Genome-wide association study of white matter hyperintensity volume in elderly persons without dementia

Guo, Yu; Shen, Xue‐Ning; Hou, Xiao-He; Ou, Ya‐Nan; Huang, Yu‐Yuan; Dong, Qiang; Tan, Lan; Yu, Jin‐Tai; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.nicl.2020.102209

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…Emerging advances in multimodal brain imaging, high throughput genotyping and sequencing techniques provide exciting new opportunities to ultimately improve our understanding of brain structure and neural dynamics, their genetic architecture and their influences on cognition and behavior ( Shen and Thompson, 2020 ). Present studies investigating direct associations among human connectomics, genomics and clinical phenotyping are primarily focused on four aspects: 1) estimating genetic heritability of basic connectome measures such as number of fibers, length of fibers and fractional anisotropy (FA) ( Jahanshad et al, 2013 ; Thompson et al, 2013 ; Elliott et al, 2018 ); 2) discovering pairwise univariate associations between single nucleotide polymorphisms (SNPs) and imaging phenotypic traits such as above mentioned basic connectome measures at each edge ( Jahanshad et al, 2013 ; Karwowski et al, 2019 ) and white matter properties at each voxel ( Kochunov et al, 2010 ; Alloza et al, 2018 ; Guo et al, 2020 ); 3) discovering pairwise univariate associations between SNPs and clinical phenotypes such as cognitive or behavioral outcomes ( Jahanshad et al, 2013 ; Elsheikh et al, 2020 ); and 4) discovering pairwise univariate associations between basic connectome measures and clinical phenotypes ( Jiang et al, 2019 ; van den Heuvel et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

et al. 2022

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Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear.Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures.Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies.Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

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Section: Introductionmentioning

confidence: 99%

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

et al. 2022

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“…(1) estimating genetic heritability of basic connectome measures such as number of fibers, length of fibers and fractional anisotropy (FA) (Elliott et al, 2018;Jahanshad et al, 2013;Thompson et al, 2013); (2) discovering pairwise univariate associations between single nucleotide polymorphisms (SNPs) and imaging phenotypic traits such as above mentioned basic connectome measures at each edge (Jahanshad et al, 2013;Karwowski et al, 2019) and white matter properties at each voxel (Alloza et al, 2018;Guo et al, 2020;Kochunov et al, 2010); (3) discovering pairwise univariate associations between SNPs and clinical phenotypes such as cognitive or behavioral outcomes (Elsheikh et al, 2020;Jahanshad et al, 2013); and (4) discovering pairwise univariate associations between basic connectome measures and clinical phenotypes (van den Heuvel et al, 2019;Jiang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

Cong

Yao

Xie

et al. 2021

Preprint

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Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies (GWAS) of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index (BMI) in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

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Genetics of Small Vessel Disease

Marini,

Parodi,

Malik

et al. 2024

Stroke Genetics

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Genome-wide association study of white matter hyperintensity volume in elderly persons without dementia

Cited by 6 publications

References 41 publications

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

Genetics of Small Vessel Disease

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