Note: Gene-environment interactions within a precision environmental health framework

Note: Gene-environment interactions within a precision environmental health framework

https://doi.org/10.1016/j.xgen.2024.100591

Disease etiology

• complex gene+environment interaction (GEI)

• measuring environmental factors
• advantage of GEI analysis
• evolution of GEI study

• Gene-environment to genome-wide interaction (GWI)
• Multi-omics

• Advancement in GEI analysis methods
• Large scale datasets

• Translation of GEI finding -- > precision environmental health (PEH)
• Significance if GEI for disease prediction and prevention
• Advocate for integrating exposome into PEH omics studies

โดยปกติแล้วมีการประมาณการว่าโรคที่เกิดขึ้นมาจาก สวล สัก 50-80%

PEH

• genetic + epigenetic + environment + system-level data

• Identifying environmental causes
• Identify biomarkers of exposure
• Develop new prevention and intervention strategies

Precise risk assessment

• Personal information -- genetic susceptibility
• Environmental exposure

Gene-environment interactions

Linear modeling frameworks are statistical approaches used to model the relationship between a dependent variable and one or more independent variables. In the context of gene-environment interactions (GEIs), linear models help assess how genetic and environmental factors jointly contribute to a phenotype or disease outcome.

• เป็นการเก็บปัจจัยต่าง ๆ ที่เอามาสัมพันธ์กับการประเมินความเสี่ยง การป้องกันโรค และการลดการเกิดโรค

Figure 2 แสดงถึงลักษณะ trait ที่สามารถเห็นได้ เมื่อมีปัจจัยทางด้าน genetic/environment (exposure) เข้ามาเกี่ยวข้องด้วย เช่น รูป A expose vs non-expose ไม่ต่างกัน เพราะว่าแต่ละปัจจัยไม่มีผลต่อ phenotype (trait) 

Exposure measurement

• Exposome describes the totality of environmental exposures over the course of an individual’s life, including external (e.g., pollution, radiation, social determinants of health) and internal (e.g., microbiome, metabolism, oxidative stress) factors.
• Untargeted MS -- The sheer volume and complexity of data generated pose significant challenges in data analysis and compound identification.
• Geographic information systems (GISs) are used in epidemiology studies for environmental exposure assessment that integrates data from diverse sources
• Smart sensors in wearable devices that provide real-time data on individual exposure levels have transformed environmental exposure monitoring and assessment
• Wearable sensors can monitor small and coarse particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), and hazardous gases including CO and NO2
• Questionnaires administered to collect detailed data on lifestyle factors, environmental exposures, and social determinants of health are a pivotal source of information in environmental health research

• significant challenges, particularly regarding the standardization of questionnaires.
• Subjective responses and recall bias further exacerbate these challenges, potentially skewing the data
• cultural and linguistic differences can influence respondents’ understanding of questions
• necessitating careful adaptation and validation across diverse populations to ensure accuracy and relevance

Challenges with the standardization of the exposome

• Environmental exposures are responsible for 70%–90% of disease risk.
• the heterogeneity of data collection methods used
• the lack of structured data and opportunities for data sharing makes it challenging to standardize measures and compare findings across studies

Advantages of GEI analysis

• GEI analyses can provide insights into disease mechanisms,
• identify sources of heterogeneity across individuals
• the proportion of heritability not explained by genetics alone
• Example: associations between
• air pollution and cardiovascular disease,
• alcohol and liver disease,
• smoking and lung cancer,
• diet and obesity,
• and pesticide exposure and Parkinson’s disease
• Polygenic scores (PGSs), which provide a measure of an individual’s risk of a disease due to their genetics, may have low predictive power when applied to populations with different genetic ancestry or socioeconomic status or those that live in different environments.
• including the exposome in polygenic models can improve the portability of PGSs

Interplay of environmental epidemiology and genetics

• Epidemiology provides a framework for investigating the distribution and determinants of health and disease in populations, and genetics provides insight into the underlying biological mechanisms that contribute to disease.
• Epidemiological studies provide a framework for investigating interactions between genetic and environmental factors in disease development.

Candidate genes

• Key gene classes involved in responses to external factors include
• cytochrome P450 enzymes (CYPs) for metabolizing drugs and detoxifying xenobiotics,
• DNA repair genes for correcting damage from agents such as UV light and chemicals,
• Metabolic genes other than CYPs that transform substances for easier excretion.
• Antioxidant genes combat oxidative stress from pollutants, and
• heat shock proteins (HSPs) protect against environmental stressors by ensuring proper protein folding.
• inflammatory response genes mediate reactions to environmental agents
• The Comparative Toxicogenomics Database (CTD) is a publicly available, manually curated resource that outlines interactions between chemicals and genes, chemicals and diseases, and genes and diseases to elucidate a broad network of these components
• microarray and proteomics datasets for various species can be accessed via the Chemical Effects in Biological Systems (CEBS) knowledgebase (http://cebs.niehs.nih.gov/cebs)

EPIGENETICS

• Epigenetics involves the study of non-sequence modifications to DNA and modifications to 
• chromatin that affect gene-DNA accessibility and subsequent gene regulation.
• CpG methylation
• hetero-chromatic histone modification
• There is growing interest in studying the accumulation of epigenetic changes, known as epigenetic drift, in individuals with sustained generational environmental stressors such as stress and insufficient or improper nutrition
• for understanding the consequences of racism, poverty, and other multigenerational disparities
• Because epigenomics alone is insufficient for a comprehensive understanding of disease pathology, several approaches have been used to examine the collective genome and epigenome.
• Aligning with the Developmental Origins of Health and Disease (DOHaD) theory, early life, prenatal, and pre-conception settings have been shown to affect long-term health and chronic illness risk
• The DOHaD theory highlights early-life events’ impact on long-term health and has major implications for public health efforts to prevent chronic disease.

MULTI-OMICS APPROACHES FOR PEH

• Integrative omics integrates and simultaneously analyzes multiple omics datasets for a comprehensive understanding of complex biological systems.
• This approach can unravel complex interactions between genetic variations, gene-expression patterns, protein profiles, metabolic changes, and epigenetic modifications in response to environmental factors.

Advancements and challenges in studying OxE (Omics-environment) interactions using multi-omics

• Advancements in single-cell omics technologies, such as single cell RNA sequencing (scRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATACseq), have enabled the identification of associations between traits and cell types from high-resolution profiling of individual cells.
• Integrative OxE analyses face significant challenges involving data harmonization, the integration of multiple types of environmental exposure data from diverse sources, consideration of cell type-specific responses, how to capture dynamic interactions, and sample-size limitations.

METHODS FOR GEI ANALYSIS

• Incorporating both genetic and environmental variables, along with their interaction terms, into a regression equation
• dependent variable typically represents a phenotype of interest, such as disease status or a quantitative trait, and the independent variables include a genetic factor (e.g., genotype or allele count), an environmental exposure, and an interaction term that represents the product of the genetic and environmental variables
• Phenotype = Genetic factor (allele frequency) * environmental factor * interaction term
• For example, in a study examining the interaction between a gene variant and smoking on lung cancer risk, the model might include:
• Dependent variable: Lung cancer status (presence or absence)
• Independent variables: Genotype at a specific locus, smoking status (smoker or non-smoker), and the interaction term (genotype * smoking)
• The interaction term in a gene-environment interaction (GEI) model is a statistical construct that captures the combined effect of both the genetic factor and the environmental exposure on the phenotype of interest. It's not simply the sum of their individual effects, but rather how they modify or influence each other.
• High dimensionality complicates analyses and requires multiple comparison adjustments, increasing the risk of type II error. (a type II error occurs when a false null hypothesis is not rejected. This means that a true effect or relationship is missed because the statistical test fails to detect it. In the field of genetic association studies, this could mean failing to identify a true genetic variant associated with a disease.)
• The lack of a standardized roadmap for quantifying the exposome and harmonizing environmental measurements across studies causes data collection and measurement protocol inconsistencies in GEI analyses.
• efforts such as those from the Human Health Exposure Analysis Resource (HHEAR) network can help expand standardized exposure analysis in human health studies
• create the Center for Exposome Research Coordination to Accelerate Precision Environmental Health, which is tasked with helping resolve the challenges associated with exposomics studies.

TRANSLATION FOR PEH

• GEI studies aim to identify exposure response relationships to predict disease risk and improve health. Analyzing exposure data across the course of life reveals inter-individual differences in susceptibility and informs the development of biomarkers of exposure/disease risk.

Exemplars of PEH translation

SOCIAL AND SOCIETAL CONSIDERATIONS IN GEI RESEARCH

Environmental justice

• PEH also plays a crucial role in addressing environmental justice issues such as unequal pollutant exposure in low-income and marginalized communities
• genetic studies have focused on populations of European descent and thus have not accurately represented the genetic and environmental diversity of populations underrepresented in clinical and scientific studies

Return of results

• Report-back of findings to communities and individuals should be routine as a matter of reciprocity, respect, transparency, and trust.
• Report-back of findings should include information on choices to reduce individual-level exposures.
• Findings can aid decision-making in clinical settings and guide policy changes
• Community engagement, guidelines, educational resources, training, and tool development are needed to support the effective communication of findings

Data privacy

• The Genetic Information Non-discrimination Act (GINA) protects genetic data, but there is no equivalent for environmental data.
• Spatial exposure data that track location can reveal routines, habits, health, and pollutant exposures, making them vulnerable to identity theft and malicious activity
• Geospatial exposure data collection and use require clear guidelines and informed consent
• Data collection, storage, and sharing should incorporate encryption, access controls, and data minimization
• location-specific environmental exposure measurements from wearable sensors capturing location, exposures, and biometrics can expose individuals to risk
• Breaches in data privacy can have severe consequences, including higher insurance costs, employability impacts, stigma at the individual and community levels, effects on home prices, and legal repercussions

• อาจมีการปฏิเสธการรับทำประกัน หรือการรับเข้าทำงาน เพราะการ exposure to สิ่งที่มีความเสี่ยงต่อสุขภาพ จะทำให้ productivity ของงานอาจได้รับผลกระทบ

• Privacy breaches and re-identification can also damage public confidence in environmental health and harm litigants and research.

• ถ้าข้อมูลจากแหล่งวิจัยหลุดไป ข้อมูลใที่นี้หมายถึงข้อมูลที่ลิงค์ถึงต้นตอ -- ซึ่งจะส่งผลต่อ trust ของ public และอาจจะมีผลกระทบต่อการทำวิจัยได้ เนื่องจากไม่สามารถรักษาความลับของข้อมูล