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Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data

Abstract:

Motivation: The identification of genetic variants influencing gene expression (known as expression quantitative trait loci or eQTLs ) is important in unravelling the genetic basis of complex traits. Detecting multiple eQTLs simultaneously in a population based on paired DNA-seq and RNA-seq assays employs two competing types of models: models which rely on appropriate transformations of RNA-seq data (and are powered by a mature mathematical theory), or count-based models , which represent dig...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/bioinformatics/btx355

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Oncology
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Oncology
Role:
Author
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Grant:
Oxford Biomedical Research Centre Program
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Grant:
WT Centre for Human Genetics Wellcome Trust Core Award Grant Number 090532/Z/09/Z
Publisher:
Oxford University Press Publisher's website
Journal:
Bioinformatics Journal website
Volume:
33
Issue:
19
Pages:
3058–3064
Publication date:
2017-05-31
DOI:
EISSN:
1367-4811
ISSN:
1367-4803
Language:
English
Pubs id:
pubs:700437
UUID:
uuid:4caec00b-3182-4aae-9f5d-ae865f162130
Local pid:
pubs:700437
Deposit date:
2017-06-28

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