FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a set of tools for performing efficient genome-wide association studies (GWAS) 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
FaST-LMM applications include single-SNP testing, SNP-set testing, tests for epistasis, and heritability estimation.<\/p>\n
FaST-LMM (python): This version is our most up-to-date release<\/span> and available on GitHub<\/span> (opens in new tab)<\/span><\/a>.\u00a0 It supports univariate GWAS, set tests, epistatic tests, and heritability estimation. The release includes ipython notebook examples (opens in new tab)<\/span><\/a> and API documentation (opens in new tab)<\/span><\/a>.\u00a0 An example of\u00a0FaST-LMM\u00a0with cloud computing is\u00a0here (opens in new tab)<\/span><\/a>.<\/p>\n FaST-LMM (C++): This version supports univariate GWAS and epistatic tests. The release includes Windows binary<\/a>, Linux binary<\/a>, and source<\/a>.<\/p>\n EWASher: This version support corrections for cellular heterogeneity in methylation and similar data.\u00a0 The release includes a python version<\/a> and R version,<\/a> although the R version has been reported to be\u00a0difficult to run so we advise sticking with the python.<\/p>\n FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a set of tools for performing efficient genome-wide association studies (GWAS) on large data sets. FaST-LMM runs on both Windows and Linux, and has been tested on data sets with over one million samples. FaST-LMM applications include single-SNP testing, SNP-set testing, tests for epistasis, and heritability estimation. […]<\/p>\n","protected":false},"featured_media":255012,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13553],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-171427","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2011-01-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[144943],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[{"id":0,"name":"","content":""}],"slides":[{"attachment_id":213102,"headline":"FaST-LMM on GitHub","cta":"Get the download here","url":"https:\/\/github.com\/microsoftgenomics\/fast-lmm","cta_style":"","slideshow_type":"feature"}],"related-researchers":[],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171427","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":7,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171427\/revisions"}],"predecessor-version":[{"id":603729,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171427\/revisions\/603729"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/255012"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=171427"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=171427"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=171427"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=171427"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=171427"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Annotated Bibliography<\/strong><\/h2>\n
Univariate GWAS<\/strong><\/h3>\n
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Set Tests\u00a0for GWAS<\/strong><\/h3>\n
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Epigenetic Cellular Heterogeneity Correction (EWAS)<\/strong><\/h3>\n
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Epistatic Genome-Wide Association <\/strong><\/h3>\n
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