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Showing content from https://doi.org/10.1038/ng.3190 below:

Efficient Bayesian mixed-model analysis increases association power in large cohorts

  • Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

    Article  CAS  PubMed  Google Scholar 

  • Kang, H.M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  • Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • Segura, V. et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44, 825–830 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Korte, A. et al. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat. Genet. 44, 1066–1071 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Listgarten, J. et al. Improved linear mixed models for genome-wide association studies. Nat. Methods 9, 525–526 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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).

    Article  CAS  PubMed  Google Scholar 

  • Listgarten, J., Lippert, C. & Heckerman, D. FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nat. Genet. 45, 470–471 (2013).

    Article  CAS  PubMed  Google Scholar 

  • Yang, J., Zaitlen, N.A., Goddard, M.E., Visscher, P.M. & Price, A.L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  • Stahl, E.A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lippert, C. et al. The benefits of selecting phenotype-specific variants for applications of mixed models in genomics. Sci. Rep. 3, 1815 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • Rakitsch, B., Lippert, C., Stegle, O. & Borgwardt, K. A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29, 206–214 (2013).

    Article  CAS  PubMed  Google Scholar 

  • Meuwissen, T.H., Hayes, B.J. & Goddard, M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  • de Los Campos, G., Hickey, J.M., Pong-Wong, R., Daetwyler, H.D. & Calus, M.P. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Meuwissen, T.H., Solberg, T.R., Shepherd, R. & Woolliams, J.A. A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genet. Sel. Evol. 41, 2 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  • Carbonetto, P. & Stephens, M. Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Anal. 7, 73–108 (2012).

    Article  Google Scholar 

  • Logsdon, B.A., Hoffman, G.E. & Mezey, J.G. A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis. BMC Bioinformatics 11, 58 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  • Jakobsdottir, J. & McPeek, M.S. MASTOR: mixed-model association mapping of quantitative traits in samples with related individuals. Am. J. Hum. Genet. 92, 652–666 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bulik-Sullivan, B. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 10.1038/ng.3211 (2 February 2015).

  • Ridker, P.M. et al. Rationale, design, and methodology of the Women's Genome Health Study: a genome-wide association study of more than 25,000 initially healthy American women. Clin. Chem. 54, 249–255 (2008).

    Article  CAS  PubMed  Google Scholar 

  • García-Cortés, L.A., Moreno, C., Varona, L. & Altarriba, J. Variance component estimation by resampling. J. Anim. Breed. Genet. 109, 358–363 (1992).

    Article  Google Scholar 

  • Matilainen, K., Mäntysaari, E.A., Lidauer, M.H., Strandén, I. & Thompson, R. Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters. PLoS ONE 8, e80821 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  • Legarra, A. & Misztal, I. Computing strategies in genome-wide selection. J. Dairy Sci. 91, 360–366 (2008).

    Article  CAS  PubMed  Google Scholar 

  • VanRaden, P.M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).

    Article  CAS  PubMed  Google Scholar 

  • Sawcer, S. et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aulchenko, Y.S., Ripke, S., Isaacs, A. & Van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  PubMed  Google Scholar 

  • 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 

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

    Article  CAS  PubMed  Google Scholar 

  • Wray, N.R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Campbell, C.D. et al. Demonstrating stratification in a European American population. Nat. Genet. 37, 868–872 (2005).

    Article  CAS  PubMed  Google Scholar 

  • Tucker, G., Price, A.L. & Berger, B.A. Improving the power of GWAS and avoiding confounding from population stratification with PC-Select. Genetics 197, 1045–1049 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  • Stephens, M. & Balding, D.J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).

    Article  CAS  PubMed  Google Scholar 

  • Logsdon, B.A., Carty, C.L., Reiner, A.P., Dai, J.Y. & Kooperberg, C. A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging. Bioinformatics 28, 1738–1744 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Styrkarsdottir, U. et al. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature 497, 517–520 (2013).

    Article  CAS  PubMed  Google Scholar 

  • Do, C.B. et al. Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease. PLoS Genet. 7, e1002141 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hayeck, T. et al. Mixed model with correction for case-control ascertainment increases association power. bioRxiv 10.1101/008755 (2014).

  • Speed, D. & Balding, D.J. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res. 24, 1550–1557 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen, W.-M. & Abecasis, G.R. Family-based association tests for genomewide association scans. Am. J. Hum. Genet. 81, 913–926 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aulchenko, Y.S., De Koning, D.-J. & Haley, C. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177, 577–585 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen, W.-M., Manichaikul, A. & Rich, S.S. A generalized family-based association test for dichotomous traits. Am. J. Hum. Genet. 85, 364–376 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Boyd, S.P. & Vandenberghe, L. Convex Optimization (Cambridge University Press, 2004).

  • Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 


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