Literature DB >> 30635622

The illusion of polygenic disease risk prediction.

Nicholas J Wald1, Robert Old2.   

Abstract

A problem at the interface of genomic medicine and medical screening is that genetic associations of etiological significance are often interpreted as having predictive significance. Genome-wide association studies (GWAS) have identified many thousands of associations between common DNA variants and hundreds of diseases and benign traits. This knowledge has generated many publications with the understandable expectation that it can be used to derive polygenic risk scores for predicting disease to identify those at sufficiently high risk to benefit from preventive intervention. However, the expectation rests on the incorrect assumption that odds ratios derived from polygenic risk scores that are important etiologically are also directly useful in risk prediction and population screening.

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Year:  2019        PMID: 30635622     DOI: 10.1038/s41436-018-0418-5

Source DB:  PubMed          Journal:  Genet Med        ISSN: 1098-3600            Impact factor:   8.822


  44 in total

Review 1.  The genetic architecture of vitiligo.

Authors:  Genevieve H L Roberts; Stephanie A Santorico; Richard A Spritz
Journal:  Pigment Cell Melanoma Res       Date:  2019-12-04       Impact factor: 4.693

2.  Polygenic risk scores and the prediction of common diseases.

Authors:  Mika Ala-Korpela; Michael V Holmes
Journal:  Int J Epidemiol       Date:  2020-02-01       Impact factor: 7.196

3.  Harveian Oration 2019: Prediction and prevention in the genomic era.

Authors:  John Burn
Journal:  Clin Med (Lond)       Date:  2020-01       Impact factor: 2.659

4.  Family Clustering of Autoimmune Vitiligo Results Principally from Polygenic Inheritance of Common Risk Alleles.

Authors:  Genevieve H L Roberts; Subrata Paul; Daniel Yorgov; Stephanie A Santorico; Richard A Spritz
Journal:  Am J Hum Genet       Date:  2019-07-18       Impact factor: 11.025

5.  From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data.

Authors:  Daniele Raimondi; Massimiliano Corso; Piero Fariselli; Yves Moreau
Journal:  Nucleic Acids Res       Date:  2022-02-22       Impact factor: 16.971

6.  Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality.

Authors:  Allison Meisner; Prosenjit Kundu; Yan Dora Zhang; Lauren V Lan; Sungwon Kim; Disha Ghandwani; Parichoy Pal Choudhury; Sonja I Berndt; Neal D Freedman; Montserrat Garcia-Closas; Nilanjan Chatterjee
Journal:  Am J Hum Genet       Date:  2020-08-05       Impact factor: 11.025

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

Authors:  Bart Penders; A Cecile J W Janssens
Journal:  Hum Genet       Date:  2021-02-15       Impact factor: 4.132

8.  Will polygenic risk scores for cancer ever be clinically useful?

Authors:  Amit Sud; Clare Turnbull; Richard Houlston
Journal:  NPJ Precis Oncol       Date:  2021-05-21

9.  Dynamic change in the association of a cigarettes-per-day polygenic risk score across the numeric range of its corresponding phenotype over adolescence and young adulthood.

Authors:  Arielle R Deutsch; Arielle S Selya
Journal:  Addict Behav       Date:  2020-07-23       Impact factor: 3.913

10.  Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes.

Authors:  Avigail Moldovan; Yedael Y Waldman; Nadav Brandes; Michal Linial
Journal:  J Pers Med       Date:  2021-06-21
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