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Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data
Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measureme...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730532/ https://www.ncbi.nlm.nih.gov/pubmed/34890141 |
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author | Bao, Jingxuan Wen, Zixuan Kim, Mansu Zhao, Xiwen Lee, Brian N. Jung, Sang-Hyuk Davatzikos, Christos Saykin, Andrew J. Thompson, Paul M. Kim, Dokyoon Zhao, Yize Shen, Li |
author_facet | Bao, Jingxuan Wen, Zixuan Kim, Mansu Zhao, Xiwen Lee, Brian N. Jung, Sang-Hyuk Davatzikos, Christos Saykin, Andrew J. Thompson, Paul M. Kim, Dokyoon Zhao, Yize Shen, Li |
author_sort | Bao, Jingxuan |
collection | PubMed |
description | Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer’s disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability. |
format | Online Article Text |
id | pubmed-8730532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-87305322022-01-05 Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data Bao, Jingxuan Wen, Zixuan Kim, Mansu Zhao, Xiwen Lee, Brian N. Jung, Sang-Hyuk Davatzikos, Christos Saykin, Andrew J. Thompson, Paul M. Kim, Dokyoon Zhao, Yize Shen, Li Pac Symp Biocomput Article Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer’s disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability. 2022 /pmc/articles/PMC8730532/ /pubmed/34890141 Text en https://creativecommons.org/licenses/by/4.0/Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License. |
spellingShingle | Article Bao, Jingxuan Wen, Zixuan Kim, Mansu Zhao, Xiwen Lee, Brian N. Jung, Sang-Hyuk Davatzikos, Christos Saykin, Andrew J. Thompson, Paul M. Kim, Dokyoon Zhao, Yize Shen, Li Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title | Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title_full | Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title_fullStr | Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title_full_unstemmed | Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title_short | Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data |
title_sort | identifying highly heritable brain amyloid phenotypes through mining alzheimer’s imaging and sequencing biobank data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730532/ https://www.ncbi.nlm.nih.gov/pubmed/34890141 |
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