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The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

A gene-based association method for mapping traits using reference transcriptome data

Open access
Gamazon, Eric R, Wheeler, Heather E, Shah, Kaanan P, Mozaffari, Sahar V, Aquino-Michaels, Keston, Carroll, Robert J, Eyler, Anne E, Denny, Joshua C, GTEx Consortium, Nicolae, Dan L, Cox, Nancy J, Im, Hae Kyung·2015·Nature Genetics
DOI 10.1038/ng.3367

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Abstract

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

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Citations

Cites

Research works printed but not linked: 33

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Metadata

Authors
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., GTEx Consortium, Nicolae, D. L., Cox, N. J., & Im, H. K.
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2015-08-10
Volume
47
Issue
9
Pages
1091-1098
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/ng.3367
OpenAlex
W2209106767
Cited by
1,829
References
20
FWCI
60.03
Type
journal article
Language
en
Open access
green
Licence

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