PRS-on-Spark (PRSoS) : a novel, efficient and flexible approach for generating polygenic risk scores
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Background: Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach.
Results: We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort (N = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t = 42.865, p = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%).
Conclusion: PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
CHEN, Lawrence M., Nelson YAO, Elika GARG, Yuecai ZHU, Thao T. T. NGUYEN, Irina POKHVISNEVA, Shantala A. HARI DASS, Eva UNTERNAEHRER, Hélène GAUDREAU, Kieran J. O'DONNELL, 2018. PRS-on-Spark (PRSoS) : a novel, efficient and flexible approach for generating polygenic risk scores. In: BMC Bioinformatics. 2018, 19(1), 295. eISSN 1471-2105. Available under: doi: 10.1186/s12859-018-2289-9BibTex
@article{Chen2018-08-08PRSon-44856, year={2018}, doi={10.1186/s12859-018-2289-9}, title={PRS-on-Spark (PRSoS) : a novel, efficient and flexible approach for generating polygenic risk scores}, number={1}, volume={19}, journal={BMC Bioinformatics}, author={Chen, Lawrence M. and Yao, Nelson and Garg, Elika and Zhu, Yuecai and Nguyen, Thao T. T. and Pokhvisneva, Irina and Hari Dass, Shantala A. and Unternaehrer, Eva and Gaudreau, Hélène and O'Donnell, Kieran J.}, note={Article Number: 295} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/44856"> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/> <dc:contributor>Yao, Nelson</dc:contributor> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44856/3/Chen_2-15mhmfk7hxvd29.pdf"/> <dc:contributor>Pokhvisneva, Irina</dc:contributor> <dc:contributor>Nguyen, Thao T. T.</dc:contributor> <dc:contributor>Unternaehrer, Eva</dc:contributor> <dc:creator>Yao, Nelson</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-05T12:22:39Z</dcterms:available> <dc:creator>Hari Dass, Shantala A.</dc:creator> <dc:contributor>Gaudreau, Hélène</dc:contributor> <dcterms:abstract xml:lang="eng">Background: Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach.<br /><br />Results: We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort (N = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t = 42.865, p = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%).<br /><br />Conclusion: PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.</dcterms:abstract> <dc:creator>Chen, Lawrence M.</dc:creator> <dc:contributor>Zhu, Yuecai</dc:contributor> <dc:creator>Nguyen, Thao T. T.</dc:creator> <dcterms:title>PRS-on-Spark (PRSoS) : a novel, efficient and flexible approach for generating polygenic risk scores</dcterms:title> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/> <dc:creator>Unternaehrer, Eva</dc:creator> <dc:creator>Zhu, Yuecai</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:rights>Attribution 4.0 International</dc:rights> <dc:creator>Pokhvisneva, Irina</dc:creator> <dc:contributor>O'Donnell, Kieran J.</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44856/3/Chen_2-15mhmfk7hxvd29.pdf"/> <dc:creator>Gaudreau, Hélène</dc:creator> <dc:creator>Garg, Elika</dc:creator> <dcterms:issued>2018-08-08</dcterms:issued> <dc:language>eng</dc:language> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:contributor>Garg, Elika</dc:contributor> <dc:contributor>Hari Dass, Shantala A.</dc:contributor> <dc:creator>O'Donnell, Kieran J.</dc:creator> <dc:contributor>Chen, Lawrence M.</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-05T12:22:39Z</dc:date> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44856"/> </rdf:Description> </rdf:RDF>