scholarly journals TH46. THE EFFECT OF DOPAMINE-RELATED GENETIC VARIANTS ON REWARD-RELATED NEURONAL ACTIVITY IN HEALTHY PARTICIPANTS: AN IMAGING GENETICS STUDY

2021 ◽  
Vol 51 ◽  
pp. e218-e219
Author(s):  
Aqeedah Roomaney ◽  
Jacqueline S. Womersley ◽  
Patricia Swart ◽  
Stefan du Plessis ◽  
Leigh vd Heuvel ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Genetic Variants ◽  
Imaging Genetics ◽  
Healthy Participants

(333) Spinal Cord Neuronal Activity Correlates with Experimental Pain Ratings of Healthy Participants: Use of BOLD fMRI

2017 ◽  
Vol 18 (4) ◽  
pp. S58
Author(s):  
J. Boissoneault ◽  
M. Robinson ◽  
P. Stroman ◽  
S. Lai ◽  
R. Staud
Keyword(s):  
Spinal Cord ◽  
Neuronal Activity ◽  
Experimental Pain ◽  
Bold Fmri ◽  
Pain Ratings ◽  
Healthy Participants

Genetics in anxiety disorders - an update

2011 ◽  
Vol 26 (S2) ◽  
pp. 2097-2097
Author(s):  
K. Domschke
Keyword(s):  
Panic Disorder ◽  
Anxiety Disorders ◽  
Candidate Genes ◽  
Genetic Variants ◽  
Emotional Processing ◽  
Disease Risk ◽  
Association Studies ◽  
Imaging Genetics ◽  
Genome Wide Association Studies ◽  
Increased Risk

Twin studies propose a strong genetic contribution to the pathogenesis of anxiety disorders with a heritability of about 50%. The dissection of the complex-genetic underpinnings of anxiety disorders requires a multi-level approach using molecular genetic, imaging genetic, (cognitive)-behavioral genetic and pharmacogenetic techniques linking basic and clinical research.The present talk will first give an overview of results from linkage and association studies yielding support for several candidate genes contributing to the genetic risk for anxiety and panic disorder in particular such as the adenosine 2A receptor, the catechol-O-methyltransferase, the neuropeptide S receptor and the serotonin receptor 1A genes. Results from the first genome-wide association studies in the field of anxiety disorders will be discussed. Additionally, studies on gene-environment interactions between anxiety disorder risk variants and environmental factors will be presented. Imaging genetics approaches have yielded evidence for several risk genes to crucially impact activation in brain regions critical for emotional processing. Gene variation has furthermore been found to potentially confer an increased risk for panic disorder via elevated autonomic arousal and dysfunctional cognitions regarding bodily sensations. Finally, there is first evidence for genetic variants impacting treatment response to antidepressant pharmacotherapy in anxiety disorders.Thus, converging lines of evidence will be presented for several candidate genes of anxiety to exert an increased disease risk potentially via a distorted cortico-limbic interaction during emotional processing, increased physiological arousal or dysfunctional cognition. Additionally, a possible impact of genetic variants on pharmacoresponse in anxiety disorders and its potential clinical implications will be discussed.

scholarly journals Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

2020 ◽  
Author(s):  
Rachel M. Brouwer ◽  
Marieke Klein ◽  
Katrina L. Grasby ◽  
Hugo G. Schnack ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Brain Development ◽  
Brain Structure ◽  
Genetic Variants ◽  
Imaging Genetics ◽  
Adult Brain ◽  
Brain Morphology ◽  
Brain Growth ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Age Dependent

AbstractHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world’s largest longitudinal imaging genetics dataset we identified genetic variants that alter age-dependent brain growth and atrophy throughout our lives.One-sentence summaryWe identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide.

scholarly journals Identifying Imaging Genetics Biomarkers of Alzheimer’s Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression

2021 ◽  
Vol 12 ◽  
Author(s):  
Fengchun Ke ◽  
Wei Kong ◽  
Shuaiqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Genetic Variants ◽  
Canonical Correlation ◽  
Brain Regions ◽  
Imaging Genetics ◽  
Sparse Canonical Correlation Analysis ◽  
Disease Specific

Imaging genetics combines neuroimaging and genetics to assess the relationships between genetic variants and changes in brain structure and metabolism. Sparse canonical correlation analysis (SCCA) models are well-known tools for identifying meaningful biomarkers in imaging genetics. However, most SCCA models incorporate only diagnostic status information, which poses challenges for finding disease-specific biomarkers. In this study, we proposed a multi-task sparse canonical correlation analysis and regression (MT-SCCAR) model to reveal disease-specific associations between single nucleotide polymorphisms and quantitative traits derived from multi-modal neuroimaging data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. MT-SCCAR uses complementary information carried by multiple-perspective cognitive scores and encourages group sparsity on genetic variants. In contrast with two other multi-modal SCCA models, MT-SCCAR embedded more accurate neuropsychological assessment information through linear regression and enhanced the correlation coefficients, leading to increased identification of high-risk brain regions. Furthermore, MT-SCCAR identified primary genetic risk factors for Alzheimer’s disease (AD), including rs429358, and found some association patterns between genetic variants and brain regions. Thus, MT-SCCAR contributes to deciphering genetic risk factors of brain structural and metabolic changes by identifying potential risk biomarkers.

scholarly journals Enhanced Brain Imaging Genetics in UK Biobank

2020 ◽  
Author(s):  
Stephen M Smith ◽  
Gwenaëlle Douaud ◽  
Winfield Chen ◽  
Taylor Hanayik ◽  
Fidel Alfaro-Almagro ◽  
...  
Keyword(s):  
Brain Imaging ◽  
X Chromosome ◽  
Genetic Variants ◽  
Imaging Genetics ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Imaging Data ◽  
Discovery Sample ◽  
Renal Malformations

AbstractUK Biobank is a major prospective epidemiological study that is carrying out detailed multimodal brain imaging on 100,000 participants, and includes genetics and ongoing health outcomes. As a step forwards in understanding genetic influence on brain structure and function, in 2018 we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 UKB subjects. Here we present a new open resource of GWAS summary statistics, resulting from a greatly expanded set of genetic associations with brain phenotypes, using the 2020 UKB imaging data release of approximately 40,000 subjects. The discovery sample has now almost tripled (22,138), the number of phenotypes increased to 3,935 and the number of genetic variants with MAF≥1% increased to 10 million. For the first time, we include associations on the X chromosome, and several new classes of image derived phenotypes (primarily, more fine-grained subcortical volumes, and cortical grey-white intensity contrast). Previously we had found 148 replicated clusters of associations between genetic variants and imaging phenotypes; here we find 692 replicating clusters of associations, including 12 on the X chromosome. We describe some of the newly found associations, focussing particularly on the X chromosome and autosomal associations involving the new classes of image derived phenotypes. Our novel associations implicate pathways involved in the rare X-linked syndrome STAR (syndactyly, telecanthus and anogenital and renal malformations), Alzheimer’s disease and mitochondrial disorders. All summary statistics are openly available for interactive viewing and download on the “BIG40” open web server.

Imaging Genetics

2018 ◽  
pp. 107-123
Author(s):  
Luanna Dixson ◽  
Heike Tost ◽  
Andreas Meyer-Lindenberg
Keyword(s):  
Protein Interactions ◽  
Genetic Variants ◽  
Historical Perspective ◽  
Molecular Mechanisms ◽  
Imaging Genetics ◽  
Gene Effects ◽  
Future Directions ◽  
Intermediate Phenotypes ◽  
Neuroimaging Data ◽  
The Brain

Imaging genetics is the study of how modifications of the DNA sequence may manifest in altered brain structure, function, and biochemistry. This field has been made possible through the combined analysis of genotyping and neuroimaging data in the same individual. Imaging genetics has had an especially significant impact in the realm of psychiatry, where the identification of genetic variants associated with heritable “intermediate phenotypes” provides important clues about underlying molecular mechanisms. More recently, integration of information on biological pathways, protein interactions, epigenetic factors, and gene expression has provided a holistic framework for developing biologically driven hypotheses of gene effects in the brain. This chapter gives a historical perspective on the imaging genetics approach, outlines a few key examples, and speculates on future directions for this exciting discipline.

scholarly journals Genetic Variants of the PLCXD3 Gene Are Associated with Risk of Metabolic Syndrome in the Emirati Population

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 665
Author(s):  
Hayat Aljaibeji ◽  
Abdul Khader Mohammed ◽  
Sami Alkayyali ◽  
Mahmood Yaseen Hachim ◽  
Hind Hasswan ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Genetic Variants ◽  
Cell Function ◽  
Hdl Cholesterol ◽  
Additional Risk ◽  
Increased Risk ◽  
Β Cell Function ◽  
Domain 3 ◽  
Healthy Participants

Phosphatidylinositol-specific phospholipase C X domain 3 (PLCXD3) has been shown to influence pancreatic β-cell function by disrupting insulin signaling. Herein, we investigated two genetic variants in the PLCXD3 gene in relation to type 2 diabetes (T2D) or metabolic syndrome (MetS) in the Emirati population. In total, 556 adult Emirati individuals (306 T2D and 256 controls) were genotyped for two PLCXD3 variants (rs319013 and rs9292806) using TaqMan genotyping assays. The frequency distribution of minor homozygous CC genotype of rs9292806 and GG genotype of rs319013 were significantly higher in subjects with MetS compared to Non-MetS (p < 0.01). The minor homozygous rs9292806-CC and rs319013-GG genotypes were significantly associated with increased risk of MetS (adj. OR 2.92; 95% CI 1.61–5.3; p < 0.001) (adj. OR 2.62; 95% CI 1.42–4.83; p = 0.002), respectively. However, no associations were detected with T2D. In healthy participants, the homozygous minor genotypes of both rs9292806 and rs319013 were significantly higher fasting glucose (adj. p < 0.005), HbA1c (adj. p < 0.005) and lower HDL-cholesterol (adj. p < 0.05) levels. Data from T2D Knowledge Portal database disclosed a nominal association of rs319013 and rs9292806 with T2D and components of MetS. Bioinformatics prediction analysis showed a deleterious effect of rs9292806 on the regulatory regions of PLCXD3. In conclusion, this study identifies rs319013 and rs9292806 variants of PLCXD3 as additional risk factors for MetS in the Emirati population.

scholarly journals A structural equation model for imaging genetics using spatial transcriptomics

2018 ◽  
Author(s):  
Sjoerd M.H. Huisman ◽  
Ahmed Mahfouz ◽  
Nematollah K. Batmanghelich ◽  
Boudewijn P.F. Lelieveldt ◽  
Marcel J.T. Reinders
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dimension Reduction ◽  
Genetic Variants ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Imaging Genetics ◽  
Brain Atlas ◽  
Imaging Features

AbstractAlzheimer’s disease is a neurodegenerative disorder that causes changes in the structure of the brain, observable with MRI scans, and that has a strong heritable component, reflected in the DNA. Imaging genetics deals with such relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored both with dimension reduction methods and model based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting, and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.

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