Journal article
Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
- Abstract:
- Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-018-05444-6
Authors
- Publisher:
- Nature Publishing Group
- Journal:
- Nature Communications More from this journal
- Volume:
- 9
- Issue:
- 1
- Pages:
- 3254
- Publication date:
- 2018-08-14
- Acceptance date:
- 2018-07-09
- DOI:
- EISSN:
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2041-1723
- Pmid:
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30108209
- Language:
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English
- Keywords:
- Pubs id:
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pubs:908884
- UUID:
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uuid:58ad1789-edb3-4fed-a496-c4f4127c41ad
- Local pid:
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pubs:908884
- Deposit date:
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2018-11-01
Terms of use
- Copyright holder:
- *Copyright holder name ("et al" as required)*
- Copyright date:
- 2018
- Notes:
- © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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