Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Niina Sandholm; Joanne B. Cole; Viji Nair; Xin Sheng; Hongbo Liu; Emma Ahlqvist; Natalie van Zuydam; Emma H. Dahlström; Damian Fermin; Laura J Smyth; Rany M Salem; Carol Forsblom; Erkka Valo; Valma Harjutsalo; Eoin P. Brennan; Gareth J. McKay; Darrell Andrews; Ross Doyle; Helen C. Looker; Robert G. Nelson; Colin Palmer; Amy Jayne McKnight; Catherine Godson; Alexander P. Maxwell; Leif Groop; Mark I. McCarthy; Matthias Kretzler; Katalin Susztak; Joel N. Hirschhorn; Jose C. Florez; Per-Henrik Groop; GENIE Consortium

doi:10.1007/s00125-022-05735-0

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Niina Sandholm, Joanne B. Cole, Viji Nair, Xin Sheng, Hongbo Liu, Emma Ahlqvist, Natalie van Zuydam, Emma H. Dahlström, Damian Fermin, Laura J Smyth, Rany M Salem, Carol Forsblom, Erkka Valo, Valma Harjutsalo, Eoin P. Brennan, Gareth J. McKay, Darrell Andrews, Ross Doyle, Helen C. Looker, Robert G. NelsonColin Palmer, Amy Jayne McKnight, Catherine Godson, Alexander P. Maxwell, Leif Groop, Mark I. McCarthy, Matthias Kretzler, Katalin Susztak, Joel N. Hirschhorn (Lead / Corresponding author), Jose C. Florez, Per-Henrik Groop (Lead / Corresponding author), GENIE Consortium

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Abstract

Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.

Methods: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.

Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m ²) and DKD (microalbuminuria or worse) phenotype (p=9.8×10 ⁻⁹; although not withstanding correction for multiple testing, p>9.3×10 ⁻⁹). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10 ⁻⁶). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10 ⁻⁶). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10 ⁻¹¹). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10 ⁻⁸] and negatively with tubulointerstitial fibrosis [p=2.0×10 ⁻⁹], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10 ⁻¹⁶], and SNX30 expression correlated positively with eGFR [p=5.8×10 ⁻¹⁴] and negatively with fibrosis [p<2.0×10 ⁻¹⁶]).

Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract: [Figure not available: see fulltext.].

Original language	English
Pages (from-to)	1495-1509
Number of pages	15
Journal	Diabetologia
Volume	65
Early online date	28 Jun 2022
DOIs	https://doi.org/10.1007/s00125-022-05735-0
Publication status	Published - Sept 2022

Keywords

Diabetes complications
Diabetic kidney disease
Genetics
Genome-wide association study; Meta-analysis; Transcriptomics

ASJC Scopus subject areas

Internal Medicine
Endocrinology, Diabetes and Metabolism

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Final Published VersionFinal published version, 2.17 MBLicence: CC BY

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Sandholm, N., Cole, J. B., Nair, V., Sheng, X., Liu, H., Ahlqvist, E., van Zuydam, N., Dahlström, E. H., Fermin, D., Smyth, L. J., Salem, R. M., Forsblom, C., Valo, E., Harjutsalo, V., Brennan, E. P., McKay, G. J., Andrews, D., Doyle, R., Looker, H. C., ... GENIE Consortium (2022). Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease. Diabetologia, 65, 1495-1509. https://doi.org/10.1007/s00125-022-05735-0

@article{58f71cbf74c24453b22907be8cf4d0f7,

title = "Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease",

abstract = "Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.Methods: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m 2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10 −9; although not withstanding correction for multiple testing, p>9.3×10 −9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10 −6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10 −6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10 −11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10 −8] and negatively with tubulointerstitial fibrosis [p=2.0×10 −9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10 −16], and SNX30 expression correlated positively with eGFR [p=5.8×10 −14] and negatively with fibrosis [p<2.0×10 −16]).Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract: [Figure not available: see fulltext.].",

keywords = "Diabetes complications, Diabetic kidney disease, Genetics, Genome-wide association study; Meta-analysis; Transcriptomics",

author = "Niina Sandholm and Cole, {Joanne B.} and Viji Nair and Xin Sheng and Hongbo Liu and Emma Ahlqvist and {van Zuydam}, Natalie and Dahlstr{\"o}m, {Emma H.} and Damian Fermin and Smyth, {Laura J} and Salem, {Rany M} and Carol Forsblom and Erkka Valo and Valma Harjutsalo and Brennan, {Eoin P.} and McKay, {Gareth J.} and Darrell Andrews and Ross Doyle and Looker, {Helen C.} and Nelson, {Robert G.} and Colin Palmer and McKnight, {Amy Jayne} and Catherine Godson and Maxwell, {Alexander P.} and Leif Groop and McCarthy, {Mark I.} and Matthias Kretzler and Katalin Susztak and Hirschhorn, {Joel N.} and Florez, {Jose C.} and Per-Henrik Groop and {GENIE Consortium}",

note = "Funding Information: Open Access funding provided by University of Helsinki including Helsinki University Central Hospital. This study was funded by JDRF (grants S-SRA-2014-276-Q-R and 17-2013-7) and by NIDDK R01 DK105154. Research in FinnDiane was supported by Folkh{\"a}lsan Research Foundation, Wilhelm and Else Stockmann Foundation, {\textquoteleft}Liv och H{\"a}lsa{\textquoteright} Society, Helsinki University Central Hospital Research Funds (EVO TYH2018207), Academy of Finland (299200 and 316664) and Novo Nordisk Foundation (NNF OC0013659). Acquisition of clinical data and samples from the Pima Indians was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases and by the ADA (Clinical Science Award 1-08-CR-42). JBC was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (K99DK127196). EA was funded by grants from the Swedish Research Council (2017-02688, 2020-02191) and the Novo Nordisk Foundation (NNF18OC0034408). Copyright: {\textcopyright} 2022. The Author(s).",

year = "2022",

month = sep,

doi = "10.1007/s00125-022-05735-0",

language = "English",

volume = "65",

pages = "1495--1509",

journal = "Diabetologia",

issn = "0012-186X",

publisher = "Springer Verlag",

}

Sandholm, N, Cole, JB, Nair, V, Sheng, X, Liu, H, Ahlqvist, E, van Zuydam, N, Dahlström, EH, Fermin, D, Smyth, LJ, Salem, RM, Forsblom, C, Valo, E, Harjutsalo, V, Brennan, EP, McKay, GJ, Andrews, D, Doyle, R, Looker, HC, Nelson, RG, Palmer, C, McKnight, AJ, Godson, C, Maxwell, AP, Groop, L, McCarthy, MI, Kretzler, M, Susztak, K, Hirschhorn, JN, Florez, JC, Groop, P-H & GENIE Consortium 2022, 'Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease', Diabetologia, vol. 65, pp. 1495-1509. https://doi.org/10.1007/s00125-022-05735-0

TY - JOUR

T1 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

AU - Sandholm, Niina

AU - Cole, Joanne B.

AU - Nair, Viji

AU - Sheng, Xin

AU - Liu, Hongbo

AU - Ahlqvist, Emma

AU - van Zuydam, Natalie

AU - Dahlström, Emma H.

AU - Fermin, Damian

AU - Smyth, Laura J

AU - Salem, Rany M

AU - Forsblom, Carol

AU - Valo, Erkka

AU - Harjutsalo, Valma

AU - Brennan, Eoin P.

AU - McKay, Gareth J.

AU - Andrews, Darrell

AU - Doyle, Ross

AU - Looker, Helen C.

AU - Nelson, Robert G.

AU - Palmer, Colin

AU - McKnight, Amy Jayne

AU - Godson, Catherine

AU - Maxwell, Alexander P.

AU - Groop, Leif

AU - McCarthy, Mark I.

AU - Kretzler, Matthias

AU - Susztak, Katalin

AU - Hirschhorn, Joel N.

AU - Florez, Jose C.

AU - Groop, Per-Henrik

AU - GENIE Consortium

N1 - Funding Information: Open Access funding provided by University of Helsinki including Helsinki University Central Hospital. This study was funded by JDRF (grants S-SRA-2014-276-Q-R and 17-2013-7) and by NIDDK R01 DK105154. Research in FinnDiane was supported by Folkhälsan Research Foundation, Wilhelm and Else Stockmann Foundation, ‘Liv och Hälsa’ Society, Helsinki University Central Hospital Research Funds (EVO TYH2018207), Academy of Finland (299200 and 316664) and Novo Nordisk Foundation (NNF OC0013659). Acquisition of clinical data and samples from the Pima Indians was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases and by the ADA (Clinical Science Award 1-08-CR-42). JBC was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (K99DK127196). EA was funded by grants from the Swedish Research Council (2017-02688, 2020-02191) and the Novo Nordisk Foundation (NNF18OC0034408). Copyright: © 2022. The Author(s).

PY - 2022/9

Y1 - 2022/9

N2 - Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.Methods: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m 2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10 −9; although not withstanding correction for multiple testing, p>9.3×10 −9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10 −6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10 −6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10 −11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10 −8] and negatively with tubulointerstitial fibrosis [p=2.0×10 −9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10 −16], and SNX30 expression correlated positively with eGFR [p=5.8×10 −14] and negatively with fibrosis [p<2.0×10 −16]).Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract: [Figure not available: see fulltext.].

AB - Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.Methods: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m 2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10 −9; although not withstanding correction for multiple testing, p>9.3×10 −9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10 −6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10 −6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10 −11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10 −8] and negatively with tubulointerstitial fibrosis [p=2.0×10 −9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10 −16], and SNX30 expression correlated positively with eGFR [p=5.8×10 −14] and negatively with fibrosis [p<2.0×10 −16]).Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract: [Figure not available: see fulltext.].

KW - Diabetes complications

KW - Diabetic kidney disease

KW - Genetics

KW - Genome-wide association study; Meta-analysis; Transcriptomics

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DO - 10.1007/s00125-022-05735-0

M3 - Article

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SN - 0012-186X

VL - 65

SP - 1495

EP - 1509

JO - Diabetologia

JF - Diabetologia

ER -

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

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UN SDGs

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Pulpotomy for the Management of Irreversible Pulpitis (PIPS)

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Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Abstract

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ASJC Scopus subject areas

UN SDGs

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Other files and links

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