Literature DB >> 33692554

Improving reporting standards for polygenic scores in risk prediction studies.

Hannah Wand1,2, Samuel A Lambert3,4,5,6,7, Cecelia Tamburro8, Michael A Iacocca1, Jack W O'Sullivan1,2, Catherine Sillari8, Iftikhar J Kullo9, Robb Rowley8, Jacqueline S Dron10,11, Deanna Brockman10, Eric Venner12, Mark I McCarthy13,14, Antonis C Antoniou15, Douglas F Easton15, Robert A Hegele11, Amit V Khera10, Nilanjan Chatterjee16,17, Charles Kooperberg18, Karen Edwards19, Katherine Vlessis20, Kim Kinnear20, John N Danesh5,6,21, Helen Parkinson6,7, Erin M Ramos8, Megan C Roberts22, Kelly E Ormond20,23, Muin J Khoury24, A Cecile J W Janssens25, Katrina A B Goddard26,27, Peter Kraft28, Jaqueline A L MacArthur7, Michael Inouye3,4,5,6,21,29, Genevieve L Wojcik30.   

Abstract

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

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Year:  2021        PMID: 33692554      PMCID: PMC8609771          DOI: 10.1038/s41586-021-03243-6

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   69.504


  60 in total

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Review 2.  Five years of GWAS discovery.

Authors:  Peter M Visscher; Matthew A Brown; Mark I McCarthy; Jian Yang
Journal:  Am J Hum Genet       Date:  2012-01-13       Impact factor: 11.025

3.  Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer.

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Journal:  J Natl Cancer Inst       Date:  2014-11-12       Impact factor: 13.506

Review 4.  Dissecting the genetics of complex traits using summary association statistics.

Authors:  Bogdan Pasaniuc; Alkes L Price
Journal:  Nat Rev Genet       Date:  2016-11-14       Impact factor: 53.242

5.  PRSice-2: Polygenic Risk Score software for biobank-scale data.

Authors:  Shing Wan Choi; Paul F O'Reilly
Journal:  Gigascience       Date:  2019-07-01       Impact factor: 6.524

6.  Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries.

Authors:  Amber N Hurson; Parichoy Pal Choudhury; Chi Gao; Anika Hüsing; Mikael Eriksson; Min Shi; Michael E Jones; D Gareth R Evans; Roger L Milne; Mia M Gaudet; Celine M Vachon; Daniel I Chasman; Douglas F Easton; Marjanka K Schmidt; Peter Kraft; Montserrat Garcia-Closas; Nilanjan Chatterjee
Journal:  Int J Epidemiol       Date:  2021-03-23       Impact factor: 9.685

7.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.

Authors:  Bjarni J Vilhjálmsson; Jian Yang; Hilary K Finucane; Alexander Gusev; Sara Lindström; Stephan Ripke; Giulio Genovese; Po-Ru Loh; Gaurav Bhatia; Ron Do; Tristan Hayeck; Hong-Hee Won; Sekar Kathiresan; Michele Pato; Carlos Pato; Rulla Tamimi; Eli Stahl; Noah Zaitlen; Bogdan Pasaniuc; Gillian Belbin; Eimear E Kenny; Mikkel H Schierup; Philip De Jager; Nikolaos A Patsopoulos; Steve McCarroll; Mark Daly; Shaun Purcell; Daniel Chasman; Benjamin Neale; Michael Goddard; Peter M Visscher; Peter Kraft; Nick Patterson; Alkes L Price
Journal:  Am J Hum Genet       Date:  2015-10-01       Impact factor: 11.025

8.  A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog.

Authors:  Joannella Morales; Danielle Welter; Emily H Bowler; Maria Cerezo; Laura W Harris; Aoife C McMahon; Peggy Hall; Heather A Junkins; Annalisa Milano; Emma Hastings; Cinzia Malangone; Annalisa Buniello; Tony Burdett; Paul Flicek; Helen Parkinson; Fiona Cunningham; Lucia A Hindorff; Jacqueline A L MacArthur
Journal:  Genome Biol       Date:  2018-02-15       Impact factor: 13.583

9.  STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement.

Authors:  Julian Little; Julian P T Higgins; John P A Ioannidis; David Moher; France Gagnon; Erik von Elm; Muin J Khoury; Barbara Cohen; George Davey-Smith; Jeremy Grimshaw; Paul Scheet; Marta Gwinn; Robin E Williamson; Guang Yong Zou; Kim Hutchings; Candice Y Johnson; Valerie Tait; Miriam Wiens; Jean Golding; Cornelia van Duijn; John McLaughlin; Andrew Paterson; George Wells; Isabel Fortier; Matthew Freedman; Maja Zecevic; Richard King; Claire Infante-Rivard; Alex Stewart; Nick Birkett
Journal:  PLoS Med       Date:  2009-02-03       Impact factor: 11.069

10.  Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.

Authors:  Jonathan R I Coleman; Wouter J Peyrot; Kirstin L Purves; Katrina A S Davis; Christopher Rayner; Shing Wan Choi; Christopher Hübel; Héléna A Gaspar; Carol Kan; Sandra Van der Auwera; Mark James Adams; Donald M Lyall; Karmel W Choi; Erin C Dunn; Evangelos Vassos; Andrea Danese; Barbara Maughan; Hans J Grabe; Cathryn M Lewis; Paul F O'Reilly; Andrew M McIntosh; Daniel J Smith; Naomi R Wray; Matthew Hotopf; Thalia C Eley; Gerome Breen
Journal:  Mol Psychiatry       Date:  2020-01-23       Impact factor: 15.992

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  59 in total

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Journal:  Am J Clin Exp Urol       Date:  2021-04-15

Review 3.  Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases.

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Journal:  Stroke       Date:  2021-08-17       Impact factor: 7.914

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Review 5.  Monogenic Versus Polygenic Forms of Hypercholesterolemia and Cardiovascular Risk: Are There Any Differences?

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6.  Ancestry-Matched and Cross-Ancestry Genetic Risk Scores of Type 2 Diabetes in Pregnant Women and Fetal Growth: A Study in an Ancestrally Diverse Cohort.

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Journal:  Diabetes       Date:  2022-02-01       Impact factor: 9.461

7.  Including diverse and admixed populations in genetic epidemiology research.

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Journal:  Genet Epidemiol       Date:  2022-07-16       Impact factor: 2.344

Review 8.  Use of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations.

Authors:  Ozan Dikilitas; Daniel J Schaid; Catherine Tcheandjieu; Shoa L Clarke; Themistocles L Assimes; Iftikhar J Kullo
Journal:  Curr Cardiol Rep       Date:  2022-07-07       Impact factor: 3.955

Review 9.  Implementation and implications for polygenic risk scores in healthcare.

Authors:  John L Slunecka; Matthijs D van der Zee; Jeffrey J Beck; Brandon N Johnson; Casey T Finnicum; René Pool; Jouke-Jan Hottenga; Eco J C de Geus; Erik A Ehli
Journal:  Hum Genomics       Date:  2021-07-20       Impact factor: 4.639

Review 10.  Monogenic and Polygenic Models of Coronary Artery Disease.

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