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Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank.

作者信息

Millard Louise A C, Davies Neil M, Gaunt Tom R, Davey Smith George, Tilling Kate

机构信息

MRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine.

Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK.

出版信息

Int J Epidemiol. 2018 Feb;47(1):29-35. doi: 10.1093/ije/dyx204. Epub 2017 Oct 5.


DOI:10.1093/ije/dyx204
PMID:29040602
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5837456/
Abstract

MOTIVATION: Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. GENERAL FEATURES: PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables. IMPLEMENTATION: The PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format. AVAILABILITY: PHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.5 corresponds to the version presented here.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb58/5837456/548db842495f/dyx204f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb58/5837456/8058c1d9d643/dyx204f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb58/5837456/548db842495f/dyx204f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb58/5837456/8058c1d9d643/dyx204f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb58/5837456/548db842495f/dyx204f2.jpg

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本文引用的文献

[1]
Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank.

Nat Genet. 2017-9

[2]
Outcome-wide Epidemiology.

Epidemiology. 2017-5

[3]
Phenotypic Characterization of Genetically Lowered Human Lipoprotein(a) Levels.

J Am Coll Cardiol. 2016-12-27

[4]
Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods.

Genet Epidemiol. 2016-12

[5]
Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Annu Rev Genomics Hum Genet. 2016-8-31

[6]
MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Sci Rep. 2015-11-16

[7]
Genetic studies of body mass index yield new insights for obesity biology.

Nature. 2015-2-12

[8]
Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis.

Am J Hum Genet. 2014-1-23

[9]
A nutrient-wide association study on blood pressure.

Circulation. 2012-10-23

[10]
Mendelian Randomisation Study of Childhood BMI and Early Menarche.

J Obes. 2011

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