Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
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 a...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Publishing Group
2018
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| _version_ | 1797069970778095616 |
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| author | Ganjgahi, H Winkler, A Glahn, D Blangero, J Donohue, B Kochunov, P Nichols, T |
| author_facet | Ganjgahi, H Winkler, A Glahn, D Blangero, J Donohue, B Kochunov, P Nichols, T |
| author_sort | Ganjgahi, H |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T22:32:17Z |
| format | Journal article |
| id | oxford-uuid:58ad1789-edb3-4fed-a496-c4f4127c41ad |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:32:17Z |
| publishDate | 2018 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | oxford-uuid:58ad1789-edb3-4fed-a496-c4f4127c41ad2022-03-26T17:05:00ZFast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:58ad1789-edb3-4fed-a496-c4f4127c41adEnglishSymplectic Elements at OxfordNature Publishing Group2018Ganjgahi, HWinkler, AGlahn, DBlangero, JDonohue, BKochunov, PNichols, TGenome 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. |
| spellingShingle | Ganjgahi, H Winkler, A Glahn, D Blangero, J Donohue, B Kochunov, P Nichols, T Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title | Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title_full | Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title_fullStr | Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title_full_unstemmed | Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title_short | Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
| title_sort | fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes |
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