T
Tingting Wang
Researcher at Baker IDI Heart and Diabetes Institute
Publications - 15
Citations - 828
Tingting Wang is an academic researcher from Baker IDI Heart and Diabetes Institute. The author has contributed to research in topics: Medicine & Framingham Risk Score. The author has an hindex of 7, co-authored 11 publications receiving 475 citations. Previous affiliations of Tingting Wang include Cooperative Research Centre & La Trobe University.
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Journal ArticleDOI
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
TL;DR: 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.
Journal ArticleDOI
Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke
Gad Abraham,Gad Abraham,Gad Abraham,Rainer Malik,Ekaterina Yonova-Doing,Agus Salim,Agus Salim,Tingting Wang,John Danesh,Adam S. Butterworth,Joanna M. M. Howson,Joanna M. M. Howson,Michael Inouye,Martin Dichgans,Martin Dichgans +14 more
TL;DR: A metaGRS is developed that is composed of several stroke-related GRSs and demonstrate improved predictive power compared with individual GRS or classic risk factors and it is suggested that, for individuals with high metaG RS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk.
Journal ArticleDOI
Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect
Irene van den Berg,Phil J. Bowman,Iona M. MacLeod,Ben J. Hayes,Tingting Wang,Sunduimijid Bolormaa,Michael E. Goddard +6 more
TL;DR: The lack of increase in prediction accuracy when applied to real data could be due to imputation errors, which demonstrates the importance of developing more accurate methods of imputation or directly genotyping sequence variants that have a major effect in the prediction equation.
Posted ContentDOI
Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and 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
TL;DR: A new genomic risk score for CAD, consisting of 1.7 million genetic variants, has been developed, enabling targeted primary intervention in combination with conventional risk factors and partially attenuated by lipid and blood pressure-lowering medication.
Journal ArticleDOI
Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping.
Tingting Wang,Tingting Wang,Yi-Ping Phoebe Chen,Iona M. MacLeod,Jennie E. Pryce,Jennie E. Pryce,Michael E. Goddard,Michael E. Goddard,Ben J. Hayes,Ben J. Hayes +9 more
TL;DR: A new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization algorithm followed by Markov Chain Monte Carlo sampling, is applied to genomic prediction in a large dairy cattle population with imputed whole genome sequence data.