scholarly journals Heritability of the Mouse Brain Connectome

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.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

scholarly journals Understanding the genetic determinants of the brain with MOSTest

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis van der Meer ◽  
Oleksandr Frei ◽  
Tobias Kaufmann ◽  
Alexey A. Shadrin ◽  
Anna Devor ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Computational Design ◽  
Brain Regions ◽  
Brain Morphology ◽  
Genome Wide Association Studies ◽  
Small Individual ◽  
Significance Threshold ◽  
Regional Brain ◽  
Genome Wide

Abstract Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10−8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.

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

2021 ◽  
Author(s):  
Shan Cong ◽  
Xiaohui Yao ◽  
Linhui Xie ◽  
Jingwen Yan ◽  
Li Shen ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Association Studies ◽  
Structural Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Middle Temporal Gyrus ◽  
Genome Wide Association Studies ◽  
Brain Connectome ◽  
Genome Wide ◽  
Changes Over Time

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.

Genetic correlations between subcortical brain volumes and psychiatric disorders

2020 ◽  
Vol 216 (5) ◽  
pp. 280-283
Author(s):  
Kazutaka Ohi ◽  
Takamitsu Shimada ◽  
Yuzuru Kataoka ◽  
Toshiki Yasuyama ◽  
Yasuhiro Kawasaki ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Variants ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Intracranial Volume ◽  
Genome Wide Association Studies ◽  
Brain Volumes ◽  
Subcortical Brain ◽  
Genome Wide

SummaryPsychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.

scholarly journals Multivariate genome-wide association study identifies 1735 unique genetic loci associated with cortical morphology

2020 ◽  
Author(s):  
Alexey A. Shadrin ◽  
Tobias Kaufmann ◽  
Dennis van der Meer ◽  
Clare E. Palmer ◽  
Carolina Makowski ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Fold Increase ◽  
Structural Mri ◽  
Genome Wide Association ◽  
Brain Morphology ◽  
Effective Sample Size ◽  
Brain Anatomy ◽  
Genome Wide Association Studies ◽  
Genome Wide

ABSTRACTBrain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) measures from N=35,657 participants in the UK Biobank. We identified 1598 loci for cortical surface area and 1054 for cortical thickness, reflecting an approximate 10-fold increase compared to the most recent report using commonly applied GWAS methods. Our power analysis indicates that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional GWAS approaches. Our gene-based analyses implicate 10% of all protein-coding genes and point towards pathways involved in neurogenesis and cell differentiation, supporting that we are capturing valid biological mechanisms underlying brain anatomy.

scholarly journals GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways

2017 ◽  
Author(s):  
Mats Nagel ◽  
Philip R Jansen ◽  
Sven Stringer ◽  
Kyoko Watanabe ◽  
Christiaan A de Leeuw ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Cell ◽  
Medium Spiny Neurons ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Signal

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3×10-8), medium spiny neurons (P=4×10-8) and serotonergic neurons (P=1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4×10-9), behavioural response to cocaine processes (P=1.84×10-7), and axon part (P=5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

scholarly journals A Combined Analysis of Genetically Correlated Traits Identifies Genes and Brain Regions for Insomnia

2020 ◽  
Vol 65 (12) ◽  
pp. 874-884
Author(s):  
Kezhi Liu ◽  
Ling Zhu ◽  
Minglan Yu ◽  
Xuemei Liang ◽  
Jin Zhang ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Genome Wide Association ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
New Genes ◽  
Genome Wide

Aims: Previous studies have inferred that there is a strong genetic component in insomnia. However, the etiology of insomnia is still unclear. This study systematically analyzed multiple genome-wide association study (GWAS) data sets with core human pathways and functional networks to detect potential gene pathways and networks associated with insomnia. Methods: We used a novel method, multitrait analysis of genome-wide association studies (MTAG), to combine 3 large GWASs of insomnia symptoms/complaints and sleep duration. The i-Gsea4GwasV2 and Reactome FI programs were used to analyze data from the result of MTAG analysis and the nominally significant pathways, respectively. Results: Through analyzing data sets using the MTAG program, our sample size increased from 113,006 subjects to 163,188 subjects. A total of 17 of 1,816 Reactome pathways were identified and showed to be associated with insomnia. We further revealed 11 interconnected functional and topologically interacting clusters (Clusters 0 to 10) that were associated with insomnia. Based on the brain transcriptome data, it was found that the genes in Cluster 4 were enriched for the transcriptional coexpression profile in the prenatal dorsolateral prefrontal cortex ( P = 7 × 10−5), inferolateral temporal cortex ( P = 0.02), medial prefrontal cortex ( P < 1 × 10−5), and amygdala ( P < 1 × 10−5), and detected RPA2, ORC6, PIAS3, and PRIM2 as core nodes in these 4 brain regions. Conclusions: The findings provided new genes, pathways, and brain regions to understand the pathology of insomnia.

scholarly journals Functional validation of CHMP7 as an ADHD risk gene

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Callum Dark ◽  
Caitlin Williams ◽  
Mark A. Bellgrove ◽  
Ziarih Hawi ◽  
Robert J. Bryson-Richardson
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Neurodevelopmental Disorder ◽  
Bioinformatic Analysis ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Zebrafish Model ◽  
Brain Volumes ◽  
Genome Wide ◽  
Total Brain

Abstract Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder of childhood with a strong genetic component. Despite the success of mapping ADHD risk loci, little work has been done to experimentally verify the contribution of these loci to ADHD phenotypes. Meta-analysis of four genome-wide association studies in ADHD suggested CHMP7 as a predisposing gene for ADHD. A DNA variant (rs2294123) mapped to CHMP7 has been shown (via bioinformatic analysis) to have a high likelihood for functionality and correlate with reduced transcript levels. We used CRISPR-Cas9 genome editing to generate a chmp7 zebrafish model for ADHD. chmp7+/− fish showed comparable reductions in mRNA levels to individuals homozygous for the CHMP7 ADHD risk allele. These fish displayed significant hyperactivity over a 24-h period at 6 days post-fertilisation compared to chmp7+/+, but this effect did not persist into juvenile and adulthood stages. In addition, chmp7+/− fish had significantly smaller total brain volumes than chmp7+/+ fish. Finally, the hyperactivity at 6 days post-fertilisation was significantly reduced through the application of methylphenidate, a mainstay pharmacological treatment for ADHD. Overall, this study highlights an important role for CHMP7 in the neurodevelopment of ADHD, and demonstrates the utility of zebrafish for modelling the functional effects of genes conferring risk to ADHD.

scholarly journals GWAS of 19,629 individuals identifies novel genetic variants for regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

2019 ◽  
Author(s):  
Bingxin Zhao ◽  
Tianyou Luo ◽  
Tengfei Li ◽  
Yun Li ◽  
Jingwen Zhang ◽  
...  
Keyword(s):  
Mental Health ◽  
Sample Size ◽  
Genetic Variants ◽  
Association Studies ◽  
Genetic Correlations ◽  
Joint Analysis ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Brain Volumes ◽  
Genome Wide

AbstractVolumetric variations of human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) and post-GWAS analyses of 101 brain volumetric phenotypes using the UK Biobank (UKB) sample including 19,629 participants. GWAS identified 287 independent SNPs exceeding genome-wide significance threshold of 4.9*10−10, adjusted for testing multiple phenotypes. Gene-based association study found 142 associated genes (113 new) and functional gene mapping analysis linked 122 more genes. Many of the discovered genetic variants have previously been implicated with cognitive and mental health traits (such as cognitive performance, education, mental disease/disorders), and significant genetic correlations were detected for 29 pairs of traits. The significant SNPs discovered in the UKB sample were supported by a joint analysis with other four independent studies (total sample size 2,192), and we performed a meta-analysis of five samples to provide GWAS summary statistics with sample size larger than 20,000. Using genome-wide polygenic risk scores prediction, up to 4.36% of phenotypic variance (p-value=2.97*10−22) in the four independent studies can be explained by the UKB GWAS results. In conclusion, our study identifies many new genetic variants at SNP, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

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