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

AbstractBackgroundHuman 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.MethodsThis 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.ResultsOur 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.DiscussionThese 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.Impact StatementThe genetic architecture underlying brain connectivity, and whether this mechanism changes over time, remain largely unknown. To understand the inter-individual variability at different life stages, this study performed genome-wide association studies of brain network connectivity measures from two age-specific neuroimaging cohorts, and identified a common association between the minor allele (G) of rs7937515 and decreased network segregation measures of the left middle temporal gyrus. The mediation analysis further elucidated neurobiological pathway of brain connectivity mediators linking the genes FAM86C1/FOLR3 with body mass index. This study provided new insights into the genetic mechanism of inter-regional connectivity alteration in the brain.

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Heritability of the Mouse Brain Connectome

10.1101/701755 ◽

2019 ◽

Author(s):

Nian Wang ◽

Robert J Anderson ◽

David G Ashbrook ◽

Vivek Gopalakrishnan ◽

Youngser Park ◽

...

Keyword(s):

Population Studies ◽

Association Studies ◽

Structural Mri ◽

Brain Regions ◽

Genome Wide Association Studies ◽

Brain Volumes ◽

Brain Connectome ◽

Genome Wide ◽

Scalar Diffusion ◽

And Diffusion

SUMMARYGenome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We derived volumes, scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 ± 0.23 SD, n = 332), followed by fractional anisotropy (0.54 ± 0.26), radial diffusivity (0.34 ± 0.022), and axial diffusivity (0.28 ± 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines.

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Integrating Psychiatry and Medical Biotechnology as a Way to Achieve Scientific, Precision, and Personalized Psychiatry

Avicenna Journal of Medical Biotechnology ◽

10.18502/ajmb.v13i4.7201 ◽

2021 ◽

Author(s):

Ahmad Shamabadi ◽

Alireza Hasanzadeh ◽

Shahin Akhondzadeh

Keyword(s):

Big Data ◽

Molecular Markers ◽

Psychiatric Disorders ◽

Clinical Symptoms ◽

Association Studies ◽

Brain Network ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Tnf Α ◽

Factor Α

Besides concerns about the increasing prevalence of psychiatric disorders and the significant burdens and costs, there are concerns about its validity. The dilemma of validity went so far that studies described the diagnoses in psychiatry as scientifically worthless. We suggest integrating psychiatry and medical biotechnology and using biotechnological products in psychiatric aspects help psychiatry become more precise, strengthen its position among other sciences, and increase its scientific credibility by giving examples. For this matter, we need different inputs to choose between the vast outputs. The most common inputs are clinical symptoms, cognitive function, individual and environmental risk factors, molecular markers, genetic markers, neuroimaging signs, and big data. Some molecular markers have been shown to have a relationship with psychiatric disorders such as Interleukin-6 (IL-6) and Tumor Necrosis Factor-α (TNF-α). Genetic studies might evolve the most accurate part of precision psychiatry. Currently, and through the developments in technology, genome-wide association studies have become available. In neuroimaging signs, psychiatric disorders are associated with generalized rather than focal brain network dysfunction, and functional magnetic resonance imaging could be performed to study them. It would exhibit different aberrancies in various psychiatric disorders. In big data, the constitution of predictive models and movement toward precision psychiatry can be led by using artificial intelligence and machine learning.

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The Genetics of Sleep Disorders in Children: A Narrative Review

Brain Sciences ◽

10.3390/brainsci11101259 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1259

Author(s):

Greta Mainieri ◽

Angelica Montini ◽

Antonio Nicotera ◽

Gabriella Di Rosa ◽

Federica Provini ◽

...

Keyword(s):

Sleep Disorders ◽

Association Studies ◽

Single Gene ◽

Brain Network ◽

Narrative Review ◽

Genome Wide Association Studies ◽

Genetic Studies ◽

Gene Environment ◽

Genome Wide

Sleep is a universal, highly preserved process, essential for human and animal life, whose complete functions are yet to be unravelled. Familial recurrence is acknowledged for some sleep disorders, but definite data are lacking for many of them. Genetic studies on sleep disorders have progressed from twin and family studies to candidate gene approaches to culminate in genome-wide association studies (GWAS). Several works disclosed that sleep-wake characteristics, in addition to electroencephalographic (EEG) sleep patterns, have a certain degree of heritability. Notwithstanding, it is rare for sleep disorders to be attributed to single gene defects because of the complexity of the brain network/pathways involved. Besides, the advancing insights in epigenetic gene-environment interactions add further complexity to understanding the genetic control of sleep and its disorders. This narrative review explores the current genetic knowledge in sleep disorders in children, following the International Classification of Sleep Disorders—Third Edition (ICSD-3) categorisation.

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Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease

10.1101/342436 ◽

2018 ◽

Cited By ~ 1

Author(s):

Samar S. M. Elsheikh ◽

Emile R. Chimusa ◽

Nicola J. Mulder ◽

Alessandro Crimi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Brain Connectivity ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Healthy Control ◽

Using Data

ABSTRACTVariations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer’s disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer’s disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer’s disease.

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Brain Network Connectivity in Adolescents At Risk for Alcohol Use Problems

10.1101/2020.10.22.20217976 ◽

2020 ◽

Author(s):

Simon T. E. Baker ◽

Murat Yücel ◽

Alex Fornito ◽

Andrew Zalesky ◽

Sarah Whittle ◽

...

Keyword(s):

Family History ◽

Alcohol Use ◽

Brain Connectivity ◽

Structural Connectivity ◽

Mean Diffusivity ◽

Network Connectivity ◽

Brain Network ◽

Increased Risk ◽

Brain Network Connectivity ◽

History Of

AbstractImaging studies of young people with a family history of alcohol use disorder (AUD) have found structural and/or functional differences within and between anatomically distributed and functionally specialised systems throughout the brain. Differences in brain connectivity among adolescents with a family history of AUD may account for the increased risk of later alcohol use problems; however, to date, no prospective studies have directly examined this hypothesis across the entire connectome in a regionally unbiased way. Our analysis included 52 adolescents (Mage = 16.5 years ± 0.6 SD) assessed with diffusion-weighted magnetic resonance imaging, of whom 20 had a family history of AUD and 32 did not. All participants were followed-up 2.3 years later and completed a questionnaire measuring past year alcohol use and alcohol-related harms. Subject-specific connectomic maps of structural connectivity were constructed using two parcellation schemes (82-node anatomical and 530-node random) and five measures of connectivity weight (streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity), and a connectome-wide network-based statistic analysis was used to compare group differences at each and every connection between adolescents with and without a family history of AUD. Baseline connectivity measures did not differentiate these groups, and we did not find an association between baseline connectivity measures and alcohol outcomes at follow-up. These findings suggest that atypical inter-regional structural connectivity may not contribute to the risk of developing alcohol use problems in this particular age group, or during this particular period of development.

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