Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk (original) (raw)

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Acknowledgements

The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration between the HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology NTNU), the Nord-Trøndelag County Council, the Central Norway Health Authority and the Norwegian Institute of Public Health. C.J.W. is supported by HL094535 and HL109946 from the National Heart, Lung and Blood Institute. M.B. is supported by DK062370 from the National Institute of Diabetes and Digestive and Kidney Diseases. Y.E.C. is supported by HL068878 and HL117491 from the National Heart, Lung and Blood Institute. G.R.A. is supported by HL117626 from the National Heart, Lung and Blood Institute and HG007022 from the National Human Genome Research Institute. For frequency look up for the RNF111 variant, we thank the following: R. Loos and K. Lu of the BioMe Clinical Care Cohort operated by The Charles Bronfman Institute for Personalized Medicine (IPM) at the Mount Sinai Medical Center (the Mount Sinai IPM Biobank Program is supported by The Andrea and Charles Bronfman Philanthropies); A. Metspalu of the Estonian Genome Center (the Estonian Biobank data were provided by E. Mihailov from the Estonian Genome Center of University of Tartu, Estonia); and A. Uitterlinden, F. Rivadeneira and K. Estrada of Erasmus University Rotterdam.

Author information

Author notes

  1. Oddgeir L Holmen, He Zhang and Yanbo Fan: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
    Oddgeir L Holmen, Arnulf Langhammer & Kristian Hveem
  2. St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
    Oddgeir L Holmen
  3. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
    He Zhang, Yanbo Fan, Daniel H Hovelson, Ellen M Schmidt, Wei Zhou, Yanhong Guo, Ji Zhang, Santhi K Ganesh, Jifeng Zhang, Jin Chen, Y Eugene Chen & Cristen J Willer
  4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
    Daniel H Hovelson, Ellen M Schmidt & Cristen J Willer
  5. Department of Community Medicine, Epidemiology of Chronic Diseases Research Group, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
    Maja-Lisa Løchen, Tom Wilsgaard & Inger Njølstad
  6. Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
    Santhi K Ganesh & Cristen J Willer
  7. Department of Public Health, Norwegian University of Science and Technology, Trondheim, Norway
    Lars Vatten
  8. Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
    Frank Skorpen
  9. Department of Medicine, Levanger Hospital, Nord-Trøndelag Health Trust, Levanger, Norway
    Håvard Dalen, Carl Platou & Kristian Hveem
  10. Department of Circulation and Medical Imaging, Medical Imaging Laboratory for Innovative Future Healthcare, Norwegian University of Science and Technology, Trondheim, Norway
    Håvard Dalen
  11. Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
    Subramaniam Pennathur
  12. Department of Clinical Medicine, Brain and Circulation Research Group, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
    Ellisiv B Mathiesen
  13. Brain and Circulation Research Group, University Hospital of North Norway, Tromsø, Norway
    Ellisiv B Mathiesen
  14. Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
    Michael Boehnke & Gonçalo R Abecasis

Authors

  1. Oddgeir L Holmen
  2. He Zhang
  3. Yanbo Fan
  4. Daniel H Hovelson
  5. Ellen M Schmidt
  6. Wei Zhou
  7. Yanhong Guo
  8. Ji Zhang
  9. Arnulf Langhammer
  10. Maja-Lisa Løchen
  11. Santhi K Ganesh
  12. Lars Vatten
  13. Frank Skorpen
  14. Håvard Dalen
  15. Jifeng Zhang
  16. Subramaniam Pennathur
  17. Jin Chen
  18. Carl Platou
  19. Ellisiv B Mathiesen
  20. Tom Wilsgaard
  21. Inger Njølstad
  22. Michael Boehnke
  23. Y Eugene Chen
  24. Gonçalo R Abecasis
  25. Kristian Hveem
  26. Cristen J Willer

Contributions

A.L., I.N., K.H., H.D., C.P., E.B.M., T.W., L.V., F.S., M.-L.L. and O.L.H. obtained, contributed and analyzed the phenotype data. O.L.H. and T.W. were responsible for sample selection. O.L.H. and H.Z. were responsible for genetic data analysis and interpretation. H.Z. and J.C. performed variant calling from sequence data. D.H.H., E.M.S. and W.Z. generated figures and performed secondary analyses. L.V., M.-L.L., S.K.G., A.L., E.B.M., I.N. and K.H. provided epidemiological expertise. H.D. and C.P. provided clinical expertise. G.R.A., F.S. and M.B. provided genotyping and genetic epidemiology expertise. Y.F., Y.G., Ji Zhang, S.P. and Jifeng Zhang conducted mouse experiments under the supervision of Y.E.C. with assistance from C.J.W. C.J.W., G.R.A. and O.L.H. drafted the manuscript with assistance from D.H.H., H.Z., M.B. and K.H. Y.F., E.M.S., W.Z., Y.G., Ji Zhang, Jifeng Zhang, A.L., M.-L.L., S.K.G., L.V., F.S., H.D., J.C., C.P., E.B.M., T.W., I.N. and Y.E.C. critically reviewed the manuscript and provided comments and feedback. C.J.W., O.L.H. and K.H. conceived the study. C.J.W., O.L.H., K.H., M.B. and G.R.A. designed the study. K.H. and C.J.W. provided overall leadership for the project.

Corresponding authors

Correspondence toKristian Hveem or Cristen J Willer.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Effect sizes for known lipid index SNPs from non-fasting HUNT samples are correlated with estimates from large GWAS

This figure shows similarity in effect sizes estimated from non-fasting HUNT samples (N=5,643) and from large population-based cohorts used in GWAS (N=196k). The effect sizes estimated from HUNT samples (N=5,643) are correlated with the previously published effect sizes (LDL cholesterol r2 = 0.46, HDL cholesterol r2 = 0.75, Total cholesterol r2 = 0.47, Triglycerides r2 = 0.84).

Supplementary Figure 2 Quantile-quantile plots for single-variant analysis results of lipid traits

Quantile-quantile plots (QQ) for single variant association analysis results for lipid traits in Stage 1 samples (N=5,643). We display separate Q-Q curves for previously known GWAS variants as published by the Global Lipid Genetic Consortium1 (red), variants within 500kb from a GWAS variant (blue), and association for other coding variants >500kb away from lead GWAS SNPs (black). 95% confidence intervals for the null hypothesis of no association are shown in grey. HDL, high-density lipoprotein; LDL, low-density lipoprotein. Genomic control lambda values were: HDL λGC = 1.06; LDL λGC = 1.04; TC λGC = 1.08; TG λGC = 1.04.

Supplementary Figure 3 Regional association plot of the LIPC locus

The regional association plots show the LIPC region on chromosome 15. The –log10 P value for association with HDL cholesterol is shown for several data sets: (a) Global Lipids Genetics Consortium GWAS results 4 in ∼95,000, (b) association in Stage 1 samples (N=5,643), and (c) association in Stage 1 samples conditioning on association with rs113298164 (LIPC p.Thr405Met). Supplementary Figure 3a shows that the LIPC p.Thr405Met and RNF111 p.Pro836Ser were not observed in GWAS. Comparison of Supplementary Figure 3b and the conditional analysis in Supplementary Figure 3c demonstrates that the association signal at RNF111 p.Pro836Ser is due to linkage disequilibrium with LIPC p.Thr405Met. Furthermore, the association signal at the variants discovered by GWAS as two independent signals (rs1532085 and rs261334) remain strongly associated after conditioning on p.Thr405Met, suggesting the association at these two SNPs is independent from p.Thr405Met.

Supplementary Figure 4 Regional association plot of the TM6SF2 locus

This figure shows a LocusZoom plot of the –log10 P value for association with total cholesterol levels for all markers represented on the exome array in the TM6SF2 region by position on the chromosome. Color-coded linkage disequilibrium (LD) metrics r2 using HapMap CEU values are provided for the most significant association to reflect their LD with the lead SNP. SNPs are functionally annotated coding (square) or other (circle), and lead SNPs are colored purple. (a) GWAS P-values as published by Teslovich et al. (N >100,000)4 showing an absence of data for the TM6SF2 coding variant. (b) Association as observed in Stage 1 samples (N = 5,643) demonstrating the most significant association for the TM6SF2 variant (in purple) and the GWAS variant (in red), with less significant association for the NCAN coding variant (yellow).

Supplementary Figure 5 Expression pattern of endogenous TM6SF2 in wild-type C57BL/6J mouse

This figure shows the tissue distribution pattern of TM6SF2 in C57BL/6J mouse. Endogenous Tm6sf2 was highly expressed in the liver at both mRNA and protein levels. (a) The mRNA level of Tm6sf2 was determined by Northern blotting. The ethidium bromide (EB) staining of 28S and 18S Ribosomal RNAs (rRNA) was used as a positive control. (b) The protein level of TM6SF2 in tissues was detected by Western blotting, and the fast green staining of blot served as an internal control. Tissues were collected from wild-type C57BL/6J mouse. SKM refers to skeletal muscle.

Supplementary Figure 6 No evidence for accumulation of triglyceride in mouse liver following adenovirus overexpression of TM6SF2 or sh_Tm6sf2_

This figure show representative photomicrographs of liver sections stained with Oil red O after mice were fasted overnight. We found no evidence of triglyceride accumulation -in mouse liver in any experimental or control mouse, suggesting that neither TM6SF2 expression changes nor adenovirus injection caused any liver damage. In total, 20 animals were analyzed, 5 in each study group (Ad-LacZ, Ad-TM6SF2, Ad-shLacZ, Ad-sh_TM6SF2_). A total of 10 tissue sections were analyzed for each animal. Scale bars in black, 2 mm.

Supplementary Figure 7 Adenovirus-mediated TM6SF2 overexpression and knockdown does not elevate alanine aminotransferase

This figure shows no significant differences in ALT levels in experimental vs. control mice after either overexpression or knockdown of Tm6sf2. ALT activity in mice was determined in serum collected from overnight-fasted wild-type mice and experimental mice including Ad-LacZ, Ad-TM6SF2, Ad-sh_LacZ_ and Ad-sh_TM6SF2_ mice. No significant differences were observed between any experimental group and the control group (no adenovirus injection). 25 animals were analyzed, 5 in each study group.

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Holmen, O., Zhang, H., Fan, Y. et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk.Nat Genet 46, 345–351 (2014). https://doi.org/10.1038/ng.2926

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