Paper
21 December 2018 Voxelwise meta-analysis of brain structural associations with genome-wide polygenic risk for Alzheimer’s disease
Linda Ding, Alyssa H. Zhu, Arvin Saremi, Joshua I. Faskowitz, Asta Håberg, Paul M. Thompson, Neda Jahanshad
Author Affiliations +
Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750L (2018) https://doi.org/10.1117/12.2506660
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico
Abstract
Polygenic risk scores (PRSs) may be used to investigate the effects of genetic risk for disease on complex human traits. Here we set out to determine how the overall genetic risk for Alzheimer’s disease (AD) shapes brain structure in non-AD populations. PRS scores were computed using results from the International Genomics of Alzheimer's Project (IGAP) study. PRSs were computed at 14 different significance thresholds in the IGAP results. The effect of PRS as a predictor of brain morphometry was mapped voxelwise on brain structure as determined by tensor-based morphometry (TBM) in three cohorts: ADNI1, ADNI2, and HUNT. Our multi-cohort TBM framework first tests associations in each cohort individually, then meta-analyzes findings in a common space. Higher PRS for AD was associated with greater ventricular and lower hippocampal volumes. Associations remained after removing the major AD risk gene, APOE, from the PRS. This cumulative influence of common genetic variants on brain-wide structural variation in nondemented individuals may pinpoint genetic and neurological pathways that contribute to the preclinical assessment of disease risk.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Linda Ding, Alyssa H. Zhu, Arvin Saremi, Joshua I. Faskowitz, Asta Håberg, Paul M. Thompson, and Neda Jahanshad "Voxelwise meta-analysis of brain structural associations with genome-wide polygenic risk for Alzheimer’s disease", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750L (21 December 2018); https://doi.org/10.1117/12.2506660
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Cited by 3 scholarly publications.
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KEYWORDS
Genetics

Brain

Alzheimer's disease

Statistical analysis

Neuroimaging

Brain mapping

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