Quantification of the overall contribution of gene-environment interaction for obesity-related traits

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Gene-environment interactions in human health

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank

The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β  = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGS BMI -covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge GWAS effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Quality control and analytic best practices for testing genetic models of sex differences in large populations

Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R ² value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

Gene-environment interaction explains a part of missing heritability in human body mass index

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.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Genetic determinants of polygenic prediction accuracy within a population

Genomic risk prediction is on the emerging path toward personalized medicine. However, the accuracy of polygenic prediction varies strongly in different individuals. Based on up to 352,277 European ancestry participants in the UK Biobank, we constructed polygenic risk scores for 15 physiological and biochemical quantitative traits. We identified a total of 185 polygenic prediction variability quantitative trait loci for 11 traits by Levene's test among 254,376 unrelated individuals. We validated the effects of prediction variability quantitative trait loci using an independent test set of 58,927 individuals. For instance, a score aggregating 51 prediction variability quantitative trait locus variants for triglycerides had the strongest Spearman correlation of 0.185 (P-value <1.0 × 10-300) with the squared prediction errors. We found a strong enrichment of complex genetic effects conferred by prediction variability quantitative trait loci compared to risk loci identified in genome-wide association studies, including 89 prediction variability quantitative trait loci exhibiting dominance effects. Incorporation of dominance effects into polygenic risk scores significantly improved polygenic prediction for triglycerides, low-density lipoprotein cholesterol, vitamin D, and platelet. In conclusion, we have discovered and profiled genetic determinants of polygenic prediction variability for 11 quantitative biomarkers. These findings may assist interpretation of genomic risk prediction in various contexts and encourage novel approaches for constructing polygenic risk scores with complex genetic effects.

The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

Gene-environment interactions explain a substantial portion of variability of common neuropsychiatric disorders

In complex diseases, the phenotypic variability can be explained by genetic variation (G), environmental stimuli (E), and interaction of genetic and environmental factors (G-by-E effects), among which the contribution G-by-E remains largely unknown. In this study, we focus on ten major neuropsychiatric disorders using data for 138,383 United States families with 404,475 unique individuals. We show that, while gene-environment interactions account for only a small portion of the total phenotypic variance for a subset of disorders (depression, adjustment disorder, substance abuse), they explain a rather large portion of the phenotypic variation of the remaining disorders: over 20% for migraine and close to or over 30% for anxiety/phobic disorder, attention-deficit/hyperactivity disorder, recurrent headaches, sleep disorders, and post-traumatic stress disorder. In this study, we have incorporated—in the same analysis—clinical data, family pedigrees, the spatial distribution of individuals, their socioeconomic and demographic confounders, and a collection of environmental measurements.

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G-by-E interactions contribute to the etiology of neuropsychiatric disorders

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Environmental quality is associated with the risk of neuropsychiatric disorders

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Geographic locations explain a part of variability of neuropsychiatric disorders

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Identifying specific G-by-E interactions might be the future of personalized medicine

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits

Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene-environment and gene-gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well.

Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.

Genetic Determinants of Body Mass Index and Fasting Glucose Are Mediators of Grade 1 Diastolic Dysfunction

Background Early (grade 1) cardiac left ventricular diastolic dysfunction (G1DD) increases the risk for heart failure with preserved ejection fraction and may improve with aggressive risk factor modification. Type 2 diabetes, obesity, hypertension, and coronary heart disease are associated with increased incidence of diastolic dysfunction. The genetic drivers of G1DD are not defined. Methods and Results We curated genotyped European ancestry G1DD cases (n=668) and controls with normal diastolic function (n=1772) from Vanderbilt's biobank. G1DD status was explored through (1) an additive model genome-wide association study, (2) shared polygenic risk through logistic regression, and (3) instrumental variable analysis using 2-sample Mendelian randomization (the inverse-variance weighted method, Mendelian randomization-Egger, and median) to determine potential modifiable risk factors. There were no common single nucleotide polymorphisms significantly associated with G1DD status. A polygenic risk score for BMI was significantly associated with increased G1DD risk (odds ratio [OR], 1.20 for 1-SD increase in BMI [95% CI, 1.08-1.32]; P=0.0003). The association was confirmed by the inverse-variance weighted method (OR, 1.89 [95% CI, 1.37-2.61]). Among the candidate mediators for BMI, only fasting glucose was significantly associated with G1DD status by the inverse-variance weighted method (OR, 4.14 for 1-SD increase in fasting glucose [95% CI, 1.55-11.02]; P=0.005). Multivariable Mendelian randomization showed a modest attenuation of the BMI association (OR, 1.84 [95% CI, 1.35-2.52]) when adjusting for fasting glucose. Conclusions These data suggest that a genetic predisposition to elevated BMI increases the risk for G1DD. Part of this effect may be mediated through altered glucose homeostasis.

Study of the Combined Effect of Maternal Tobacco Smoking and Polygenic Risk Scores on Birth Weight and Body Mass Index in Childhood

Background: Maternal smoking during pregnancy has adverse health effects on the offspring, including lower birth weight and increased risk for obesity. These outcomes are also influenced by common genetic polymorphisms. We aimed to investigate the combined effect of maternal smoking during pregnancy and genetic predisposition on birth weight and body mass index (BMI)-related traits in 1,086 children of the Human Early Life Exposome (HELIX) project.

Methods: Maternal smoking during pregnancy was self-reported. Phenotypic traits were assessed at birth or at the age of 8 years. Ten polygenic risk scores (PRSs) per trait were calculated using the PRSice v2 program. For birth weight, we estimated two sets of PRSs based on two different base GWAS summary statistics: PRS-EGG, which includes HELIX children, and PRS-PanUK, which is completely independent. The best PRS per trait (highest R2) was selected for downstream analyses, and it was treated in continuous or categorized into three groups. Multivariate linear regression models were applied to evaluate the association of the explanatory variables with the traits of interest. The combined effect was evaluated by including an interaction term in the regression models and then running models stratified by the PRS group.

Results: BMI-related traits were correlated among them but not with birth weight. A similar pattern was observed for their PRSs. On average, the PRSs explained ∼4% of the phenotypic variation, with higher PRS values related to higher trait values (p-value &lt;5.55E-08). Sustained maternal smoking was associated with lower birth weight and higher BMI and related traits (p-value &lt;2.99E-02). We identified a gene by environment (GxE) interaction for birth weight between sustained maternal smoking and the PRS-EGG in three groups (p-value interaction = 0.01), which was not replicated with the PRS-PanUK (p-value interaction = 0.341). Finally, we did not find any statistically significant GxE interaction for BMI-related traits (p-value interaction &gt;0.237).

Conclusion: Sustained maternal smoking and the PRSs were independently associated with birth weight and childhood BMI-related traits. There was low evidence of GxE interactions.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

MC4R Gene Polymorphisms Interact With the Urbanized Living Environment on Obesity: Results From the Yi Migrant Study

Objectives: This study aimed to determine the association of MC4R gene polymorphisms (rs17782313 and rs12970134) and urbanized living environment and the gene–environment interaction with obesity in Yi people in China.

Methods: A 1:2 frequency-matched case–control study based on the cross-sectional data was designed. Those with BMI ≥28 kg/m² were included as the case group. The age- and sex-matched controls were selected from those with BMI <24 kg/m². Unconditional logistic models were used to determine the association of SNPs with obesity. Additive interaction was evaluated by calculating relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI).

Results: A total of 322 cases and 643 controls were included. After adjustment, allele C of rs17782313 was significantly associated with obesity (additive model, OR = 1.52, 95%CI: 1.18–1.96) in Yi people. A similar association was found in allele A of rs12970134 (additive model, OR = 1.45, 95%CI: 1.13–1.89). Yi rural-to-urban migrants were found at 2.59-fold (95%CI: 1.70–3.95) higher odds of obesity than Yi farmers. Additive interactions were found between the two SNPs and rural-to-urban migration (rs17782313: AP = 0.65, 95%CI: 0.22–1.09; rs12970134: AP = 0.59, 95%CI: 0.02–1.17).

Conclusion:MC4R gene polymorphisms positively interacted with the urbanized living environment on obesity in Yi people. The effect of the MC4R gene on obesity was modified by the living environment.

Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Genetic and plastic rewiring of food webs under climate change

Climate change is altering ecological and evolutionary processes across biological scales. These simultaneous effects of climate change pose a major challenge for predicting the future state of populations, communities and ecosystems. This challenge is further exacerbated by the current lack of integration of research focused on these different scales.

We propose that integrating the fields of quantitative genetics and food web ecology will reveal new insights on how climate change may reorganize biodiversity across levels of organization. This is because quantitative genetics links the genotypes of individuals to population‐level phenotypic variation due to genetic (G), environmental (E) and gene‐by‐environment (G × E) factors. Food web ecology, on the other hand, links population‐level phenotypes to the structure and dynamics of communities and ecosystems.

We synthesize data and theory across these fields and find evidence that genetic (G) and plastic (E and G × E) phenotypic variation within populations will change in magnitude under new climates in predictable ways. We then show how changes in these sources of phenotypic variation can rewire food webs by altering the number and strength of species interactions, with consequences for ecosystem resilience. We also find evidence suggesting there are predictable asymmetries in genetic and plastic trait variation across trophic levels, which set the pace for phenotypic change and food web responses to climate change. Advances in genomics now make it possible to partition G, E and G × E phenotypic variation in natural populations, allowing tests of the hypotheses we propose.

By synthesizing advances in quantitative genetics and food web ecology, we provide testable predictions for how the structure and dynamics of biodiversity will respond to climate change.

Modeling genome-wide by environment interactions through omnigenic interactome networks

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Dietary Management of Obesity: A Review of the Evidence

Obesity is a multi-factorial disease and its prevention and management require knowledge of the complex interactions underlying it and adopting a whole system approach that addresses obesogenic environments within country specific contexts. The pathophysiology behind obesity involves a myriad of genetic, epigenetic, physiological, and macroenvironmental factors that drive food intake and appetite and increase the obesity risk for susceptible individuals. Metabolically, food intake and appetite are regulated via intricate processes and feedback systems between the brain, gastrointestinal system, adipose and endocrine tissues that aim to maintain body weight and energy homeostasis but are also responsive to environmental cues that may trigger overconsumption of food beyond homeostatic needs. Under restricted caloric intake conditions such as dieting, these processes elicit compensatory metabolic mechanisms that promote energy intake and weight regain, posing great challenges to diet adherence and weight loss attempts. To mitigate these responses and enhance diet adherence and weight loss, different dietary strategies have been suggested in the literature based on their differential effects on satiety and metabolism. In this review article, we offer an overview of the literature on obesity and its underlying pathological mechanisms, and we present an evidence based comparative analysis of the effects of different popular dietary strategies on weight loss, metabolic responses and diet adherence in obesity.