Gene-environment interactions in human diseases

Exploring the complex information processes underlying plant behavior

Newly discovered plant behaviors, linked to historical observations, contemporary technologies, and emerging knowledge of signaling mechanisms, argue that plants utilize complex information processing systems. Plants are goal-oriented in an evolutionary and physiological sense; they demonstrate agency and learning. While most studies on plant plasticity, learning, and memory deal with the responsiveness of individual plants to resource availability and biotic stresses, adaptive information is often perceived from and coordinated with neighboring plants, while competition occurs for limited resources. Based on existing knowledge, technologies, and sustainability principles, climate-smart agricultural practices are now being adopted to enhance crop resilience and productivity. A deeper understanding of the dynamics of plant behavior offers a rich palette of potential amelioration strategies for improving the productivity and health of natural and agricultural ecosystems.

Influence of structural racism on cancer health disparities: Tailoring measures relevant to multiple myeloma

This commentary highlights a need for comprehensive measures of structural racism tailored to cancer health disparities, in particular Black-White disparities in multiple myeloma (MM). Recent political and social calls and advances in the ability to quantitate structural racism have led to rapidly growing research on the health consequences of structural racism. However, to date, most studies have used unidimensional measures of structural racism that do not capture cumulative influences or enable the identification of factors most responsible for driving disparities. Furthermore, measures may not reflect aspects of structural racism most relevant to underlying disease processes and risks. This study proposes a multifaceted approach to measuring structural racism relevant to MM that includes comprehensive, disease- and at-risk population-tailored social and environmental data and biomarkers of susceptibility and progression related to underlying biological changes associated with structural racism. Such novel measures of structural racism may improve the ability to assess the influence of structural racism on cancer health disparities, which may advance understanding of disease etiology and differences observed by racialized groups.

The utility of obesity polygenic risk scores from research to clinical practice: A review

Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study

Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventions. We developed a predictive model integrating genetic, environmental, and clinical factors using data from 7819 IS patients in the Third China National Stroke Registry. Employing an 80:20 split, we randomly divided the dataset into development and internal validation cohorts. The discrimination and calibration performance of models were evaluated using the area under the receiver operating characteristic curves (AUC) for discrimination and Brier score with calibration curve in the internal validation cohort. We conducted genome-wide association studies (GWAS) in the development cohort, identifying rs11109607 (ANKS1B) as the most significant variant associated with IS functional outcome. We employed principal component analysis to reduce dimensionality on the top 100 significant variants identified by the GWAS, incorporating them as genetic factors in the predictive model. We employed a machine learning algorithm capable of identifying nonlinear relationships to establish predictive models for IS patient functional outcome. The optimal model was the XGBoost model, which outperformed the logistic regression model (AUC 0.818 versus 0.756, P < .05) and significantly improved reclassification efficiency. Our study innovatively incorporated genetic, environmental, and clinical factors for predicting the IS functional outcome in East Asian populations, thereby offering novel insights into IS functional outcome.

Patient Perceptions of Illness Causes and Treatment Preferences for Obsessive-Compulsive Disorder: A Mixed-Methods Study

Obsessive-compulsive disorder (OCD) is a condition with high patient morbidity and mortality. Research shows that eliciting patient explanations about illness causes and treatment preferences promotes cross-cultural work and engagement in health services. These topics are in the Cultural Formulation Interview (CFI), a semi-structured interview first published in DSM-5 that applies anthropological approaches within mental health services to promote person-centered care. This study focuses on the New York City site of an international multi-site study that used qualitative-quantitative mixed methods to: (1) analyze CFI transcripts with 55 adults with OCD to explore perceived illness causes and treatment preferences, and (2) explore whether past treatment experiences are related to perceptions about causes of current symptoms. The most commonly named causes were circumstantial stressors (n = 16), genetics (n = 12), personal psychological traits (n = 9), an interaction between circumstantial stressors and participants' brains (n = 6), and a non-specific brain problem (n = 6). The most common treatment preferences were psychotherapy (n = 42), anything (n = 4), nothing (n = 4), and medications (n = 2). Those with a prior medication history had twice the odds of reporting a biological cause, though this was not a statistically significant difference. Our findings suggest that providers should ask patients about illness causes and treatment preferences to guide treatment choice.

Red and Processed Meat Intake, Polygenic Risk and the Prevalence of Colorectal Neoplasms: Results from a Screening Colonoscopy Population

High red and processed meat intake and genetic predisposition are risk factors of colorectal cancer (CRC). However, evidence of their independent and joint associations on the risk of colorectal neoplasms is limited. We assessed these associations among 4774 men and women undergoing screening colonoscopy. Polygenic risk scores (PRSs) were calculated based on 140 loci related to CRC. We used multiple logistic regression models to evaluate the associations of red and processed meat intake and PRS with the risk of colorectal neoplasms. Adjusted odds ratios (aORs) were translated to genetic risk equivalents (GREs) to compare the strength of the associations with colorectal neoplasm risk of both factors. Compared to ≤1 time/week, processed meat intake >1 time/week was associated with a significantly increased risk of colorectal neoplasm [aOR (95% CI): 1.28 (1.12-1.46)]. This risk increase was equivalent to the risk increase associated with a 19 percentile higher PRS. The association of red meat intake with colorectal neoplasm was weaker and did not reach statistical significance. High processed meat intake and PRS contribute to colorectal neoplasm risk independently. Limiting processed meat intake may offset a substantial proportion of the genetically increased risk of colorectal neoplasms.

Uncovering the shared genetic components of thyroid disorders and reproductive health

OBJECTIVE

The aim of the study is to map the shared genetic component and relationships between thyroid and reproductive health traits to improve the understanding of the interplay between those domains.

DESIGN

A large-scale genetic analysis of thyroid traits (hyper- and hypothyroidism, and thyroid-stimulating hormone levels) was conducted in up to 743 088 individuals of European ancestry from various cohorts.

METHODS

We evaluated genetic associations using genome-wide association study (GWAS) meta-analysis, GWAS Catalog lookup, gene prioritization, mouse phenotype lookup, and genetic correlation analysis.

RESULTS

GWAS meta-analysis results for thyroid phenotypes showed that 50 lead variants out of 253 (including 5/52 of the novel hits) were linked to reproductive health in previous literature. Genetic correlation analyses revealed significant correlations between hypothyroidism and reproductive phenotypes. The results showed that 31.9% of thyroid-associated genes also had an impact on reproductive phenotypes, with the most affected functions being related to genitourinary tract issues.

CONCLUSIONS

The study discovers novel genetic loci linked to thyroid phenotypes and highlights the shared genetic determinants between thyroid function and reproductive health, providing evidence for the genetic pleiotropy and shared biological mechanisms between these traits in both sexes.

Gene‒Environment Interaction Between CAST Gene and Eye-Rubbing in the Chinese Keratoconus Cohort Study: A Case-Only Study

Purpose

Keratoconus (KC), characterized by progressive corneal protrusion and thinning, is a complex disease influenced by the combination of genetic and environmental factors. The purpose of this study was to explore potential gene‒environment interaction between the calpastatin (CAST) gene and eye-rubbing in KC.

Methods

A case-only study including 930 patients (676 patients with eye-rubbing and 254 patients without eye-rubbing) from the Chinese Keratoconus (CKC) cohort study was performed in the present study. Genotyping of single nucleotide polymorphism (SNP) was conducted using the Illumina Infinium Human Asian Screening Array (ASA) Beadchip. The gene‒environment interactions between CAST gene and eye-rubbing were analyzed using PLINK version 1.90. The interactions between CAST genotypes and eye-rubbing were analyzed by logistic regression models. The SNP-SNP-environment interactions were analyzed using generalized multifactor dimensionality reduction (GMDR).

Results

Three SNPs in CAST gene, namely, rs26515, rs27991, and rs9314177, reached the significance threshold for interactions (defined as P < 2.272 × 10-3). Notably, the minor alleles of these three SNPs exhibited negative interactions with eye-rubbing in KC. The results of logistic regression models revealed that the minor allele homozygotes and heterozygotes of rs26515, rs27991, and rs9314177 also exhibited negative interactions with eye-rubbing. Furthermore, GMDR analysis revealed the significant SNP-SNP-environment interactions among rs26515, rs27991, rs9314177, and eye-rubbing in KC.

Conclusions

This study identified rs26515, rs27991, and rs9314177 in CAST gene existed gene-environment interactions with eye-rubbing in KC, which is highly important for understanding the underlying biological mechanisms of KC and guiding precision prevention and proper management.

Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples

PURPOSE

Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears.

METHODS

A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs.

RESULTS

The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears.

CONCLUSION

The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.

Breastfeeding may reduce the effects of maternal smoking on lung cancer mortality in adult offspring: a prospective cohort study

BACKGROUND

Although previous research has indicated a correlation between smoking and the mortality rate in patients with lung cancer, the impact of early life factors on this relationship remains unclear and requires further investigation. This study aimed to investigate the hypothesis that breastfeeding reduces the risk of lung cancer-related death.

METHODS

The authors conducted a prospective cohort study involving 501 859 participants recruited from the United Kingdom Biobank to explore the potential association between breastfeeding and the risk of lung cancer mortality using a Cox proportional hazards model. Subsequently, the polygenic risk score for lung cancer was calculated to detect interactions between genes and the environment.

RESULTS

Over a median follow-up duration of 11.8 years, encompassing a total of 501 859 participants, breastfeeding was found to reduce the risk of lung cancer-related death and the impact of maternal smoking on lung cancer mortality in adult offspring. This association remained consistent after stratification. Furthermore, the influence of maternal smoking and breastfeeding on the risk of lung cancer mortality was significant at a high genetic risk level.

CONCLUSION

Breastfeeding can reduce the risk of lung cancer-related death and the impact of maternal smoking on lung cancer mortality in adult offspring. This correlation has the potential to reduce the probability of lung-cancer-related deaths in subsequent generations.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks

Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Difference in Gastrointestinal Cancer Risk and Mortality by Dietary Pattern Analysis: A Systematic Review and Meta-Analysis

CONTEXT

Several studies have demonstrated that dietary patterns identified by a posteriori and hybrid methods are associated with gastrointestinal (GI) cancer risk and mortality. These studies applied different methods for analyzing dietary data and reported inconsistent findings.

OBJECTIVE

This systematic review and meta-analysis were aimed to determine the association between dietary patterns, derived using principal component analysis (PCA) and reduced rank regression (RRR), and GI cancer risk and GI cancer-caused mortality.

DATA SOURCE

Articles published up to June 2023 in English were eligible for inclusion. The Medline, SCOPUS, Cochrane Library, CINHAL, PsycINFO, ProQuest, and Web of Sciences databases were used to identify prospective studies. The Preferred Reporting Item for Systematic Review and Meta-analysis Protocol 2020 was used to report results.

DATA EXTRACTION

A total of 28 studies were eligible for inclusion. Varied approaches to deriving dietary patterns were used, including PCA (n = 22), RRR (n = 2), combined PCA and RRR (n = 1), cluster analysis (CA; n = 2) and combined PCA and CA (n = 1).

DATA ANALYSIS

Two dietary patterns, "healthy" and "unhealthy," were derived using PCA and RRR. The healthy dietary pattern was characterized by a higher intake of fruits, whole grains, legumes, vegetables, milk, and other dairy products, whereas the unhealthy dietary pattern was characterized by a higher intake of red and processed meat, alcohol, and both refined and sugar-sweetened beverages. The findings indicated that the PCA-derived healthy dietary pattern was associated with an 8% reduced risk (relative risk [RR], 0.92; 95% CI, 0.87-0.98), and the unhealthy dietary pattern was associated with a 14% increased risk (RR, 1.14; 95% CI, 1.07-1.22) of GI cancers. Similarly, the RRR-derived healthy dietary pattern (RR, 0.83; 95% CI, 0.61-1.12) may be associated with reduced risk of GI cancers. In contrast, the RRR-derived unhealthy dietary pattern (RR, 0.93; 95% CI, 0.57-1.52) had no association with a reduced risk of GI cancers. Similarly, evidence suggested that PCA-derived healthy dietary patterns may reduce the risk of death from GI cancers, whereas PCA-derived unhealthy dietary patterns may increase the risk.

CONCLUSION

Findings from prospective studies on the association of PCA-derived dietary patterns and the risk of GI cancers support the evidence of healthy and unhealthy dietary patterns as either protective or risk-increasing factors for GI cancers and for survivorship, respectively. The findings also suggest that the RRR-derived healthy dietary pattern reduces the risk of GI cancers (albeit with low precision), but no association was found for the RRR-derived unhealthy dietary pattern. Prospective studies are required to further clarify disparities in the association between PCA- and RRR-derived dietary patterns and the risk of GI cancers. Systematic review registration: PROSPERO registration no. CRD42022321644.

Genetic and environmental interactions contribute to immune variation in rewilded mice

The relative and synergistic contributions of genetics and environment to interindividual immune response variation remain unclear, despite implications in evolutionary biology and medicine. Here we quantify interactive effects of genotype and environment on immune traits by investigating C57BL/6, 129S1 and PWK/PhJ inbred mice, rewilded in an outdoor enclosure and infected with the parasite Trichuris muris. Whereas cellular composition was shaped by interactions between genotype and environment, cytokine response heterogeneity including IFNγ concentrations was primarily driven by genotype with consequence on worm burden. In addition, we show that other traits, such as expression of CD44, were explained mostly by genetics on T cells, whereas expression of CD44 on B cells was explained more by environment across all strains. Notably, genetic differences under laboratory conditions were decreased following rewilding. These results indicate that nonheritable influences interact with genetic factors to shape immune variation and parasite burden.

DNA Methylation Profiles of PSMA6, PSMB5, KEAP1, and HIF1A Genes in Patients with Type 1 Diabetes and Diabetic Retinopathy

We explored differences in the DNA methylation statuses of PSMA6, PSMB5, HIF1A, and KEAP1 gene promoter regions in patients with type 1 diabetes and different diabetic retinopathy (DR) stages. Study subjects included individuals with no DR (NDR, n = 41), those with non-proliferative DR (NPDR, n = 27), and individuals with proliferative DR or those who underwent laser photocoagulation (PDR/LPC, n = 46). DNA methylation was determined by Zymo OneStep qMethyl technique. The methylation of PSMA6 (NDR 5.9 (3.9-8.7) %, NPDR 4.5 (3.8-5.7) %, PDR/LPC 6.6 (4.7-10.7) %, p = 0.003) and PSMB5 (NDR 2.2 (1.9-3.7) %, NPDR 2.2 (1.9-3.0) %, PDR/LPC 3.2 (2.5-7.1) %, p < 0.01) differed across the groups. Consistent correlations were observed between the methylation levels of HIF1A and PSMA6 in all study groups. DNA methylation levels of PSMA6, PSMB5, and HIF1A genes were positively correlated with the duration of diabetes, HbA1c, and albuminuria in certain study groups. Univariate regression models revealed a significant association between the methylation level z-scores of PSMA6, PSMB5, and HIF1A and severe DR (PSMA6: OR = 1.96 (1.15; 3.33), p = 0.013; PSMB5: OR = 1.90 (1.14; 3.16), p = 0.013; HIF1A: OR = 3.19 (1.26; 8.06), p = 0.014). PSMB5 remained significantly associated with DR in multivariate analysis. Our findings suggest significant associations between the severity of DR and the DNA methylation levels of the genes PSMA6, PSMB5, and HIF1A, but not KEAP1 gene.

The Chinese keratoconus (CKC) cohort study

The Chinese keratoconus (CKC) cohort study is a population-based longitudinal prospective cohort study in the Chinese population involving a clinical database and biobanks. This ongoing study focuses on the prevention of KC progression and is the first to involve the effect of gene‒environment interactions on KC progression. The CKC cohort is hospital-based and dynamic and was established in Zhengzhou, China; KC patients (n = 1114) from a large geographical area were enrolled from January 2019 to June 2023, with a mean age of 22.23 years (6‒57 years). Demographic details, socioeconomic characteristics, lifestyle, disease history, surgical history, family history, and visual and social function data are being collected using questionnaires. General physical examination, eye examination, biological specimen collection, and first-degree relative data were collected and analyzed in the present study. The primary focus of the present study was placed on gene, environment and the effect of gene‒environment interactions on KC progression. The follow-up of the CKC cohort study is expected to include data collection at 3 months, 6 months, and 1 year after the initial examination and then at the annual follow-up examinations. The first follow-up of the CKC cohort study was recorded. A total of 918 patients completed the follow-up by June 1, 2023, with a response rate of 82.40%. Aside from the younger age of patients who were followed up, no significant differences were found between patients who were followed up and patients who were not.

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.

Teaching gene-environment interaction concepts with narrative vignettes: Effects on knowledge, stigma, and behavior motivation

Gene-environment interaction (GxE) concepts underlie a proper understanding of complex disease risk and risk-reducing behavior. Communicating GxE concepts is a challenge. This study designed an educational intervention that communicated GxE concepts in the context of eating behavior and its impact on weight, and tested its efficacy in changing knowledge, stigma, and behavior motivation. The study also explored whether different framings of GxE education and matching frames with individual eating tendencies would result in stronger intervention impact. The experiment included four GxE education conditions and a control condition unrelated to GxE concepts. In the education conditions, participants watched a video introducing GxE concepts then one of four narrative vignettes depicting how a character's experience with eating hyperpalatable or bitter tasting food (reward-based eating drive vs. bitter taste perception scenario) is influenced by genetic or environmental variations (genetic vs. environmental framings). The education intervention increased GxE knowledge, genetic causal attributions, and empathetic concern. Mediation analyses suggest that causal attributions, particularly to genetics and willpower, are key factors that drive downstream stigma and eating behavior outcomes and could be targeted in future interventions. Tailoring GxE education frames to individual traits may lead to more meaningful outcomes. For example, genetic (vs. environmental) framed GxE education may reduce stigma toward individuals with certain eating tendencies among individuals without such tendencies. GxE education interventions would be most likely to achieve desired outcomes such as reducing stigma if they target certain causal beliefs and are strategically tailored to individual attributes.

Excess Weight, Polygenic Risk Score, and Findings of Colorectal Neoplasms at Screening Colonoscopy

INTRODUCTION

Excess weight is an established risk factor of colorectal cancer (CRC). However, evidence is lacking on how its impact varies by polygenic risk at different stages of colorectal carcinogenesis.

METHODS

We assessed the individual and joint associations of body mass index (BMI) and polygenic risk scores (PRSs) with findings of colorectal neoplasms among 4,784 participants of screening colonoscopy. Adjusted odds ratios (aORs) for excess weight derived by multiple logistic regression were converted to genetic risk equivalents (GREs) to quantify the impact of excess weight compared with genetic predisposition.

RESULTS

Overweight and obesity (BMI 25-<30 and ≥30 kg/m 2 ) were associated with increased risk of any colorectal neoplasm (aOR [95% confidence interval, CI] 1.26 [1.09-1.45] and 1.47 [1.24-1.75]). Obesity was associated with increased risk of advanced colorectal neoplasm (aOR [95% CI] 1.46 [1.16-1.84]). Dose-response relationships were seen for the PRS (stronger for advanced neoplasms than any neoplasms), with no interaction with BMI, suggesting multiplicative effects of both factors. Obese participants with a PRS in the highest tertile had a 2.3-fold (95% CI 1.7-3.1) and 2.9-fold (95% CI 1.9-4.3) increased risk of any colorectal neoplasm and advanced colorectal neoplasm, respectively. The aOR of obesity translated into a GRE of 38, meaning that its impact was estimated to be equivalent to the risk caused by 38 percentiles higher PRS for colorectal neoplasm.

DISCUSSION

Excess weight and polygenic risk are associated with increased risk of colorectal neoplasms in a multiplicative manner. Maintaining normal weight is estimated to have an equivalent effect as having 38 percentiles lower PRS.

Time-dependent gene-environment interactions are essential drivers of asthma initiation and persistence

Asthma is a clinical syndrome caused by heterogeneous underlying mechanisms with some of them having a strong genetic component. It is known that up to 82% of atopic asthma has a genetic background with the rest being influenced by environmental factors that cause epigenetic modification(s) of gene expression. The interaction between the gene(s) and the environment has long been regarded as the most likely explanation of asthma initiation and persistence. Lately, much attention has been given to the time frame the interaction occurs since the host response (immune or biological) to environmental triggers, differs at different developmental ages. The integration of the time variant into asthma pathogenesis is appearing to be equally important as the gene(s)-environment interaction. It seems that, all three factors should be present to trigger the asthma initiation and persistence cascade. Herein, we introduce the importance of the time variant in asthma pathogenesis and emphasize the long-term clinical significance of the time-dependent gene-environment interactions in childhood.

The use of effect biomarkers in chemical mixtures risk assessment - Are they still important?

Human epidemiological studies with biomarkers of effect play an invaluable role in identifying health effects with chemical exposures and in disease prevention. Effect biomarkers that measure genetic damage are potent tools to address the carcinogenic and/or mutagenic potential of chemical exposures, increasing confidence in regulatory risk assessment decision-making processes. The micronucleus (MN) test is recognized as one of the most successful and reliable assays to assess genotoxic events, which are associated with exposures that may cause cancer. To move towards the next generation risk assessment is crucial to establish bridges between standard approaches, new approach methodologies (NAMs) and tools for increase the mechanistically-based biological plausibility in human studies, such as the adverse outcome pathways (AOPs) framework. This paper aims to highlight the still active role of MN as biomarker of effect in the evolution and applicability of new methods and approaches in human risk assessment, with the positive consequence, that the new methods provide a deeper knowledge of the mechanistically-based biology of these endpoints.

An approach to identify gene-environment interactions and reveal new biological insight in complex traits

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

The genetic and dietary landscape of the muscle insulin signalling network

Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway's organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasising the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.

The sulfur microbial diet and risk of nonalcoholic fatty liver disease: a prospective gene-diet study from the UK Biobank

BACKGROUND

The gut microbiota is closely related to liver diseases. The dietary pattern associated with sulfur-metabolizing bacteria in stool has been found to influence intestinal health.

OBJECTIVE

We aimed to investigate whether consuming the sulfur microbial diet is associated with nonalcoholic fatty liver disease (NAFLD).

METHODS

We included 143,918 participants of European descent from the UK Biobank. Information on serving sizes used per diet component was recorded by an online 24-h dietary assessment tool (Oxford WebQ). The total sulfur microbial diet score was constructed by summing the product of β-coefficients and corresponding serving sizes. NAFLD was ascertained using hospital inpatient and death records. Cox proportional hazard models were used to estimate the adjusted hazard ratio (HR) and 95% confidence interval (CI). Mediation analyses were used to investigate underlying mediators including body mass index, waist circumference, glucose, triglyceride, urate, and C-reactive protein. A polygenic risk score for NAFLD was constructed and stratified to assess whether the association is modified by genetic predisposition.

RESULTS

After a median follow-up of 11.7 y (interquartile range: 11.3-12.5 y), we documented 1540 incident cases of NAFLD. After adjustment for covariates, we observed an overall J-shaped relationship between the sulfur microbial diet and risk of NAFLD. Those in the highest quartile of sulfur microbial diet score had a 46% increased risk of NAFLD [HRQ4vsQ1 (95% CI): 1.46 (1.26, 1.69)]. We also found that this association is partly mediated by metabolic disorders and systemic inflammation. In addition, the positive association was stronger among individuals at higher genetic risk for NAFLD (Pinteraction = 0.044).

CONCLUSIONS

The sulfur microbial diet had adverse associations with incident NAFLD, particularly in those at a higher genetic risk. Our study may provide evidence on the role of sulfur-metabolizing bacteria in the diet-NAFLD association.

Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function

BACKGROUND

Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions.

METHODS

We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking.

RESULTS

We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857).

CONCLUSIONS

Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

Modifiable lifestyle factors, genetic and acquired risk, and the risk of severe liver disease in the UK Biobank cohort: (Lifestyle factors and SLD)

BACKGROUND

Lifestyle intervention is important for the treatment of liver diseases.

AIMS

To clarify the association of healthy lifestyle with severe liver disease (SLD) and assessed whether genetic susceptibility and acquired fibrosis risk can modify the association.

METHODS

We included 417,986 UK Biobank participants who were free of SLD at baseline. Information on seven modifiable lifestyle factors was collected through a baseline questionnaire. SLD was defined as a medical diagnosis of cirrhosis, hepatocellular carcinoma or liver failure. Cox proportional hazards models were used to evaluate the association between healthy lifestyle factors and risk of incident SLD. The polygenic risk score (PRS) and fibrosis-4 index (FIB-4) were calculated and set as an interaction term.

RESULTS

During a median follow-up of 12.6 years, 4542 fatal and non-fatal SLD incidents were identified. A higher overall lifestyle score was associated with a significantly lower SLD risk (Ptrend <0.001). An increment of 1-point lifestyle score combined with a 1-SD increment in FIB-4 or PRS was associated with an additional reduction of 3% or 2% in SLD risk.

CONCLUSIONS

In European individuals, a healthy lifestyle is associated with a lower risk of incident SLD, which is more pronounced among individuals with a higher genetic and fibrosis risk.

Overcoming combination fatigue: Addressing high-dimensional effect measure modification and interaction in clinical, biomedical, and epidemiologic research using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Growing interest in precision medicine, gene-environment interactions, health equity, expanding diversity in research, and the generalizability results, requires researchers to evaluate how the effects of treatments or exposures differ across numerous subgroups. Evaluating combination complexity, in the form of effect measure modification and interaction, is therefore a common study aim in the biomedical, clinical, and epidemiologic sciences. There is also substantial interest in expanding the combinations of factors analyzed to include complex treatment protocols (e.g., multiple study arms or factorial randomization), comorbid medical conditions or risk factors, and sociodemographic and other subgroup identifiers. However, expanding the number of subgroup category combinations creates combination fatigue problems, including concerns over small sample size, reduced power, multiple testing, spurious results, and design and analytic complexity. Creative new approaches for managing combination fatigue and evaluating high-dimensional effect measure modification and interaction are needed. Intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) has already attracted substantial interest in social epidemiology, and has been hailed as the new gold standard for investigating health inequities across complex intersections of social identity. Leveraging the inherent advantages of multilevel models, a more general multicategorical MAIHDA can be used to study statistical interactions and predict effects across high-dimensional combinations of conditions, with important advantages over alternative approaches. Though it has primarily been used thus far as an analytic approach, MAIHDA should also be used as a framework for study design. In this article, I introduce MAIHDA to the broader health sciences research community, discuss its advantages over conventional approaches, and provide an overview of potential applications in clinical, biomedical, and epidemiologic research.

Common genetic variation impacts stress response in the brain

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.

Pathological imaging-assisted cancer gene-environment interaction analysis

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance and stability.

Using biophysical cues and biomaterials to improve genetic models

With the advent of induced pluripotent stem cells and modern differentiation protocols, many advances in our understanding of disease have been made possible by in vitro disease modeling; in some cases, their use may have supplanted animal models. Yet in vitro models often rely on rigid cell culture substrates that could limit our ability to completely reproduce human disease in a dish. Nascent work, however, suggests that the combination of biomaterials and/or advanced microphysiological systems-which better recapitulate tissue properties-with stem cells expressing disease mimicking genetics, could substantially improve current disease modeling efforts where genetics alone is insufficient. This review will highlight such recent advances as well as review current challenges that the fields must overcome to create more personalized therapeutics in the future.

Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study

(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.

The Exposome and Nutritional Pharmacology and Toxicology: A New Application for Metabolomics

The exposome refers to all of the internal and external life-long exposures that an individual experiences. These exposures, either acute or chronic, are associated with changes in metabolism that will positively or negatively influence the health and well-being of individuals. Nutrients and other dietary compounds modulate similar biochemical processes and have the potential in some cases to counteract the negative effects of exposures or enhance their beneficial effects. We present herein the concept of Nutritional Pharmacology/Toxicology which uses high-information metabolomics workflows to identify metabolic targets associated with exposures. Using this information, nutritional interventions can be designed toward those targets to mitigate adverse effects or enhance positive effects. We also discuss the potential for this approach in precision nutrition where nutrients/diet can be used to target gene-environment interactions and other subpopulation characteristics. Deriving these "nutrient cocktails" presents an opportunity to modify the effects of exposures for more beneficial outcomes in public health.

Niche construction and the environmental term of the price equation: How natural selection changes when organisms alter their environments

Organisms construct their own environments and phenotypes through the adaptive processes of habitat choice, habitat construction, and phenotypic plasticity. We examine how these processes affect the dynamics of mean fitness change through the environmental change term of the Price Equation. This tends to be ignored in evolutionary theory, owing to the emphasis on the first term describing the effect of natural selection on mean fitness (the additive genetic variance for fitness of Fisher's Fundamental Theorem). Using population genetic models and the Price Equation, we show how adaptive niche constructing traits favorably alter the distribution of environments that organisms encounter and thereby increase population mean fitness. Because niche-constructing traits increase the frequency of higher-fitness environments, selection favors their evolution. Furthermore, their alteration of the actual or experienced environmental distribution creates selective feedback between niche constructing traits and other traits, especially those with genotype-by-environment interaction for fitness. By altering the distribution of experienced environments, niche constructing traits can increase the additive genetic variance for such traits. This effect accelerates the process of overall adaption to the niche-constructed environmental distribution and can contribute to the rapid refinement of alternative phenotypic adaptations to different environments. Our findings suggest that evolutionary biologists revisit and reevaluate the environmental term of the Price Equation: owing to adaptive niche construction, it contributes directly to positive change in mean fitness; its magnitude can be comparable to that of natural selection; and, when there is fitness G × E, it increases the additive genetic variance for fitness, the much-celebrated first term.

Associations between the Genetic Heritability of Dyslipidemia and Dietary Patterns in Korean Adults Based on Sex Differences

Dyslipidemia can be defined as an abnormality in serum lipid levels that is substantially linked to genetic variations and lifestyle factors, such as diet patterns, and has distinct sex-specific characteristics. We aimed to elucidate the genetic impact of dyslipidemia according to sex and explore the associations between genetic variants and dietary patterns in large-scale population-based cohorts. After performing genome-wide association studies (GWASs) in male, female, and entire cohorts, significant single nucleotide polymorphisms (SNPs) were identified in the three groups, and genetic risk scores (GRSs) were calculated by summing the risk alleles from the selected SNPs. After adjusting for confounding variables, the risk of dyslipidemia was 2.013-fold and 2.535-fold higher in the 3rd quartile GRS group in the male and female cohorts, respectively, than in the 1st quartile GRS group. While instant noodle and soft drink intake were significantly associated with GRS related to hyperlipidemia in male cohorts, coffee consumption was substantially related to GRS related to hyperlipidemia in female cohorts. Considering the influence of genetic factors and dietary patterns, the findings of this study suggest the potential for implementing sex-specific strategic interventions to avoid dyslipidemia.

Towards modifying the genetic predisposition for glaucoma: An overview of the contribution and interaction of genetic and environmental factors

Glaucoma, the leading cause of irreversible blindness worldwide, is a complex human disease, with both genetic and environmental determinants. The availability of large-scale, population-based cohorts and biobanks, combining genotyping and detailed phenotyping, has greatly accelerated research into the aetiology of glaucoma in recent years. Hypothesis-free genome-wide association studies have furthered our understanding of the complex genetic architecture underpinning the disease, while epidemiological studies have provided advances in the identification and characterisation of environmental risk factors. It is increasingly recognised that the combined effects of genetic and environmental factors may confer a disease risk that reflects a departure from the simple additive effect of the two. These gene-environment interactions have been implicated in a host of complex human diseases, including glaucoma, and have several important diagnostic and therapeutic implications for future clinical practice. Importantly, the ability to modify the risk associated with a particular genetic makeup promises to lead to personalised recommendations for glaucoma prevention, as well as novel treatment approaches in years to come. Here we provide an overview of genetic and environmental risk factors for glaucoma, as well as reviewing the evidence and discussing the implications of gene-environment interactions for the disease.

Systematic review on gene-sun exposure interactions in skin cancer

BACKGROUND

The risk of skin cancer is determined by environmental factors like ultraviolet radiation (UVR), personal habits like time spent outdoors and genetic factors. This review aimed to survey existing studies in gene-environment (GxE) interaction on skin cancer risk, and report on GxE effect estimates.

METHODS

We searched Embase, Medline (Ovid) and Web of Science (Core Collection) and included only primary research that reported on GxE on the risk of the three most common types of skin cancer: basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma. Quality assessment followed the Newcastle-Ottawa Scale. Meta-analysis was not possible because no two studies examined the same interaction. This review was registered on PROSPERO (CRD42021238064).

RESULTS

In total 260 records were identified after exclusion of duplicates. Fifteen studies were included in the final synthesis-12 used candidate gene approach. We found some evidence of GxE interactions with sun exposure, notably, with MC1R, CAT and NOS1 genes in melanoma, HAL and IL23A in BCC and HAL and XRCC1 in SCC.

CONCLUSION

Sun exposure seems to interact with genes involved in pigmentation, oxidative stress and immunosuppression, indicating that excessive UV exposure might exhaust oxidative defence and repair systems differentially, dependent on genetic make-up. Further research is warranted to better understand skin cancer epidemiology and develop sun exposure recommendations. A genome-wide approach is recommended as it might uncover unknown disease pathways dependent on UV radiation.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

Misclassification Bias in the Assessment of Gene-by-Environment Interactions

BACKGROUND

Misclassification bias is a common concern in epidemiologic studies. Despite strong bias on main effects, gene-environment interactions have been shown to be biased towards the null under gene-environment independence. In the context of a recent article examining the interaction between nerve agent exposure and paraoxonase-1 gene on Gulf War Illness, we aimed to assess the impact of recall bias-a common misclassfication bias-on the identification of gene-environment interactions when the independence assumption is violated.

METHODS

We derive equations to quantify the bias of the interaction, and numerically illustrate these results by simulating a case-control study of 1000 cases and 1000 controls. Simulation input parameters included exposure prevalence, strength of gene-environment dependence, strength of the main effect, exposure specificity among cases, and strength of the gene-environment interaction.

RESULTS

We show that, even if gene-environment independence is violated, we can bound possible gene-environment interactions by knowing the strength and direction of the gene-environment dependence ( ) and the observed gene-environment interaction ( )-thus often still allowing for the identification of such interactions. Depending on whether is larger or smaller than the inverse of , is a lower (if ) or upper (if ) bound for the true interaction. In addition, the bias magnitude is somewhat predictable by examining other characteristics such as exposure prevalence, the strength of the exposure main effect, and directions of the recall bias and gene-environment dependence.

CONCLUSIONS

Even if gene-environment dependence exists, we may still be able to identify gene-environment interactions even when misclassification bias is present.

Establishment of the early prediction models of low-birth-weight reveals influential genetic and environmental factors: a prospective cohort study

BACKGROUND

Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction.

METHODS

We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups.

RESULTS

We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model.

CONCLUSIONS

We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns' health throughout life.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.