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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Autores principales: | Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov, Thomas E. Nichols |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
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Materias: | |
Acceso en línea: | https://doaj.org/article/3315ced0f5074d3a9344c50e4a584c44 |
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