UNLABELLED: High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project. AVAILABILITY AND IMPLEMENTATION: The browser is available at http://www.uk10k.org/dalliance.html. Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv

Down, Thomas A

Geihs, Matthias

Huang, Jie

Hubbard, Tim J

Mead, Daniel

Memari, Yasin

Min, Josine L

Soranzo, Nicole

Timpson, Nicholas J

UK10K Consortium

Walter, Klaudia

Yan, Ying

English

PubMed

High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project.</p

Min, Josine L.

Hubbard, Tim J.

Timpson, Nicholas J.

Down, Thomas A.

King's Research Portal

                       King’s Research Portal  DOI:10.1093/bioinformatics/btv491Document VersionPublisher's PDF, also known as Version of recordLink to publication record in King's Research PortalCitation for published version (APA):Geihs, M., Yan, Y., Walter, K., Huang, J., Memari, Y., Min, J. L., Mead, D., UK10 Consortium, Hubbard, T. J.,Timpson, N. J., Down, T. A., & Soranzo, N. (2015). An interactive genome browser of association results fromthe UK10K cohorts project. BIOINFORMATICS, 31(24), 4029-4031.https://doi.org/10.1093/bioinformatics/btv491Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. 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Oct. 2023Genetic and population analysisAn interactive genome browser of associationresults from the UK10K cohorts projectMatthias Geihs1, Ying Yan1, Klaudia Walter1, Jie Huang1,Yasin Memari1, Josine L. Min2, Daniel Mead1, UK10K Consortium,Tim J. Hubbard1,3, Nicholas J. Timpson2, Thomas A. Down4,* andNicole Soranzo1,5,*1Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK, 2MRC Integrative Epidemiology Unit,University of Bristol, Oakfield Grove, Bristol, UK, 3Department of Medical and Molecular Genetics, Division of Geneticsand Molecular Medicine, King’s College London School of Medicine, Guy’s Hospital, London SE1 9RT, UK, 4EMBL-EBI,Hinxton CB10 1SD, UK and 5Department of Haematology, University of Cambridge, Cambridge CB2 1TN, UK*To whom correspondence should be addressed.Associate Editor: Alfonso ValenciaReceived on March 31, 2015; revised on July 7, 2015; accepted on August 17, 2015AbstractSummary: High-throughput sequencing technologies survey genetic variation at genome scaleand are increasingly used to study the contribution of rare and low-frequency genetic variants tohuman traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7) whole genome sequencing of 3621 cohort individuals were analysed forstatistical associations with 64 different phenotypic traits of biomedical importance. Here, we de-scribe a novel genome browser based on the Biodalliance platform developed to provide inter-active access to the association results of the project.Availability and implementation: The browser is available at http://www.uk10k.org/dalliance.html.Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.Contact: ns6@sanger.ac.uk or thomas@biodalliance.orgSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionRare and low-frequency genetic variants play an important role indetermining population variance of complex traits and disease; how-ever, until recently their systematic evaluation has been beyond thereach of empirical population-based genetic studies.The UK10K project was designed to characterize rare and low-frequency variation in the UK genome wide, and study its contribu-tion to a broad spectrum of biomedically relevant quantitative traitsand diseases with different predicted genetic architectures. The datagenerated by the different arms of the UK10K project and theiruse are described elsewhere (UK10K Consortium, 2015). Here, wedescribe the development of a novel browser for genetic associationdata based on the Biodalliance platform (Down et al., 2011), de-signed to facilitate the retrieval of genotype–phenotype associationresults from the UK10K-cohorts arm of the project, and their visual-ization in the context of different annotation features (Fig. 1). Inparticular, we developed a novel interactive display for genetic vari-ants showing both the strength of association with a trait, and thepattern of linkage disequilibrium (LD) within the cohort.2 ImplementationBiodalliance is a pure JavaScript genome browser, using HTML5canvas for displays. Biodalliance’s preferred approach for fetchingdata is to use indexed binary files such as bigWig or bigBed filesVC The Author 2015. Published by Oxford University Press. 4029This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 31(24), 2015, 4029–4031doi: 10.1093/bioinformatics/btv491Advance Access Publication Date: 26 August 2015Applications Note at King's College London - Journals Dept on August 11, 2016http://bioinformatics.oxfordjournals.org/Downloaded from (Kent et al., 2010) which can be made available from normal web-servers and are frequently organized as track-hubs (Raney et al.,2014). Because Biodalliance is pure JavaScript it can be embedded inany normal webpage making it easy to create custom browsers suchas presented here.However, creating a display showing genetic association dataposed additional challenges. Firstly, there is no perfect file formatfor representing the association results themselves (which compriseboth a P-value from the association test and additional informationabout the SNP). Secondly, our desire to present LD data in a flex-ible, interactive manner does not fit well with the usual indexed-fileapproach of retrieving a single block of data corresponding to somerange of genomic coordinates.To represent the association results, we used a pair of binaryfiles. Firstly, a bigWig file contains the association test result (ex-pressed as a log P-value) for each position in the genome where avariant was tested. A bigBed file containing the variant ID and otherinformation such as SNP consequence predictions supplements this.We added support in the core Biodalliance code for merging mul-tiple data sources into a single track. The Biodalliance renderer andstylesheet system runs on this merged dataset, so in our associationtracks we can show association score (from a bigWig file) as the y-axis position of the feature, but choose a point style based on theSNP consequence (from the bigBed file). The y-axis encoding hadthe added benefit of enabling a feature in the browser to searchfor the next variant in the genome above an association scorethreshold (exploiting information encoded in the reduced resolutionviews of bigWig files) or by identifier (using a name index in thebigBed file).Dynamic display of LD information poses a different challengesince all-against-all data are unwieldy but we do not know in ad-vance which variants users might want to select as reference. Oneoption would be to create a Biodalliance plug-in that calculates LDon the client side. However, this would require complete genotypedata for the cohort, which is also very large, and would raise data se-curity concerns. Instead, we developed a simple server componentfor LD calculation on the fly for a selected (or multiple) referencevariant(s), allowing the genotype data itself to remain securelyon the server. We also developed Biodalliance plug-ins for selectingreference variants, and for communicating with the LD server.Once again, the capability to merge results from multiple data sour-ces proved useful, and the LD scores simply get merged into the restof the feature data before it is passed to the Biodalliance renderer.Having extended the Biodalliance code in this way, bigWig andfiles containing P-values were prepared for each combination of thetraits in Supplementary Appendix S1, and the statistical tests inSupplementary Appendix S2. A description of the statistical tests im-plemented is given in Supplementary Appendix 3. The large numberof resulting files were organized in a track-hub, which the UK10KGenome Browser was configured to use by default. The browserfunctionality is described in Supplementary Appendix 4. A referencesummary of test statistics, and key navigation functions, is given inSupplementary Appendix 5. Finally, a step-by-step user tutorial tothe browser is also provided (Supplementary Appendix 6).3 ResultsThe UK10K Genome Browser exploits the key basic features of theBiodalliance Genome Browser. A number of custom features werespecifically designed to aid navigation and interpretation of theUK10K-cohorts data. First, the genome browser supports dynamic es-timation of local LD statistics (i.e. the metric r2) for inferring statis-tical independence between local association signals and one or moreSNP(s) of interest. Calculations are performed in real time using theUK10K-cohorts sequence data. Second, it allows exporting of local as-sociation results into high-quality images (in scalable vector graphics,svg) that can be used in publications. Finally, it provides dynamic an-notation of each SNP, including information on minor allele fre-quency, SNP quality metrics, as well as links to SNP annotationresources such as dbSNP and functional genome annotation track-hubs such as those generated by the ENCODE (Encode ProjectConsortium et al., 2012), NIH Roadmap (Roadmap EpigenomicsConsortium et al., 2015) and BLUEPRINT (Adams et al., 2012).4 ConclusionsIn summary, the UK10K browser provides an intuitive and efficientplatform to access association statistics for common, low-frequencyand rare variants against a large number of human phenotypic traits.As efforts progress to systematically map the contribution of humangenetic variation to healthy and disease phenotypes, and to integrate itwith genome functional resources, the development of platforms likeours will become essential enabling instruments for the integration andcross-validation of genetic discoveries within the scientific community.AcknowledgementsThis study makes use of data generated by the UK10K Consortium, derivedfrom samples from the ALSPAC and TwinsUK datasets. A full list of the in-vestigators who contributed to the generation of the data is available fromwww.uk10k.org. We acknowledge James Smith for deploying the server codeon the Sanger Institute web infrastructure.FundingThe Wellcome Trust under award WT091310 provided funding for UK10K.N.S. and team are supported by the Wellcome Trust (Grant Codes WT098051and WT091310), the EU-FP7 (EPIGENESYS Grant Code 257082 andBLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR. We fur-ther acknowledge the BBSRC (grant BB/K015427/1).Conflict of Interest: none declared.ReferencesAdams,D. et al. (2012) BLUEPRINT to decode the epigenetic signature writ-ten in blood. Nat. Biotechnol., 30, 224–226.Down,T.A. et al. (2011) Dalliance: interactive genome viewing on the web.Bioinformatics, 27, 889–890.Encode Project Consortium et al. (2012) An integrated encyclopedia of DNAelements in the human genome. Nature, 489, 57–74.Fig. 1. Example screenshot of the UK10K Genome Browser. The panel showsa 30-kb region of the human genome where UK10K SNPs associate with HDLCholesterol with high P-values around the CETP gene. Colouring identifiesgroups of independent SNPs4030 M.Geihs et al. at King's College London - Journals Dept on August 11, 2016http://bioinformatics.oxfordjournals.org/Downloaded from Kent,W.J. et al. (2010) BigWig and BigBed: enabling browsing of large distrib-uted datasets. Bioinformatics, 26, 2204–2207.Raney,B.J. et al. (2014) Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. Bioinformatics, 30, 1003–1005.Roadmap Epigenomics,C. et al. (2015) Integrative analysis of 111 referencehuman epigenomes. Nature, 518, 317–330.UK10K Consortium. (2015) The UK10K project identifies rare variants inhealth and disease, Nature, 526, 82–90.An interactive genome browser based on the Biodalliance platform 4031 at King's College London - Journals Dept on August 11, 2016http://bioinformatics.oxfordjournals.org/Downloaded from 

An interactive genome browser of association results from the UK10K cohorts project

Geihs, M

Yan, Y

Walter, K

Huang, J

Memari, Y

Min, JL

Mead, D

Hubbard, TJ

Timpson, NJ

Down, TA

Soranzo, N

St George's Online Research Archive

Genetic and population analysisAn interactive genome browser of associationresults from the UK10K cohorts projectMatthias Geihs1, Ying Yan1, Klaudia Walter1, Jie Huang1,Yasin Memari1, Josine L. Min2, Daniel Mead1, UK10K Consortium,Tim J. Hubbard1,3, Nicholas J. Timpson2, Thomas A. Down4,* andNicole Soranzo1,5,*1Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK, 2MRC Integrative Epidemiology Unit,University of Bristol, Oakfield Grove, Bristol, UK, 3Department of Medical and Molecular Genetics, Division of Geneticsand Molecular Medicine, King’s College London School of Medicine, Guy’s Hospital, London SE1 9RT, UK, 4EMBL-EBI,Hinxton CB10 1SD, UK and 5Department of Haematology, University of Cambridge, Cambridge CB2 1TN, UK*To whom correspondence should be addressed.Associate Editor: Alfonso ValenciaReceived on March 31, 2015; revised on July 7, 2015; accepted on August 17, 2015AbstractSummary: High-throughput sequencing technologies survey genetic variation at genome scaleand are increasingly used to study the contribution of rare and low-frequency genetic variants tohuman traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7) whole genome sequencing of 3621 cohort individuals were analysed forstatistical associations with 64 different phenotypic traits of biomedical importance. Here, we de-scribe a novel genome browser based on the Biodalliance platform developed to provide inter-active access to the association results of the project.Availability and implementation: The browser is available at http://www.uk10k.org/dalliance.html.Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.Contact: ns6@sanger.ac.uk or thomas@biodalliance.orgSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionRare and low-frequency genetic variants play an important role indetermining population variance of complex traits and disease; how-ever, until recently their systematic evaluation has been beyond thereach of empirical population-based genetic studies.The UK10K project was designed to characterize rare and low-frequency variation in the UK genome wide, and study its contribu-tion to a broad spectrum of biomedically relevant quantitative traitsand diseases with different predicted genetic architectures. The datagenerated by the different arms of the UK10K project and theiruse are described elsewhere (UK10K Consortium, 2015). Here, wedescribe the development of a novel browser for genetic associationdata based on the Biodalliance platform (Down et al., 2011), de-signed to facilitate the retrieval of genotype–phenotype associationresults from the UK10K-cohorts arm of the project, and their visual-ization in the context of different annotation features (Fig. 1). Inparticular, we developed a novel interactive display for genetic vari-ants showing both the strength of association with a trait, and thepattern of linkage disequilibrium (LD) within the cohort.2 ImplementationBiodalliance is a pure JavaScript genome browser, using HTML5canvas for displays. Biodalliance’s preferred approach for fetchingdata is to use indexed binary files such as bigWig or bigBed filesVC The Author 2015. Published by Oxford University Press. 4029This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 31(24), 2015, 4029–4031doi: 10.1093/bioinformatics/btv491Advance Access Publication Date: 26 August 2015Applications Note(Kent et al., 2010) which can be made available from normal web-servers and are frequently organized as track-hubs (Raney et al.,2014). Because Biodalliance is pure JavaScript it can be embedded inany normal webpage making it easy to create custom browsers suchas presented here.However, creating a display showing genetic association dataposed additional challenges. Firstly, there is no perfect file formatfor representing the association results themselves (which compriseboth a P-value from the association test and additional informationabout the SNP). Secondly, our desire to present LD data in a flex-ible, interactive manner does not fit well with the usual indexed-fileapproach of retrieving a single block of data corresponding to somerange of genomic coordinates.To represent the association results, we used a pair of binaryfiles. Firstly, a bigWig file contains the association test result (ex-pressed as a log P-value) for each position in the genome where avariant was tested. A bigBed file containing the variant ID and otherinformation such as SNP consequence predictions supplements this.We added support in the core Biodalliance code for merging mul-tiple data sources into a single track. The Biodalliance renderer andstylesheet system runs on this merged dataset, so in our associationtracks we can show association score (from a bigWig file) as the y-axis position of the feature, but choose a point style based on theSNP consequence (from the bigBed file). The y-axis encoding hadthe added benefit of enabling a feature in the browser to searchfor the next variant in the genome above an association scorethreshold (exploiting information encoded in the reduced resolutionviews of bigWig files) or by identifier (using a name index in thebigBed file).Dynamic display of LD information poses a different challengesince all-against-all data are unwieldy but we do not know in ad-vance which variants users might want to select as reference. Oneoption would be to create a Biodalliance plug-in that calculates LDon the client side. However, this would require complete genotypedata for the cohort, which is also very large, and would raise data se-curity concerns. Instead, we developed a simple server componentfor LD calculation on the fly for a selected (or multiple) referencevariant(s), allowing the genotype data itself to remain securelyon the server. We also developed Biodalliance plug-ins for selectingreference variants, and for communicating with the LD server.Once again, the capability to merge results from multiple data sour-ces proved useful, and the LD scores simply get merged into the restof the feature data before it is passed to the Biodalliance renderer.Having extended the Biodalliance code in this way, bigWig andfiles containing P-values were prepared for each combination of thetraits in Supplementary Appendix S1, and the statistical tests inSupplementary Appendix S2. A description of the statistical tests im-plemented is given in Supplementary Appendix 3. The large numberof resulting files were organized in a track-hub, which the UK10KGenome Browser was configured to use by default. The browserfunctionality is described in Supplementary Appendix 4. A referencesummary of test statistics, and key navigation functions, is given inSupplementary Appendix 5. Finally, a step-by-step user tutorial tothe browser is also provided (Supplementary Appendix 6).3 ResultsThe UK10K Genome Browser exploits the key basic features of theBiodalliance Genome Browser. A number of custom features werespecifically designed to aid navigation and interpretation of theUK10K-cohorts data. First, the genome browser supports dynamic es-timation of local LD statistics (i.e. the metric r2) for inferring statis-tical independence between local association signals and one or moreSNP(s) of interest. Calculations are performed in real time using theUK10K-cohorts sequence data. Second, it allows exporting of local as-sociation results into high-quality images (in scalable vector graphics,svg) that can be used in publications. Finally, it provides dynamic an-notation of each SNP, including information on minor allele fre-quency, SNP quality metrics, as well as links to SNP annotationresources such as dbSNP and functional genome annotation track-hubs such as those generated by the ENCODE (Encode ProjectConsortium et al., 2012), NIH Roadmap (Roadmap EpigenomicsConsortium et al., 2015) and BLUEPRINT (Adams et al., 2012).4 ConclusionsIn summary, the UK10K browser provides an intuitive and efficientplatform to access association statistics for common, low-frequencyand rare variants against a large number of human phenotypic traits.As efforts progress to systematically map the contribution of humangenetic variation to healthy and disease phenotypes, and to integrate itwith genome functional resources, the development of platforms likeours will become essential enabling instruments for the integration andcross-validation of genetic discoveries within the scientific community.AcknowledgementsThis study makes use of data generated by the UK10K Consortium, derivedfrom samples from the ALSPAC and TwinsUK datasets. A full list of the in-vestigators who contributed to the generation of the data is available fromwww.uk10k.org. We acknowledge James Smith for deploying the server codeon the Sanger Institute web infrastructure.FundingThe Wellcome Trust under award WT091310 provided funding for UK10K.N.S. and team are supported by the Wellcome Trust (Grant Codes WT098051and WT091310), the EU-FP7 (EPIGENESYS Grant Code 257082 andBLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR. We fur-ther acknowledge the BBSRC (grant BB/K015427/1).Conflict of Interest: none declared.ReferencesAdams,D. et al. (2012) BLUEPRINT to decode the epigenetic signature writ-ten in blood. Nat. Biotechnol., 30, 224–226.Down,T.A. et al. (2011) Dalliance: interactive genome viewing on the web.Bioinformatics, 27, 889–890.Encode Project Consortium et al. (2012) An integrated encyclopedia of DNAelements in the human genome. Nature, 489, 57–74.Fig. 1. Example screenshot of the UK10K Genome Browser. The panel showsa 30-kb region of the human genome where UK10K SNPs associate with HDLCholesterol with high P-values around the CETP gene. Colouring identifiesgroups of independent SNPs4030 M.Geihs et al.Kent,W.J. et al. (2010) BigWig and BigBed: enabling browsing of large distrib-uted datasets. Bioinformatics, 26, 2204–2207.Raney,B.J. et al. (2014) Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. Bioinformatics, 30, 1003–1005.Roadmap Epigenomics,C. et al. (2015) Integrative analysis of 111 referencehuman epigenomes. Nature, 518, 317–330.UK10K Consortium. (2015) The UK10K project identifies rare variants inhealth and disease, Nature, 526, 82–90.An interactive genome browser based on the Biodalliance platform 4031

An interactive genome browser of association results from the UK10K cohorts project.

UNLABELLED: High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project.AVAILABILITY AND IMPLEMENTATION: The browser is available at http://www.uk10k.org/dalliance.html. Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.CONTACT: ns6@sanger.ac.uk or thomas@biodalliance.orgSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p

Explore Bristol Research

Apollo (Cambridge)

Genetic and population analysisAn interactive genome browser of associationresults from the UK10K cohorts projectMatthias Geihs1, Ying Yan1, Klaudia Walter1, Jie Huang1,Yasin Memari1, Josine L. Min2, Daniel Mead1, UK10K Consortium,Tim J. Hubbard1,3, Nicholas J. Timpson2, Thomas A. Down4,* andNicole Soranzo1,5,*1Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK, 2MRC Integrative Epidemiology Unit,University of Bristol, Oakfield Grove, Bristol, UK, 3Department of Medical and Molecular Genetics, Division of Geneticsand Molecular Medicine, King’s College London School of Medicine, Guy’s Hospital, London SE1 9RT, UK, 4EMBL-EBI,Hinxton CB10 1SD, UK and 5Department of Haematology, University of Cambridge, Cambridge CB2 1TN, UK*To whom correspondence should be addressed.Associate Editor: Alfonso ValenciaReceived on March 31, 2015; revised on July 7, 2015; accepted on August 17, 2015AbstractSummary: High-throughput sequencing technologies survey genetic variation at genome scaleand are increasingly used to study the contribution of rare and low-frequency genetic variants tohuman traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7) whole genome sequencing of 3621 cohort individuals were analysed forstatistical associations with 64 different phenotypic traits of biomedical importance. Here, we de-scribe a novel genome browser based on the Biodalliance platform developed to provide inter-active access to the association results of the project.Availability and implementation: The browser is available at http://www.uk10k.org/dalliance.html.Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.Contact: ns6@sanger.ac.uk or thomas@biodalliance.orgSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionRare and low-frequency genetic variants play an important role indetermining population variance of complex traits and disease; how-ever, until recently their systematic evaluation has been beyond thereach of empirical population-based genetic studies.The UK10K project was designed to characterize rare and low-frequency variation in the UK genome wide, and study its contribu-tion to a broad spectrum of biomedically relevant quantitative traitsand diseases with different predicted genetic architectures. The datagenerated by the different arms of the UK10K project and theiruse are described elsewhere (UK10K Consortium, 2015). Here, wedescribe the development of a novel browser for genetic associationdata based on the Biodalliance platform (Down et al., 2011), de-signed to facilitate the retrieval of genotype–phenotype associationresults from the UK10K-cohorts arm of the project, and their visual-ization in the context of different annotation features (Fig. 1). Inparticular, we developed a novel interactive display for genetic vari-ants showing both the strength of association with a trait, and thepattern of linkage disequilibrium (LD) within the cohort.2 ImplementationBiodalliance is a pure JavaScript genome browser, using HTML5canvas for displays. Biodalliance’s preferred approach for fetchingdata is to use indexed binary files such as bigWig or bigBed filesVC The Author 2015. Published by Oxford University Press. 4029This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 31(24), 2015, 4029–4031doi: 10.1093/bioinformatics/btv491Advance Access Publication Date: 26 August 2015Applications Note(Kent et al., 2010) which can be made available from normal web-servers and are frequently organized as track-hubs (Raney et al.,2014). Because Biodalliance is pure JavaScript it can be embedded inany normal webpage making it easy to create custom browsers suchas presented here.However, creating a display showing genetic association dataposed additional challenges. Firstly, there is no perfect file formatfor representing the association results themselves (which compriseboth a P-value from the association test and additional informationabout the SNP). Secondly, our desire to present LD data in a flex-ible, interactive manner does not fit well with the usual indexed-fileapproach of retrieving a single block of data corresponding to somerange of genomic coordinates.To represent the association results, we used a pair of binaryfiles. Firstly, a bigWig file contains the association test result (ex-pressed as a log P-value) for each position in the genome where avariant was tested. A bigBed file containing the variant ID and otherinformation such as SNP consequence predictions supplements this.We added support in the core Biodalliance code for merging mul-tiple data sources into a single track. The Biodalliance renderer andstylesheet system runs on this merged dataset, so in our associationtracks we can show association score (from a bigWig file) as the y-axis position of the feature, but choose a point style based on theSNP consequence (from the bigBed file). The y-axis encoding hadthe added benefit of enabling a feature in the browser to searchfor the next variant in the genome above an association scorethreshold (exploiting information encoded in the reduced resolutionviews of bigWig files) or by identifier (using a name index in thebigBed file).Dynamic display of LD information poses a different challengesince all-against-all data are unwieldy but we do not know in ad-vance which variants users might want to select as reference. Oneoption would be to create a Biodalliance plug-in that calculates LDon the client side. However, this would require complete genotypedata for the cohort, which is also very large, and would raise data se-curity concerns. Instead, we developed a simple server componentfor LD calculation on the fly for a selected (or multiple) referencevariant(s), allowing the genotype data itself to remain securelyon the server. We also developed Biodalliance plug-ins for selectingreference variants, and for communicating with the LD server.Once again, the capability to merge results from multiple data sour-ces proved useful, and the LD scores simply get merged into the restof the feature data before it is passed to the Biodalliance renderer.Having extended the Biodalliance code in this way, bigWig andfiles containing P-values were prepared for each combination of thetraits in Supplementary Appendix S1, and the statistical tests inSupplementary Appendix S2. A description of the statistical tests im-plemented is given in Supplementary Appendix 3. The large numberof resulting files were organized in a track-hub, which the UK10KGenome Browser was configured to use by default. The browserfunctionality is described in Supplementary Appendix 4. A referencesummary of test statistics, and key navigation functions, is given inSupplementary Appendix 5. Finally, a step-by-step user tutorial tothe browser is also provided (Supplementary Appendix 6).3 ResultsThe UK10K Genome Browser exploits the key basic features of theBiodalliance Genome Browser. A number of custom features werespecifically designed to aid navigation and interpretation of theUK10K-cohorts data. First, the genome browser supports dynamic es-timation of local LD statistics (i.e. the metric r2) for inferring statis-tical independence between local association signals and one or moreSNP(s) of interest. Calculations are performed in real time using theUK10K-cohorts sequence data. Second, it allows exporting of local as-sociation results into high-quality images (in scalable vector graphics,svg) that can be used in publications. Finally, it provides dynamic an-notation of each SNP, including information on minor allele fre-quency, SNP quality metrics, as well as links to SNP annotationresources such as dbSNP and functional genome annotation track-hubs such as those generated by the ENCODE (Encode ProjectConsortium et al., 2012), NIH Roadmap (Roadmap EpigenomicsConsortium et al., 2015) and BLUEPRINT (Adams et al., 2012).4 ConclusionsIn summary, the UK10K browser provides an intuitive and efficientplatform to access association statistics for common, low-frequencyand rare variants against a large number of human phenotypic traits.As efforts progress to systematically map the contribution of humangenetic variation to healthy and disease phenotypes, and to integrate itwith genome functional resources, the development of platforms likeours will become essential enabling instruments for the integration andcross-validation of genetic discoveries within the scientific community.AcknowledgementsThis study makes use of data generated by the UK10K Consortium, derivedfrom samples from the ALSPAC and TwinsUK datasets. A full list of the in-vestigators who contributed to the generation of the data is available fromwww.uk10k.org. We acknowledge James Smith for deploying the server codeon the Sanger Institute web infrastructure.FundingThe Wellcome Trust under award WT091310 provided funding for UK10K.N.S. and team are supported by the Wellcome Trust (Grant Codes WT098051and WT091310), the EU-FP7 (EPIGENESYS Grant Code 257082 andBLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR. We fur-ther acknowledge the BBSRC (grant BB/K015427/1).Conflict of Interest: none declared.ReferencesAdams,D. et al. (2012) BLUEPRINT to decode the epigenetic signature writ-ten in blood. Nat. Biotechnol., 30, 224–226.Down,T.A. et al. (2011) Dalliance: interactive genome viewing on the web.Bioinformatics, 27, 889–890.Encode Project Consortium et al. (2012) An integrated encyclopedia of DNAelements in the human genome. Nature, 489, 57–74.Fig. 1. Example screenshot of the UK10K Genome Browser. The panel showsa 30-kb region of the human genome where UK10K SNPs associate with HDLCholesterol with high P-values around the CETP gene. Colouring identifiesgroups of independent SNPs4030 M.Geihs et al.Kent,W.J. et al. (2010) BigWig and BigBed: enabling browsing of large distrib-uted datasets. Bioinformatics, 26, 2204–2207.Raney,B.J. et al. (2014) Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser.Bioinformatics, 30, 1003–1005.Roadmap Epigenomics,C. et al. (2015) Integrative analysis of 111 referencehuman epigenomes. Nature, 518, 317–330.UK10K Consortium. (2015) The UK10K project identifies rare variants inhealth and disease, Nature, 526, 82–90.An interactive genome browser based on the Biodalliance platform 4031

Matthias Geihs

Ying Yan

Klaudia Walter

Jie Huang

Yasin Memari

Josine L. Min

Daniel Mead

Tim J. Hubbard

Nicholas J. Timpson

Thomas A. Down

Nicole Soranzo

null null

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https://www.repository.cam.ac.uk/bitstream/handle/1810/277458/An%20interactive%20genome%20browser%20of%20association%20results%20from%20the%20UK10K%20cohorts%20project.pdf?sequence=1&isAllowed=y

An interactive genome browser of association results from the UK10K cohorts project.

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