Gene-by-environment interactions in urban populations modulate risk phenotypes

Quantification of heavy metal exposure in a British population cohort links total mercury levels in plasma with skin tissue-specific changes in mitochondrial-related gene expression

Heavy metals in our direct environment have profound effects on human health and while some are essential for life, others can be toxic. In vivo studies often focus on clinical features caused by overexposure to, or by deprivation of a heavy metal. However, to understand the cellular impact of heavy metals on health, studies in healthy volunteers before symptom onset are needed. Here, we explored the impact of mercury, lead and selenium in over 800 British female twins on multi-tissue gene expression levels as an intermediate phenotype. Total mercury, lead and selenium concentrations were determined in plasma as a proxy for heavy metal exposure. We identified significant associations between total mercury levels measured in plasma, that fall within normal ranges, and expression of 873 genes within skin tissue, including PUSL1, SAMD10, ERCC1, MRPL17, NDUFB8, SELENOH, SEC31A, and KAT7P1. Functional analysis of genes associated with total mercury levels in plasma show a strong link to the mitochondrial oxidative phosphorylation pathway (p-value = 3.02 × 10-10). Analysis of mitochondrial-specific gene expression supported involvement of genes of oxidative phosphorylation complexes (MT-ND4L, and MT-ND5), which are encoded in mitochondrial DNA. These results suggest that mercury is likely detrimental to the energy metabolism of mitochondria. We also tested for associations between total mercury levels in plasma and gene expression in adipose and whole blood samples, but did not identify significant associations in these tissues, nor with lead or selenium in any tissue. Our results demonstrate that subtoxic mercury exposure leaves a clear molecular signature. It also underscores the necessity of conducting tissue-specific association studies to accurately capture the molecular impact of environmental exposures, as only relevant tissues will manifest a response to environmental exposures.

Association of dust exposure with anxiety and depression in the occupational population: The important role of sleep duration

BACKGROUND

Mental health issues, particularly anxiety and depression, are increasingly prevalent among the occupational population. Environmental factors, such as dust exposure, may contribute to the worsening of these symptoms. While previous studies have examined the association between dust exposure and mental health, the moderating effect of sleep duration on this link in occupational settings remains under-explored.

METHODS

This study was conducted from July to October 2023 at The Fifth People's Hospital of Ningxia and recruited dust-expose d occupational workers from different coal enterprises. After a series of screening 1274 valid subjects were finally included. Binary logistic regression was used to explore the association of dust exposure with anxiety and depression. Generalized additive models (GAM) were constructed to explore the nonlinear relationships between dust exposure duration, sleep duration, and mental health outcomes. Mediating variable contributions were isolated using the Karlson-Holm-Breen (KHB) method and mediated effects models were fitted.

RESULTS

The prevalence of anxiety and depression was found to be 6.44% and 4.24%, respectively. Dust exposure duration was positively associated with both anxiety and depression, while stratification by monthly income level had no significant effect. The contribution of sleep duration to the indirect effect accounted for 21.76% and 43.54% of the total effect of dust exposure duration on anxiety and depression scores, respectively. A nonlinear relationship was observed between dust exposure duration, sleep duration, and the scores of anxiety and depression. In the mediation analysis, shorter sleep duration explained 12.87% of the association between dust exposure duration and anxiety scores.

CONCLUSIONS

Dust exposure duration was associated with anxiety and depression, with a nonlinear relationship between them. Changes in sleep duration may effectively influence mental health problems in occupationally dust-exposed populations.

Association of acute exposure to PM2.5 constituents and sources with kidney injury: A longitudinal panel study of Nrf2 promoter polymorphism

Evidence on the effects of fine particulate matter (PM2.5) constituents and sources on kidney injury is limited. We designed a panel study with 4 repeated measurements to investigate the association of acute exposure to chemical constituents and source-specific PM2.5 with kidney function and renal tubular injury. We further evaluated the modifying effect of Nrf2 promoter polymorphism. In this study, a total of 64 participants were recruited and ambient PM2.5 constituents were monitored at a fixed-site station. We used a positive matrix factorization (PMF) model to identify emission sources and linear mixed-effect models to explore the associations. An interquartile range (IQR) increase in PM2.5 concentration was associated with a 1.40 % and 3.15 % decrease in eGFR-Cr (eGFR assessed by creatinine) and eGFR-Cys (eGFR assessed by cystatin-C), respectively, and 10.2 % higher kidney injury molecule 1 (KIM-1) levels. Carbonaceous components (EC and OC), metallic elements (Cr, K, Pb, Zn) and Cl- were robustly responsible for kidney injury. Per IQR increase in these constituents accounted for 0.57 % to 1.62 % declines in eGFR-Cr; 1.36 % to 3.66 % declines in eGFR-Cys; and 7.50 % to 19.83 % increments in KIM-1. Specific source analysis revealed that PM2.5 emitted by combustion was associated with the largest reduction in eGFR, while the secondary source played a more prominent role in renal tubular injury. The dominant models showed that the magnitudes of the effect estimates of PM2.5 and its constituents were generally larger in the participants with minor alleles of the Nrf2 promoter. These findings suggest that acute exposure to EC, OC, Cl- and several metallic constituents may be responsible for kidney injury induced by PM2.5, especially in individuals with unfavorable Nrf2 genotypes. PM2.5 from combustion and secondary sources impairs kidney health, highlighting the importance of source-oriented PM2.5 control strategies.

Unraveling the influence of childhood emotional support on adult aging: Insights from the UK Biobank

BACKGROUND

Exploring the association between Childhood Emotional Support (CES) and the mechanisms of aging is pivotal for understanding its potential to lessen the incidence of age-related pathologies and promote a milieu for healthy aging.

METHODS

Utilizing data from the UK Biobank comprising nearly 160,000 individuals, comprehensive analyses were conducted to explore associations between CES levels and age-related diseases, biological age and aging hallmarks. Cox proportional hazards regression models were used to investigate the relationship between CES and the risk of hospitalization for age-related diseases. Linear regression models were employed to explore the associations between CES and the frailty index (FI), Klemera-Doubal method (KDM) biological age acceleration, homeostatic dysregulation (HD), C-reactive protein (CRP), white blood cell (WBC) count, and telomere length.

RESULTS

The analyses revealed a significant association between higher CES levels and a decreased risk of hospitalization for age-related diseases in later life. After adjustments for covariates, the hazard ratio for age-related diseases was 0.87 (95 % confidence interval, 0.83-0.91, p < 0.001) in those with the highest CES level compared to those with the lowest CES level. Participants with the highest CES level exhibited lower FI scores (coefficient = -0.033, p < 0.001), reduced CRP level (coefficient = -0.097, p < 0.05) and lower WBC counts (coefficient = -0.034, p < 0.05). Stratified analyses based on genetic susceptibility further elucidated the protective role of CES against age-related diseases.

CONCLUSION

These findings underscore the potential of early interventions targeting CES to promote healthy aging and alleviating the burden of age-related diseases.

Liver DE(HP)toxification: luteolin as "phthalates-cleaner" to protect from environmental pollution

A Mengozzi and F Cappelli discuss a potential pharmacological strategy for phthalate detoxification of the liver as reported by S Chen, C Liu, W Zhang and colleagues, in this issue of EMBO Mol Med .

The advances of E2A-PBX1 fusion in B-cell acute lymphoblastic Leukaemia

The E2A-PBX1 gene fusion is a common translocation in B-cell acute lymphoblastic leukaemia. Patients harbouring the E2A-PBX1 fusion gene typically exhibit an intermediate prognosis. Furthermore, minimal residual disease has unsatisfactory prognostic value in E2A-PBX1 B-cell acute lymphoblastic leukaemia. However, the mechanism of E2A-PBX1 in the occurrence and progression of B-cell acute lymphoblastic leukaemia is not well understood. Here, we mainly review the roles of E2A and PBX1 in the differentiation and development of B lymphocytes, the mechanism of E2A-PBX1 gene fusion in B-cell acute lymphoblastic leukaemia, and the potential therapeutic approaches.

Integrating the Thrifty Genotype and Evolutionary Mismatch Hypotheses to understand variation in cardiometabolic disease risk

More than 60 years ago, James Neel proposed the Thrifty Genotype Hypothesis to explain the widespread prevalence of type 2 diabetes in Western, industrial contexts. This hypothesis posits that variants linked to conservative energy usage and increased fat deposition would have been favored throughout human evolution due to the advantages they could provide during periods of resource limitation. However, in industrial environments, these variants instead produce an increased risk of obesity, metabolic syndrome, type 2 diabetes, and related health issues. This hypothesis has been popular and impactful, with thousands of citations, many ongoing debates, and several spin-off theories in biomedicine, evolutionary biology, and anthropology. However, despite great attention, the applicability and utility of the Thrifty Genotype Hypothesis (TGH) to modern human health remains, in our opinion, unresolved. To move research in this area forward, we first discuss the original formulation of the TGH and its critiques. Second, we trace the TGH to updated hypotheses that are currently at the forefront of the evolutionary medicine literature-namely, the Evolutionary Mismatch Hypothesis. Third, we lay out empirical predictions for updated hypotheses and evaluate them against the current literature. Finally, we discuss study designs that could be fruitful for filling current knowledge gaps; here, we focus on partnerships with subsistence-level groups undergoing lifestyle transitions, and we present data from an ongoing study with the Orang Asli of Malaysia to illustrate this point. Overall, we hope this synthesis will guide new empirical research aimed at understanding how the human evolutionary past interacts with our modern environments to influence cardiometabolic health.

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.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Increasing diversity of functional genetics studies to advance biological discovery and human health

In this perspective we discuss the current lack of genetic and environmental diversity in functional genomics datasets. There is a well-described Eurocentric bias in genetic and functional genomic research that has a clear impact on the benefit this research can bring to underrepresented populations. Current research focused on genetic variant-to-function experiments aims to identify molecular QTLs, but the lack of data from genetically diverse individuals has limited analyses to mostly populations of European ancestry. Although some efforts have been established to increase diversity in functional genomic studies, much remains to be done to consistently generate data for underrepresented populations from now on. We discuss the major barriers for this continuity and suggest actionable insights, aiming to empower research and researchers from underserved populations.

Interaction between mitochondria and microbiota modulating cellular metabolism in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a prototypic complex disease in the gastrointestinal tract that has been increasing in incidence and prevalence in recent decades. Although the precise pathophysiology of IBD remains to be elucidated, a large body of evidence suggests the critical roles of mitochondria and intestinal microbiota in the pathogenesis of IBD. In addition to their contributions to the disease, both mitochondria and gut microbes may interact with each other and modulate disease-causing cell activities. Therefore, we hypothesize that dissecting this unique interaction may help to identify novel pathways involved in IBD, which will further contribute to discovering new therapeutic approaches to the disease. As poorly treated IBD significantly affects the quality of life of patients and is associated with risks and complications, successful treatment is crucial. In this review, we stratify previously reported experimental and clinical observations of the role of mitochondria and intestinal microbiota in IBD. Additionally, we review the intercommunication between mitochondria, and the intestinal microbiome in patients with IBD is reviewed along with the potential mediators for these interactions. We specifically focus on their roles in cellular metabolism in intestinal epithelial cells and immune cells. To this end, we propose a potential therapeutic intervention strategy for IBD.

Gene-environment interactions that influence CVD, lipid traits, obesity, diabetes, and hypertension appear to be able to influence gene therapy

Most mind boggling diseases are accepted to be impacted by both genetic and environmental elements. As of late, there has been a flood in the improvement of different methodologies, concentrate on plans, and measurable and logical techniques to examine gene-environment cooperations (G × Es) in enormous scope studies including human populaces. The many-sided exchange between genetic elements and environmental openings has long charmed the consideration of clinicians and researchers looking to grasp the complicated starting points of diseases. While single variables can add to disease, the blend of genetic variations and environmental openings frequently decides disease risk. The fundamental point of this paper is to talk about the Gene-Environment Associations That Impact CVD, Lipid Characteristics, Obesity, Diabetes, and Hypertension Have all the earmarks of being Ready to Impact Gene Therapy. This survey paper investigates the meaning of gene-environment collaborations (G × E) in disease advancement. The intricacy of genetic and environmental communications in disease causation is explained, underlining the multifactorial idea of many circumstances. The job of gene-environment cooperations in cardiovascular disease, lipid digestion, diabetes, obesity, and hypertension is investigated. This audit fixates on Gene by Environment (G × E) collaborations, investigating their importance in disease etiology.

The Utility of Mixed Effects Models in the Evaluation of Complex Genomic Traits In Vitro

In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this in silico study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.

Study on the correlation and interaction between metals and dyslipidemia: a case-control study in Chinese community-dwelling elderly

Previous studies on the association between metals and dyslipidemia are not completely consistent. There are few studies investigating the relationship between mixed metal exposure and dyslipidemia as well as the effects of metals on dyslipidemia in community-dwelling elderly. To evaluate the correlations and interaction effect between the urinary concentrations of metals and the risk of dyslipidemia in community-dwelling elderly. We designed a case-control study to assess the correlation between urine metals and dyslipidemia in elderly people in the Yinchuan. The urinary levels of 13 metals, including calcium, vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, and thallium, were measured by inductively coupled plasma-mass spectrometry (ICP-MS), and the blood biochemical analyzer was used to measure the blood lipid levels of 3384 senior individuals from four different areas of Yinchuan city. Logistic regression and restricted cubic splines (RCS) were used to explore the correlation and dose-response relationship between urinary metals and the risk of dyslipidemia. Least absolute shrinkage and selection operator (LASSO) regression was used to select metals, and then weighted quantile sum (WQS) regression was used to explore the weight of each metal in mixed metals. Bayesian kernel machine regression (BKMR) was used to explore the interactions between metals on dyslipidemia risk. (1) After selection by LASSO regression, in the multi-metal model, compared with the lowest quartile, the adjusted ORs (95%CI) of the highest quartiles were 0.47 (0.37-0.60) for Fe, 1.43 (1.13-1.83) for Zn, 1.46 (1.11-1.92) for As, 0.59 (0.44-0.80) for Se, 1.53 (1.18-2.00) for Mo, and 1.36 (1.07-1.73) for Te. (2) In the WQS regression model, Fe and Mo accounted for the largest weight in the negative and positive effects of dyslipidemia, respectively. (3) In the BKMR model, there may be a positive interaction between Te and Se on dyslipidemia. Among the mixed metals, Fe, As, Se, Mo, and Te were associated with the prevalence of dyslipidemia, with Fe and Mo contributing the most. There may be certain interactions between Te and Se.

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population

BACKGROUND

Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource.

RESULTS

We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue.

CONCLUSIONS

By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

The effect of environmental pollution on gene expression of seabirds: A review

One of the biggest challenges for ecotoxicologists is to detect harmful effects of contaminants on individual organisms before they have caused significant harm to natural populations. One possible approach for discovering sub-lethal, negative health effects of pollutants is to study gene expression, to identify metabolic pathways and physiological processes affected by contaminants. Seabirds are essential components of ecosystems but highly threatened by environmental changes. Being at the top of the food chain and exhibiting a slow pace of life, they are highly exposed to contaminants and to their ultimate impacts on populations. Here we provide an overview of the currently available seabird-related gene expression studies in the context of environmental pollution. We show that studies conducted, so far, mainly focus on a small selection of xenobiotic metabolism genes, often using lethal sampling protocols, while the greater promise of gene expression studies for wild species may lie in non-invasive procedures focusing on a wider range of physiological processes. However, as whole genome approaches might still be too expensive for large-scale assessments, we also bring out the most promising candidate biomarker genes for future studies. Based on the biased geographical representativeness of the current literature, we suggest expanding studies to temperate and tropical latitudes and urban environments. Also, as links with fitness traits are very rare in the current literature, but would be highly relevant for regulatory purposes, we point to an urgent need for establishing long-term monitoring programs in seabirds that would link pollutant exposure and gene expression to fitness traits.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

The pollutome-connectome axis: a putative mechanism to explain pollution effects on neurodegeneration

The study of pollutant effects is extremely important to address the epochal challenges we are facing, where world populations are increasingly moving from rural to urban centers, revolutionizing our world into an urban world. These transformations will exacerbate pollution, thus highlighting the necessity to unravel its effect on human health. Epidemiological studies have reported that pollution increases the risk of neurological diseases, with growing evidence on the risk of neurodegenerative disorders. Air pollution and water pollutants are the main chemicals driving this risk. These chemicals can promote inflammation, acting in synergy with genotype vulnerability. However, the biological underpinnings of this association are unknown. In this review, we focus on the link between pollution and brain network connectivity at the macro-scale level. We provide an updated overview of epidemiological findings and studies investigating brain network changes associated with pollution exposure, and discuss the mechanistic insights of pollution-induced brain changes through neural networks. We explain, in detail, the pollutome-connectome axis that might provide the functional substrate for pollution-induced processes leading to cognitive impairment and neurodegeneration. We describe this model within the framework of two pollutants, air pollution, a widely recognized threat, and polyfluoroalkyl substances, a large class of synthetic chemicals which are currently emerging as new neurotoxic source.

Application of Metabolomics in Obesity-Related Childhood Asthma Subtyping: A Narrative Scoping Review

Obesity-related asthma is a heterogeneous childhood asthma phenotype with rising prevalence. Observational studies identify early-life obesity or weight gain as risk factors for childhood asthma development. The reverse association is also described, children with asthma have a higher risk of being obese. Obese children with asthma have poor symptom control and an increased number of asthma attacks compared to non-obese children with asthma. Clinical trials have also identified that a proportion of obese children with asthma do not respond as well to usual treatment (e.g., inhaled corticosteroids). The heterogeneity of obesity-related asthma phenotypes may be attributable to different underlying pathogenetic mechanisms. Although few childhood obesity-related asthma endotypes have been described, our knowledge in this field is incomplete. An evolving analytical profiling technique, metabolomics, has the potential to link individuals' genetic backgrounds and environmental exposures (e.g., diet) to disease endotypes. This will ultimately help define clinically relevant obesity-related childhood asthma subtypes that respond better to targeted treatment. However, there are challenges related to this approach. The current narrative scoping review summarizes the evidence for metabolomics contributing to asthma subtyping in obese children, highlights the challenges associated with the implementation of this approach, and identifies gaps in research.

Addressing the challenges of polygenic scores in human genetic research

The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.

Childhood Trauma and Epigenetics: State of the Science and Future

PURPOSE OF REVIEW

There is a great deal of interest regarding the biological embedding of childhood trauma and social exposures through epigenetic mechanisms, including DNA methylation (DNAm), but a comprehensive understanding has been hindered by issues of limited reproducibility between studies. This review presents a summary of the literature on childhood trauma and DNAm, highlights issues in the field, and proposes some potential solutions.

RECENT FINDINGS

Investigations of the associations between DNAm and childhood trauma are commonly performed using candidate gene approaches, specifically involving genes related to neurological and stress pathways. Childhood trauma is defined in a wide range of ways in several societal contexts. However, although variations in DNAm are frequently found in stress-related genes, unsupervised epigenome-wide association studies (EWAS) have shown limited reproducibility both between studies and in relating these changes to exposures. The reproducibility of childhood trauma DNAm studies, and the field of social epigenetics in general, may be improved by increasing sample sizes, standardizing variables, making use of effect size thresholds, collecting longitudinal and intervention samples, appropriately accounting for known confounding factors, and applying causal analysis wherever possible, such as "two-step epigenetic Mendelian randomization."

Human variation in population-wide gene expression data predicts gene perturbation phenotype

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.

Analysis of Genetic Variants in the Glucocorticoid Receptor Gene NR3C1 and Stenosis of the Carotid Artery in a Polish Population with Coronary Artery Disease

Background: Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Early diagnosis and elimination of risk factors are crucial for better managing CVDs. Atherosclerosis, whose development might be associated with glucocorticoids (GCs), is a critical factor in the development of carotid artery (CA) stenosis and most other CVDs. Aim: To investigate the association of Tth111I, N363S, and ER22/23EK-NR3C1 polymorphisms and the incidence of CA stenosis. Methods: The study group consisted of 117 patients diagnosed with coronary artery disease (CAD) and CA stenosis and 88 patients with CAD and ruled out CA stenosis. Genomic DNA was extracted from blood, and genotyping was carried out using Tth111I, N363S, and ER22/23EK-NR3C1 polymorphism sequencing. Results: No significant association between studied polymorphisms and the incidence or the severity of CA stenosis in the Polish population with CAD was found. Conclusion: This is the first study that proves that common NR3C1 gene variants do not influence CA stenosis and probably are not associated with atherosclerosis. The search for genes that can act as prognostic markers in predicting CA stenosis is still ongoing.

The Genetic Variants in the Renin-Angiotensin System and the Risk of Heart Failure in Polish Patients

(1) Background: Heart failure (HF) is a complex disease and one of the major causes of morbidity and mortality in the world. The renin-angiotensin system (RAS) may contribute to the pathogenesis of HF. (2) Aim: To investigate the association of RAS key genetic variants, rs5051 (A-6G) in the gene encoding angiotensinogen (AGT), rs4646994 (I/D) in the gene for angiotensin I converting enzyme (ACE), and rs5186 (A1166C) in the gene encoding type 1 receptor for angiotensin II (AGTR1), with the HF risk in the cohort of Polish patients. (3) Methods: The study group consisted of 415 patients that were diagnosed with HF, while the control group comprised of 152 healthy individuals. Genomic DNA were extracted from blood and genotyping was carried out using either PCR or PCR-RFLP for ACE or AGT and AGTR1 variants, respectively. (4) Results: No association has been found between the I/D ACE and heart failure. The HF risk was significantly higher for AG AGT heterozygotes (overdominance: AG versus AA + GG) and for carriers of the G AGT allele in codominant and dominant modes of inheritance. However, the risk of HF was significantly lower in the carriers of at least one C AGTR1 allele (AC or CC genotypes) or in AC AGTR1 heterozygotes (overdominant mode). There was a significant relationship for AGT and HF patients in NYHA Class I-II for whom the risk was higher for the carriers of the G allele, and for the AG heterozygotes. There was also a significant interaction between heterozygote advantage of AGT and BMI increasing the risk for HF. (5) Conclusion: Our results suggest that the A(-6)G AGT polymorphism may be associated with HF in the Polish population and the HF risk seems to be modulated by the A1166C AGTR1 polymorphism.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Recombination affects allele-specific expression of deleterious variants in human populations

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

Primitive detection of Alzheimer’s disease using neuroimaging: A progression model for Alzheimer’s disease: Their applications, benefits, and drawbacks

Alzheimer’s disease (A.D.) is the most widespread type of Dementia, and it is not a curable neurodegenerative disease that affects millions of older people. Researchers were able to use their understanding of Alzheimer’s disease risk variables to develop enrichment processes for longitudinal imaging studies. Using this method, they reduced their sample size and study time. This paper describes the primitive detective of Alzheimer’s diseases using Neuroimaging techniques. Several preprocessing methods were used to ensure that the dataset was ready for subsequent feature extraction and categorization. The noise was reduced by converting and averaging many scan frames from real to DCT space. Both sides of the averaged image were filtered and combined into a single shot after being converted to real space. InceptionV3 and DenseNet201 are two pre-trained models used in the suggested model. The PCA approach was used to select the traits, and the resulting explained variance ratio was 0.99The Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC)—and UCI machine learning datasets showed that our method is faster and more successful at identifying complete long-risk patterns when compared to existing methods.

Gene-environment interactions in childhood asthma revisited; expanding the interaction concept

Investigation of gene-environment interactions (GxE) may provide important insights into the gene regulatory framework in response to environmental factors of relevance for childhood asthma. Over the years, different methodological strategies have been applied, more recently using genome-wide approaches. The best example to date is the major asthma locus on the 17q12-21 chromosome region, viral infections, and airway epithelium processes where recent studies have shed much light on mechanisms in childhood asthma. However, there are challenges with the traditional single variant-single exposure interaction models, as they do not encompass the complexity and cumulative effects of multiple exposures or multiple genetic variants. As such, we need to redefine our traditional GxE thinking, and we propose in this review to expand the GxE concept by also evaluating other omics layers, such as epigenetics, transcriptomics, metabolomics, and proteomics. In addition, host factors such as age, gender, and other exposures are very likely to influence GxE effects and need firmly to be considered in future studies.

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers

Background

For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.

Methods and results

From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants’ medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.

Conclusions

Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.

Epigenetics in Ecology, Evolution, and Conservation

Epigenetic variation is often characterized by modifications to DNA that do not alter the underlying nucleotide sequence, but can influence behavior, morphology, and physiological phenotypes by affecting gene expression and protein synthesis. In this review, we consider how the emerging field of ecological epigenetics (eco-epi) aims to use epigenetic variation to explain ecologically relevant phenotypic variation and predict evolutionary trajectories that are important in conservation. Here, we focus on how epigenetic data have contributed to our understanding of wild populations, including plants, animals, and fungi. First, we identified published eco-epi literature and found that there was limited taxonomic and ecosystem coverage and that, by necessity of available technology, these studies have most often focused on the summarized epigenome rather than locus- or nucleotide-level epigenome characteristics. We also found that while many studies focused on adaptation and heritability of the epigenome, the field has thematically expanded into topics such as disease ecology and epigenome-based ageing of individuals. In the second part of our synthesis, we discuss key insights that have emerged from the epigenetic field broadly and use these to preview the path toward integration of epigenetics into ecology. Specifically, we suggest moving focus to nucleotide-level differences in the epigenome rather than whole-epigenome data and that we incorporate several facets of epigenome characterization (e.g., methylation, chromatin structure). Finally, we also suggest that incorporation of behavior and stress data will be critical to the process of fully integrating eco-epi data into ecology, conservation, and evolutionary biology.

Ambient air pollution, healthy diet and vegetable intakes, and mortality: a prospective UK Biobank study

BACKGROUND

Recent studies suggest potential interactions of air pollutants with dietary factors and genetic susceptibility on mortality risk; however, evidence from prospective studies is still lacking. We aimed to assess the association between air pollution and mortality, and investigate the modification effects of a healthy diet and genetic susceptibility.

METHODS

A total of 386 937 participants were enrolled from 2006 to 2010 and followed up to 2018 in the UK Biobank study. The annual average air pollutant concentrations of particulate matter (PM) with diameters ≤2.5 (PM2.5), ≤10 (PM10) and between 2.5 and 10 µm (PM2.5-10) and nitrogen oxides (NO2 and NOx) were calculated and linked to participants' residential addresses. Healthy dietary patterns were evaluated by a healthy diet score (HDS) based on intakes of vegetables, fruit, fish, unprocessed red meat and processed meat. We also calculated genetic risk score (GRS) of the lifespan. We examined potential interactions by setting variable cross-product terms of air pollutants with diets or GRS in the models.

RESULTS

We identified 11 881 deaths [2426 from cardiovascular diseases (CVD), 1211 from coronary heart disease (CHD) and 466 from stroke] during a median follow-up of 8.9 years. We found that PM2.5 [hazard ratio (HR), 1.27; 95% CI, 1.05-1.55], PM10 (HR, 1.18; 95% CI, 1.04-1.34), NO2 (HR, 1.05; 95% CI, 1.01-1.08), and NOx (HR, 1.02; 95% CI, 1.01-1.03) were associated with all-cause mortality. PM2.5 was also associated with increased risks of CVD mortality (HR, 1.68; 95% CI, 1.10-2.56) and CHD mortality (HR, 2.08; 95% CI, 1.16-3.75). In addition, we found that adherence to healthy dietary patterns modified associations of PM2.5, NO2 and NOx with all-cause mortality (P-interaction = 0.006, 0.006 and 0.02, respectively). Among the individual dietary components, vegetable intakes showed interactions with PM2.5, NO2 and NOx (P-interaction = 0.007, 0.004 and 0.02, respectively). The associations between air pollutants and increased risks of all-cause mortality were attenuated among participants with higher vegetable intakes. We did not observe interactions between air pollutants and HDS on CVD, CHD or stroke mortality (P-interaction > 0.05). Besides, we did not find interactions between air pollutants and genetic risk for lifespan on mortality risk.

CONCLUSION

This study provides evidence linking long-term exposure to various air pollutants to the risk of all-cause, CVD and CHD mortality, and the potential attenuation of a healthy diet, especially high vegetable intakes, on such relations. Our findings highlight the importance of adherence to a healthy diet in lowering ambient air-pollution-related mortality risk.

Sex, ancestry, senescence, and aging (SAnSA) are stark drivers of nontuberculous mycobacterial pulmonary disease

Nontuberculous mycobacterial (NTM) pulmonary disease (PD) disproportionately affects otherwise healthy, older, Caucasian females. The reasons behind this are likely multifactorial involving several conspiring factors. A variety of factors are thought to contribute to increased susceptibility to NTM in the older adult including exposure to various environmental conditions and contaminants across the lifespan, genetic risk factors, hormonal changes, and immunodeficiency. Independent of sex and ancestry, respiratory muscle atrophy intensifies with age and an aging immune system can show functional decline of macrophages, poor lung migration and homing of dendritic cells, promotion of aberrant pro-inflammatory responses, acceleration of inflammation related to aging, and increased immunosenescence. The purpose of this review is to synthesize the current body of knowledge regarding the roles of sex, ancestry, senescence, and aging (SAnSA) in NTM acquisition and the possible mechanisms involved in NTM PD, highlighting age-related respiratory and immune system changes. We also summarize molecular tools and biomarkers of these fields and contextualize these into the study of NTM PD. Finally, we discuss the relevance of biomarkers described for senescence and aging and senolytic therapies as potentially new adjunctive strategies to reduce the burden of NTM PD.

BMI, Blood Pressure, and Plasma Lipids among Centenarians and Their Offspring

Background

The burden of cardiovascular diseases (CVDs) is increasing substantially due to population growth and aging. Determining effective prevention and understanding the underlying mechanisms remain desirable pursuits for increasing the quality of life. As centenarians and their offspring may have genetic advantages, they may present with healthier cardiovascular-related profiles.

Methods

We launched a cross-sectional household-based survey of centenarian families, including 253 centenarians, 217 centenarian offspring, and 116 offspring spouses without centenarian parents from county-level Chinese longevity city Rugao. Among offspring and offspring spouses were the following arrangements: 101 paired offspring and offspring spouses who lived together, 116 unpaired offspring, and 16 unpaired spouses. We investigated their cardiovascular-related health status including waist circumference, body mass index (BMI), blood pressure, and plasma lipids and compared results among centenarians, centenarian offspring, and offspring spouses.

Results

Centenarians ranged from 99 to 109 years with a median age of 100 years. Centenarian offspring, with a median age of 70 years, and offspring spouses, with a median age of 69 years, shared similar age. Results of blood pressure, plasma lipid levels, and BMI displayed no significant difference between centenarian offspring and offspring spouses. However, centenarians appeared to have lower waist circumference, BMI, TC, LDL-C, TG, and diastolic blood pressure but higher levels of systolic blood pressure (p < 0.05). Multivariate analysis showed the prevalence of obesity, hypertension, and dyslipidemia was similar between centenarian offspring and offspring spouses, while centenarians appeared to have a lower prevalence of obesity and a higher prevalence of hypertension (p < 0.05).

Conclusions

Centenarians and centenarian offspring did not present healthier BMI, blood pressure, or plasma lipids than offspring spouses. Further research on longevity and cardiovascular diseases are desirable.

Functional genetic variants in complement component 7 confer susceptibility to gastric cancer

BACKGROUND

Complement system plays an important role in innate immunity which involved in the changes tumor immune microenvironment by mediating the inflammatory response. This study aims to explore the relationship between complement component 7 (C7) polymorphisms and the risk of gastric cancer (GC).

MATERIALS AND METHODS

All selected SNPs of C7 were genotyped in 471 patients and 471 controls using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by unconditional Logistic regression to analyze the relationship between each genotype and the genetic susceptibility to gastric cancer. The level of C7 expression in GC was analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and detected by Enzyme Linked Immunosorbent Assay. Kaplan-Meier plotter were used to reveal C7 of prognostic value in GC. We examined SNPs associated with the expression of C7 using the GTEx database. The effect of C7 polymorphisms on the regulatory activity of C7 was detected by luciferase reporter assay.

RESULTS

Unconditional logistic regression showed that individuals with C7 rs1376178 AA or CA genotype had a higher risk of GC with OR (95% CI) of 2.09 (1.43-3.03) and 1.88 (1.35-2.63), respectively. For C7 rs1061429 C > A polymorphism, AA genotype was associated with the elevated risk for developing gastric cancer (OR = 2.16, 95% CI [1.37-3.38]). In stratified analysis, C7 rs1376178 AA genotype increased the risk of GC among males (OR = 2.88, 95% CI [1.81-4.58]), but not among females (OR = 1.06, 95% CI [0.55-2.06]). Individuals carrying rs1061429 AA significantly increased the risk of gastric cancer among youngers (OR = 2.84, 95% CI [1.39-5.80]) and non-smokers (OR = 2.79, 95% CI [1.63-4.77]). C7 was overexpressed in gastric cancer tissues and serum of cancer patients and was significantly associated with the prognosis. C7 rs1061429 C > A variant contributed to reduced protein level of C7 (P = 0.029), but rs1376178 didn't. Luciferase reporter assay showed that rs1376178C-containing plasmid exhibited 2.86-fold higher luciferase activity than rs1376178 A-containing plasmid (P < 0.001). We also found that rs1061429A allele contributed 1.34-fold increased luciferase activity than rs1061429C allele when co-transfected with miR-591 (P = 0.0012).

CONCLUSIONS

These findings highlight the role of C7 in the development of gastric cancer.

Genetic architecture of gene regulation in Indonesian populations identifies QTLs associated with global and local ancestries

Lack of diversity in human genomics limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities. To map genetic effects on gene regulation in the underrepresented Indonesian population, we have integrated genotype, gene expression, and CpG methylation data from 115 participants across three island populations that capture the major sources of genomic diversity in the region. In a comparison with European datasets, we identify eQTLs shared between Indonesia and Europe as well as population-specific eQTLs that exhibit differences in allele frequencies and/or overall expression levels between populations. By combining local ancestry and archaic introgression inference with eQTLs and methylQTLs, we identify regulatory loci driven by modern Papuan ancestry as well as introgressed Denisovan and Neanderthal variation. GWAS colocalization connects QTLs detected here to hematological traits, and further comparison with European datasets reflects the poor overall transferability of GWAS statistics across diverse populations. Our findings illustrate how population-specific genetic architecture, local ancestry, and archaic introgression drive variation in gene regulation across genetically distinct and in admixed populations and highlight the need for performing association studies on non-European populations.

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.

Environmental Influences on Sleep in the California Teachers Study Cohort

Only two-thirds of Americans meet the recommended 7 hours of sleep nightly. Insufficient sleep and circadian disruption have been associated with adverse health outcomes, including diabetes and cardiovascular disease. Several environmental disruptors of sleep have been reported, such as artificial light at night (ALAN) and noise. These studies tended to evaluate exposures individually. We evaluated several spatially derived environmental exposures (ALAN, noise, green space, and air pollution) and self-reported sleep outcomes obtained in 2012-2015 in a large cohort of 51,562 women in the California Teachers Study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for sleep duration and latency. After adjusting for age, race/ethnicity, chronotype, use of sleep medication, and self-reported trouble sleeping, ALAN (per 5 millicandela (mcd)/m2 luminance, OR = 1.13, 95% CI: 1.07, 1.20) and air pollution (per 5 μg/m3 PM2.5, OR = 1.06, 95% CI: 1.04, 1.09) were associated with shorter sleep duration (<7 hours), and noise was associated with longer latency (>15 minutes) (per 10 decibels, OR = 1.05, 95% CI: 1.01, 1.10). Green space was associated with increased duration (per 0.1 units, OR = 0.41, 95% CI: 0.28, 0.60) and decreased latency (per 0.1 units, OR = 0.55, 95% CI: 0.39, 0.78). Further research is necessary to understand how these and other exposures (e.g., diet) perturb an individuals' inherited sleep patterns and contribute to downstream health outcomes.

A sex-specific evolutionary interaction between ADCY9 and CETP

Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points toward a biological link between dalcetrapib's pharmacogene ADCY9 and its therapeutic target CETP.

The impact of age on genetic risk for common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.

Revealing consensus gene pathways associated with respiratory functions and disrupted by PM2.5 nitrate exposure at bulk tissue and single cell resolution

BACKGROUND

Nitrate is a major pollutant component in ambient PM_2.5. It is known that chronic exposure to PM_2.5 NO₃^- damages respiratory functions. We aim to explore the underlying toxicological mechanism at single cell resolution.

METHODS

We systematically conducted exposure experiments on forty C57BL/6 mice, assessed respiratory functions, and profiled lung transcriptome. . Afterward, we estimated the cell type compositions from RNA-seq data using deconvolution analysis. The genes and pathways associated with respiratory function and dysregulated by to PM_2.5 NO₃^- exposure were characterized at bulk-tissue and single-cell resolution.

RESULTS

PM_2.5 NO₃^- exposure did not significantly modify the cell type composition in lung, but profoundly altered the gene expression within each cell type. At ambient concentration (22 μg/m³), exposure significantly (FDR<10%) altered 95 genes' expression. Among the genes associated with respiratory functions, a large fraction (74.6-91.7%) were significantly perturbed by PM_2.5 NO₃^- exposure. For example, among the 764 genes associated with peak expiratory flow (PEF), 608 (79.6%) were affected by exposure (p = 1.92e-345). Pathways known to play role in lung disease pathogenesis, including circadian rhythms, sphingolipid metabolism, immune response and lysosome, were found significantly associated with respiratory functions and disrupted by PM_2.5 NO₃^- exposure.

CONCLUSIONS

This study extended our knowledge of PM_2.5 NO₃^- exposure's effect to the levels of lung gene expression, pathways, lung cell type composition and cell specific transcriptome. At single cell resolution, we provided insights in toxicological mechanism of PM_2.5 NO₃^- exposure and subsequent pulmonary disease risks.

Common variants in the CD36 gene are associated with dietary fat intake, high-fat food consumption and serum triglycerides in a cohort of Quebec adults

BACKGROUND

The CD36 gene is a candidate for sensory detection of fatty acids and has been associated with individual differences in fat preferences and consumption. Excess adiposity may compromise sensory detection, but few studies have examined whether associations between CD36 variants and fat consumption differ between underweight/normal weight (UW/NW) and overweight/obese (OW/OB) individuals.

METHODS

Diet (assessed by food frequency questionnaire), genetic (nine variants), body mass index (BMI), lifestyle and biomarker data were obtained from the CARTaGENE biobank (n = 12,065), a Quebec cohort of middle-aged adults. Primary outcome variables included intakes (%kcal/day) of total, saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids. Secondary outcome variables included consumption (servings/day) of four food categories with high-fat content (added fats and oils, high-fat foods, desserts and MUFA- and PUFA-rich foods) and biomarkers of chronic disease. Multivariable regression models stratified by BMI category were used to assess associations between CD36 variants and outcome variables.

RESULTS

Among UW/NW, rs1049654 and rs10499859 were associated with higher intakes of total fat, MUFA and PUFA (all P < 0.05), while rs1527483 and rs3211956 were associated with higher SFA (P = 0.0278) and lower PUFA (P = 0.0466) intake, respectively. Rs1527483 and rs3211956 were also associated with higher consumption of high-fat foods and desserts (all P < 0.05). Among OW, rs1054516 and rs3173798 were associated with higher SFA intake (both P < 0.05), and rs1054516 was also associated with higher serum triglycerides (P = 0.0065).

CONCLUSIONS

CD36 variants are associated with habitual fat consumption, which may play a role in subsequent associations with chronic-disease biomarkers. Associations differ by BMI status and dietary fat type.

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures

The COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, and mortality. Expression profiles represent snapshots of combined genetic, socio-environmental (including socioeconomic and environmental factors), and physiological effects on the molecular phenotype. As such, they have potential to improve biological understanding of differences among populations, and provide therapeutic biomarkers and environmental mitigation strategies. Here, we undertook a large-scale assessment of patterns of gene expression between African Americans and European Americans, mining RNA-Seq data from 25 non-diseased and diseased (tumor) tissue-types. We observed the widespread enrichment of pathways implicated in COVID-19 and integral to inflammation and reactive oxygen stress. Chemokine CCL3L3 expression is up-regulated in African Americans. GSTM1, encoding a glutathione S-transferase that metabolizes reactive oxygen species and xenobiotics, is upregulated. The little-studied F8A2 gene is up to 40-fold more highly expressed in African Americans; F8A2 encodes HAP40 protein, which mediates endosome movement, potentially altering the cellular response to SARS-CoV-2. African American expression signatures, superimposed on single cell-RNA reference data, reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. Our findings establish basal prognostic signatures that can be used to refine approaches to minimize risk of severe infection and improve precision treatment of COVID-19 for African Americans. To enable dissection of causes of divergent molecular phenotypes, we advocate routine inclusion of metadata on genomic and socio-environmental factors for human RNA-sequencing studies.

Integration of gene expression and DNA methylation identifies epigenetically controlled modules related to PM2.5 exposure

Air pollution has been associated with adverse health effects across the life-course. Although underlying mechanisms are unclear, several studies suggested pollutant-induced changes in transcriptomic profiles. In this meta-analysis of transcriptome-wide association studies of 656 children and adolescents from three European cohorts participating in the MeDALL Consortium, we found two differentially expressed transcript clusters (FDR p < 0.05) associated with exposure to particulate matter < 2.5 µm in diameter (PM_2.5) at birth, one of them mapping to the MIR1296 gene. Further, by integrating gene expression with DNA methylation using Functional Epigenetic Modules algorithms, we identified 9 and 6 modules in relation to PM_2.5 exposure at birth and at current address, respectively (including NR1I2, MAPK6, TAF8 and SCARA3). In conclusion, PM_2.5 exposure at birth was linked to differential gene expression in children and adolescents. Importantly, we identified several significant interactome hotspots of gene modules of relevance for complex diseases in relation to PM_2.5 exposure.

What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health

The environment plays an important role in mediating human health. In this session we consider research addressing ways to overcome the challenges associated with studying the multifaceted and ever-changing environment. Environmental health research has a need for technological and methodological advances which will further our knowledge of how exposures precipitate complex phenotypes and exacerbate disease.

The Contextualized Genetics of Human Longevity: JACC Focus Seminar

The genetics of human longevity has long been studied, and in this regard, centenarians represent a very informative model. Centenarians are characterized by 2 main features: 1) the capability to avoid or postpone the major age-related diseases; and 2) a high level of heterogeneity of their phenotype. The first suggests that longevity and resistance to diseases are mediated by shared mechanisms, the latter that many strategies can be used to become long lived, likely as a result of variable genome-environment interactions. The authors suggest that the complexity of genome-environment interactions must be considered within an evolutionary and ecological perspective and that the concept of "risk allele" is highly context dependent, changing with age, time, and geography. Genes involved in both longevity and cardiovascular diseases, taken as a paradigmatic example of age-related diseases, as well as other emerging topics in genetics of longevity, such as micro-ribonucleic acid (miRNA) genetics, polygenic risk scores, environmental pollutants, and somatic mutations are discussed.

Targeted Genetic Analysis in a Chinese Cohort of 208 Patients Related to Familial Hypercholesterolemia

AIM

Familial hypercholesterolemia (FH) is the most commonly encountered genetic condition that predisposes individuals to severe autosomal dominant lipid metabolism dysfunction. Although more than 75% of the European population has been scrutinized for FH-causing mutations, the genetic diagnosis proportion among Chinese people remains very low (less than 0.5%). The aim of this study was to identify genetic mutations and help make a precise diagnosis in Chinese FH patients.

METHODS

We designed a gene panel containing 20 genes responsible for FH and tested 208 unrelated Chinese possible/probable or definite FH probands. In addition, we called LDLR copy number variation (CNVs) with the panel data by panelcn.MOPS, and multiple ligation-dependent probe amplification (MLPA) was used to search for CNVs in LDLR, APOB, and PCSK9.

RESULTS

A total of 79 probands (38.0%) tested positive for a (likely) pathogenic mutation, most of which were LDLR mutations, and three LDLR CNVs called from the panel data were all successfully confirmed by MLPA analysis. In total, 48 different mutations were identified, including 45 LDLR mutations, 1 APOB mutation, 1 ABCG5 mutation, and 1 APOE mutation. Among them, the five most frequent mutations (LDLR c.1879G＞A, c.1747C＞T, c.313＋1G＞A, c.400T＞C, and APOB c.10579C＞T) were detected. Moreover, we also found that patients with LDLR variants of CNVs and splicing and nonsense had increased low-density lipoprotein cholesterol levels when compared with those who carried missense variants.

CONCLUSIONS

The spectrum of FH-causing mutations in the Chinese population is refined and expanded. Analyses of FH causal genes have been a great help in clinical diagnosis and have deep implications in disease treatment. These data can serve as a considerable dataset for next-generation sequencing analysis of the Chinese population with FH and contribute to the genetic diagnosis and counseling of FH patients.

Identifying county-level factors for female breast cancer incidence rate through a large-scale population study

Female breast cancer (FBC) incidence rate (IR) varies greatly across counties in the United States (U.S.). Factors contributing to these geographic disparities have not been fully understood at the population level. In this study, we investigated the relationships between the county-level FBC IR and a diverse set of variables in demographics, socioeconomics, life style, health care accessibility, and environment. Our study included 1,277 counties in the U.S. where the female population was 10,000 or above for at least one race/ethnicity. After controlling for the racial/ethnic and other significant factors, percent of husband-wife family households (pHWFH) for a racial/ethnic group in a county is negatively associated with FBC IR (p < 0.001). A 10% increase in married family households may lower a county's IR by 5.2 cases per 100,000 females per year. We also found that PM2.5 (fine inhalable particles with a diameter of 2.5 micrometers or less) is positively associated with FBC IR (p < 0.001). Counties with the highest level of PM2.5 have approximately 4 additional FBC new cases per 100,000 females per year than counties with the lowest level of PM2.5. Furthermore, we found that the county-level factors contributing to FBC IR vary significantly for different racial groups using race-specific models. While confirming most of the previously known patient- and neighborhood-level risk factors (such as race/ethnicity, income, and health care accessibility), our study identified two significant county-level factors contributing to the spatial disparity of FBC IR across the U.S. The newly-identified beneficial factor (marriage) and risk factor (PM2.5), together with the verified known factors, may help provide insights to officials of health departments/organizations for them to make decisions on cancer intervention strategies.

Genetic Polymorphisms of MMP1, MMP9, COL1A1, and COL1A2 in Polish Patients with Thoracic Aortopathy

BACKGROUND

The pathogenesis of thoracic aortopathy is complex, and much evidence suggests the influence of genetic factors. Some genes with polymorphisms are widely considered critical factors in the initiation and development of aortic aneurysm. The aim of our study was to analyze the association of genetic polymorphisms of MMP1 rs1799750 (c.-1607G>GG), MMP9 rs3918242 (c.-1562C>T), COL1A1 rs1800012 (c.1245G>T), and COL1A2 rs42524 (c.1645G>C) with predisposition to thoracic aortopathy in Polish patients and with clinical characteristics of these patients.

METHODS

The study was carried out with 96 patients with thoracic aortopathy (47 patients with ascending aortic aneurysm and 49 patients with thoracic aortic dissection) and 61 control subjects without thoracic aortopathy. The MMP1, MMP9, COL1A1, and COL1A2 polymorphisms were determined by PCR-RFLP.

RESULTS

No significant differences in the frequency distributions of MMP1, MMP9, COL1A1, and COL1A2 genotypes or alleles were found (1) between the control group and patients with ascending aortic aneurysm (AsAA), (2) between the control group and patients with thoracic aortic dissection (TAD), or (3) between AsAA and TAD patients. Multivariate logistic regression analysis revealed that MMP1 and MMP9 polymorphisms were associated with the degree of aortic valve regurgitation.

CONCLUSION

The results of our study did not support associations between MMP1, MMP9, COL1A1, and COL1A2 genetic variants with the risk of thoracic artery disease in Polish patients. However, rs1799750 MMP1 and rs3918242 MMP9 seem to be associated with the degree of aortic regurgitation.