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

Diabetologia. 2022 Sep;65(9):1495-1509. doi: 10.1007/s00125-022-05735-0. Epub 2022 Jun 28.

Authors

Niina Sandholm^{1

2

3}, Joanne B Cole^{4

5

6}, Viji Nair⁷, Xin Sheng^{8

9

10}, Hongbo Liu^{8

9

10}, Emma Ahlqvist¹¹, Natalie van Zuydam^{12

13

14}, Emma H Dahlström^{1

2

3}, Damian Fermin⁷, Laura J Smyth¹⁵, Rany M Salem¹⁶, Carol Forsblom^{1

2

3}, Erkka Valo^{1

2

3}, Valma Harjutsalo^{1

2

3

17}, Eoin P Brennan¹⁸, Gareth J McKay¹⁵, Darrell Andrews¹⁸, Ross Doyle¹⁸, Helen C Looker¹⁹, Robert G Nelson¹⁹, Colin Palmer¹², Amy Jayne McKnight¹⁵, Catherine Godson¹⁸, Alexander P Maxwell^{15

20}, Leif Groop^{11

21}, Mark I McCarthy^{13

14}, Matthias Kretzler⁷, Katalin Susztak^{8

9

10}, Joel N Hirschhorn^{22

23

24}, Jose C Florez^{4

6

25}, Per-Henrik Groop^{26

27

28

29}; GENIE Consortium

Affiliations

¹ Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
² Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
³ Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
⁴ Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
⁶ Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁷ Michigan Medicine, Ann Arbor, MI, USA.
⁸ Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
⁹ Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
¹⁰ Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
¹¹ Department of Clinical Sciences, Lund University Diabetes Centre, Lund University and Skåne University Hospital, Malmö, Sweden.
¹² Pat Macpherson Centre for Pharmacogenetics & Pharmacogenomics, Cardiovascular & Diabetes Medicine, School of Medicine, University of Dundee, Dundee, UK.
¹³ Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
¹⁴ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
¹⁵ Molecular Epidemiology Research Group, Centre for Public Health, Queen's University Belfast, Belfast, UK.
¹⁶ Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
¹⁷ Finnish Institute for Health and Welfare, Helsinki, Finland.
¹⁸ Diabetes Complications Research Centre, Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland.
¹⁹ Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.
²⁰ Regional Nephrology Unit, Belfast City Hospital, Belfast, Northern Ireland, UK.
²¹ Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
²² Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.
²³ Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.
²⁴ Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA. Joel.Hirschhorn@childrens.harvard.edu.
²⁵ Department of Medicine, Harvard Medical School, Boston, MA, USA.
²⁶ Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland. per-henrik.groop@helsinki.fi.
²⁷ Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. per-henrik.groop@helsinki.fi.
²⁸ Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. per-henrik.groop@helsinki.fi.
²⁹ Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia. per-henrik.groop@helsinki.fi.

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 m²) 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 ).

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

Publication types

Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Diabetes Mellitus, Type 2* / complications
Diabetic Nephropathies* / metabolism
Doublecortin-Like Kinases
Fibrosis
Genome-Wide Association Study
Humans
Intracellular Signaling Peptides and Proteins / genetics
Kidney / metabolism
Polymorphism, Single Nucleotide / genetics
Protein Serine-Threonine Kinases / genetics

Substances

Intracellular Signaling Peptides and Proteins
DCLK1 protein, human
Doublecortin-Like Kinases
Protein Serine-Threonine Kinases

Abstract

Publication types

MeSH terms

Substances

Grants and funding