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Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability

Nature Genetics volume 49, pages 1398–1402 (2017)Cite this article

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Abstract

Sequence variants that affect mean fasting glucose levels do not necessarily affect risk for type 2 diabetes (T2D). We assessed the effects of 36 reported glucose-associated sequence variants1 on between- and within-subject variance in fasting glucose levels in 69,142 Icelanders. The variant in TCF7L2 that increases fasting glucose levels increases between-subject variance (5.7% per allele, P = 4.2 × 10−10), whereas variants in GCK and G6PC2 that increase fasting glucose levels decrease between-subject variance (7.5% per allele, P = 4.9 × 10−11 and 7.3% per allele, P = 7.5 × 10−18, respectively). Variants that increase mean and between-subject variance in fasting glucose levels tend to increase T2D risk, whereas those that increase the mean but reduce variance do not (r2 = 0.61). The variants that increase between-subject variance increase fasting glucose heritability estimates. Intuitively, our results show that increasing the mean and variance of glucose levels is more likely to cause pathologically high glucose levels than increase in the mean offset by a decrease in variance.

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Figure 1: Effects of 36 published fasting-glucose-associated variants on between-subject and within-subject variance in fasting glucose levels and between-subject variance in HbA1c levels.
Figure 2: Effects of 36 published fasting-glucose-associated variants on fasting glucose and HbA1c, and between-subject variance in fasting glucose and HbA1c versus their effects on type 2 diabetes risk.
Figure 3: The percentage of type 2 diabetes cases in each quantile of the genetic risk scores.
Figure 4: Covariance between genotype-concordant relative pairs.

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References

  1. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  Google Scholar 

  2. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  Google Scholar 

  3. Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

    Article  CAS  Google Scholar 

  4. Manning, A.K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

    Article  CAS  Google Scholar 

  5. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    Article  CAS  Google Scholar 

  6. Gaulton, K.J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

    Article  CAS  Google Scholar 

  7. Rönnegård, L. & Valdar, W. Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genet. 13, 63 (2012).

    Article  Google Scholar 

  8. Wei, W.-H. et al. Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis. Sci. Rep. 6, 25014 (2016).

    Article  CAS  Google Scholar 

  9. Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).

    Article  CAS  Google Scholar 

  10. Topless, R.K. et al. Association of SLC2A9 genotype with phenotypic variability of serum urate in pre-menopausal women. Front. Genet. 6, 313 (2015).

    Article  Google Scholar 

  11. Paré, G., Cook, N.R., Ridker, P.M. & Chasman, D.I. On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study. PLoS Genet. 6, e1000981 (2010).

    Article  Google Scholar 

  12. Perry, G.M.L. et al. Sex modifies genetic effects on residual variance in urinary calcium excretion in rat (Rattus norvegicus). Genetics 191, 1003–1013 (2012).

    Article  CAS  Google Scholar 

  13. Mackay, T.F. & Lyman, R.F. Drosophila bristles and the nature of quantitative genetic variation. Phil. Trans. R. Soc. Lond. B 360, 1513–1527 (2005).

    Article  CAS  Google Scholar 

  14. Shen, X., Pettersson, M., Rönnegård, L. & Carlborg, Ö. Inheritance beyond plain heritability: variance-controlling genes in Arabidopsisthaliana. PLoS Genet. 8, e1002839 (2012).

    Article  CAS  Google Scholar 

  15. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294–298 (2014).

    Article  CAS  Google Scholar 

  16. Grant, S.F. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38, 320–323 (2006).

    Article  CAS  Google Scholar 

  17. Perry, J.R. et al. Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases. PLoS Genet. 8, e1002741 (2012).

    Article  CAS  Google Scholar 

  18. Cauchi, S. et al. The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies. BMC Med. Genet. 9, 45 (2008).

    Article  Google Scholar 

  19. Azizi, F. et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials 10, 5 (2009).

    Article  Google Scholar 

  20. Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    Article  CAS  Google Scholar 

  21. Falconer, D.S. & Mackay, T.F.C. in Introduction to Quantitative Genetics 4th edn, Ch. 10 (Pearson, 1996).

  22. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    Article  CAS  Google Scholar 

  23. Mitchell, R.K. et al. Selective disruption of Tcf7l2 in the pancreatic β cell impairs secretory function and lowers β cell mass. Hum. Mol. Genet. 24, 1390–1399 (2015).

    Article  CAS  Google Scholar 

  24. Gudbjartsson, D.F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    Article  CAS  Google Scholar 

  25. Hastie, T. & Tibshirani, R. Generalized additive models. Stat. Sci. 1, 297–310 (1986).

    Article  Google Scholar 

  26. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank the subjects of the Icelandic deCODE study and the Iranian study for their participation. We also thank the staff at deCODE Genetics core facilities and all our colleagues for their contributions to this work. This research project has been supported by grant no. 121 NRCI Research Project and with the support of the National Research Council of the Islamic Republic of Iran.

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Authors and Affiliations

  1. deCODE Genetics/Amgen, Inc., Reykjavik, Iceland

    Erna V Ivarsdottir, Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Patrick Sulem, Hilma Holm, Snaevar Sigurdsson, Unnur Thorsteinsdottir, Daniel F Gudbjartsson & Kari Stefansson

  2. School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland

    Erna V Ivarsdottir & Daniel F Gudbjartsson

  3. Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Maryam S Daneshpour

  4. Department of Endocrinology and Metabolic Medicine, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland

    Astradur B Hreidarsson, Gunnar Sigurdsson & Rafn Benediktsson

  5. Children's Medical Center, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland

    Ragnar Bjarnason & Arni V Thorsson

  6. Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland

    Ragnar Bjarnason, Rafn Benediktsson, Unnur Thorsteinsdottir & Kari Stefansson

  7. Laboratory in Mjodd, RAM, Reykjavik, Iceland

    Gudmundur Eyjolfsson

  8. Department of Clinical Biochemistry, Akureyri Hospital, Akureyri, Iceland

    Olof Sigurdardottir

  9. Department of Clinical Biochemistry, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland

    Isleifur Olafsson

  10. Iranian Molecular Medicine Network, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

    Sirous Zeinali

  11. Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Fereidoun Azizi

Authors
  1. Erna V Ivarsdottir
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Contributions

E.V.I., V.S., P.S., H.H., U.T., D.F.G. and K.S. designed the study and interpreted the results. E.V.I. and D.F.G. performed statistical analysis. M.S.D., G.T., S.S., A.B.H., G.S., R. Bjarnason, A.V.T., R. Benediktsson, G.E., O.S., I.O., S.Z. and F.A. performed recruitment and phenotyping. The manuscript was drafted by E.V.I., V.S., D.F.G. and K.S. All authors contributed to the final version of the manuscript.

Corresponding authors

Correspondence to Daniel F Gudbjartsson or Kari Stefansson.

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Competing interests

E.V.I., V.S., G.T., P.S., H.H., S.S., U.T., D.F.G. and K.S. are employees of deCODE Genetics/Amgen, Inc.

Integrated supplementary information

Supplementary Figure 1 Mean and variance of glucose levels against age.

(a) Average of fasting and non-fasting glucose levels (mM) for all measurements on subjects belonging to a 5-year age interval (age = 20 for individuals between 16 and 20 years) against age, for both sexes. (b) Variance of fasting and non-fasting glucose levels against age, for both sexes.

Supplementary Figure 2 BMI interaction effect on fasting glucose.

The BMI interaction effect of 36 published fasting-glucose-associated variants on fasting glucose against their effect on fasting glucose between-subject variance.

Supplementary Figure 3 Effect of glucose-associated variants on mean and between-subject variance in HbA1c levels.

(a) Effect of 36 published fasting-glucose-associated variants on HbA1c and between-subject variance in HbA1c. Variants are colored blue if they significantly decrease the variance and red if they significantly increase it. (b) Effect of 36 published fasting-glucose-associated variants on HbA1c between-subject variance against fasting glucose between-subject variance.

Supplementary Figure 4 Glucose-associated variants and their effect on between-subject variance in HbA1c levels.

(a) Effect of 36 published fasting-glucose-associated variants on HbA1c between-subject variance (I) against the effect on HbA1c between-subject variance after removing all subjects with levels below 4.5 and above 6.5 (II). (b) Effect of 36 published fasting-glucose-associated variants on HbA1c between-subject variance (I) against the effect on HbA1c between-subject variance after setting values above 6.5 and below 4.5 to the respective boundary values (III).

Supplementary Figure 5 HbA1c variants and their effect on between-subject variance in HbA1c levels.

(a) Effect of eight published HbA1c-associated variants on HbA1c between-subject variance (I) against the effect on HbA1c between-subject variance after removing all subjects with levels below 4.5 and above 6.5 (II). (b) Effect of eight published HbA1c-associated variants on HbA1c between-subject variance (I) against the effect on HbA1c between-subject variance after setting values above 6.5 and below 4.5 to the respective boundary values (III).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1–15 and Supplementary Note (PDF 1990 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Code

Code to detect variance effects. (TXT 3 kb)

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Ivarsdottir, E., Steinthorsdottir, V., Daneshpour, M. et al. Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability. Nat Genet 49, 1398–1402 (2017). https://doi.org/10.1038/ng.3928

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