Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention.
Michael Inouye,Gad Abraham,Christopher P. Nelson,Angela M. Wood,Michael J. Sweeting,Frank Dudbridge,Florence Lai,Stephen Kaptoge,Marta Brozynska,Tingting Wang,Shu Ye,Tom R. Webb,Martin K. Rutter,Ioanna Tzoulaki,Riyaz S. Patel,Ruth J. F. Loos,Bernard Keavney,Harry Hemingway,John F. Thompson,Hugh Watkins,Panos Deloukas,Emanuele Di Angelantonio,Adam S. Butterworth,John Danesh,Nilesh J. Samani +24 more
TLDR
The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.About:
This article is published in Journal of the American College of Cardiology.The article was published on 2018-10-16 and is currently open access. It has received 522 citations till now. The article focuses on the topics: Framingham Risk Score.read more
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Prediction of Coronary Artery Disease and Major Adverse Cardiovascular Events Using Clinical and Genetic Risk Scores for Cardiovascular Risk Factors
TL;DR: In this article , the added value of genetic risk scores for a variety of cardiovascular traits (CV GRSs) for predicting Coronary artery disease (CAD) and major adverse cardiovascular events (MACE) are the leading causes of death in the general population.
Posted ContentDOI
A polygenic risk score to predict sudden cardiac arrest in patients with coronary artery disease
Emanuele Porcu,Christian W. Thorball,A. Porretta,Etienne Pruvot,Kim Wiskott,Francesca Gilardi,A. Thomas,Claire Redin,Zoltán Kutalik,Tony Fracasso,Olivier Muller,Jacques Fellay +11 more
TL;DR: The results show that a combined genetic risk score for CVD and associated risk factors has the potential to predict the occurrence of SCA in patients with myocardial infarction, hence to identify patients who could benefit from further preventive measures.
Journal ArticleDOI
Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction
TL;DR: In this article , the authors performed an extensive evaluation of age and sex effects in genetic CAD risk prediction, and they found that the sensitivity for females was lower than males in all models including genetic information.
Posted ContentDOI
A novel hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation
TL;DR: In this paper , the performance of the H-matrix best linear unbiased prediction (HBLUP) method has been evaluated using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle.
Posted ContentDOI
Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores
Vesela P. Kovacheva,B. W. Eberhard,Raphael Y. Cohen,M. Maher,Rajiv Kumar Saxena,Kathryn J. Gray +5 more
TL;DR: In this article , the authors developed machine learning and linear regression models to predict preeclampsia risk using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study.
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UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
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TL;DR: UNLABELLED ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R that features over 25 performance measures that can be freely combined to create two-dimensional performance curves.
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A reference panel of 64,976 haplotypes for genotype imputation
Shane A. McCarthy,Sayantan Das,Warren W. Kretzschmar,Olivier Delaneau,Andrew R. Wood,Alexander Teumer,Hyun Min Kang,Christian Fuchsberger,Petr Danecek,Kevin Sharp,Yang Luo,C Sidore,Alan Kwong,Nicholas J. Timpson,Seppo Koskinen,Scott I. Vrieze,Laura J. Scott,He Zhang,Anubha Mahajan,Jan H. Veldink,Ulrike Peters,Ulrike Peters,Carlos N. Pato,Cornelia M. van Duijn,Christopher E. Gillies,Ilaria Gandin,Massimo Mezzavilla,Arthur Gilly,Massimiliano Cocca,Michela Traglia,Andrea Angius,Jeffrey C. Barrett,D.I. Boomsma,Kari Branham,Gerome Breen,Gerome Breen,Chad M. Brummett,Fabio Busonero,Harry Campbell,Andrew T. Chan,Sai Chen,Emily Y. Chew,Francis S. Collins,Laura J Corbin,George Davey Smith,George Dedoussis,Marcus Dörr,Aliki-Eleni Farmaki,Luigi Ferrucci,Lukas Forer,Ross M. Fraser,Stacey Gabriel,Shawn Levy,Leif Groop,Leif Groop,Tabitha A. Harrison,Andrew T. Hattersley,Oddgeir L. Holmen,Kristian Hveem,Matthias Kretzler,James Lee,Matt McGue,Thomas Meitinger,David Melzer,Josine L. Min,Karen L. Mohlke,John B. Vincent,Matthias Nauck,Deborah A. Nickerson,Aarno Palotie,Aarno Palotie,Michele T. Pato,Nicola Pirastu,Melvin G. McInnis,J. Brent Richards,J. Brent Richards,Cinzia Sala,Veikko Salomaa,David Schlessinger,Sebastian Schoenherr,P. Eline Slagboom,Kerrin S. Small,Tim D. Spector,Dwight Stambolian,Marcus A. Tuke,Jaakko Tuomilehto,Leonard H. van den Berg,Wouter van Rheenen,Uwe Völker,Cisca Wijmenga,Daniela Toniolo,Eleftheria Zeggini,Paolo Gasparini,Matthew G. Sampson,James F. Wilson,Timothy M. Frayling,Paul I.W. de Bakker,Morris A. Swertz,Steven A. McCarroll,Charles Kooperberg,Annelot M. Dekker,David Altshuler,Cristen J. Willer,William G. Iacono,Samuli Ripatti,Nicole Soranzo,Nicole Soranzo,Klaudia Walter,Anand Swaroop,Francesco Cucca,Carl A. Anderson,Richard M. Myers,Michael Boehnke,Mark I. McCarthy,Mark I. McCarthy,Richard Durbin,Gonçalo R. Abecasis,Jonathan Marchini +117 more
TL;DR: A reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies.
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Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population.
Anna Fry,Thomas J. Littlejohns,Catherine Sudlow,Nicola Doherty,Ligia Adamska,Tim Sprosen,Rory Collins,Naomi E. Allen +7 more
TL;DR: UK Biobank is not representative of the sampling population; there is evidence of a “healthy volunteer” selection bias; valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be Representative of the population at large.
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A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
Majid Nikpay,Anuj Goel,Won H-H.,Leanne M. Hall,Christina Willenborg,Stavroula Kanoni,Danish Saleheen,Theodosios Kyriakou,Christopher P. Nelson,Christopher P. Nelson,Jemma C. Hopewell,Tom R. Webb,Tom R. Webb,Lingyao Zeng,Abbas Dehghan,Maris Alver,Sebastian M. Armasu,Kirsi Auro,Kirsi Auro,Andrew Bjonnes,Daniel I. Chasman,Shufeng Chen,Ian Ford,Nora Franceschini,Christian Gieger,Christopher Grace,Stefan Gustafsson,Jie Huang,Hwang S-J.,Yun Kyoung Kim,Marcus E. Kleber,King Wai Lau,Xiangfeng Lu,Yingchang Lu,Lyytikäinen L-P.,Evelin Mihailov,Alanna C. Morrison,Natalia Pervjakova,Natalia Pervjakova,Natalia Pervjakova,Liming Qu,Lynda M. Rose,Elias Salfati,Richa Saxena,Markus Scholz,Albert V. Smith,Emmi Tikkanen,André G. Uitterlinden,Xueli Yang,Weihua Zhang,Wei Zhao,M. De Andrade,P. De Vries,N. R. van Zuydam,N. R. van Zuydam,Sonia S. Anand,Lars Bertram,Frank Beutner,George Dedoussis,Philippe M. Frossard,Dominique Gauguier,Alison H. Goodall,Omri Gottesman,Marc Haber,Han B-G.,Shapour Jalilzadeh,Thorsten Kessler,Inke R. König,Lars Lannfelt,Wolfgang Lieb,Lars Lind,Cecilia M. Lindgren,Lokki M-L.,Lokki M-L.,Patrik K. E. Magnusson,Nadeem Hayyat Mallick,Narinder K. Mehra,Thomas Meitinger,Memon F-U-R.,Andrew P. Morris,Andrew P. Morris,Markku S. Nieminen,Nancy L. Pedersen,Annette Peters,Loukianos S. Rallidis,Adil Rasheed,M Samuel,Svati H. Shah,Juha Sinisalo,Kathleen Stirrups,Stella Trompet,Ligui Wang,Ligui Wang,Khan Shah Zaman,Diego Ardissino,Eric Boerwinkle,Ingrid B. Borecki,Erwin P. Bottinger,Julie E. Buring,John C. Chambers,Rory Collins,L. A. Cupples,L. A. Cupples,John Danesh,Ilja Demuth,Roberto Elosua,Stephen E. Epstein,Tõnu Esko,Mary F. Feitosa,Oscar H. Franco,MariaGrazia Franzosi,Christopher B. Granger,Dongfeng Gu,Vilmundur Gudnason,Alistair S. Hall,Anders Hamsten,Tamara B. Harris,Stanley L. Hazen,Christian Hengstenberg,Albert Hofman,Erik Ingelsson,Carlos Iribarren,J W Jukema,Pekka J. Karhunen,Kim B-J.,Jaspal S. Kooner,Iftikhar J. Kullo,Terho Lehtimäki,Loos Rjf.,Olle Melander,Andres Metspalu,Winfried März,Winfried März,Winfried März,Colin N. A. Palmer,Markus Perola,Markus Perola,Markus Perola,Thomas Quertermous,Daniel J. Rader,Paul M. Ridker,Samuli Ripatti,Samuli Ripatti,Robert Roberts,Veikko Salomaa,Dharambir K. Sanghera,Stephen M. Schwartz,Udo Seedorf,Alexandre F.R. Stewart,David J. Stott,Joachim Thiery,Pierre Zalloua,Christopher J. O'Donnell,Christopher J. O'Donnell,Muredach P. Reilly,Themistocles L. Assimes,John R. Thompson,Jeanette Erdmann,Robert Clarke,Hugh Watkins,Sekar Kathiresan,Ruth McPherson,Panos Deloukas,Panos Deloukas,Heribert Schunkert,Nilesh J. Samani,Nilesh J. Samani,Martin Farrall +167 more
TL;DR: This article conducted a meta-analysis of coronary artery disease (CAD) cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 millions low-frequency (0.005 < MAF < 0.5) variants.
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