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Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification

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Abstract

Aims/hypothesis

Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD.

Methods

We exploited a novel algorithm, ‘Bag of Naive Bayes’, whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK–Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US).

Results

Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case–control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno.

Conclusions/interpretation

This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

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Abbreviations

ACR:

Albumin/creatinine ratio

BAI:

Body adiposity index

BoNB:

Bag of Naive Bayes

DBP:

Diastolic blood pressure

eGFR:

Estimated GFR

ESRD:

End-stage renal disease

FDR:

False discovery rate

FinnDiane:

Finnish Diabetic Nephropathy Study

GENIE:

Genetics of Nephropathy–an International Effort

GoKinD US:

Genetics of Kidneys in Diabetes US Study

GWAS:

Genome-wide association study

MCC:

Matthews correlation coefficient

NBC:

Naive Bayes classifier

SBP:

Systolic blood pressure

SNP:

Single nucleotide polymorphism

UK-ROI:

All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK and Republic of Ireland)

References

  1. Andersen AR, Christiansen JS, Andersen JK, Kreiner S, Deckert T (1983) Diabetic nephropathy in type 1 (insulin-dependent) diabetes: an epidemiological study. Diabetologia 25:496–501

    Article  CAS  PubMed  Google Scholar 

  2. Groop PH, Thomas MC, Moran JL et al (2009) The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58:1651

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Parving HH (1996) Initiation and progression of diabetic nephropathy. N Engl J Med 335:1682–1683

    Article  CAS  PubMed  Google Scholar 

  4. Rossing P, Hougaard P, Parving HH (2002) Risk factors for development of incipient and overt diabetic nephropathy in type 1 diabetic patients: a 10-year prospective observational study. Diabetes Care 25:859–864

    Article  PubMed  Google Scholar 

  5. Forsblom C, Harjutsalo V, Thorn LM et al (2011) Competing-risk analysis of ESRD and death among patients with type 1 diabetes and macroalbuminuria. J Am Soc Nephrol 22:537–544

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Seaquist ER, Goetz FC, Rich S, Barbosa J (1989) Familial clustering of diabetic kidney disease. N Engl J Med 320:1161–1165

    Article  CAS  PubMed  Google Scholar 

  7. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J (2004) Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes 53:2449–2454

    Article  CAS  PubMed  Google Scholar 

  8. Pezzolesi MG, Poznik GD, Mychaleckyj JC et al (2009) Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes. Diabetes 58:1403–1410

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Craig DW, Millis MP, DiStefano JK (2009) Genome-wide SNP genotyping study using pooled DNA to identify candidate markers mediating susceptibility to end-stage renal disease attributed to Type 1 diabetes. Diabet Med 26:1090–1098

    Article  CAS  PubMed  Google Scholar 

  10. Sandholm N, Salem RM, McKnight AJ et al (2012) New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet 8:e1002921

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Bansal V, Libiger O, Torkamani A, Schork NJ (2010) Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773–785

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Thorn LM, Forsblom C, Fagerudd J et al (2005) Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care 28:2019–2024

    Article  PubMed  Google Scholar 

  13. Sambo F, Trifoglio E, Di Camillo B, Toffolo GM, Cobelli C (2012) Bag of Naive Bayes: biomarker selection and classification from genome-wide SNP data. BMC Bioinforma 13(Suppl 14):S2

    Article  Google Scholar 

  14. Tarnow L, Groop PH, Hadjadj S et al (2008) European rational approach for the genetics of diabetic complications—EURAGEDIC: patient populations and strategy. Nephrol Dial Transplant 23:161–168

    Article  CAS  PubMed  Google Scholar 

  15. Makinen VP, Forsblom C, Thorn LM et al (2008) Metabolic phenotypes, vascular complications, and premature deaths in a population of 4,197 patients with type 1 diabetes. Diabetes 57:2480–2487

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Bergman RN, Stefanovski D, Buchanan TA et al (2011) A better index of body adiposity. Obesity (Silver Spring) 19:1083–1089

    Article  PubMed Central  Google Scholar 

  17. Niemi J, Makinen VP, Heikkonen J et al (2009) Estimation of VLDL, IDL, LDL, HDL2, apoA-I, and apoB from the Friedewald inputs—apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes. Ann Med 41:451–461

    Article  CAS  PubMed  Google Scholar 

  18. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499–502

    CAS  PubMed  Google Scholar 

  19. Levey AS, Stevens LA (2010) Estimating GFR using the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation: more accurate GFR estimates, lower CKD prevalence estimates, and better risk predictions. Am J Kidney Dis 55:622–627

    Article  PubMed Central  PubMed  Google Scholar 

  20. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  22. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5:e1000529

    Article  PubMed Central  PubMed  Google Scholar 

  23. 1000 Genomes Project Consortium, Abecasis GR, Auton A et al (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491:56–65

    Article  PubMed  Google Scholar 

  24. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424

    Article  CAS  PubMed  Google Scholar 

  25. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300

    Google Scholar 

  27. O'Connor BP, Eun SY, Ye Z et al (2008) Semaphorin 6D regulates the late phase of CD4+ T cell primary immune responses. Proc Natl Acad Sci U S A 105:13015–13020

    Article  PubMed Central  PubMed  Google Scholar 

  28. Zhou T, He X, Cheng R et al (2012) Implication of dysregulation of the canonical wingless-type MMTV integration site (WNT) pathway in diabetic nephropathy. Diabetologia 55:255–266

    Article  CAS  PubMed  Google Scholar 

  29. Stark K, Vainio S, Vassileva G, McMahon AP (1994) Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4. Nature 372:679–683

    Article  CAS  PubMed  Google Scholar 

  30. Kispert A, Vainio S, McMahon AP (1998) Wnt-4 is a mesenchymal signal for epithelial transformation of metanephric mesenchyme in the developing kidney. Development 125:4225–4234

    CAS  PubMed  Google Scholar 

  31. Surendran K, McCaul SP, Simon TC (2002) A role for Wnt-4 in renal fibrosis. Am J Physiol Renal Physiol 282:F431–F441

    CAS  PubMed  Google Scholar 

  32. Terada Y, Tanaka H, Okado T et al (2003) Expression and function of the developmental gene Wnt-4 during experimental acute renal failure in rats. J Am Soc Nephrol 14:1223–1233

    Article  CAS  PubMed  Google Scholar 

  33. Lin CL, Wang JY, Huang YT, Kuo YH, Surendran K, Wang FS (2006) Wnt/beta-catenin signaling modulates survival of high glucose-stressed mesangial cells. J Am Soc Nephrol 17:2812–2820

    Article  CAS  PubMed  Google Scholar 

  34. Moore JH, Asselbergs FW, Williams SM (2010) Bioinformatics challenges for genome-wide association studies. Bioinformatics 26:445–455

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Gibson G (2012) Rare and common variants: twenty arguments. Nat Rev Genet 13:135–145

    Article  CAS  PubMed  Google Scholar 

  36. Mukhopadhyay I, Feingold E, Weeks DE, Thalamuthu A (2010) Association tests using kernel-based measures of multi-locus genotype similarity between individuals. Genet Epidemiol 34:213–221

    PubMed Central  PubMed  Google Scholar 

  37. Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 91:224–237

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Di Camillo B, Sambo F, Toffolo G, Cobelli C (2013) ABACUS: an entropy based cumulative bivariate statistic robust to rare variants and different direction of genotype effect. Bioinformatics 30:384–391

    PubMed  Google Scholar 

  39. Di Camillo B, Sanavia T, Martini M et al (2012) Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment. PLoS One 7:e32200

    Article  PubMed Central  PubMed  Google Scholar 

  40. Sebastiani P, Ramoni MF, Nolan V, Baldwin CT, Steinberg MH (2005) Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet 37:435–440

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Sebastiani P, Solovieff N, Dewan AT et al (2012) Genetic signatures of exceptional longevity in humans. PLoS One 7:e29848

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Malovini A, Barbarini N, Bellazzi R, de Michelis F (2012) Hierarchical naive Bayes for genetic association studies. BMC Bioinforma 13(Suppl 14):S6

    Article  Google Scholar 

Download references

Acknowledgements

We thank M. Parkkonen, A. Sandelin, A.-Reetta Salonen, T. Soppela and J. Tuomikangas for skilful laboratory assistance. We also thank all the patients who participated in the study and gratefully acknowledge all the physicians and nurses at each centre involved in the recruitment of participants (ESM Table 5). The members of the GENIE Consortium are listed in ESM Table 6.

Funding

The FinnDiane Study was supported by grants from the Folkhälsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och Hälsa Foundation, Helsinki University Central Hospital Research Funds (EVO), the Sigrid Juselius Foundation, the Signe and Arne Gyllenberg Foundation, Finska Läkaresällskapet, the Novo Nordisk Foundation, the Academy of Finland (134379) and Tekes. The research was supported by the European Union's Seventh Framework Program (FP7/2007-2013) for the Innovative Medicine Initiative under grant agreement IMI/115006 (the SUMMIT consortium). The GENIE consortium was funded by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (NIH NIDDK) R01DK081923 and the Northern Ireland Research and Development Office. M. Stavarachi was supported by the Sectoral Operational Programme Human Resources Development (SOP HRD), financed from the European Social Fund and by the Romanian Government under contract number POSDRU/89/1.5/S/64109. V.-P. Mäkinen was supported by American Heart Association (13POST17240095).

Duality of interest

P-HG has received lecture honoraria from Abbot, Boehringer Ingelheim, Cebix, Eli Lilly, Genzyme, Novartis, Novo Nordisk and MSD, and research grants from Eli Lilly, Roche. P-HG is also an advisory board member for Boehringer Ingelheim, Novartis and Medscape.

Contribution statement

FS, AM, NS and M. Stavarachi analysed data and wrote the manuscript. CF contributed to the conception and design and acquisition of data, and revised the manuscript critically. V-PM contributed to the conception and design and revised the manuscript critically. VH, RL, DG, MP, M. Saraheimo, LMT, NT, JW, BH, A-MÖ, JT, ML, AJM, LT, KT, NMP and P-HG contributed to the acquisition of the genotypic and/or phenotypic data and revised the manuscript critically. RMS and AJM analysed data and revised the manuscript. NB, BD and GMT contributed to the interpretation of the results, revised and edited the manuscript. RB, CC and P-HG designed and supervised the study and revised the manuscript. All the authors approved the final version of the manuscript. P-HG is the guarantor of this work.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Francesco Sambo, Barbara Di Camillo, Gianna M. Toffolo & Claudio Cobelli

  2. Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy

    Alberto Malovini, Nicola Barbarini & Riccardo Bellazzi

  3. IRCCS Fondazione Salvatore Maugeri, Pavia, Italy

    Alberto Malovini

  4. Folkhälsan Institute of Genetics, Folkhälsan Research Centre, Helsinki, Finland

    Niina Sandholm, Monica Stavarachi, Carol Forsblom, Valma Harjutsalo, Raija Lithovius, Daniel Gordin, Maija Parkkonen, Markku Saraheimo, Lena M. Thorn, Nina Tolonen, Johan Wadén, Nicolae M. Panduru & Per-Henrik Groop

  5. Division of Nephrology, Department of Medicine, Helsinki University Central Hospital, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FI-00014, Helsinki, Finland

    Niina Sandholm, Carol Forsblom, Valma Harjutsalo, Raija Lithovius, Daniel Gordin, Maija Parkkonen, Markku Saraheimo, Lena M. Thorn, Nina Tolonen, Johan Wadén & Per-Henrik Groop

  6. Department of Biomedical Engineering and Computational Science, Aalto University, School of Science, Helsinki, Finland

    Niina Sandholm

  7. Department of Genetics, University of Bucharest, Bucharest, Romania

    Monica Stavarachi

  8. Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA

    Ville-Petteri Mäkinen

  9. South Australian Health and Medical Research Institute, Adelaide, Australia

    Ville-Petteri Mäkinen

  10. Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland

    Valma Harjutsalo, Jaako Tuomilehto & Karl Tryggvason

  11. Division of Matrix Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Bing He & Anne-May Österholm

  12. Centre for Vascular Prevention, Danube-University Krems, Krems, Austria

    Jaako Tuomilehto

  13. Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia

    Jaako Tuomilehto

  14. Clinical Research Department, Steno Diabetes Centre, Gentofte, Denmark

    Maria Lajer

  15. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Rany M. Salem

  16. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    Rany M. Salem

  17. Department of Endocrinology, Children’s Hospital Boston, Boston, MA, USA

    Rany M. Salem

  18. Nephrology Research, Centre for Public Health, Queen’s University of Belfast, Belfast, UK

    Amy Jayne McKnight

  19. Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark

    Lise Tarnow

  20. Research Unit, Nordsjaellands Hospital, Hilleroed, Denmark

    Lise Tarnow

  21. Department of Pathophysiology, 2nd Clinical Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania

    Nicolae M. Panduru

  22. Baker IDI Heart & Diabetes Institute, Melbourne, Australia

    Per-Henrik Groop

Authors
  1. Francesco Sambo
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  2. Alberto Malovini
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  3. Niina Sandholm
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  4. Monica Stavarachi
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  5. Carol Forsblom
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  6. Ville-Petteri Mäkinen
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  7. Valma Harjutsalo
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  8. Raija Lithovius
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  9. Daniel Gordin
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  10. Maija Parkkonen
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  11. Markku Saraheimo
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  12. Lena M. Thorn
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  13. Nina Tolonen
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  14. Johan Wadén
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  15. Bing He
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  16. Anne-May Österholm
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  17. Jaako Tuomilehto
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  18. Maria Lajer
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  19. Rany M. Salem
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  20. Amy Jayne McKnight
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  21. Lise Tarnow
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  22. Nicolae M. Panduru
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  23. Nicola Barbarini
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  24. Barbara Di Camillo
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  25. Gianna M. Toffolo
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  26. Karl Tryggvason
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  27. Riccardo Bellazzi
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  28. Claudio Cobelli
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  29. Per-Henrik Groop
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Consortia

The GENIE Consortium

The FinnDiane Study Group

Corresponding author

Correspondence to Per-Henrik Groop.

Additional information

F. Sambo, A. Malovini, N. Sandholm and M. Stavarachi contributed equally to this work.

The list of GENIE members is provided in the electronic supplementary material.

Electronic supplementary material

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ESM Table 1

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ESM Table 2

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ESM Table 3

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ESM Table 4

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ESM Table 5

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ESM Table 6

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Sambo, F., Malovini, A., Sandholm, N. et al. Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification. Diabetologia 57, 1611–1622 (2014). https://doi.org/10.1007/s00125-014-3256-2

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