JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.

Newcombe, Paul; Conti, David V; Richardson, Sylvia

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Authors

Newcombe, Paul

Conti, David V

Richardson, Sylvia

Publication Date

2016-04

Journal Title

Genetic epidemiology

ISSN

0741-0395

Volume

Issue

Pages

188-201

Language

eng

Type

Article

This Version

VoR

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Citation

Newcombe, P., Conti, D. V., & Richardson, S. (2016). JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.. Genetic epidemiology, 40 (3), 188-201. https://doi.org/10.1002/gepi.21953

Keywords

Humans, Insulin, Glucose, Fasting, Bayes Theorem, Genomics, Phenotype, Polymorphism, Single Nucleotide, Algorithms, Models, Genetic, Computer Simulation, Genome-Wide Association Study, Adenylyl Cyclases

Sponsorship

Wellcome Trust (076113/C/04/Z)

Wellcome Trust (061858/Z/00/E)

Identifiers

External DOI: https://doi.org/10.1002/gepi.21953

This record's URL: https://www.repository.cam.ac.uk/handle/1810/275140

Rights

Attribution 4.0 International

Licence URL: http://creativecommons.org/licenses/by/4.0/

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Creative Commons

Except where otherwise noted, this item's licence is described as Attribution 4.0 International