Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
Genome-wide association studies (GWAS) of neuroimaging data pose a significant computational burden because of the need to correct for multiple testing in both the genetic and the imaging data. Here, Ganjgahi et al. develop WLS-REML which significantly reduces computation running times in brain imag...
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| Main Authors: | , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2018
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| Subjects: | |
| Online Access: | https://doaj.org/article/3315ced0f5074d3a9344c50e4a584c44 |
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