Evidence map›Paper›PMID 32758451›Full record

ArticleAmerican journal of human genetics2020

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

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 and 1 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  14. Blood pressure polygenic risk scores tackle hard endpoints.Hypertension research : official journal of the Japanese Society of Hypertension · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Allison MeisnerDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Prosenjit KunduDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Yan Dora ZhangDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Statistics, University of Hong Kong, 999077, Hong Kong.
Lauren V LanDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Sungwon KimDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Disha GhandwaniDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Indian Statistical Institute, Kolkata, West Bengal 700108, India.
Parichoy Pal ChoudhuryDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Sonja I BerndtDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Neal D FreedmanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Montserrat Garcia-ClosasDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address: nilanjan@jhu.edu.

Funding

Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk PredictionR01HG010480 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2019 to 2023
$2.8M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010480
6 · The paper itself

Abstract

While genome-wide association studies have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS). These cPRS were moderately associated with all-cause mortality in independent data within the UK Biobank: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively. These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry (i.e., middle age for most participants). The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.

Indexed as

Genetic Predisposition to DiseaseBiological Specimen BanksFemaleGenetic Diseases, InbornGenome-Wide Association StudyHumansMaleMiddle AgedMultifactorial InheritancePhenotypePolymorphism, Single NucleotideProportional Hazards ModelsRisk AssessmentRisk FactorsUnited Kingdomall-cause mortalitycause-specific mortalitygenetic risk stratificationgenome-wide association studieslifestyle modificationpolygenic risk scores

Identifiers

PMID32758451
PMCPMC7477009

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.