Literature DB >> 33587168

Do we measure or compute polygenic risk scores? Why language matters.

Bart Penders1, A Cecile J W Janssens2.   

Abstract

Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ways, blood flow has pressure and sodium is available in a concentration that can be quantified and visualised. In stark contrast, PRSs are calculated, compiled or constructed through the statistical assemblage of genetic variants. How researchers frame and name PRSs has consequences for how we interpret and value their results. We distinguish between the tangible and inferential understanding of PRS and the corresponding languages of measurement and computation, respectively. The conflation of these frames obscures important questions we need to ask: what PRS seeks to represent, whether current ways of 'doing PRS' are optimal and responsible, and upon what we base the credibility of PRS-based knowledge claims.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Entities:  

Year:  2021        PMID: 33587168     DOI: 10.1007/s00439-021-02262-7

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  15 in total

Review 1.  Polygenic influences on dyslipidemias.

Authors:  Jacqueline S Dron; Robert A Hegele
Journal:  Curr Opin Lipidol       Date:  2018-04       Impact factor: 4.776

2.  Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration.

Authors:  Julian Maller; Sarah George; Shaun Purcell; Jes Fagerness; David Altshuler; Mark J Daly; Johanna M Seddon
Journal:  Nat Genet       Date:  2006-08-27       Impact factor: 38.330

3.  Genotype score in addition to common risk factors for prediction of type 2 diabetes.

Authors:  James B Meigs; Peter Shrader; Lisa M Sullivan; Jarred B McAteer; Caroline S Fox; Josée Dupuis; Alisa K Manning; Jose C Florez; Peter W F Wilson; Ralph B D'Agostino; L Adrienne Cupples
Journal:  N Engl J Med       Date:  2008-11-20       Impact factor: 91.245

Review 4.  Tutorial: a guide to performing polygenic risk score analyses.

Authors:  Shing Wan Choi; Timothy Shin-Heng Mak; Paul F O'Reilly
Journal:  Nat Protoc       Date:  2020-07-24       Impact factor: 13.491

5.  Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk.

Authors:  Hana Lango; Colin N A Palmer; Andrew D Morris; Eleftheria Zeggini; Andrew T Hattersley; Mark I McCarthy; Timothy M Frayling; Michael N Weedon
Journal:  Diabetes       Date:  2008-06-30       Impact factor: 9.461

6.  Bioinformatics: indispensable, yet hidden in plain sight?

Authors:  Andrew Bartlett; Bart Penders; Jamie Lewis
Journal:  BMC Bioinformatics       Date:  2017-06-21       Impact factor: 3.169

7.  Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income.

Authors:  W David Hill; Neil M Davies; Stuart J Ritchie; Nathan G Skene; Julien Bryois; Steven Bell; Emanuele Di Angelantonio; David J Roberts; Shen Xueyi; Gail Davies; David C M Liewald; David J Porteous; Caroline Hayward; Adam S Butterworth; Andrew M McIntosh; Catharine R Gale; Ian J Deary
Journal:  Nat Commun       Date:  2019-12-16       Impact factor: 14.919

8.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Authors:  James J Lee; Robbee Wedow; Aysu Okbay; Edward Kong; Omeed Maghzian; Meghan Zacher; Tuan Anh Nguyen-Viet; Peter Bowers; Julia Sidorenko; Richard Karlsson Linnér; Mark Alan Fontana; Tushar Kundu; Chanwook Lee; Hui Li; Ruoxi Li; Rebecca Royer; Pascal N Timshel; Raymond K Walters; Emily A Willoughby; Loïc Yengo; Maris Alver; Yanchun Bao; David W Clark; Felix R Day; Nicholas A Furlotte; Peter K Joshi; Kathryn E Kemper; Aaron Kleinman; Claudia Langenberg; Reedik Mägi; Joey W Trampush; Shefali Setia Verma; Yang Wu; Max Lam; Jing Hua Zhao; Zhili Zheng; Jason D Boardman; Harry Campbell; Jeremy Freese; Kathleen Mullan Harris; Caroline Hayward; Pamela Herd; Meena Kumari; Todd Lencz; Jian'an Luan; Anil K Malhotra; Andres Metspalu; Lili Milani; Ken K Ong; John R B Perry; David J Porteous; Marylyn D Ritchie; Melissa C Smart; Blair H Smith; Joyce Y Tung; Nicholas J Wareham; James F Wilson; Jonathan P Beauchamp; Dalton C Conley; Tõnu Esko; Steven F Lehrer; Patrik K E Magnusson; Sven Oskarsson; Tune H Pers; Matthew R Robinson; Kevin Thom; Chelsea Watson; Christopher F Chabris; Michelle N Meyer; David I Laibson; Jian Yang; Magnus Johannesson; Philipp D Koellinger; Patrick Turley; Peter M Visscher; Daniel J Benjamin; David Cesarini
Journal:  Nat Genet       Date:  2018-07-23       Impact factor: 38.330

9.  Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients.

Authors:  J Frank; M Lang; S H Witt; J Strohmaier; D Rujescu; S Cichon; F Degenhardt; M M Nöthen; D A Collier; S Ripke; D Naber; M Rietschel
Journal:  Mol Psychiatry       Date:  2014-06-03       Impact factor: 15.992

Review 10.  Validity of polygenic risk scores: are we measuring what we think we are?

Authors:  A Cecile J W Janssens
Journal:  Hum Mol Genet       Date:  2019-11-21       Impact factor: 6.150

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  1 in total

1.  Genomics and justice: mitigating the potential harms and inequities that arise from the implementation of genomics in medicine.

Authors:  A J Clarke; C G van El
Journal:  Hum Genet       Date:  2022-04-12       Impact factor: 5.881

  1 in total

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