scholarly journals Investigation of gene–environment interactions in relation to tic severity

2021 ◽  
Author(s):  
Mohamed Abdulkadir ◽  
Dongmei Yu ◽  
Lisa Osiecki ◽  
Robert A. King ◽  
Thomas V. Fernandez ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Association Studies ◽  
Autism Spectrum ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Linear Regression Models ◽  
Compulsive Disorder ◽  
Gene Environment ◽  
Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

scholarly journals Investigation of gene-environment interactions in relation to tic severity

2021 ◽  
Author(s):  
Mohamed Abdulkadir ◽  
Dongmei Yu ◽  
Lisa Osiecki ◽  
Robert King ◽  
Thomas Fernandez ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Association Studies ◽  
Autism Spectrum ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Linear Regression Models ◽  
Compulsive Disorder ◽  
Gene Environment ◽  
Tic Severity

Tourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N=518) design. We assessed 98 single nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate gene-studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene-environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene-environment studies.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

2011 ◽  
Vol 38 (3) ◽  
pp. 564-566 ◽  
Author(s):  
PROTON RAHMAN
Keyword(s):  
Psoriatic Arthritis ◽  
Annual Meeting ◽  
Association Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

scholarly journals Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17305 ◽  
Author(s):  
Yaping Wang ◽  
Donghui Li ◽  
Peng Wei
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Score Test ◽  
Principal Component ◽  
Case Control ◽  
Degree Of Freedom ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Missing Heritability ◽  
Gene Environment

Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of G WAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene-gene (G × G) and gene-environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey's one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey's 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey's 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn's disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case-control study of pancreatic cancer.

scholarly journals HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

Genetics

2018 ◽  
Author(s):  
Carol Kan ◽  
Ma-Li Wong
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Twin Studies ◽  
Epidemiological Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Small Magnitude ◽  
Gene Environment ◽  
Genetic Overlap

An association between type 2 diabetes mellitus (T2DM) and depression has been reported in epidemiological studies. Finding a genetic overlap between T2DM and depression will provide evidence to support a common biological pathway to both disorders. Genetic correlations observed from twin studies indicate that a small magnitude of the variance in liability can be attributed to genetic factors. However, no genetic overlap has been observed between T2DM and depression in genome-wide association studies using both the polygenic score and the linkage disequilibrium score regression approaches. Clarifying the shared heritability between these two complex traits is an important next step towards better therapy and treatment. Another area that needs to be explored is gene–environment interaction, since genotypes can affect an individual’s responses to the environment and environment can differentially affect genotypes expression.

scholarly journals Combining multivariate genomic approaches to elucidate the comorbidity between ASD and ADHD

2020 ◽  
Author(s):  
Hugo Peyre ◽  
Tabea Schoeler ◽  
Chaoyu Liu ◽  
Camille Michèle Williams ◽  
Nicolas Hoertel ◽  
...  
Keyword(s):  
Structural Equation ◽  
Association Studies ◽  
Autism Spectrum ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hyperactivity Disorder ◽  
Risk Variants ◽  
Genome Wide ◽  
Summary Data ◽  
Shared Genetic

ABSTRACTBackgroundSeveral lines of evidence point toward the presence of shared genetic factors underlying Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). However, Genome-Wide Association Studies (GWAS) have yet to identify risk variants (i.e. Single-Nucleotide Polymorphisms, SNPs) shared by ADHD and ASD.MethodsTwo complementary multivariate analyses – genomic structural equation modelling (SEM) and colocalization analysis – were exploited to identify the shared SNPs for ASD and ADHD, using summary data from two independent GWAS of ASD (N=46,350) and ADHD individuals (N=55,374).ResultsGenomic SEM identified 7 novel SNPs shared between ASD and ADHD (pgenome-wide<5e-8), including three SNPs that were not identified in any of the original univariate GWAS of ASD and ADHD (rs227378, rs2391769 and rs325506). We also mapped 4 novel genes (MANBA, DPYD, INSM1, and PAX1) to SNPs shared by ASD and ADHD, as well as 4 genes that had already been mapped to SNPs identified in either ASD or ADHD GWAS (SORCS3, XRN2, PTBP2 and NKX2-4). All the shared genes between ADHD and ASD were more prominently expressed in the brain than the genes mapped to SNPs specific to ASD or ADHD. Colocalization analyses revealed that 44% percent of the SNPs associated with ASD (p<1e-6) colocalized with ADHD SNPs and 26% of the SNPs associated with ADHD (p<1e-6) colocalized with ASD SNPs.ConclusionsUsing multivariate genomic analyses, the present study reveals the shared genetic pathways that underlie ASD and ADHD. Further investigation of these pathways may help identify new targets for treatment of these disorders.

scholarly journals Measuring epigenetics as the mediator of gene/environment interactions in DOHaD

2014 ◽  
Vol 6 (1) ◽  
pp. 10-16 ◽  
Author(s):  
M.-L. Ong ◽  
X. Lin ◽  
J. D. Holbrook
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Mediating Effect ◽  
Genome Wide Association Studies ◽  
Methylation Data ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Gene Environment ◽  
Genome Wide ◽  
Sources Of Variation

Analysis of DNA methylation data in epigenome-wide association studies provides many bioinformatics and statistical challenges. Not least of these, are the non-independence of individual DNA methylation marks from each other, from genotype and from technical sources of variation. In this review we discuss DNA methylation data from the Infinium450K array and processing methodologies to reduce technical variation. We describe recent approaches to harness the concordance of neighbouring DNA methylation values to improve power in association studies. We also describe how the non-independence of genotype and DNA methylation has been used to infer causality (in the case of Mendelian randomization approaches); suggest the mediating effect of DNA methylation in linking intergenic single nucleotide polymorphisms, identified in genome-wide association studies, to phenotype; and to uncover the widespread influence of gene and environment interactions on methylation levels.

scholarly journals Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective

2020 ◽  
Vol 4 ◽  
pp. 247054702092484 ◽  
Author(s):  
Frank R. Wendt ◽  
Gita A. Pathak ◽  
Daniel S. Tylee ◽  
Aranyak Goswami ◽  
Renato Polimanti
Keyword(s):  
Psychiatric Disorders ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Data ◽  
Autism Spectrum ◽  
Genome Wide Association Studies ◽  
Compulsive Disorder ◽  
Genome Wide ◽  
A Genome ◽  
Chronic Tic Disorder

Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.

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