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63 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 from: Context-dependent expression of the foraging gene in field colonies of ants: the interacting roles of age, environment and task
Task allocation among social insect workers is an ideal framework for studying the molecular mechanisms underlying behavioural plasticity because workers of similar genotype adopt different behavioural phenotypes. Elegant laboratory studies have pioneered this effort, but field studies involving the genetic regulation of task allocation are rare. Here, we investigate the expression of the foraging gene in harvester ant workers from five age- and task-related groups in a natural population, and we experimentally test how exposure to light affects foraging expression in brood workers and foragers. Results from our field study show that the regulation of the foraging gene in harvester ants occurs at two time scales: levels of foraging mRNA are associated with ontogenetic changes over weeks in worker age, location and task, and there are significant daily oscillations in foraging expression in foragers. The temporal dissection of foraging expression reveals that gene expression changes in foragers occur across a scale of hours and the level of expression is predicted by activity rhythms: foragers have high levels of foraging mRNA during daylight hours when they are most active outside the nests. In the experimental study, we find complex interactions in foraging expression between task behaviour and light exposure. Oscillations occur in foragers following experimental exposure to 13 L : 11 D (LD) conditions, but not in brood workers under similar conditions. No significant differences were seen in foraging expression over time in either task in 24 h dark (DD) conditions. Interestingly, the expression of foraging in both undisturbed field and experimentally treated foragers is also significantly correlated with the expression of the circadian clock gene, cycle. Our results provide evidence that the regulation of this gene is context-dependent and associated with both ontogenetic and daily behavioural plasticity in field colonies of harvester ants. Our results underscore the importance of assaying temporal patterns in behavioural gene expression and suggest that gene regulation is an integral mechanism associated with behavioural plasticity in harvester ants.
Data from: Context-dependent expression of the foraging gene in field colonies of ants: the interacting roles of age, environment and task
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A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
<p>It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. In this study, we developed a data-driven approach termed <u>E</u>nvironmental <u>C</u>ovariate Search Affecting <u>G</u>enetic <u>C</u>orrelations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (<i>Glycine max</i>) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields. This dataset provides the data used in this study and supplementary tables cited in the manuscript.</p>
Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions
<p>This repository contains raw data, preprocessing scripts, and analysis code to replicate S-LDXR analysis from Wang et al.</p> <p>The project directory is organized as follows:<br>- `compute_de.r`: The script to compute anc-de genes<br>- `create_annot.100kb.sh`: code to create annotations for S-LDXR<br>- `create_annot.r`: R function used to create annotations for S-LDXR<br>- `DEstats.tgz`: Differential gene expression statistics between EAS and EUR for each 7 main cell types + PCs of each cell within each cell type<br>- `list_genes.txt`: Gene list as QCed as in Gazal et al. 2022 Nat Genet<br>- `Lupus_study_adjusted_compressed.h5ad.gz`: The single-cell dataset of Perez*, Gordon*, Subramaniam* et al. 2022 Science used in this study<br>- `README.sh`: This README file<br>- `sldxr_annotations.tgz`: Main S-LDXR annotations used in this study<br>- `sldxr_ref_files.tgz`: S-LDXR reference files (baseline-LD-X annotations, regression weights and EAS and EUR 1000G genotype reference files)<br>- `sumstats.tgz`: GWAS summary statistics of 31 traits in EAS and EUR populations<br>- `code_figures.tgz`: Code files for main figures</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-Interaction: Influence of the COMT Genotype on the Effects of Different Cannabinoids
ClinicalTrials.gov study NCT02487381. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
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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.
Data from: Environmental heterogeneity generates opposite gene-by-environment interactions for two fitness-related traits within a population
Theory predicts that environmental heterogeneity offers a potential solution to the maintenance of genetic variation within populations, but empirical evidence remains sparse. The livebearing fish Xiphophorus variatus exhibits polymorphism at a single locus, with different alleles resulting in up to five distinct melanistic "tailspot" patterns within populations. We investigated the effects of heterogeneity in two ubiquitous environmental variables (temperature and food availability) on two fitness-related traits (upper thermal limits and body condition) in two different tailspot types (wildtype and upper cut crescent). We found gene-by-environment (GxE) interactions between tailspot type and food level affecting upper thermal limits (UTL), as well as between tailspot type and thermal environment affecting body condition. Exploring mechanistic bases underlying these GxE patterns, we found no differences between tailspot types in hsp70 gene expression despite significant overall increases in expression under both thermal and food stress. Similarly, there was no difference in routine metabolic rates between the tailspot types. The reversal of relative performance of the two tailspot types under different environmental conditions revealed a mechanism by which environmental heterogeneity can balance polymorphism within populations through selection on different fitness-related traits.
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: Environmental heterogeneity generates opposite gene-by-environment interactions for two fitness-related traits within a population
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