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Toppar: an interactive browser for viewing association study results

Toppar: an interactive browser for viewing association study results Summary: Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases. Availability and implementation: Detailed information on Toppar’s features and functionality are on our website http://mccarthy.well.ox.ac.uk/toppar/docs along with instructions on how to down- load, install and run Toppar. Our online version of Toppar is accessible from the website and can be test-driven using Firefox, Safari or Chrome on sub-sets of publicly available genome-wide asso- ciation study anthropometric waist and body mass index data (Locke et al., 2015; Shungin et al., 2015) from the Genetic Investigation of ANthropometric Traits consortium. Contact: [email protected] 1 Introduction 2 Implementation Analytical challenges facing genetic studies increase with larger, 2.1 Main features and functionality more complex datasets, more extensive phenotypic trait informa- Toppar is an extension of GSCANDB (Taylor et al., 2007), and the tion, a greater array of statistical tests and a variety of genetic refer- display includes filtering and interactive viewing panels (Fig. 1). ence panels and annotation tools. Here, we present Toppar a Association results can be viewed in the latter by either genome or customizable database-driven browser for genetic association gen- region view, with association P-values on the –log10 scale displayed ome-wide association study (GWAS) data. It allows integration and on the vertical axes and chromosomal position on the horizontal visualization of analyses generated across multiple platforms and axis. Association results mapped to a specific genome build methodologies. It combines a whole-genome overview of GWAS re- (GRCh38 by default) for a given study or population can be viewed sults with an interactive regional display for loci of interest. User through various filters. data can be uploaded and explored in conjunction with gene and Toppar’s functionality is best illustrated using published geno- exon annotation, as well as already published genome-wide associ- type/phenotype association results, such as the waist and body mass ation catalogs (Welter et al., 2014). The Toppar browser can filter index (BMI) datasets released by the international Genetic its display using user-defined sub-categories, e.g. to visualize both Investigation of ANthropometric Trait (GIANT) consortium, which single variant and gene-based association results. This enables com- includes GWAS data from a number of anthropometric traits strati- parison across multiple traits, using different tests and filters. fied by ancestry and gender (Locke et al., 2015; Shungin et al., V The Author(s) 2018. Published by Oxford University Press. 1922 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Toppar 1923 Fig. 1. Toppar’s main interface. The filtering panel (A) allows the user to navigate and view the data by selecting available sub-categories. From the genome view (B), one navigates to a specific genomic locus by selecting it on the plot. This refocuses the display to the region (chromosome) view (C). Beyond a certain level of zoom-in the appearance of the plotted dots that indicate association results changes from small filled circles (D) to larger open circles, indicating they have be- come interactive and that test statistic associated with the point/test are displayed on mouseover. An interactive point becomes red when selected, with the vari- ant name displayed above it, and the LD pattern of all data points relative to the one selected can be displayed (D). At further zoom-in, exonic regions are also displayed for each gene (E). GWAS catalog SNPs can be added and colored based on traits of interest and hovering over a GWAS variant displays its rs identifier, P-value and associated disease trait and pubmed id, while selecting the variant takes the user to the referenced pubmed article (E). Multiple traits can be viewed simultaneously (F), where each trait appears on a separate but synchronized plot: zooming in on one plot automatically refocuses the other plots 2015). We uploaded sets of top 40 000 genetic variants associated Association results and GWAS catalog data are also listed in tables with BMI and with traits related to fat distribution including waist- that are hidden from view by default. hip-ratio, with and without adjusting for BMI, to Toppar’s local Existing browsers that display genotype/phenotype association database (see full instructions on website). The upload time per 40k results include the regional plotter Locuszoom (Pruim et al., 2010), set was around 3 s. The entire upload, which only has to be done UK10K genome browser (Geihs et al., 2015) which is based on the once, took approximately 3 min on a Mac OS v10.9.5 with a 3 GHz Biodalliance platform (Down et al., 2011) and the AMP T2D know- Intel Core i7 processor and 16 GB 1600 MHz memory. ledge portal (http://www.type2diabetesgenetics.org). Locuszoom is Once uploaded, the different phenotypes, ethnicity, gender and optimized for plotting regions of association test results for single test appear in Toppar’s four filtering menus (Fig. 1A). To view the traits and displaying the pattern of LD, whereas the UK10K genome GIANT results (e.g. for BMI) one can use the optional filters before and AMP T2D are publicly available web browsers, which allow for selecting genome view and pressing submit. From the genome view the extensive exploration of pre-uploaded data. Toppar has the abil- (Fig. 1B) one can navigate to the region (chromosome) view ity to display multiple trait data and integrate it with a wider selec- (Fig. 1C), where results for the area of interest and neighboring tion of relevant external data as well as provide LD information for genes can be further explored through zooming and panning and small genetic regions (GRCh38). It thus complements Locuszoom as linkage disequilibrium (LD) patterns can be displayed for selected well as larger genome browsers. variants. Genes linked with disease according to the DISEASE web resource, based on automatic text mining of scientific literature (Pletscher-Frankild et al., 2015) are highlighted in red and the 2.2 Installation and usage gene name and associated disease are displayed by hovering over the Toppar was written in JavaScript and HTML and uses the Flot li- gene (Fig. 1C and D). Annotation for displayed genes, including brary (http://www.flotcharts.org) for plotting, zooming and pan- gene name, links to Ensembl and UCSC, genomic interval and gene ning. All data displayed on the plots are also listed in DataTables description appears in a table below the gene plot (as in Fig. 1D). (https://datatables.net). Toppar stores the data in a MySQL 1924 T.Juliusdottir et al. database and uses Perl and DBI (Database Interface) with Common applicationofTopparisthatitcan serve as a persistent local interface Gateway Interface for database communication. Installation of for association results and simultaneously integrate information from Toppar is straightforward, requiring an Internet-connected UNIX other sources for comparison and annotation. Future development of platform with a webserver (e.g. Apache) and Perl (required Toppar will focus on inclusion and display of regulatory elements [e.g. JavaScripts are included in the Toppar download package). The enhancers, promoters and topologically associating domains]. package also includes a Perl script for managing all database- dependent tasks, such as creating the database, and uploading and Funding deleting data from it. The same script can also be used to update the database’s GWAS catalog and gene annotations making it straight- This work was supported by the Wellcome Trust awards [090532 to R.M. forward to keep up with the most current releases. Data can also be and M.I.M, 098381] and the JDRF [2-SRA-2014-276-Q-R]. M.I.M is a uploaded and deleted through the online GUI. Wellcome Trust Senior Investigator. The data format required for upload of association data is a text Conflict of Interest: none declared. file containing at least four columns (chr, pos, marker_id and pvalue), where the exact order and naming of the columns is flexible (i.e. the chr label can be either chr, chrom or chromosome and is References case insensitive) to suit a variety of output formats. Alternatively, Down,T.A. et al. (2011) Dalliance: interactive genome viewing on the web. the data can be uploaded from two separate files, one with variant Bioinformatics, 27, 889–890. information (chr, pos and marker_id) and the other with association Geihs,M. et al. (2015) An interactive genome browser of association results results (marker_id and pvalue). Gene-based test results can also be from the UK10K cohorts project. Bioinformatics, 31, 4029–4031. directly uploaded to the database if the group file used to generate Locke,A.E. et al. (2015) Genetic studies of body mass index yield new insights them is provided (see website for more information). for obesity biology. Nature, 518, 197–206. Pletscher-Frankild,S. et al. (2015) DISEASES: text mining and data integration of disease-gene associations. Methods, 74, 83–89. 3 Conclusions Pruim,R.J. et al. (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics, 26, 2336–2337. We have created a user-friendly tool to view and navigate phenotype/ Shungin,D. et al. (2015) New genetic loci link adipose and insulin biology to genotype association results. A distinctive feature of Toppar is that it fa- body fat distribution. Nature, 518, 187–196. cilitates navigation through results using a hierarchical filtering menu. Taylor,M. et al. (2007) Management, presentation and interpretation of gen- Toppar is optimized for comparison of results obtained in bulk and im- ome scans using GSCANDB. Bioinformatics, 23, 1545–1549. mediately after receiving association data the user can view and browse Welter,D. et al. (2014) The NHGRI GWAS Catalog, a curated resource the results. The user can store all data in one place, revisit it later, and of SNP-trait associations. Nucleic Acids Res., 42(Database issue), add more results or external data to it as relevant. Thus, a key D1001–D1006. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

Toppar: an interactive browser for viewing association study results

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Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press.
ISSN
1367-4803
eISSN
1460-2059
DOI
10.1093/bioinformatics/btx840
Publisher site
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Abstract

Summary: Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases. Availability and implementation: Detailed information on Toppar’s features and functionality are on our website http://mccarthy.well.ox.ac.uk/toppar/docs along with instructions on how to down- load, install and run Toppar. Our online version of Toppar is accessible from the website and can be test-driven using Firefox, Safari or Chrome on sub-sets of publicly available genome-wide asso- ciation study anthropometric waist and body mass index data (Locke et al., 2015; Shungin et al., 2015) from the Genetic Investigation of ANthropometric Traits consortium. Contact: [email protected] 1 Introduction 2 Implementation Analytical challenges facing genetic studies increase with larger, 2.1 Main features and functionality more complex datasets, more extensive phenotypic trait informa- Toppar is an extension of GSCANDB (Taylor et al., 2007), and the tion, a greater array of statistical tests and a variety of genetic refer- display includes filtering and interactive viewing panels (Fig. 1). ence panels and annotation tools. Here, we present Toppar a Association results can be viewed in the latter by either genome or customizable database-driven browser for genetic association gen- region view, with association P-values on the –log10 scale displayed ome-wide association study (GWAS) data. It allows integration and on the vertical axes and chromosomal position on the horizontal visualization of analyses generated across multiple platforms and axis. Association results mapped to a specific genome build methodologies. It combines a whole-genome overview of GWAS re- (GRCh38 by default) for a given study or population can be viewed sults with an interactive regional display for loci of interest. User through various filters. data can be uploaded and explored in conjunction with gene and Toppar’s functionality is best illustrated using published geno- exon annotation, as well as already published genome-wide associ- type/phenotype association results, such as the waist and body mass ation catalogs (Welter et al., 2014). The Toppar browser can filter index (BMI) datasets released by the international Genetic its display using user-defined sub-categories, e.g. to visualize both Investigation of ANthropometric Trait (GIANT) consortium, which single variant and gene-based association results. This enables com- includes GWAS data from a number of anthropometric traits strati- parison across multiple traits, using different tests and filters. fied by ancestry and gender (Locke et al., 2015; Shungin et al., V The Author(s) 2018. Published by Oxford University Press. 1922 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Toppar 1923 Fig. 1. Toppar’s main interface. The filtering panel (A) allows the user to navigate and view the data by selecting available sub-categories. From the genome view (B), one navigates to a specific genomic locus by selecting it on the plot. This refocuses the display to the region (chromosome) view (C). Beyond a certain level of zoom-in the appearance of the plotted dots that indicate association results changes from small filled circles (D) to larger open circles, indicating they have be- come interactive and that test statistic associated with the point/test are displayed on mouseover. An interactive point becomes red when selected, with the vari- ant name displayed above it, and the LD pattern of all data points relative to the one selected can be displayed (D). At further zoom-in, exonic regions are also displayed for each gene (E). GWAS catalog SNPs can be added and colored based on traits of interest and hovering over a GWAS variant displays its rs identifier, P-value and associated disease trait and pubmed id, while selecting the variant takes the user to the referenced pubmed article (E). Multiple traits can be viewed simultaneously (F), where each trait appears on a separate but synchronized plot: zooming in on one plot automatically refocuses the other plots 2015). We uploaded sets of top 40 000 genetic variants associated Association results and GWAS catalog data are also listed in tables with BMI and with traits related to fat distribution including waist- that are hidden from view by default. hip-ratio, with and without adjusting for BMI, to Toppar’s local Existing browsers that display genotype/phenotype association database (see full instructions on website). The upload time per 40k results include the regional plotter Locuszoom (Pruim et al., 2010), set was around 3 s. The entire upload, which only has to be done UK10K genome browser (Geihs et al., 2015) which is based on the once, took approximately 3 min on a Mac OS v10.9.5 with a 3 GHz Biodalliance platform (Down et al., 2011) and the AMP T2D know- Intel Core i7 processor and 16 GB 1600 MHz memory. ledge portal (http://www.type2diabetesgenetics.org). Locuszoom is Once uploaded, the different phenotypes, ethnicity, gender and optimized for plotting regions of association test results for single test appear in Toppar’s four filtering menus (Fig. 1A). To view the traits and displaying the pattern of LD, whereas the UK10K genome GIANT results (e.g. for BMI) one can use the optional filters before and AMP T2D are publicly available web browsers, which allow for selecting genome view and pressing submit. From the genome view the extensive exploration of pre-uploaded data. Toppar has the abil- (Fig. 1B) one can navigate to the region (chromosome) view ity to display multiple trait data and integrate it with a wider selec- (Fig. 1C), where results for the area of interest and neighboring tion of relevant external data as well as provide LD information for genes can be further explored through zooming and panning and small genetic regions (GRCh38). It thus complements Locuszoom as linkage disequilibrium (LD) patterns can be displayed for selected well as larger genome browsers. variants. Genes linked with disease according to the DISEASE web resource, based on automatic text mining of scientific literature (Pletscher-Frankild et al., 2015) are highlighted in red and the 2.2 Installation and usage gene name and associated disease are displayed by hovering over the Toppar was written in JavaScript and HTML and uses the Flot li- gene (Fig. 1C and D). Annotation for displayed genes, including brary (http://www.flotcharts.org) for plotting, zooming and pan- gene name, links to Ensembl and UCSC, genomic interval and gene ning. All data displayed on the plots are also listed in DataTables description appears in a table below the gene plot (as in Fig. 1D). (https://datatables.net). Toppar stores the data in a MySQL 1924 T.Juliusdottir et al. database and uses Perl and DBI (Database Interface) with Common applicationofTopparisthatitcan serve as a persistent local interface Gateway Interface for database communication. Installation of for association results and simultaneously integrate information from Toppar is straightforward, requiring an Internet-connected UNIX other sources for comparison and annotation. Future development of platform with a webserver (e.g. Apache) and Perl (required Toppar will focus on inclusion and display of regulatory elements [e.g. JavaScripts are included in the Toppar download package). The enhancers, promoters and topologically associating domains]. package also includes a Perl script for managing all database- dependent tasks, such as creating the database, and uploading and Funding deleting data from it. The same script can also be used to update the database’s GWAS catalog and gene annotations making it straight- This work was supported by the Wellcome Trust awards [090532 to R.M. forward to keep up with the most current releases. Data can also be and M.I.M, 098381] and the JDRF [2-SRA-2014-276-Q-R]. M.I.M is a uploaded and deleted through the online GUI. Wellcome Trust Senior Investigator. The data format required for upload of association data is a text Conflict of Interest: none declared. file containing at least four columns (chr, pos, marker_id and pvalue), where the exact order and naming of the columns is flexible (i.e. the chr label can be either chr, chrom or chromosome and is References case insensitive) to suit a variety of output formats. Alternatively, Down,T.A. et al. (2011) Dalliance: interactive genome viewing on the web. the data can be uploaded from two separate files, one with variant Bioinformatics, 27, 889–890. information (chr, pos and marker_id) and the other with association Geihs,M. et al. (2015) An interactive genome browser of association results results (marker_id and pvalue). Gene-based test results can also be from the UK10K cohorts project. Bioinformatics, 31, 4029–4031. directly uploaded to the database if the group file used to generate Locke,A.E. et al. (2015) Genetic studies of body mass index yield new insights them is provided (see website for more information). for obesity biology. Nature, 518, 197–206. Pletscher-Frankild,S. et al. (2015) DISEASES: text mining and data integration of disease-gene associations. Methods, 74, 83–89. 3 Conclusions Pruim,R.J. et al. (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics, 26, 2336–2337. We have created a user-friendly tool to view and navigate phenotype/ Shungin,D. et al. (2015) New genetic loci link adipose and insulin biology to genotype association results. A distinctive feature of Toppar is that it fa- body fat distribution. Nature, 518, 187–196. cilitates navigation through results using a hierarchical filtering menu. Taylor,M. et al. (2007) Management, presentation and interpretation of gen- Toppar is optimized for comparison of results obtained in bulk and im- ome scans using GSCANDB. Bioinformatics, 23, 1545–1549. mediately after receiving association data the user can view and browse Welter,D. et al. (2014) The NHGRI GWAS Catalog, a curated resource the results. The user can store all data in one place, revisit it later, and of SNP-trait associations. Nucleic Acids Res., 42(Database issue), add more results or external data to it as relevant. Thus, a key D1001–D1006.

Journal

Bioinformatics – Oxford University Press

Published: Jan 8, 2018

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