A version of FaST-LMM has now been optimized for use in the cloud and cloud sized data.\u00a0 If you are interested in reading about it, click here (opens in new tab)<\/span><\/a>.\u00a0 If you are interested in using this, please click here<\/a> to\u00a0send an\u00a0email to genomics@microsoft.com with “GWAS use request” as the\u00a0subject.<\/p>\n FaST-LMM, (Factored Spectrally Transformed Linear Mixed Models) is a set of tools for efficiently performing genome-wide association studies (GWAS), prediction, and heritability estimation on large data sets. FaST-LMM runs on both Windows and Linux, and has been tested on data sets with over one million samples.<\/p>\n The most up-to-date version of FaST-LMM is written in python and available on GitHub (opens in new tab)<\/span><\/a>.\u00a0 It supports univariate GWAS [1, 4], tests for epistasis, corrections for cellular heterogeneity via the inclusion of\u00a0principal components [2], set association\u00a0tests [3], and heritability estimation [5].\u00a0 A C++ version, including Windows binary<\/a>, Linux binary<\/a>, and source<\/a>, supports univariate GWAS and limited epistatic testing. Another version supporting corrections for cellular heterogeneity is available in python <\/u>and R<\/u>.\u00a0 An example of\u00a0FaST-LMM\u00a0with cloud computing is\u00a0here (opens in new tab)<\/span><\/a>.<\/p>\n [1] Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, D. Heckerman. FaST linear mixed models for genome-wide association studies. (opens in new tab)<\/span><\/a> Nature Methods<\/em>, 8: 833-835, Oct 2011 (doi:10.1038\/nmeth.1681).<\/p>\n [2] Zou, C. Lippert, D. Heckerman, M. Aryee, J. Listgarten. Epigenome-wide association studies without the need for cell-type composition. (opens in new tab)<\/span><\/a> Nature Methods<\/em>, 11: 309\u2013311, Jan 2014 (doi:10.1038\/nmeth.2815).<\/p>\n [3] Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl M. Kadie, D. Heckerman, J. Listgarten. Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. (opens in new tab)<\/span><\/a> Bioinformatics<\/em>, 30, July 2014 (doi: 10.1093\/bioinformatics\/btu504).<\/p>\n [4] Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman. Further Improvements to Linear Mixed Models for Genome-Wide Association Studies. (opens in new tab)<\/span><\/a> Scientific Reports<\/em>, 4, 6874, Nov 2014 (doi:10.1038\/srep06874).<\/p>\n [5] Heckerman, D. Gurdasani, C. Kadie, C. Pomilla, T. Carstensen, H. Martin, K. Ekoru, R.N. Nsubuga, G. Ssenyomo A. Kamali, P. Kaleebu, C. Widmer, and M.S. Sandhu. Linear mixed model for heritability estimation that explicitly addresses environmental variation (opens in new tab)<\/span><\/a>. PNAS<\/em>, 113: 7377\u20137382, July 2016 (doi: 10.1073\/pnas.1510497113).<\/p>\n\t Univariate GWAS<\/strong><\/p>\n [1] H. Kang, N. Zaitlen, C. Wade, A. Kirby, D. Heckerman, M. Daly, and E. Eskin, Efficient Control of Population Structure in Model Organism Association Mapping (opens in new tab)<\/span><\/a>, Genetics, 178:1709-1723, March, 2008 (doi: 10.1534\/genetics.107.080101).<\/p>\n Describes early efforts to make linear mixed models more computationally efficient.<\/p>\n [2] Lippert*<\/sup><\/strong>, J. Listgarten*<\/sup><\/strong>, Y. Liu, C.M. Kadie, R.I. Davidson, D. Heckerman*<\/sup><\/strong>.\u00a0FaST linear mixed models for genome-wide association studies (opens in new tab)<\/span><\/a>.\u00a0Nature Methods<\/em>, 8: 833-835, Oct 2011 (doi:10.1038\/nmeth.1681). (*<\/sup>equal contributions)<\/p>\n Shows how exact linear-mixed-model computations can be performed in time and memory linear<\/em> in the number of individuals when the number of SNPs used in the similarity matrix is less than the number of individuals (i.e.,<\/em> when the similarity matrix is low rank). This work also describes an approach to select SNPs to achieve this condition with linkage-disequilibrium-based pruning. In addition, this work shows that computations are quadratic in time and memory when the similarity matrix is full rank.<\/p>\n [3] J. Listgarten*<\/sup><\/strong>, C. Lippert*<\/sup><\/strong>, C.M. Kadie, R.I. Davidson, E. Eskin, D. Heckerman*<\/sup><\/strong>. Improved linear mixed models for genome-wide association studies (opens in new tab)<\/span><\/a>.\u00a0Nature Methods<\/em>, 9: 525-526, June 2012 (doi:10.1038\/nmeth.2037). (*<\/sup>equal contributions)<\/p>\n Describes a method for selecting SNPs for the linear-mixed-model similarity matrix by identifying SNPs that are predictive of the phenotype. A later publication [6] shows this approach yields poor control of type I error, whereas the original selection method in [2] performs well. This work also shows that the inclusion of irrelevant SNPs in the similarity matrix leads to inflated test statistics and reduced power, a phenomenon called \u201cdilution\u201d. Although an incorrect explanation for dilution is offered here, a correction is given in [5]. Finally, there is a bug in the analysis of the synthetic data, which makes the prediction-based selection method appear to perform better than it actually does.<\/p>\n [4] J. Listgarten*<\/sup><\/strong>, C. Lippert*<\/sup><\/strong>, D. Heckerman*<\/sup><\/strong>. FaST-LMM-Select for addressing confounding from spatial structure and rare variants (opens in new tab)<\/span><\/a>.\u00a0Nature Genetics <\/em>(2013) doi:10.1038\/ng.2620 (*<\/sup>equal contributions)<\/p>\n Shows how the feature-selection method in [3] addresses an open problem in statistical genetics that had been published in Nature Genetics. Based on results in [6], however, we recommend that the selection approach in [2] be used instead.<\/p>\n [5] C. Lippert*<\/sup><\/strong>, Gerald Quon, Eun Youg Kang, Carl M. Kadie, J. Listgarten*<\/sup><\/strong>, D. Heckerman*<\/sup><\/strong>.\u00a0The benefits of selecting phenotype-specific variants for applications of mixed models in genomics (opens in new tab)<\/span><\/a>.\u00a0Scientific Reports<\/em>(2013) doi:10.1038\/srep01815 (*<\/sup>equal contributions)<\/p>\n Describes additional experiments regarding the feature-selection method in [3] as applied to GWAS and prediction. Again, based on the results in [6], we recommend that the selection approach in [2] be used instead.<\/p>\n [6] C. Widmer*, C. Lippert*, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman*.\u00a0Further Improvements to Linear Mixed Models for Genome-Wide Association Studies (opens in new tab)<\/span><\/a>. Scientific Reports<\/em>, 4, 6874, Nov 2014 (doi:10.1038\/srep06874). (*<\/sup>equal contributions)<\/p>\n Describes the latest version of FaST-LMM. It shows that selecting SNPs for the linear-mixed-model similarity matrix through pruning via linkage disequilibrium (as in [2]) works well to control type I error, whereas selecting SNPs that are predictive of the phenotype (as in [3]) does not.<\/p>\n [7] C. Lippert and D. Heckerman. Computational and statistical issues in personalized medicine (opens in new tab)<\/span><\/a>. XRDS<\/em> 21, 24-27, Summer 2015 (doi:10.1145\/2788502).<\/p>\n Describes statistical issues in GWAS with linear mixed models from a graphical-model perspective.<\/p>\n Set Tests\u00a0for GWAS<\/strong><\/p>\n [8] Listgarten*<\/sup><\/strong>, C. Lippert*<\/sup><\/strong>, Eun Youg Kang, Jing Xiang, Carl M. Kadie, D. Heckerman*<\/sup><\/strong>.\u00a0A powerful and efficient set test for genetic markers that handles confounders. (opens in new tab)<\/span><\/a> Bioinformatics<\/em>, 29:1526-1533, April 2013 (doi:10.1093\/bioinformatics\/btt177). (*<\/sup>equal contributions)<\/p>\n Shows that the LRT can be more powerful than a score test for set association tests. This work is limited to similarity matrices that are low rank and includes an efficient algorithm for this case. This limitation is relaxed in [9].<\/p>\n [9] C. Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl M. Kadie, D. Heckerman*, J. Listgarten. Greater power and computational efficiency for kernel-based association testing of sets of genetic variants (opens in new tab)<\/span><\/a>.\u00a0Bioinformatics<\/em>, 2014 (doi: 10.1093\/bioinformatics\/btu504). (*corresponding author)<\/p>\n Makes theoretical arguments and demonstrates empirically that the LRT is often more powerful than the traditionally-used score test (e.g. SKAT). It also has exposition on how to do a number of algebraic computations for set tests with either a low- or full-rank background kernel efficiently.<\/p>\n Data Transformations\/Pre-processing for GWAS<\/strong><\/p>\n [10] N. Fusi*, C. Lippert, N. D. Lawrence and O. Stegle*. Warped linear mixed models for the genetic analysis of transformed phenotypes (opens in new tab)<\/span><\/a>. Nature Communications<\/em>, 2014.<\/p>\n Shows how monotonically transforming the phenotype can increase power in genome-wide association studies and increase the accuracy of heritability estimation and phenotype prediction.<\/p>\n [11] O. Weissbrod, C. Lippert, D. Geiger, and D. Heckerman.\u00a0 Accurate liability estimation improves power in ascertained case-control studies (opens in new tab)<\/span><\/a>.\u00a0 Nature Methods<\/em>, Feb 2015 (doi:10.1038\/nmeth.3285).<\/p>\n Describes an approach to pre-process ascertained case-control-study data that leads to improved power when analyzed with a linear mixed model.<\/p>\n Epigenetic Cellular Heterogeneity Correction<\/strong><\/p>\n [12] Zou, C. Lippert, D. Heckerman, M. Aryee, Jennifer Listgarten.\u00a0Epigenome-wide association studies without the need for cell-type composition (opens in new tab)<\/span><\/a>.\u00a0Nature Methods<\/em>, doi:10.1038\/NMETH.2815.<\/p>\n Shows how FaST-LMM, with the inclusion of principal components (PCs) as covariates, can correct for the confounding effects of multiple cell types. Although a method for selecting PCs is presented here, the method in [6] is now recommended.<\/p>\n Epistatic Genome-Wide Association<\/strong><\/p>\n [13] Lippert*<\/sup><\/strong>, J. Listgarten*<\/sup><\/strong>, Robert Davidson, Scott Baxter, Hoifung Poon, Carl M. Kadie, D. Heckerman*<\/sup><\/strong>.\u00a0An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data (opens in new tab)<\/span><\/a>, Scientific Reports<\/em>, 2013, doi:10.1038\/srep01099 (*<\/sup>equal contributions)<\/p>\n Presents results for all pairwise-epistatic tests for all phenotypes in the WTCCC1 data, using a linear mixed model with a low-rank similarity matrix based on the feature-selection method in [3]. As described, based on the results in [6], we now recommend that the feature-selection method in [2] be used instead. The rank order of the hits may be approximately correct, and therefore we have left these results on the Azure marketplace http:\/\/datamarket.azure.com\/dataset\/microsoftresearch\/epistasisgwas (opens in new tab)<\/span><\/a>.<\/p>\n GWAS for\u00a0“functional traits”\u00a0such as longitudinal traits<\/strong><\/p>\n [14] Fusi and J. Listgarten.\u00a0\u00a0Leveraging Non-Linear Genetic Effects on Functional Traits for GWAS,\u00a0Proceedings of RECOMB 2016.<\/em><\/p>\n Introduces a model for performing GWAS for\u00a0vector-valued traits which vary smoothly in time.\u00a0The\u00a0framework is expressive and\u00a0computationally efficient, but the null model is not nested inside of the\u00a0alternative model, something we are currently\u00a0addressing in ongoing work.<\/p>\n Heritability estimation<\/strong><\/p>\n [15] N. Furlotte, D. Heckerman, and C. Lippert.\u00a0 Quantifying the uncertainty in heritability (opens in new tab)<\/span><\/a>.\u00a0 Journal of Human Genetics<\/em> 27, March 2014 (doi: 10.1038\/jhg.2014.15).<\/p>\n Applies the spectral-decomposition trick from FaST-LMM [2] to speed up Bayesian estimates of heritability.<\/p>\nClick here to download standard version of\u00a0FaST-LMM (opens in new tab)<\/span><\/a><\/h3>\n
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