Author

Nicola Voyle

King's College London

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:Bayesian inference· 1 Research Focus:differential expression· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Richard DobsoniD· 2 shared Steven John KiddleiD· 2 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Amos FolariniD· 1 shared Angela HodgesiD· 1 shared Anne Brækhus· 1 shared Aoife KeohaneiD· 1 shared Aree WitoelariD· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersGenetics

Richard DobsonORCID

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Author profile →

2 papers

Recent·Most cited

Genetic meta-analysis identifies 9 novel loci and functional pathways for Alzheimers disease risk

Iris E. Jansen, Jeanne E. Savage, Kyoko Watanabe, Julien Bryois, Dylan M. Williams, Stacy Steinberg, Julia Sealock, Ida Karlsson, Sara Hägg, Lavinia Athanasiu, Nicola Voyle, Petroula Proitsi, Aree Witoelar, Sven Stringer, Dag Aarsland, , Daniëlle Posthuma

This study identifies 9 novel genetic loci and 215 potential causative genes associated with Alzheimer's disease risk through a large genome-wide association analysis.

bioRxiv · Feb 22, 2018 · PreprintGenetics

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

Nicola Voyle, Willemijn J. Jansen, Aoife Keohane, Hamel Patel, Amos Folarin, Stephen Newhouse, Caroline Johnston, Kuang Lin, Pieter Jelle Visser, Angela Hodges, Richard Dobson, Steven John Kiddle, Alzheimer’s Disease Neuroimaging Initiative, EDAR and DESCRIPA study groups

This study uses Bayesian logistic regression to assess if genetic risk factors predict amyloid burden in non-demented individuals, finding that known factors like age and APOE are more predictive than polygenic risk scores.

bioRxiv · Aug 10, 2017 · PreprintBioinformatics