Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
Date
2018Journal
Nature CommunicationsPublisher
Nature Publishing GroupType
Article
Metadata
Show full item recordAbstract
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. Copyright 2018, The Author(s).Identifier to cite or link to this item
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051673084&doi=10.1038%2fs41467-018-05444-6&partnerID=40&md5=3f6b7d37586630333ac4328ddfb86953; http://hdl.handle.net/10713/9204ae974a485f413a2113503eed53cd6c53
10.1038/s41467-018-05444-6
Scopus Count
Collections
Related articles
- Multiple phenotype association tests using summary statistics in genome-wide association studies.
- Authors: Liu Z, Lin X
- Issue date: 2018 Mar
- Fast and powerful heritability inference for family-based neuroimaging studies.
- Authors: Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Kochunov P, Nichols TE
- Issue date: 2015 Jul 15
- Genome-wide efficient mixed-model analysis for association studies.
- Authors: Zhou X, Stephens M
- Issue date: 2012 Jun 17
- Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.
- Authors: Wang Y, Goh W, Wong L, Montana G, Alzheimer's Disease Neuroimaging Initiative.
- Issue date: 2013
- An efficient genome-wide association test for mixed binary and continuous phenotypes with applications to substance abuse research.
- Authors: Buu A, Williams LK, Yang JJ
- Issue date: 2018 Mar