PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Bauer, A. ; Zierer, A. ; Gieger, C. ; Büyüközkan, M. ; Müller-Nurasyid, M. ; Grallert, H. ; Meisinger, C. ; Strauch, K. ; Prokisch, H. ; Roden, M.* ; Peters, A. ; Krumsiek, J. ; Herder, C. ; Koenig, W.* ; Thorand, B. ; Huth, C.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

Genet. Epidemiol. 45, 633-650 (2021)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
2.135
0.000
1
1
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Framingham Risk Score ; Metabochip ; Coronary Heart Disease ; Genomic Risk Prediction ; Priority-lasso; Myocardial-infarction; Clinical Utility; Artery-disease; Scores; Association; Imputation; Accuracy; Events; Loci; Architecture
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 0741-0395
e-ISSN 1098-2272
Zeitschrift Genetic Epidemiology
Quellenangaben Band: 45, Heft: 6, Seiten: 633-650 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Institute of Computational Biology (ICB)
Institute of Genetic Epidemiology (IGE)
Independent Research Group Clinical Epidemiology (KEPI)
Institute of Human Genetics (IHG)
Institute of Neurogenomics (ING)
POF Topic(s) 30202 - Environmental Health
30205 - Bioengineering and Digital Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
Forschungsfeld(er) Genetics and Epidemiology
Enabling and Novel Technologies
PSP-Element(e) G-504000-006
G-504090-001
G-504000-002
G-504000-010
G-504091-004
G-554100-001
G-504100-001
G-504091-002
G-502900-001
G-500700-001
G-503292-001
Förderungen Helmholtz Zentrum Munchen
Ludwig-Maximilians-Universitat Munchen
German Federal Ministry of Health
Bavarian State Ministry of Health and Care (DigiMed Bayern)
German Federal Ministry of Education and Research
Ministry of Culture and Science of the State of North Rhine-Westphalia
Helmholtz Alliance 'Aging and Metabolic Programming, AMPro'
Deutsche Forschungsgemeinschaft
DOI 10.1002/gepi.22389
WOS ID WOS:000657467200001
Scopus ID 85107124136
PubMed ID 34082474
Erfassungsdatum 2021-07-06
[➜Korrektur melden]