethnic groups, we performed genome-wide association studies (GWAS) in diverse populations within the Population Architecture using Genomics and Epidemiology (PAGE) study.

Methods

We assembled multi-ethnic genome-wide imputed data on CKD non-overlapping cases [4,150 mild to moderate CKD, 1,105 end-stage kidney disease (ESKD)] and non-CKD controls for up to 41,041 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian, and American Indians). We implemented a generalized estimating equation approach for GWAS using ancestry combined data while adjusting for age, sex, principal components, study, and ethnicity.

Results

The GWAS identified a novel genome-wide associated locus for mild to moderate CKD nearby NMT2 (rs10906850, p = 3.7 × 10^-8) that replicated in the United Kingdom Biobank white British (p = 0.008). Several variants at the APOL1 locus were associated with ESKD including the APOL1 G1 rs73885319 (p = 1.2 × 10^-9). There was no overlap among associated loci for CKD and ESKD traits, even at the previously reported APOL1 locus (p = 0.76 for CKD). Several additional loci were associated with CKD or ESKD at p-values below the genome-wide threshold. These loci were often driven by variants more common in non-European ancestry.

Conclusion

Our genetic study identified a novel association at NMT2 for CKD and showed for the first time strong associations of the APOL1 variants with ESKD across multi-ethnic populations. Our findings suggest differences in genetic effects across CKD severity and provide information for study design of genetic studies of CKD in diverse populations.