Meta-analysis of gene-level tests for rare variant association (original) (raw)

References

  1. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
  2. Kiezun, A. et al. Exome sequencing and the genetic basis of complex traits. Nat. Genet. 44, 623–630 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  3. Li, B. & Leal, S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  4. Kryukov, G.V., Shpunt, A., Stamatoyannopoulos, J.A. & Sunyaev, S.R. Power of deep, all-exon resequencing for discovery of human trait genes. Proc. Natl. Acad. Sci. USA 106, 3871–3876 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  5. Morris, A.P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010).
    Article PubMed Google Scholar
  6. Price, A.L. et al. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86, 832–838 (2010).
    Article PubMed PubMed Central Google Scholar
  7. Liu, D.J. & Leal, S.M. Replication strategies for rare variant complex trait association studies via next-generation sequencing. Am. J. Hum. Genet. 87, 790–801 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  8. Zawistowski, M. et al. Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes. Am. J. Hum. Genet. 87, 604–617 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  9. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  10. Lin, D.Y. & Tang, Z.Z. A general framework for detecting disease associations with rare variants in sequencing studies. Am. J. Hum. Genet. 89, 354–367 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  11. Lin, X. Variance component testing in generalised linear models with random effects. Biometrika 84, 309–326 (1997).
    Article Google Scholar
  12. Neale, B.M. et al. Testing for an unusual distribution of rare variants. PLoS Genet. 7, e1001322 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  13. Ladouceur, M., Dastani, Z., Aulchenko, Y.S., Greenwood, C.M. & Richards, J.B. The empirical power of rare variant association methods: results from Sanger sequencing in 1,998 individuals. PLoS Genet. 8, e1002496 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  14. Morris, A.P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010).
    Article PubMed Google Scholar
  15. Madsen, B.E. & Browning, S.R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009).
    Article PubMed PubMed Central Google Scholar
  16. Hudson, R.R. Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).
    Article CAS PubMed Google Scholar
  17. Adams, A.M. & Hudson, R.R. Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics 168, 1699–1712 (2004).
    Article CAS PubMed PubMed Central Google Scholar
  18. Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008).
    CAS PubMed PubMed Central Google Scholar
  19. Nelson, M.R. et al. An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337, 100–104 (2012).
    Article CAS PubMed Google Scholar
  20. Besag, J. & Clifford, P. Sequential Monte Carlo _p_-values. Biometrika 78, 301–304 (1991).
    Article Google Scholar
  21. Tennessen, J.A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).
    Article CAS PubMed Google Scholar
  22. McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316, 1488–1491 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  23. Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240–1249 (2008).
    Article CAS PubMed Google Scholar
  24. Clarke, R. et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361, 2518–2528 (2009).
    Article CAS PubMed Google Scholar
  25. Krokstad, S. et al. Cohort Profile: the HUNT Study, Norway. Int. J. Epidemiol. 42, 968–977 (2013).
    Article CAS PubMed Google Scholar
  26. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  27. Albrechtsen, A. et al. Exome sequencing–driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).
    Article CAS PubMed Google Scholar
  28. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  29. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
    Article CAS PubMed Google Scholar
  30. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  31. Lee, S. et al. 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 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  32. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  33. Abecasis, G.R., Cherny, S.S., Cookson, W.O. & Cardon, L.R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).
    Article CAS PubMed Google Scholar
  34. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  35. Hu, Y.J. et al. Meta-analysis of gene-level associations for rare variants based on single-variant statistics. Am. J. Hum. Genet. 93, 236–248 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  36. Lee, S., Teslovich, T.M., Boehnke, M. & Lin, X. General framework for meta-analysis of rare variants in sequencing association studies. Am. J. Hum. Genet. 93, 42–53 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  37. Tang, Z.Z. & Lin, D.Y. MASS: meta-analysis of score statistics for sequencing studies. Bioinformatics 29, 1803–1805 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  38. Genz, A. Numerical computation of multivariate normal probabilities. J. Comput. Graph. Statist. 1, 141–149 (1992).
    Google Scholar
  39. Zou, F., Fine, J.P., Hu, J. & Lin, D.Y. An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait loci. Genetics 168, 2307–2316 (2004).
    Article CAS PubMed PubMed Central Google Scholar
  40. Coventry, A. et al. Deep resequencing reveals excess rare recent variants consistent with explosive population growth. Nat. Commun. 1, 131 (2010).
    Article PubMed Google Scholar

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Acknowledgements

The authors would like to thank M. Boehnke, X. Wen and S. Zoellner for helpful discussions. This work was supported by research grants R01HG007022 from the National Human Genome Research Institute, R01EY022005 from the National Eye Institute and R01HL117626 from the National Heart, Lung, and Blood Institute. G.M.P. was supported by award T32HL007208 from the National Heart, Lung, and Blood Institute. S.K. is supported by a Research Scholar award from Massachusetts General Hospital (MGH), the Howard Goodman Fellowship from MGH, the Donovan Family Foundation and grant R01HL107816 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the US National Institutes of Health. The WHI program is funded by the National Heart, Lung, and Blood Institute, US National Institutes of Health, US Department of Health and Human Services through contracts N01WH22110, N01WH24152, N01WH32100-2, N01WH32105-6, N01WH32108-9, N01WH32111-13, N01WH32115, N01WH32118-32119, N01WH32122, N01WH42107-26, N01WH42129-32 and N01WH44221. This manuscript was prepared in collaboration with investigators from the WHI and has been approved by the WHI. WHI investigators are listed at https://cleo.whi.org/researchers/SitePages/WHI%20Investigators.aspx. The full list of PROCARDIS acknowledgments is available at http://www.procardis.org/. The Ottawa Heart Genomics Study was supported by Canadian Institutes of Health Research (CIHR) grants MOP-82810, MOP-77682 and MOP-2380941 and Canada Foundation for Innovation (CFI) grant 11966. The studies for the Malmö Diet and Cancer cohort were supported by grants from the Swedish Research Council, the Swedish Heart and Lung Foundation, the Påhlsson Foundation, the Novo Nordic Foundation and European Research Council starting grant StG-282255.

Author information

Author notes

  1. Dajiang J Liu, Gina M Peloso, Xiaowei Zhan and Oddgeir L Holmen: These authors contributed equally to this work.
  2. Sekar Kathiresan and Gonçalo R Abecasis: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
    Dajiang J Liu, Xiaowei Zhan, Matthew Zawistowski, Shuang Feng & Gonçalo R Abecasis
  2. Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    Gina M Peloso & Sekar Kathiresan
  3. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
    Gina M Peloso & Sekar Kathiresan
  4. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
    Gina M Peloso & Sekar Kathiresan
  5. Department of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
    Oddgeir L Holmen & Kristian Hveem
  6. St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway
    Oddgeir L Holmen
  7. University of Ottawa Heart Institute, Ottawa, Ontario, Canada
    Majid Nikpay & Ruth McPherson
  8. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
    Paul L Auer, Ulrike Peters & Charles Kooperberg
  9. School of Public Health, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin, USA
    Paul L Auer
  10. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
    Anuj Goel, Martin Farrall & Hugh Watkins
  11. Department of Cardiovascular Medicine, University of Oxford, Oxford, UK
    Anuj Goel, Martin Farrall, Marju Orho-Melander, Hugh Watkins & Olle Melander
  12. Division of Cardiology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
    He Zhang & Cristen J Willer
  13. Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, USA
    He Zhang & Cristen J Willer
  14. Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, USA
    Ulrike Peters
  15. Department of Clinical Sciences, Lund University, Malmö, Sweden
    Marju Orho-Melander & Olle Melander
  16. Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington, USA
    Charles Kooperberg
  17. Department of Medicine, Levanger Hospital, Nord-Trøndelag Health Trust, Levanger, Norway
    Kristian Hveem
  18. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
    Sekar Kathiresan

Authors

  1. Dajiang J Liu
  2. Gina M Peloso
  3. Xiaowei Zhan
  4. Oddgeir L Holmen
  5. Matthew Zawistowski
  6. Shuang Feng
  7. Majid Nikpay
  8. Paul L Auer
  9. Anuj Goel
  10. He Zhang
  11. Ulrike Peters
  12. Martin Farrall
  13. Marju Orho-Melander
  14. Charles Kooperberg
  15. Ruth McPherson
  16. Hugh Watkins
  17. Cristen J Willer
  18. Kristian Hveem
  19. Olle Melander
  20. Sekar Kathiresan
  21. Gonçalo R Abecasis

Contributions

D.J.L., S.K. and G.R.A. conceived and designed the study. D.J.L., G.M.P. and X.Z. carried out primary data analysis. D.J.L., X.Z. and S.F. wrote the software package implementing the proposed methodologies. O.L.H., M.N., P.L.A., A.G., H.Z., U.P., M.F., M.O.-M., C.K., R.M., H.W., C.J.W., K.H. and O.M. contributed phenotypes, exome array genotypes and analyses for the study. M.Z. conducted population genetics simulation analysis. D.J.L. and G.R.A. wrote the first version of the manuscript. All authors critically reviewed and approved the manuscript. S.K. and G.R.A. jointly supervised the study.

Corresponding authors

Correspondence toDajiang J Liu or Gonçalo R Abecasis.

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Competing interests

The authors declare no competing financial interests.

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Liu, D., Peloso, G., Zhan, X. et al. Meta-analysis of gene-level tests for rare variant association.Nat Genet 46, 200–204 (2014). https://doi.org/10.1038/ng.2852

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