Dataset Information

Segway 2.0: Gaussian mixture models and minibatch training.

ABSTRACT: Summary:Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters. Availability and implementation:Segway and its source code are freely available for download at http://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802939) and datasets (https://doi.org/10.5281/zenodo.802906) for this paper's analysis. Contact:michael.hoffman@utoronto.ca. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Chan RCW

PROVIDER: S-EPMC5860603 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Segway 2.0: Gaussian mixture models and minibatch training.

Chan Rachel C W RCW Libbrecht Maxwell W MW Roberts Eric G EG Bilmes Jeffrey A JA Noble William Stafford WS Hoffman Michael M MM

Bioinformatics (Oxford, England) 20180201 4

<h4>Summary</h4>Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.<h4>Availability and implemen ...[more]

PMID: 29028889

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Project description:Motivation:Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together. Results:We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially non-informative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP in terms of both high association mapping power and accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. Availability and implementation:iMAP is freely available at http://www.xzlab.org/software.html. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Segway 2.0: Gaussian mixture models and minibatch training.

Publications

Segway 2.0: Gaussian mixture models and minibatch training.

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