Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts.
Shan Cong1, Xiaohui Yao1, Linhui Xie2
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Frontiers in Genetics
|March 3, 2022
Summary
This study links genetic variations to human brain structural connectivity, identifying a specific gene (rs7937515) associated with brain network changes and obesity risk across the lifespan. These findings offer insights into neurodevelopment and aging.
Area of Science:
- Neuroimaging Genetics
- Brain Connectomics
- Systems Neuroscience
Background:
- Human brain structural connectivity is a key trait for development and aging.
- Understanding gene-to-connectome mechanisms and their temporal changes is crucial.
- Mapping connectivity to phenotypes provides insights into brain function and dysfunction.
Purpose of the Study:
- To investigate genetic influences on brain structural connectivity across the lifespan.
- To explore the relationship between genetic makeup, brain networks, and phenotypic outcomes.
- To identify neuroimaging biomarkers for conditions like obesity.
Main Methods:
- Analysis of diffusion-weighted imaging data from two age-specific cohorts.
- Extraction of structural connectome topological network measures.
- Genome-wide association studies (GWAS) and causality examination of genetic influences.
Main Results:
- Identified genetic underpinnings of brain connectome changes in both young and elderly adults.
- Discovered a consistent genetic effect of rs7937515 on brain connectivity across the lifespan.
- Revealed rs7937515 as a marker for body mass index in young adults and brain network alterations as a potential biomarker for obesity.
- Demonstrated hemispheric asymmetry in structural network organization.
Conclusions:
- Genetic variations significantly influence human brain structural connectivity.
- Brain network segregation alterations may serve as neuroimaging biomarkers for obesity.
- These findings highlight the potential of imaging genetics for understanding complex brain disorders.
Keywords:
body mass indexcausal inferencegenome-wide association studyhuman connectomicsnetwork segregationMore Related Videos
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