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Cited by 1,544

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

Zhou, Wei, Nielsen, Jonas B., Fritsche, Lars G., Dey, Rounak, Gabrielsen, Maiken E., Wolford, Brooke N., LeFaive, Jonathon, VandeHaar, Peter, Gagliano, Sarah A., Gifford, Aliya, Bastarache, Lisa A., Wei, Wei-Qi, Denny, Joshua C., Lin, Maoxuan, Hveem, Kristian, Kang, Hyun Min, Abecasis, Goncalo R., Willer, Cristen J., Lee, Seunggeun·2018·Nature Genetics
DOI 10.1038/s41588-018-0184-yPMC PMC6119127

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  • Association analysis for LDL cholesterol was performed using SAIGE for 388,629 individuals in the white British subset of UK Biobank.

    “Association analysis for LDL cholesterol was performed using SAIGE[37] for 388,629 individuals in the white British subset of UK Biobank[38].”

    From Genome-scale CRISPR screening for modifiers of cellular LDL uptake (2021) · cites this paper as [37]

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Research works printed but not linked: 22

  1. Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016). · DOI
  2. Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013). · DOI
  3. Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010). · DOI
  4. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011). · DOI · Paper page
  5. Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011). · DOI · Paper page
  6. Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015). · DOI · Paper page
  7. Chen, H. et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98, 653–666 (2016). · DOI
  8. Ma, C., Blackwell, T., Boehnke, M. & Scott, L. J., GoT2D investigators. Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet. Epidemiol 37, 539–550 (2013). · DOI
  9. Kuonen, D. Saddlepoint approximations for distributions of quadratic forms in normal variables. Biometrika 86, 4 (1999). · DOI
  10. Dey, R., Schmidt, E. M., Abecasis, G. R. & Lee, S. A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS. Am. J. Hum. Genet. 101, 37–49 (2017). · DOI
  11. Kaasschieter, E. F. Preconditioned conjugate gradients for solving singular systems. J. Comput. Appl. Math. 24, 265–275 (1988). · DOI
  12. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at bioRxiv, https://doi.org/10.1101/166298 (2017). · DOI
  13. Gilmour, A. R., Thompson, R. & Cullis, B. R. Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51, 1440–1450 (1995). · DOI
  14. Svishcheva, G. R., Axenovich, T. I., Belonogova, N. M., van Duijn, C. M. & Aulchenko, Y. S. Rapid variance components-based method for whole-genome association analysis. Nat. Genet. 44, 1166–1170 (2012). · DOI
  15. Nelis, M. et al. Genetic structure of Europeans: a view from the North-East. PLoS One 4, e5472 (2009). · DOI
  16. Shameer, K. et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum. Genet. 133, 95–109 (2014). · DOI
  17. Listgarten, J. et al. Improved linear mixed models for genome-wide association studies. Nat. Methods 9, 525–526 (2012). · DOI
  18. Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc 88, 9–25 (1993). · DOI
  19. Hestenes, M. R. & Stiefel, E. Methods of conjugate gradients for solving linear systems. J. Res. Natl Bur. Stand. 49, 409–436 (1952). · DOI
  20. Imhof, J. P. Computing the distribution of quadratic forms in normal variables. Biometrika 48, 419–426 (1961). · DOI
  21. 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). · DOI
  22. de Villemereuil, P., Schielzeth, H., Nakagawa, S. & Morrissey, M. General methods for evolutionary quantitative genetic inference from generalized mixed models. Genetics 204, 1281–1294 (2016). · DOI

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Metadata

Authors
Zhou, W., Nielsen, J. B., Fritsche, L. G., Dey, R., Gabrielsen, M. E., Wolford, B. N., LeFaive, J., VandeHaar, P., Gagliano, S. A., Gifford, A., Bastarache, L. A., Wei, W.-Q., Denny, J. C., Lin, M., Hveem, K., Kang, H. M., Abecasis, G. R., Willer, C. J., & Lee, S.
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2018-08-13
Volume
50
Issue
9
Pages
1335-1341
ISSN
  • 1061-4036
  • 1546-1718
DOI
10.1038/s41588-018-0184-y
PMC
PMC6119127
Cited by
1,544
References
7
Language
en
Licence
www.springer.com/tdm (tdm)

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