PUMA: a unified framework for penalized multiple regression analysis of GWAS data.

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely appl...

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Autores principales:	Gabriel E Hoffman, Benjamin A Logsdon, Jason G Mezey
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/df33d74e86dc45a2bc28814d4c499522
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spelling	oai:doaj.org-article:df33d74e86dc45a2bc28814d4c4995222021-11-18T05:52:04ZPUMA: a unified framework for penalized multiple regression analysis of GWAS data.1553-734X1553-735810.1371/journal.pcbi.1003101https://doaj.org/article/df33d74e86dc45a2bc28814d4c4995222013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23825936/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns's disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn's disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.Gabriel E HoffmanBenjamin A LogsdonJason G MezeyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 6, p e1003101 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Gabriel E Hoffman Benjamin A Logsdon Jason G Mezey PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
description	Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns's disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn's disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.
format	article
author	Gabriel E Hoffman Benjamin A Logsdon Jason G Mezey
author_facet	Gabriel E Hoffman Benjamin A Logsdon Jason G Mezey
author_sort	Gabriel E Hoffman
title	PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
title_short	PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
title_full	PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
title_fullStr	PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
title_full_unstemmed	PUMA: a unified framework for penalized multiple regression analysis of GWAS data.
title_sort	puma: a unified framework for penalized multiple regression analysis of gwas data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/df33d74e86dc45a2bc28814d4c499522
work_keys_str_mv	AT gabrielehoffman pumaaunifiedframeworkforpenalizedmultipleregressionanalysisofgwasdata AT benjaminalogsdon pumaaunifiedframeworkforpenalizedmultipleregressionanalysisofgwasdata AT jasongmezey pumaaunifiedframeworkforpenalizedmultipleregressionanalysisofgwasdata
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PUMA: a unified framework for penalized multiple regression analysis of GWAS data.

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