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Jeffrey P. Spence on Muck Rack

Jeffrey P. Spence

As seen in: Nature, PLOS, Science Magazine, BioRxiv, The American Journal of Human Genetics, Cell Press, medRxiv, eLife, The Hospitalist
staff scientist at stanford interested in statistics, genomics, etc... also, swimming (open water), cycling (road), and running (trail)
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Articles

Predicting the effect of CRISPR-Cas9-based epigenome editing

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By Sanjit Singh Batra, Alan Cabrera, Jeffrey P. Spence, Jacob Goell
| eLife
eLife Assessment This study presents an advance in efforts to use histone post-translational modification (PTM) data to model gene expression and predict epigenetic editing activity. Such models are broadly useful to the research community, especially ones that can model and predict epigenetic editing activity, which is novel; additionally, the authors have nicely integrated datasets across cell types into their model.
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Causal modelling of gene effects from regulators to programs to traits - Nature

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By Mineto Ota, Jeffrey P. Spence, Tony Zeng, Emma Dann, Alexander Marson
| Nature Verified
Abstract Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits.
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Specificity, length and luck drive gene rankings in association studies - Nature

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By Jeffrey P. Spence, Hakhamanesh Mostafavi, Mineto Ota, Courtney Smith
| Nature Verified
Abstract Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes1. Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank2,3,4, we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized.
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