SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates 'phenome scans', where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ('PhenoScanner V2'), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.This work was supported by the UK Medical Research Council
[G0800270; MR/L003120/1], the British Heart Foundation [SP/09/002;
RG/13/13/30194; RG/18/13/33946], Pfizer [G73632], the European Research Council [268834], the European Commission Framework Programme
7 [HEALTH-F2-2012-279233], the National Institute for Health Research
and Health Data Research UK (*). *The views expressed are those of the
authors and not necessarily those of the NHS or the NIHR

Blackshaw, James A

Burgess, Stephen

Butterworth, Adam S

Danesh, John

Kamat, Mihir A

Staley, James R

Surendran, Praveen

Young, Robin

English

SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates "phenome scans", where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ("PhenoScanner V2"), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.SUPPLEMENTARY INFORMATION: Full documentation: www.phenoscanner.medschl.cam.ac.uk/information.</p

Explore Bristol Research

                          Kamat, M. A., Blackshaw, J. A., Young, R., Surendran, P., Burgess, S.,Danesh, J., ... Staley, J. R. (2019). PhenoScanner V2: an expanded tool forsearching human genotype-phenotype associations. Bioinformatics, [btz469].https://doi.org/10.1093/bioinformatics/btz469Publisher's PDF, also known as Version of recordLicense (if available):CC BYLink to published version (if available):10.1093/bioinformatics/btz469Link to publication record in Explore Bristol ResearchPDF-documentThis is the final published version of the article (version of record). It first appeared online via Oxford UniversityPress at https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz469/5522366.Please refer to any applicable terms of use of the publisher.University of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-termsDatabases and ontologiesPhenoScanner V2: an expanded tool forsearching human genotype–phenotypeassociationsMihir A. Kamat1, James A. Blackshaw1, Robin Young1,Praveen Surendran1, Stephen Burgess1,2, John Danesh1,3,4,Adam S. Butterworth1,4,* and James R. Staley1,51MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University ofCambridge, Cambridge CB1 8RN, UK, 2MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK,3Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK, 4NIHR Blood and Transplant Research Unit, Departmentof Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK and 5MRC IntegrativeEpidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK* To whom correspondence should be addressed.Associate Editor: Janet KelsoReceived on December 20, 2018; revised on May 1, 2019; editorial decision on May 29, 2019; accepted on June 19, 2019AbstractSummary: PhenoScanner is a curated database of publicly available results from large-scale genet-ic association studies in humans. This online tool facilitates ‘phenome scans’, where geneticvariants are cross-referenced for association with many phenotypes of different types. Here wepresent a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million geneticvariants and more than 65 billion associations (compared to 350 million associations inPhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, andepigenetic markers. The query options have been extended to include searches by genes, genomicregions and phenotypes, as well as for genetic variants. All variants are positionally annotatedusing the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontologyterms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search forphenotype associations with proxy variants.Availability and implementation: PhenoScanner V2 is available atwww.phenoscanner.medschl.cam.ac.uk.Contact: asb38@medschl.cam.ac.uk1 IntroductionDense array-based human genetic studies, such as genome-wideassociation studies (GWAS), have identified many thousands ofassociations between genetic variants and a diverse set of pheno-types. The challenge now facing the human genomics communityis to understand the mechanisms underlying these associations. Oneapproach to aid biological insight into disease mechanisms is tocross-reference genetic associations across a range of phenotypes,including disease states, cellular traits and other intermediate traits.To enable such ‘phenome scans’ we developed the online toolPhenoScanner (Staley et al., 2016). Since its release in 2016,PhenoScanner has been accessed by hundreds of users to assist arange of analyses from analyses linking proteins to disease (Sunet al., 2018) to interrogating novel loci associated with blood cellphenotypes (Astle et al., 2016).In recent years, there has been a rapid expansion in the availabil-ity of genetic association statistics with the maturation of geneticbiobanks with rich phenotypic information. Moreover, the scope ofVC The Author(s) 2019. Published by Oxford University Press. 1This 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, 2019, 1–3doi: 10.1093/bioinformatics/btz469Advance Access Publication Date: 24 June 2019Applications NoteDownloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Bristol Library user on 16 August 2019molecular phenotypes in genetic association studies has increasedwith the publication of multi-tissue gene expression GWAS (GTExConsortium et al., 2017) and GWAS of thousands of plasma pro-teins (Sun et al., 2018). However, integrating genetic associationsacross this vast array of data sources remains challenging. Hence, tofacilitate improved ‘phenome scans’, we have released an updatedversion of PhenoScanner (PhenoScanner V2) with new featuresincluding: (i) an expanded database of human genotype–phenotypeassociations associations split into phenotype classes (diseases andtraits, gene expression, proteins, metabolites and epigenetics); (ii)additional search options including gene, genomic region andphenotype-based queries; (iii) linkage disequilibrium (LD) informa-tion for the five super-ancestries in 1000 Genomes; (vi) variant an-notation and trait ontology mappings and (v) a brand new webinterface and API.2 Materials and methodsPhenoScanner V2 consists of a Python-R interface which connects toa series of MySQL databases. To develop the catalogue of humangenotype–phenotype associations, we identified and collated >5000genetic association datasets from publicly available lists of full sum-mary associations results compiled by the NHGRI-EBI (https://www.ebi.ac.uk/gwas/downloads/summary-statistics) and NHLBI(https://grasp.nhlbi.nih.gov/FullResults.aspx), as well as from recentliterature reviews and lists of omics GWAS (e.g. Sun et al., 2018 forprotein levels). The catalogue currently contains results for diseasesand traits (30 billion associations), gene expression (84 millionassociations), protein levels (35 billion associations), metabolitelevels (3 billion associations) and epigenetic markers (13 millionassociations). To ensure consistent formatting across datasets, all ofthe variants were aligned to the NCBI plus strand, rsIDs wereupdated to dbSNP 147 (Sherry et al., 2001) and chromosome-positions [GRCh37 (hg19) and GRCh38 (hg38)] were added orupdated using dbSNP 147 and liftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver). LD measures between neighbouring variants inthe autosomal chromosomes were calculated using phased haplo-types for the five super-ancestries (European, African, AdmixedAmerican, East Asian and South Asian) in the 1000 GenomesProject phase 3 (1000 Genomes Project Consortium et al., 2015).We calculated D’ and r2 for pairs of variants within 500 Kb andkept LD statistics with r2  0.5. All phenotypes were mapped toExperimental Factor Ontology terms (Malone et al., 2010) usingZOOMA (https://www.ebi.ac.uk/spot/zooma/). Variant and geneannotation for all of the variants was performed using EnsemblVariant Effect Predictor V88 (McLaren et al., 2016) withGENCODE transcripts V26 (Harrow et al., 2012) mapped to build37 positions. Nearest genes for intergenic variants were retrievedusing the BEDOPS tool version 2.4.26 (Neph et al., 2012).Users may enter one genetic variant, gene, genomic region ortrait into the text box on the home page (www.phenoscanner.medschl.cam.ac.uk) or upload up to 100 genetic variants, 10 genesor 10 genomic regions as a tab-delimited text file. PhenoScanner V2also has an API with an associated R package and Python commandline tool (www.phenoscanner.medschl.cam.ac.uk/tools), allowingusers to search for genotype–phenotype associations fromPhenoScanner V2 inside R or from a terminal. When querying genet-ic variants, all results regardless of P-value can be displayed allow-ing the user to identify evidence against associations withphenotypes. To produce manageable results sets, only results withP < 1105 are returned for queries of genes, genomic regions orphenotypes. Once a query is evoked, the Python-R interface anno-tates the genetic variant, gene, genomic region or phenotype usingdbSNP (or ZOOMA for trait queries), before searching therequested association databases and filtering the results based on thespecified P-value threshold. The new web interface then presents theresults and makes them available to download. All associations foreach genetic variant are aligned such that the effect allele is the sameacross all results. The associations with proxy variants are alignedsuch that their effect alleles are given with respect to the effect alleleof the corresponding queried variant.3 ResultsTo demonstrate the value of the expanded database and additionalfunctionality of PhenoScanner V2, we searched for ‘rs10840293’,‘SWAP70’ and ‘coronary heart disease’. PhenoScanner V2 found>150 000 results with rs10840293 (variant annotation: intronicvariant in SWAP70) or one of its proxies (r2  0.8 in Europeans),more than 100 times the number of associations found for the samevariant query using PhenoScanner V1 (1405 associations); theNHGRI-EBI GWAS Catalog (MacArthur et al., 2017) only returnsfour results for rs10840293. In particular, PhenoScanner V2 identi-fies strong associations of rs10840293 with coronary heart disease(van der Harst and Verweij, 2018), blood pressure (https://www.nealelab.is/uk-biobank) and platelet width (Astle et al., 2016), as wellas with whole blood gene expression (V~osa et al., 2018) and plasmaprotein levels (Sun et al., 2018) of SWAP70 (all with P < 5108),suggesting a possible blood pressure related mechanism affectingcoronary heart disease risk at this locus potentially regulated viaSWAP70 expression. Variants in the SWAP70 gene had >6000 asso-ciations with P < 1105 (compared with 27 associations found bythe GWAS Catalog), while there were >50 000 genetic associationswith coronary heart disease with P < 1105 across the genome(compared with 1092 associations found by the GWAS Catalog).4 ConclusionPhenoScanner V2 is a large curated database of human genotype–phenotype associations from publicly available genetic associationstudies. This catalogue of results greatly extends PhenoScanner V1in both scale and phenotypic breadth, with tables of genetic associa-tions for diseases and traits, gene expression, protein levels, metabo-lites levels and epigenetic markers. PhenoScanner V2 also hasadditional annotation and functionality. The database can now besearched for genes, genomic regions and traits, while variant annota-tion, phenotype ontology mappings and LD statistics from a widerrange of ethnic groups have been incorporated to enhance utilityand interpretation.FundingThis work was supported by the UK Medical Research Council [G0800270,MR/L003120/1]; the British Heart Foundation [SP/09/002, RG/13/13/30194,RG/18/13/33946]; Pfizer [G73632]; the European Research Council[268834]; the European Commission Framework Programme 7 [HEALTH-F2-2012-279233]; the National Institute for Health Research; and HealthData Research UK. The views expressed are those of the authors and not ne-cessarily those of the NHS or the NIHR.Conflict of Interest: none declared.2 M.A.Kamat et al.Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Bristol Library user on 16 August 2019References1000 Genomes Project Consortium et al. (2015) A global reference for humangenetic variation. Nature, 526, 68–74.Astle,W.J. et al. (2016) The allelic landscape of human blood cell trait vari-ation and links to common complex disease. Cell, 167, 1415–1429.GTEx Consortium et al. (2017) Genetic effects on gene expression acrosshuman tissues. Nature, 550, 204–213.Harrow,J. et al. (2012) GENCODE: the reference human genome annotationfor the encode project. Genome Res., 22, 1760–1774.MacArthur,J. et al. (2017) The new NHGRI-EBI Catalog of publishedgenome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45,D896–D901.Malone,J. et al. (2010) Modeling sample variables with an experimental factorontology. Bioinformatics, 26, 1112–1118.McLaren,W. et al. (2016) The ensembl variant effect predictor. Genome Biol.,17, 122.Neph,S. et al. (2012) BEDOPS: high-performance genomic feature operations.Bioinformatics, 28, 1919–1920.Sherry,S.T. et al. (2001) dbSNP: the NCBI database of genetic variation.Nucleic Acids Res., 29, 308–311.Staley,J.R. et al. (2016) PhenoScanner: a database of human genotype–pheno-type associations. Bioinformatics, 32, 3207–3209.Sun,B.B. et al. (2018) Genomic atlas of the human plasma proteome. Nature,558, 73–79.van der Harst,P. and Verweij,N. (2018) Identification of 64 novel genetic lociprovides an expanded view on the genetic architecture of coronary arterydisease. Circ. Res., 122, 433–443.V~osa,U. et al. (2018) Unraveling the polygenic architecture of complex traitsusing blood eQTL meta-analysis. bioRxiv, doi: 10.1101/447367.PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations 3Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Bristol Library user on 16 August 2019

PhenoScanner V2:an expanded tool for searching human genotype-phenotype associations

Apollo (Cambridge)

“manuscript” — 2019/5/14 — page 1 — #1Bioinformaticsdoi.10.1093/bioinformatics/xxxxxxAdvance Access Publication Date: Day Month YearApplications NoteDatabases and ontologiesPhenoScanner V2: an expanded tool for searchinghuman genotype-phenotype associationsMihir A Kamat 1, James A Blackshaw 1, Robin Young 1, Praveen Surendran 1,Stephen Burgess 1, 2, John Danesh 1,3,4, Adam S Butterworth 1,4,∗ and JamesR Staley 1,51MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge,CB1 8RN,UK, 2MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK, 3Wellcome Trust Sanger Institute, Hinxton,CB10 1SA, UK, 4NIHR Blood and Transplant Research Unit, Department of Public Health and Primary Care, University of Cambridge,Cambridge, CB1 8RN, UK, 5MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.∗To whom correspondence should be addressed.Associate Editor: XXXXXXXReceived on XXXXX; revised on XXXXX; accepted on XXXXXAbstractSummary: PhenoScanner is a curated database of publicly available results from large-scale geneticassociation studies in humans. This online tool facilitates “phenome scans”, where genetic variants arecross-referenced for association with many phenotypes of different types. Here we present a major updateof PhenoScanner (“PhenoScanner V2”), including over 150 million genetic variants and more than 65billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits,gene expression, metabolite and protein levels, and epigenetic markers. The query options have beenextended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants.All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mappedto Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes projectcan be used to search for phenotype associations with proxy variants.Availability and implementation: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.ukContact: asb38@medschl.cam.ac.ukSupplementary information: Full documentation: www.phenoscanner.medschl.cam.ac.uk/information.1 IntroductionDense array-based human genetic studies, such as genome-wide asso-ciation studies (GWAS), have identified many thousands of associationsbetween genetic variants and a diverse set of phenotypes. The challengenow facing the human genomics community is to understand the mechani-sms underlying these associations. One approach to aid biological insightinto disease mechanisms is to cross-reference genetic associations acrossa range of phenotypes, including disease states, cellular traits and otherintermediate traits. To enable such “phenome scans” we developed the toolPhenoScanner (Staley et al., 2016). Since its release in 2016, PhenoScan-ner has been accessed by thousands of users to assist a range of analysesfrom analyses linking proteins to disease (Sun et al., 2018) to interrogatingnovel loci associated with blood cell phenotypes (Astle et al., 2016).In recent years, there has been a rapid expansion in the availability ofgenetic association statistics with the maturation of genetic biobanks withrich phenotypic information. Moreover, the scope of molecular phenotypesin genetic association studies has increased with the publication of multi-tissue gene expression GWAS (GTEx Consortium et al., 2017) and GWASof thousands of plasma proteins (Sun et al., 2018). However, integratinggenetic associations across this vast array of data sources remains challen-ging. Hence, to facilitate improved “phenome scans”, we have released anupdated version of PhenoScanner (PhenoScanner V2) with new featuresincluding: (i) a greatly expanded database of human genotype-phenotypeassociations split into phenotype classes (diseases and traits, gene expres-sion, proteins, metabolites and epigenetics); (ii) additional search optionsincluding gene, genomic region and phenotype-based queries; (iii) Lin-kage disequilibrium (LD) information for the five super-ancestries in 1000Genomes; (iv) variant annotation and trait ontology mappings; and (v) abrand new web interface and API.© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 1“manuscript” — 2019/5/14 — page 2 — #22 M.A. Kamat et al.2 MethodsPhenoScanner V2 consists of a Python-R interface which conne-cts to a series of MySQL databases. To develop the catalogueof human genotype-phenotype associations, we identified and colla-ted >5000 genetic association datasets from publicly available listsof full summary associations results compiled by the NHGRI-EBI(https://www.ebi.ac.uk/gwas/downloads/summary-statistics) and NHLBI(https://grasp.nhlbi.nih.gov/FullResults.aspx), as well as from recent lite-rature reviews and lists of omics GWAS (e.g. Sun et al. 2018 forprotein levels). The catalogue currently contains results for diseases andtraits (∼30 billion associations), gene expression (∼84 million associ-ations), protein levels (∼35 billion associations), metabolite levels (∼3billion associations) and epigenetic markers (∼13 million associations).To ensure consistent formatting across datasets, all of the variants werealigned to the NCBI plus strand, rsIDs were updated to dbSNP 147(Sherry et al., 2001) and chromosome-positions (GRCh37 [hg19] andGRCh38 [hg38]) were added or updated using dbSNP 147 and liftO-ver (https://genome.ucsc.edu/cgi-bin/hgLiftOver). LD measures betweenneighbouring variants in the autosomal chromosomes were calculatedusing phased haplotypes for the five super-ancestries (European, Afri-can, Admixed American, East Asian and South Asian) in the 1000Genomes Project phase 3 (Genomes Project Consortium et al. 2015).We calculated D’ and r2 for pairs of variants within 500Kb and keptLD statistics with r2 ≥ 0.5. All phenotypes were mapped to Expe-rimental Factor Ontology terms (Malone et al., 2010) using ZOOMA(https://www.ebi.ac.uk/spot/zooma/). Variant and gene annotation for allof the variants was performed using Ensembl Variant Effect Predictor V88(McLaren et al., 2016) with GENCODE transcripts V26 (Harrow et al.,2012) mapped to build 37 positions. Nearest genes for intergenic variantswere retrieved using the BEDOPS tool version 2.4.26 (Neph et al., 2012).Users may enter one genetic variant, gene, genomic region or trait intothe text box on the home page (www.phenoscanner.medschl.cam.ac.uk)or upload up to 100 genetic variants, 10 genes or 10 genomicregions as a tab-delimited text file. PhenoScanner V2 also has anAPI with an associated R package and Python command line tool(www.phenoscanner.medschl.cam.ac.uk/tools), allowing users to searchfor genotype-phenotype associations from PhenoScanner V2 inside R orfrom a terminal. When querying genetic variants, all results regardless ofp-value can be displayed allowing the user to identify evidence againstassociations with phenotypes. To produce manageable results sets, onlyresults with p < 1 × 10−5 are returned for queries of genes, genomicregions or phenotypes. Once a query is evoked, the Python-R interfaceannotates the genetic variant, gene, genomic region or phenotype usingdbSNP (or ZOOMA for trait queries), before searching the requested asso-ciation databases and filtering the results based on the specified p-valuethreshold. The new web interface then presents the results and makes theresults available to download. All associations for each genetic variant arealigned such that the effect allele is the same across all results. The asso-ciations with proxy variants are aligned such that their effect alleles aregiven with respect to the effect allele of the corresponding queried variant.3 ResultsTo demonstrate the value of the expanded database and additional functio-nality of PhenoScanner V2, we searched for “rs10840293”, the “SWAP70”gene and “coronary heart disease”. PhenoScanner V2 found >150,000results with rs10840293 (PhenoScanner variant annotation: intronic vari-ant in SWAP70) or one of its proxies (r2 ≥ 0.8 in Europeans), morethan 100 times the number of associations found for the same variantquery using PhenoScanner V1 (1,405 associations); the NHGRI-EBIGWAS Catalog (MacArthur et al., 2016) only returns four results forrs10840293. In particular, PhenoScanner V2 identifies strong associationsof rs10840293 with coronary heart disease (van der Harst and Verweij,2018), blood pressure (http://www.nealelab.is/uk-biobank) and plateletwidth (Astle et al., 2016), as well as with whole blood gene expression(GTEx Consortium et al., 2017) and plasma protein levels (Sun et al., 2018)of SWAP70 (all with p < 5×10−8), suggesting a possible blood pressurerelated mechanism affecting coronary heart disease risk at this locus poten-tially regulated via SWAP70 expression. Variants in the SWAP70 gene had>6,000 associations with p < 1× 10−5 (compared with 27 associationsfound by the GWAS Catalog), while there were >50,000 genetic associa-tions with coronary heart disease with p < 1× 10−5 across the genome(compared with 1,092 associations found by the GWAS Catalog).4 ConclusionPhenoScanner V2 is a large curated database of human genotypeâŁ“ph-enotype associations from publicly available genetic association studies.This catalogue of results greatly extends PhenoScanner V1 in both scaleand phenotypic breadth, with tables of genetic associations for diseases andtraits, gene expression, protein levels, metabolites levels and epigeneticmarkers. PhenoScanner V2 also has additional annotation and functiona-lity. The database can now be searched for genes, genomic regions andtraits, while variant annotation, phenotype ontology mappings and LDstatistics from a wider range of ethnic groups have been incorporated toenhance utility and interpretation.FundingThis work was supported by the UK Medical Research Council[G0800270; MR/L003120/1], the British Heart Foundation [SP/09/002;RG/13/13/30194; RG/18/13/33946], Pfizer [G73632], the European Rese-arch Council [268834], the European Commission Framework Programme7 [HEALTH-F2-2012-279233], the National Institute for Health Researchand Health Data Research UK (*). *The views expressed are those of theauthors and not necessarily those of the NHS or the NIHR.ReferencesAstle,W.J. et al. (2016) The allelic landscape of human blood cell trait variation andlinks to common complex disease. Cell, 167 (5), 1415–1429.GTEx Consortium et al. (2017) Genetic effects on gene expression across humantissues. Nature, 550 (7675), 204.Harrow,J. et al. (2012) GENCODE: the reference human genome annotation for theencode project. Genome research, 22 (9), 1760–1774.MacArthur,J. et al. (2016) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic acids research, 45 (D1),D896–D901.Malone,J. et al. (2010) Modeling sample variables with an experimental factorontology. Bioinformatics, 26 (8), 1112–1118.McLaren,W. et al. (2016) The ensembl variant effect predictor. Genome biology, 17(1), 122.Neph,S. et al. (2012) Bedops: high-performance genomic feature operations.Bioinformatics, 28 (14), 1919–1920.Sherry,S.T. et al. (2001) dbSNP: the NCBI database of genetic variation. NucleicAcids Res, 29 (1), 308–311.Staley,J.R. et al. (2016) Phenoscanner: a database of human genotype–phenotypeassociations. Bioinformatics, 32 (20), 3207–3209.Sun,B.B. et al. (2018) Genomic atlas of the human plasma proteome. Nature, 558(7708), 73.van der Harst,P. and Verweij,N. (2018) Identification of 64 novel genetic loci providesan expanded view on the genetic architecture of coronary artery disease. Circulationresearch, 122 (3), 433–443.

PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.



Availability and implementation: 

PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk

Kamat, Mihir A.

Butterworth, Adam S.

Staley, James R.

Enlighten

Databases and ontologiesPhenoScanner V2: an expanded tool forsearching human genotype–phenotypeassociationsMihir A. Kamat1, James A. Blackshaw1, Robin Young1,Praveen Surendran1, Stephen Burgess1,2, John Danesh1,3,4,Adam S. Butterworth1,4,* and James R. Staley1,51MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University ofCambridge, Cambridge CB1 8RN, UK, 2MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK,3Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK, 4NIHR Blood and Transplant Research Unit, Departmentof Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK and 5MRC IntegrativeEpidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK* To whom correspondence should be addressed.Associate Editor: Janet KelsoReceived on December 20, 2018; revised on May 1, 2019; editorial decision on May 29, 2019; accepted on June 19, 2019AbstractSummary: PhenoScanner is a curated database of publicly available results from large-scale genet-ic association studies in humans. This online tool facilitates ‘phenome scans’, where geneticvariants are cross-referenced for association with many phenotypes of different types. Here wepresent a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million geneticvariants and more than 65 billion associations (compared to 350 million associations inPhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, andepigenetic markers. The query options have been extended to include searches by genes, genomicregions and phenotypes, as well as for genetic variants. All variants are positionally annotatedusing the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontologyterms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search forphenotype associations with proxy variants.Availability and implementation: PhenoScanner V2 is available atwww.phenoscanner.medschl.cam.ac.uk.Contact: asb38@medschl.cam.ac.uk1 IntroductionDense array-based human genetic studies, such as genome-wideassociation studies (GWAS), have identified many thousands ofassociations between genetic variants and a diverse set of pheno-types. The challenge now facing the human genomics communityis to understand the mechanisms underlying these associations. Oneapproach to aid biological insight into disease mechanisms is tocross-reference genetic associations across a range of phenotypes,including disease states, cellular traits and other intermediate traits.To enable such ‘phenome scans’ we developed the online toolPhenoScanner (Staley et al., 2016). Since its release in 2016,PhenoScanner has been accessed by hundreds of users to assist arange of analyses from analyses linking proteins to disease (Sunet al., 2018) to interrogating novel loci associated with blood cellphenotypes (Astle et al., 2016).In recent years, there has been a rapid expansion in the availabil-ity of genetic association statistics with the maturation of geneticbiobanks with rich phenotypic information. Moreover, the scope ofVC The Author(s) 2019. Published by Oxford University Press. 1This 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, 2019, 1–3doi: 10.1093/bioinformatics/btz469Advance Access Publication Date: 24 June 2019Applications Note Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Glasgow user on 26 July 2019molecular phenotypes in genetic association studies has increasedwith the publication of multi-tissue gene expression GWAS (GTExConsortium et al., 2017) and GWAS of thousands of plasma pro-teins (Sun et al., 2018). However, integrating genetic associationsacross this vast array of data sources remains challenging. Hence, tofacilitate improved ‘phenome scans’, we have released an updatedversion of PhenoScanner (PhenoScanner V2) with new featuresincluding: (i) an expanded database of human genotype–phenotypeassociations associations split into phenotype classes (diseases andtraits, gene expression, proteins, metabolites and epigenetics); (ii)additional search options including gene, genomic region andphenotype-based queries; (iii) linkage disequilibrium (LD) informa-tion for the five super-ancestries in 1000 Genomes; (vi) variant an-notation and trait ontology mappings and (v) a brand new webinterface and API.2 Materials and methodsPhenoScanner V2 consists of a Python-R interface which connects toa series of MySQL databases. To develop the catalogue of humangenotype–phenotype associations, we identified and collated >5000genetic association datasets from publicly available lists of full sum-mary associations results compiled by the NHGRI-EBI (https://www.ebi.ac.uk/gwas/downloads/summary-statistics) and NHLBI(https://grasp.nhlbi.nih.gov/FullResults.aspx), as well as from recentliterature reviews and lists of omics GWAS (e.g. Sun et al., 2018 forprotein levels). The catalogue currently contains results for diseasesand traits (30 billion associations), gene expression (84 millionassociations), protein levels (35 billion associations), metabolitelevels (3 billion associations) and epigenetic markers (13 millionassociations). To ensure consistent formatting across datasets, all ofthe variants were aligned to the NCBI plus strand, rsIDs wereupdated to dbSNP 147 (Sherry et al., 2001) and chromosome-positions [GRCh37 (hg19) and GRCh38 (hg38)] were added orupdated using dbSNP 147 and liftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver). LD measures between neighbouring variants inthe autosomal chromosomes were calculated using phased haplo-types for the five super-ancestries (European, African, AdmixedAmerican, East Asian and South Asian) in the 1000 GenomesProject phase 3 (1000 Genomes Project Consortium et al., 2015).We calculated D’ and r2 for pairs of variants within 500 Kb andkept LD statistics with r2  0.5. All phenotypes were mapped toExperimental Factor Ontology terms (Malone et al., 2010) usingZOOMA (https://www.ebi.ac.uk/spot/zooma/). Variant and geneannotation for all of the variants was performed using EnsemblVariant Effect Predictor V88 (McLaren et al., 2016) withGENCODE transcripts V26 (Harrow et al., 2012) mapped to build37 positions. Nearest genes for intergenic variants were retrievedusing the BEDOPS tool version 2.4.26 (Neph et al., 2012).Users may enter one genetic variant, gene, genomic region ortrait into the text box on the home page (www.phenoscanner.medschl.cam.ac.uk) or upload up to 100 genetic variants, 10 genesor 10 genomic regions as a tab-delimited text file. PhenoScanner V2also has an API with an associated R package and Python commandline tool (www.phenoscanner.medschl.cam.ac.uk/tools), allowingusers to search for genotype–phenotype associations fromPhenoScanner V2 inside R or from a terminal. When querying genet-ic variants, all results regardless of P-value can be displayed allow-ing the user to identify evidence against associations withphenotypes. To produce manageable results sets, only results withP < 1105 are returned for queries of genes, genomic regions orphenotypes. Once a query is evoked, the Python-R interface anno-tates the genetic variant, gene, genomic region or phenotype usingdbSNP (or ZOOMA for trait queries), before searching therequested association databases and filtering the results based on thespecified P-value threshold. The new web interface then presents theresults and makes them available to download. All associations foreach genetic variant are aligned such that the effect allele is the sameacross all results. The associations with proxy variants are alignedsuch that their effect alleles are given with respect to the effect alleleof the corresponding queried variant.3 ResultsTo demonstrate the value of the expanded database and additionalfunctionality of PhenoScanner V2, we searched for ‘rs10840293’,‘SWAP70’ and ‘coronary heart disease’. PhenoScanner V2 found>150 000 results with rs10840293 (variant annotation: intronicvariant in SWAP70) or one of its proxies (r2  0.8 in Europeans),more than 100 times the number of associations found for the samevariant query using PhenoScanner V1 (1405 associations); theNHGRI-EBI GWAS Catalog (MacArthur et al., 2017) only returnsfour results for rs10840293. In particular, PhenoScanner V2 identi-fies strong associations of rs10840293 with coronary heart disease(van der Harst and Verweij, 2018), blood pressure (https://www.nealelab.is/uk-biobank) and platelet width (Astle et al., 2016), as wellas with whole blood gene expression (V~osa et al., 2018) and plasmaprotein levels (Sun et al., 2018) of SWAP70 (all with P < 5108),suggesting a possible blood pressure related mechanism affectingcoronary heart disease risk at this locus potentially regulated viaSWAP70 expression. Variants in the SWAP70 gene had >6000 asso-ciations with P < 1105 (compared with 27 associations found bythe GWAS Catalog), while there were >50 000 genetic associationswith coronary heart disease with P < 1105 across the genome(compared with 1092 associations found by the GWAS Catalog).4 ConclusionPhenoScanner V2 is a large curated database of human genotype–phenotype associations from publicly available genetic associationstudies. This catalogue of results greatly extends PhenoScanner V1in both scale and phenotypic breadth, with tables of genetic associa-tions for diseases and traits, gene expression, protein levels, metabo-lites levels and epigenetic markers. PhenoScanner V2 also hasadditional annotation and functionality. The database can now besearched for genes, genomic regions and traits, while variant annota-tion, phenotype ontology mappings and LD statistics from a widerrange of ethnic groups have been incorporated to enhance utilityand interpretation.FundingThis work was supported by the UK Medical Research Council [G0800270,MR/L003120/1]; the British Heart Foundation [SP/09/002, RG/13/13/30194,RG/18/13/33946]; Pfizer [G73632]; the European Research Council[268834]; the European Commission Framework Programme 7 [HEALTH-F2-2012-279233]; the National Institute for Health Research; and HealthData Research UK. The views expressed are those of the authors and not ne-cessarily those of the NHS or the NIHR.Conflict of Interest: none declared.2 M.A.Kamat et al.Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Glasgow user on 26 July 2019References1000 Genomes Project Consortium et al. (2015) A global reference for humangenetic variation. Nature, 526, 68–74.Astle,W.J. et al. (2016) The allelic landscape of human blood cell trait vari-ation and links to common complex disease. Cell, 167, 1415–1429.GTEx Consortium et al. (2017) Genetic effects on gene expression acrosshuman tissues. Nature, 550, 204–213.Harrow,J. et al. (2012) GENCODE: the reference human genome annotationfor the encode project. Genome Res., 22, 1760–1774.MacArthur,J. et al. (2017) The new NHGRI-EBI Catalog of publishedgenome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45,D896–D901.Malone,J. et al. (2010) Modeling sample variables with an experimental factorontology. Bioinformatics, 26, 1112–1118.McLaren,W. et al. (2016) The ensembl variant effect predictor. Genome Biol.,17, 122.Neph,S. et al. (2012) BEDOPS: high-performance genomic feature operations.Bioinformatics, 28, 1919–1920.Sherry,S.T. et al. (2001) dbSNP: the NCBI database of genetic variation.Nucleic Acids Res., 29, 308–311.Staley,J.R. et al. (2016) PhenoScanner: a database of human genotype–pheno-type associations. Bioinformatics, 32, 3207–3209.Sun,B.B. et al. (2018) Genomic atlas of the human plasma proteome. Nature,558, 73–79.van der Harst,P. and Verweij,N. (2018) Identification of 64 novel genetic lociprovides an expanded view on the genetic architecture of coronary arterydisease. Circ. Res., 122, 433–443.V~osa,U. et al. (2018) Unraveling the polygenic architecture of complex traitsusing blood eQTL meta-analysis. bioRxiv, doi: 10.1101/447367.PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations 3Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz469/5522366 by University of Glasgow user on 26 July 2019

PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations

Enlighten: Publications

Databases and ontologies
PhenoScanner V2: an expanded tool for
searching human genotype–phenotype
associations
Mihir A. Kamat1, James A. Blackshaw1, Robin Young1,
Praveen Surendran1, Stephen Burgess1,2, John Danesh1,3,4,
Adam S. Butterworth1,4,* and James R. Staley1,5
1MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of
Cambridge, Cambridge CB1 8RN, UK, 2MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK,
3Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK, 4NIHR Blood and Transplant Research Unit, Department
of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK and 5MRC Integrative
Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
* To whom correspondence should be addressed.
Associate Editor: Janet Kelso
Received on December 20, 2018; revised on May 1, 2019; editorial decision on May 29, 2019; accepted on June 19, 2019
Abstract
Summary: PhenoScanner is a curated database of publicly available results from large-scale genet-
ic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic
variants are cross-referenced for association with many phenotypes of different types. Here we
present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic
variants and more than 65 billion associations (compared to 350 million associations in
PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and
epigenetic markers. The query options have been extended to include searches by genes, genomic
regions and phenotypes, as well as for genetic variants. All variants are positionally annotated
using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology
terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for
phenotype associations with proxy variants.
Availability and implementation: PhenoScanner V2 is available at
www.phenoscanner.medschl.cam.ac.uk.
Contact: asb38@medschl.cam.ac.uk
1 Introduction
Dense array-based human genetic studies, such as genome-wide
association studies (GWAS), have identified many thousands of
associations between genetic variants and a diverse set of pheno-
types. The challenge now facing the human genomics community
is to understand the mechanisms underlying these associations. One
approach to aid biological insight into disease mechanisms is to
cross-reference genetic associations across a range of phenotypes,
including disease states, cellular traits and other intermediate traits.
To enable such ‘phenome scans’ we developed the online tool
PhenoScanner (Staley et al., 2016). Since its release in 2016,
PhenoScanner has been accessed by hundreds of users to assist a
range of analyses from analyses linking proteins to disease (Sun
et al., 2018) to interrogating novel loci associated with blood cell
phenotypes (Astle et al., 2016).
In recent years, there has been a rapid expansion in the availabil-
ity of genetic association statistics with the maturation of genetic
biobanks with rich phenotypic information. Moreover, the scope of
VC The Author(s) 2019. Published by Oxford University Press. 1
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.
Bioinformatics, 2019, 1–3
doi: 10.1093/bioinformatics/btz469
Advance Access Publication Date: 24 June 2019
Applications Note Dow
nloaded from
 https://academ
ic.oup.com
/bioinform
atics/advance-article-abstract/doi/10.1093/bioinform
atics/btz469/5522366 by U
niversity of G
lasgow
 user on 26 July 2019
molecular phenotypes in genetic association studies has increased
with the publication of multi-tissue gene expression GWAS (GTEx
Consortium et al., 2017) and GWAS of thousands of plasma pro-
teins (Sun et al., 2018). However, integrating genetic associations
across this vast array of data sources remains challenging. Hence, to
facilitate improved ‘phenome scans’, we have released an updated
version of PhenoScanner (PhenoScanner V2) with new features
including: (i) an expanded database of human genotype–phenotype
associations associations split into phenotype classes (diseases and
traits, gene expression, proteins, metabolites and epigenetics); (ii)
additional search options including gene, genomic region and
phenotype-based queries; (iii) linkage disequilibrium (LD) informa-
tion for the five super-ancestries in 1000 Genomes; (vi) variant an-
notation and trait ontology mappings and (v) a brand new web
interface and API.
2 Materials and methods
PhenoScanner V2 consists of a Python-R interface which connects to
a series of MySQL databases. To develop the catalogue of human
genotype–phenotype associations, we identified and collated >5000
genetic association datasets from publicly available lists of full sum-
mary associations results compiled by the NHGRI-EBI (https://
www.ebi.ac.uk/gwas/downloads/summary-statistics) and NHLBI
(https://grasp.nhlbi.nih.gov/FullResults.aspx), as well as from recent
literature reviews and lists of omics GWAS (e.g. Sun et al., 2018 for
protein levels). The catalogue currently contains results for diseases
and traits (30 billion associations), gene expression (84 million
associations), protein levels (35 billion associations), metabolite
levels (3 billion associations) and epigenetic markers (13 million
associations). To ensure consistent formatting across datasets, all of
the variants were aligned to the NCBI plus strand, rsIDs were
updated to dbSNP 147 (Sherry et al., 2001) and chromosome-
positions [GRCh37 (hg19) and GRCh38 (hg38)] were added or
updated using dbSNP 147 and liftOver (https://genome.ucsc.edu/
cgi-bin/hgLiftOver). LD measures between neighbouring variants in
the autosomal chromosomes were calculated using phased haplo-
types for the five super-ancestries (European, African, Admixed
American, East Asian and South Asian) in the 1000 Genomes
Project phase 3 (1000 Genomes Project Consortium et al., 2015).
We calculated D’ and r2 for pairs of variants within 500 Kb and
kept LD statistics with r2  0.5. All phenotypes were mapped to
Experimental Factor Ontology terms (Malone et al., 2010) using
ZOOMA (https://www.ebi.ac.uk/spot/zooma/). Variant and gene
annotation for all of the variants was performed using Ensembl
Variant Effect Predictor V88 (McLaren et al., 2016) with
GENCODE transcripts V26 (Harrow et al., 2012) mapped to build
37 positions. Nearest genes for intergenic variants were retrieved
using the BEDOPS tool version 2.4.26 (Neph et al., 2012).
Users may enter one genetic variant, gene, genomic region or
trait into the text box on the home page (www.phenoscanner.
medschl.cam.ac.uk) or upload up to 100 genetic variants, 10 genes
or 10 genomic regions as a tab-delimited text file. PhenoScanner V2
also has an API with an associated R package and Python command
line tool (www.phenoscanner.medschl.cam.ac.uk/tools), allowing
users to search for genotype–phenotype associations from
PhenoScanner V2 inside R or from a terminal. When querying genet-
ic variants, all results regardless of P-value can be displayed allow-
ing the user to identify evidence against associations with
phenotypes. To produce manageable results sets, only results with
P < 1105 are returned for queries of genes, genomic regions or
phenotypes. Once a query is evoked, the Python-R interface anno-
tates the genetic variant, gene, genomic region or phenotype using
dbSNP (or ZOOMA for trait queries), before searching the
requested association databases and filtering the results based on the
specified P-value threshold. The new web interface then presents the
results and makes them available to download. All associations for
each genetic variant are aligned such that the effect allele is the same
across all results. The associations with proxy variants are aligned
such that their effect alleles are given with respect to the effect allele
of the corresponding queried variant.
3 Results
To demonstrate the value of the expanded database and additional
functionality of PhenoScanner V2, we searched for ‘rs10840293’,
‘SWAP70’ and ‘coronary heart disease’. PhenoScanner V2 found
>150 000 results with rs10840293 (variant annotation: intronic
variant in SWAP70) or one of its proxies (r2  0.8 in Europeans),
more than 100 times the number of associations found for the same
variant query using PhenoScanner V1 (1405 associations); the
NHGRI-EBI GWAS Catalog (MacArthur et al., 2017) only returns
four results for rs10840293. In particular, PhenoScanner V2 identi-
fies strong associations of rs10840293 with coronary heart disease
(van der Harst and Verweij, 2018), blood pressure (https://www.nea
lelab.is/uk-biobank) and platelet width (Astle et al., 2016), as well
as with whole blood gene expression (V~osa et al., 2018) and plasma
protein levels (Sun et al., 2018) of SWAP70 (all with P < 5108),
suggesting a possible blood pressure related mechanism affecting
coronary heart disease risk at this locus potentially regulated via
SWAP70 expression. Variants in the SWAP70 gene had >6000 asso-
ciations with P < 1105 (compared with 27 associations found by
the GWAS Catalog), while there were >50 000 genetic associations
with coronary heart disease with P < 1105 across the genome
(compared with 1092 associations found by the GWAS Catalog).
4 Conclusion
PhenoScanner V2 is a large curated database of human genotype–
phenotype associations from publicly available genetic association
studies. This catalogue of results greatly extends PhenoScanner V1
in both scale and phenotypic breadth, with tables of genetic associa-
tions for diseases and traits, gene expression, protein levels, metabo-
lites levels and epigenetic markers. PhenoScanner V2 also has
additional annotation and functionality. The database can now be
searched for genes, genomic regions and traits, while variant annota-
tion, phenotype ontology mappings and LD statistics from a wider
range of ethnic groups have been incorporated to enhance utility
and interpretation.
Funding
This work was supported by the UK Medical Research Council [G0800270,
MR/L003120/1]; the British Heart Foundation [SP/09/002, RG/13/13/30194,
RG/18/13/33946]; Pfizer [G73632]; the European Research Council
[268834]; the European Commission Framework Programme 7 [HEALTH-
F2-2012-279233]; the National Institute for Health Research; and Health
Data Research UK. The views expressed are those of the authors and not ne-
cessarily those of the NHS or the NIHR.
Conflict of Interest: none declared.
2 M.A.Kamat et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/bioinform
atics/advance-article-abstract/doi/10.1093/bioinform
atics/btz469/5522366 by U
niversity of G
lasgow
 user on 26 July 2019
References
1000 Genomes Project Consortium et al. (2015) A global reference for human
genetic variation. Nature, 526, 68–74.
Astle,W.J. et al. (2016) The allelic landscape of human blood cell trait vari-
ation and links to common complex disease. Cell, 167, 1415–1429.
GTEx Consortium et al. (2017) Genetic effects on gene expression across
human tissues. Nature, 550, 204–213.
Harrow,J. et al. (2012) GENCODE: the reference human genome annotation
for the encode project. Genome Res., 22, 1760–1774.
MacArthur,J. et al. (2017) The new NHGRI-EBI Catalog of published
genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45,
D896–D901.
Malone,J. et al. (2010) Modeling sample variables with an experimental factor
ontology. Bioinformatics, 26, 1112–1118.
McLaren,W. et al. (2016) The ensembl variant effect predictor. Genome Biol.,
17, 122.
Neph,S. et al. (2012) BEDOPS: high-performance genomic feature operations.
Bioinformatics, 28, 1919–1920.
Sherry,S.T. et al. (2001) dbSNP: the NCBI database of genetic variation.
Nucleic Acids Res., 29, 308–311.
Staley,J.R. et al. (2016) PhenoScanner: a database of human genotype–pheno-
type associations. Bioinformatics, 32, 3207–3209.
Sun,B.B. et al. (2018) Genomic atlas of the human plasma proteome. Nature,
558, 73–79.
van der Harst,P. and Verweij,N. (2018) Identification of 64 novel genetic loci
provides an expanded view on the genetic architecture of coronary artery
disease. Circ. Res., 122, 433–443.
V~osa,U. et al. (2018) Unraveling the polygenic architecture of complex traits
using blood eQTL meta-analysis. bioRxiv, doi: 10.1101/447367.
PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations 3
D
ow
nloaded from
 https://academ
ic.oup.com
/bioinform
atics/advance-article-abstract/doi/10.1093/bioinform
atics/btz469/5522366 by U
niversity of G
lasgow
 user on 26 July 2019


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PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

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