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34 results for “gene-environment interaction”
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
Gene-environment interaction explains a part of missing heritability in human body mass index
<p>Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.</p>
Data for "Distinct explanations underlie gene-environment interactions in the UK Biobank."
<p>Raw output of scripts run from this repository: https://github.com/arundurvasula/GxE</p>
Gene-environment Interactions and Brain Functional Connectivity in Attention Deficit Hyperactivity Disorder
ClinicalTrials.gov study NCT01912352. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Data from: Candidate gene-environment interactions and their relationships with timing of breeding in a wild bird population
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Data from: Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions
Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G·E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G·E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman's ρ=0.401 for triglycerides, and ρ=0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ=0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial<0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann-Whitney=1.46E-5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G·E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial=8.63E-9 and 8.52E-7 for SNP·smoking and SNP·physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G·E, and variance-based prioritization can be used to identify them.
Data from: Landscape genomics in Atlantic salmon (Salmo salar): searching for gene-environment interactions driving local adaptation
A growing number of studies are examining the factors driving historical and contemporary evolution in wild populations. By combining surveys of genomic variation with a comprehensive assessment of environmental parameters, such studies can increase our understanding of the genomic and geographical extent of local adaptation in wild populations. We utilized a large-scale landscape genomics approach to examine adaptive and neutral differentiation across 54 North American populations of Atlantic salmon representing seven previously defined genetically distinct regional groups. Over 5500 genome-wide SNPs were genotyped in 641 individuals and 28 bulk assays of 25 pooled individuals each. Genome scans, linkage map and 49 environmental variables were combined to conduct an innovative landscape genomic analysis. Our results provide valuable insight into the links between environmental variation and both neutral and potentially adaptive genetic divergence. In particular, we identified markers potentially under divergent selection, as well as associated selective environmental factors and biological functions with the observed adaptive divergence. Multivariate landscape genetic analysis revealed strong associations of both genetic and environmental structures. We found an enrichment of growth related functions among outlier markers. Climate (temperature-precipitation) and geological characteristics were significantly associated with both potentially adaptive and neutral genetic divergence and should be considered as candidate loci involved in adaptation at the regional scale in Atlantic salmon. Hence, this study significantly contributes to the improvement of tools used in modern conservation and management schemes of Atlantic salmon wild populations.
Gene-Environment interactions in the influence of maternal education on adolescent neurodevelopment using ABCD Study
<p>Summary statistics for gene by maternal education effects</p>
Gene-Environment Interactions in Complex Disease
ClinicalTrials.gov study NCT00064506. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Gene-Environment Interaction and Precision Nutrition on Adiposity, Glycemic, Lipids Changes, and Bone Health
ClinicalTrials.gov study NCT03258203. IPD Sharing: NO. Countries: 0. Publications: 2.
Data from: Landscape genomics in Atlantic salmon (Salmo salar): searching for gene-environment interactions driving local adaptation
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Data from: Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression
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Data from: Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions
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Gene-environment interaction elicits dystonia-like features and impaired translational regulation in a DYT-TOR1A mouse model (RNA-Seq)
GEO Series GSE249875. Mus musculus. 72 samples. Type: Expression profiling by high throughput sequencing.
Gene-environment interaction elicits dystonia-like features and impaired translational regulation in a DYT-TOR1A mouse model (miRNA-Seq)
GEO Series GSE249876. Mus musculus. 71 samples. Type: Non-coding RNA profiling by high throughput sequencing.
The Nature and Nurture of Cell Heterogeneity: Accounting for Macrophage Gene-environment Interactions with Single-cell RNA-Seq
GEO Series GSE87849. Homo sapiens. 503 samples. Type: Expression profiling by high throughput sequencing.
Reverse gene-environment interaction approach to identify variants influencing body mass index in humans
GEO Series GSE129574. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
A genomic signature of gene-environment interactions in C. elegans
GEO Series GSE34650. Caenorhabditis elegans. 90 samples. Type: Expression profiling by array.
Gene-environment interaction in yeast gene expression
GEO Series GSE9376. Saccharomyces cerevisiae. 246 samples. Type: Expression profiling by array.
Gene-environment interaction between Notch1 and oxidative stress disrupts cardiac developmental pathways
GEO Series GSE279323. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.