Evidence map›Paper›PMID 28986454›Full record

SynthesisCirculation. Cardiovascular genetics2017

Validation of Polygenic Scores for QT Interval in Clinical Populations.

Michael A Rosenberg, Steven A Lubitz, Honghuang Lin, Gulum Kosova, Victor M Castro, Paul Huang, Patrick T Ellinor, Roy H Perlis, Christopher Newton-Cheh

Open access · diamondAbstract readMeta-AnalysisMulticenter Study
In one paragraph

Synthesis in Circulation. Cardiovascular genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 17% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
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  5. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Michael A RosenbergFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.). michael.a.rosenberg@ucdenver.edu.
Steven A LubitzFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Honghuang LinFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Gulum KosovaFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Victor M CastroFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Paul HuangFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Patrick T EllinorFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Roy H PerlisFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
Christopher Newton-ChehFrom the University of Colorado School of Medicine, Aurora (M.A.R.); Massachusetts General Hospital, Boston (S.A.L., G.K., V.M.C., P.H., P.T.E., R.H.P., C.N.-C.); and Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L.).
University of Colorado Denver · US

Funding

IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
AFGen Consortium: Atrial Fibrillation Genomics to Functional AnalysesR01HL128914 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2015 to 2018
$3.1M
Genetics of Cardiotoxic Drug Response to Psychiatric TherapyR01HL124262 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI NEWTON-CHEH, CHRISTOPHER HOLMES, PERLIS, ROY H. · 2015 to 2018
$2.7M
Mentoring in Arrhythmia ResearchK24HL105780 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS · 2011 to 2020
$1.2M
Genetics of Cardiotoxic Drug ResponseK23HL127296 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI ROSENBERG, MICHAEL A · 2015 to 2020
$932k
Atrial Fibrillation: Bridging genetic discovery and clinical associationsK23HL114724 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI LUBITZ, STEVEN A · 2013 to 2017
$893k
NHLBI NIH HHS K23 HL114724NHLBI NIH HHS K23 HL127296NHLBI NIH HHS K24 HL105780NHLBI NIH HHS L30 HL123413NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL124262NHLBI NIH HHS R01 HL128914
6 · The paper itself

Abstract

backgroundPolygenic risk scores (PGS) enable rapid estimation of genome-wide susceptibility for traits, which may be useful in clinical settings, such as prediction of QT interval. In this study, we sought to validate PGS for QT interval in 2 real-world cohorts of European ancestry (EA) and African ancestry (AA). METHODS AND

resultsTwo thousand nine hundred and fifteen participants of EA and 366 of AA in the MGH CAMP study (Cardiology and Metabolic Patient) were genotyped on a genome-wide array and imputed to the 1000 Genomes reference panel. An additional 820 EA and 57 AA participants in the Partners Biobank were genotyped and used for validation. PGS were created for each individual using effect estimates from association tests with QT interval obtained from prior genome-wide association studies, with variants selected based from multiple significance thresholds in the original study. In regression models, clinical variables explained ≈9% to 10% of total variation in resting QTc in EA individuals and ≈12% to 18% in AA individuals. The PGS significantly increased variation explained at most significance thresholds (

conclusionsFor individuals of European descent, PGS provided a significant increase in variation in QT interval explained compared with a model with only nongenetic factors at nearly every significance level. There was no apparent benefit gained by relaxing the significance threshold from conventional genome-wide significance (

Indexed as

Genome-Wide Association StudyMultifactorial InheritancePolymorphism, Single NucleotideBlack PeopleFemaleHumansLong QT SyndromeMaleMetabolic DiseasesWhite Peopleepidemiologygenetics, bioinformaticsgenome-wide association studiespredictionrepolarization

Identifiers

PMID28986454
PMCPMC5679734
OpenAlexW2762919016

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.