The inherent complexities of gene-environment interactions

Spreading the Wealth: Developing Assessments of Cognitive Abilities in Non-WEIRD Countries

In this brief essay I reminisce on the ideas I encountered in Lev Vygotsky's lectures on pedology as an undergraduate student at Moscow State University in the USSR. Some of these ideas have been reliably stored in my professional memory and have influenced how my colleagues and I have approached the assessment of IQ (or general cognitive abilities) in countries other than the ones in which they were developed. Whereas the essay is autobiographical in nature, it attempts to make a generalizable point that spreading the wealth of existing knowledge, principles, and practice is as central to the progress of science as generating new knowledge.

Mini-Review: The Contribution of Intermediate Phenotypes to GxE Effects on Disorders of Body Composition in the New OMICS Era

Studies of gene-environment (GxE) interactions describe how genetic and environmental factors influence the risk of developing disease. Intermediate (molecular or clinical) phenotypes (IPs) are traits or metabolic biomarkers that mediate the effects of gene-environment influences on risk behaviors. Functional systems genomics discovery offers mechanistic insights into how DNA variations affect IPs in order to detect genetic causality for a given disease. Disorders of body composition include obesity (OB), Type 2 diabetes (T2D), and osteoporosis (OSTP). These pathologies are examples of how a GxE interaction contributes to their development. IPs as surrogates for inherited genotypes play a key role in models of genetic and environmental interactions in health outcomes. Such predictive models may unravel relevant genomic and molecular pathways for preventive and therapeutic interventions for OB, T2D, and OSTP. Annotation strategies for genomes, in contrast to phenomes, are well advanced. They generally do not measure specific aspects of the environment. Therefore, the concepts of deep phenotyping and the exposome generate new avenues to exploit with high-resolution technologies for analyzing this sophisticated phenome. With the successful characterization of phenomes, exposomes, and genomes, environmental and genetic determinants of chronic diseases can be united with multi-OMICS studies that better examine GxE interactions.

Fifteen years of quantitative trait loci studies in fish: challenges and future directions

Understanding the genetic basis of phenotypic variation is a major challenge in biology. Here, we systematically evaluate 146 quantitative trait loci (QTL) studies on teleost fish over the last 15 years to investigate (i) temporal trends and (ii) factors affecting QTL detection and fine-mapping. The number of fish QTL studies per year increased over the review period and identified a cumulative number of 3632 putative QTLs. Most studies used linkage-based mapping approaches and were conducted on nonmodel species with limited genomic resources. A gradual and moderate increase in the size of the mapping population and a sharp increase in marker density from 2011 onwards were observed; however, the number of QTLs and variance explained by QTLs changed only minimally over the review period. Based on these findings, we discuss the causative factors and outline how larger sample sizes, phenomics, comparative genomics, epigenetics and software development could improve both the quantity and quality of QTLs in future genotype-phenotype studies. Given that the technical limitations on DNA sequencing have mostly been overcome in recent years, a renewed focus on these and other study design factors will likely lead to significant improvements in QTL studies in the future.

Power Analysis for Population-Based Longitudinal Studies Investigating Gene-Environment Interactions in Chronic Diseases: A Simulation Study

Conventional methods for sample size calculation for population-based longitudinal studies tend to overestimate the statistical power by overlooking important determinants of the required sample size, such as the measurement errors and unmeasured etiological determinants, etc. In contrast, a simulation-based sample size calculation, if designed properly, allows these determinants to be taken into account and offers flexibility in accommodating complex study design features. The Canadian Longitudinal Study on Aging (CLSA) is a Canada-wide, 20-year follow-up study of 30,000 people between the ages of 45 and 85 years, with in-depth information collected every 3 years. A simulation study, based on an illness-death model, was conducted to: (1) investigate the statistical power profile of the CLSA to detect the effect of environmental and genetic risk factors, and their interaction on age-related chronic diseases; and (2) explore the design alternatives and implementation strategies for increasing the statistical power of population-based longitudinal studies in general. The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction on a disease was boosted when: (1) the prevalence of the risk exposures increased; (2) the disease of interest is relatively common in the population; and (3) risk exposures were measured accurately. In addition, the frequency of data collection every three years in the CLSA led to a slightly lower statistical power compared to the design assuming that participants underwent health monitoring continuously. The CLSA had sufficient power to detect a small (1<hazard ratio (HR)≤1.5) or moderate effect (1.5< HR≤2.0) of the environmental risk exposure, as long as the risk exposure and the disease of interest were not rare. It had enough power to detect a moderate or large (2.0<HR≤3.0) effect of the genetic risk exposure when the prevalence of the risk exposure was not very low (≥0.1) and the disease of interest was not rare (such as diabetes and dementia). The CLSA had enough power to detect a large effect of the gene-environment interaction only when both risk exposures had relatively high prevalence (0.2) and the disease of interest was very common (such as diabetes). The minimum detectable hazard ratios (MDHR) of the CLSA for the environmental and genetic risk exposures obtained from this simulation study were larger than those calculated according to the conventional sample size calculation method. For example, the MDHR for the environmental risk exposure was 1.15 according to the conventional method if the prevalence of the risk exposure was 0.1 and the disease of interest was dementia. In contrast, the MDHR was 1.61 if the same exposure was measured every 3 years with a misclassification rate of 0.1 according to this simulation study. With a given sample size, higher statistical power could be achieved by increasing the measuring frequency in participants with high risk of declining health status or changing risk exposures, and by increasing measurement accuracy of diseases and risk exposures. A properly designed simulation-based sample size calculation is superior to conventional methods when rigorous sample size calculation is necessary.

Intrinsic and extrinsic mortality reunited

Intrinsic and extrinsic mortality are often separated in order to understand and measure aging. Intrinsic mortality is assumed to be a result of aging and to increase over age, whereas extrinsic mortality is assumed to be a result of environmental hazards and be constant over age. However, allegedly intrinsic and extrinsic mortality have an exponentially increasing age pattern in common. Theories of aging assert that a combination of intrinsic and extrinsic stressors underlies the increasing risk of death. Epidemiological and biological data support that the control of intrinsic as well as extrinsic stressors can alleviate the aging process. We argue that aging and death can be better explained by the interaction of intrinsic and extrinsic stressors than by classifying mortality itself as being either intrinsic or extrinsic. Recognition of the tight interaction between intrinsic and extrinsic stressors in the causation of aging leads to the recognition that aging is not inevitable, but malleable through the environment.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Burden of disease, health indicators and challenges for epidemiology in North America

BACKGROUND

Commissioned by the International Epidemiological Association, this article is part of a series on burden of disease, health indicators and the challenges faced by epidemiologists in bringing their discoveries to provide equitable benefit to the populations in their regions and globally. This report covers the health status and epidemiological capacity in the North American region (USA and Canada).

METHODS

We assessed data from country-specific sources to identify health priorities and areas of greatest need for modifiable risk factors. We examined inequalities in health as a function of social deprivation. We also reviewed information on epidemiological capacity building and scientific contributions by epidemiologists in the region.

FINDINGS

The USA and Canada enjoy technologically advanced healthcare systems that, in principle, prioritize preventive services. Both countries experience a life expectancy at birth that is higher than the global mean. Health indicator measures are consistently worse in the USA than in Canada for many outcomes, although typically by only marginal amounts. Socio-economic and racial/ethnic disparities in indicators exist for many diseases and risk factors in the USA. To a lesser extent, these social inequalities also exist in Canada, particularly among the Aboriginal populations. Epidemiology is a well-established discipline in the region, with many degree-granting schools, societies and job opportunities in the public and private sectors. North American epidemiologists have made important contributions in disease control and prevention and provide nearly a third of the global scientific output via published papers.

CONCLUSIONS

Critical challenges for North American epidemiologists include social determinants of disease distribution and the underlying inequalities in access to and benefit from preventive services and healthcare, particularly in the USA. The gains in life expectancy also underscore the need for research on health promotion and prevention of disease and disability in older adults. The diversity in epidemiological subspecialties poses new challenges in training and accreditation and has occurred in parallel with a decrease in research funding.

Hispanic Baby Boomers: health inequities likely to persist in old age

PURPOSE

As the Baby-Boom generation enters the ranks of the elderly adults over the next 4 decades, the United States will witness an unprecedented growth in racial/ethnic diversity among the older adult population. Hispanics will comprise 20% of the next generation of older adults, representing the largest minority population aged 65 years and older, with those of Mexican-origin comprising the majority of Hispanics. Little is known about the health status of this population. DATA/METHODS: Data are for Baby Boomers born between 1946 and 1964 (ages 43-61) in the 2007 California Health Interview Survey. Logistic regression estimates the odds of diabetes, hypertension, obesity, fair/poor self-rated health (SRH), and functional difficulties among U.S.-born non-Hispanic Whites (NHW), U.S.-born Mexicans, naturalized Mexican immigrants, and noncitizen Mexican immigrants.

RESULTS

The Mexican-origin populations are disadvantaged relative to NHW for all socioeconomic status (SES) and several health outcomes. The Mexican origin disadvantage in health attenuates when controlling for SES and demographics, but the disadvantage remains for diabetes, obesity, and fair/poor SRH.

IMPLICATIONS

Baby Boomers of Mexican origin do not share the advantages of health, income, and educational attainment enjoyed by U.S.-born NHW. As this cohort moves into old age, the cumulative disadvantage of existing disparities are likely to result in continued or worse health disparities. Reductions in federal entitlement programs for the elderly adults that delay eligibility, scale back programs and services, or increase costs to consumers may exacerbate those inequities.

APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population

APOA5 is one of the strongest regulators of plasma TG concentrations; nevertheless, its mechanisms of action are poorly characterized. Genetic variability at the APOA5 locus has also been associated with increased cardiovascular disease risk; however, this predisposition could be attenuated in the context of a prudent diet as traditionally consumed in the Mediterranean countries. We have investigated the interaction between a single nucleotide polymorphism (SNP) at the APOA5 gene (-1131T > C) and dietary fat that may modulate TG-rich lipoprotein concentrations and anthropometric measures in overweight and obese participants. We recruited 1465 participants from a Spanish population (20-65 y old; BMI 25-40 kg/m(2)) attending outpatient obesity clinics. Consistent with previous reports, we found an association between the APOA5-1131T > C SNP and TG-rich lipoprotein concentrations that were higher in carriers of the minor allele than in noncarriers (P < 0.001). Moreover, we found a significant genotype-dietary fat interaction for obesity traits. Participants homozygous for the -1131T major allele had a positive association between fat intake and obesity, whereas in those carrying the APOA5-1131C minor allele, higher fat intakes were not associated with higher BMI. Likewise, we found genotype-dietary fat interactions for TG-rich lipoproteins (P < 0.001). In conclusion, we have replicated previous gene-diet interactions between APOA5 -1131T > C SNP and fat intake for obesity traits and detected a novel interaction for TG-rich lipoprotein concentrations. Our data support the hypothesis that the minor C-allele may protect those consuming a high-fat diet from obesity and elevated concentrations of TG-rich lipoproteins.

Gene X environment interactions in reading disability and attention-deficit/hyperactivity disorder

This article examines Gene x Environment (G x E) interactions in two comorbid developmental disorders--reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD)--as a window on broader issues on G x E interactions in developmental psychology. The authors first briefly review types of G x E interactions, methods for detecting them, and challenges researchers confront in interpreting such interactions. They then review previous evidence for G x E interactions in RD and ADHD, the directions of which are opposite to each other: bioecological for RD and diathesis stress for ADHD. Given these results, the authors formulate and test predictions about G x E interactions that would be expected at the favorable end of each symptom dimension (e.g., above-average reading or attention). Consistent with their prediction, the authors found initial evidence for a resilience interaction for above-average reading: higher heritability in the presence of lower parental education. However, they did not find a G x E interaction at the favorable end of the ADHD symptom dimension. The authors conclude with implications for future research.

A review of current approaches to identifying human genes involved in myopia

The prevalence of myopia is high in many parts of the world, particularly among the Orientals such as Chinese and Japanese. Like other complex diseases such as diabetes and hypertension, myopia is likely to be caused by both genetic and environmental factors, and possibly their interactions. Owing to multiple genes with small effects, genetic heterogeneity and phenotypic complexity, the study of the genetics of myopia poses a complex challenge. This paper reviews the current approaches to the genetic analysis of complex diseases and how these can be applied to the identification of genes that predispose humans to myopia. These approaches include parametric linkage analysis, non-parametric linkage analysis like allele-sharing methods and genetic association studies. Basic concepts, advantages and disadvantages of these approaches are discussed and explained using examples from the literature on myopia. Microsatellites and single nucleotide polymorphisms are common genetic markers in the human genome and are indispensable tools for gene mapping. High throughput genotyping of millions of such markers has become feasible and efficient with recent technological advances. In turn, this makes the identification of myopia susceptibility genes a reality.

Cumulative Disadvantage and Black-White Disparities in Life-Course Health Trajectories

In this study, the authors use longitudinal data from the Panel Study of Income Dynamics and growth curve models to examine the utility of the concept of cumulative disadvantage as an explanation for race differences in life-course health (self-rated) in the United States. The authors ask whether socioeconomic resources equally benefit the health of Blacks and Whites, or if Whites receive higher rates of return to resources across the life course. The authors find that the relationship differs depending on the indicator of socioeconomic status that is examined. Education does not offer the same advantages for the health of Blacks as it does for Whites, particularly at higher levels of education, and this is compounded with age. In contrast, returns to income and wealth are similar for Blacks and Whites, and these resources remain equally important to protecting the health of Blacks and Whites across the life course. Over time, Blacks are at an increasing health disadvantage relative to Whites, a result that is not attenuated by educational attainment.

Gene × Environment interactions in speech sound disorder predict language and preliteracy outcomes

Few studies have investigated the role of gene × environment interactions (G × E) in speech, language, and literacy disorders. Currently, there are two theoretical models, the diathesis–stress model and the bioecological model, that make opposite predictions about the expected direction of G × E, because environmental risk factors may either strengthen or weaken the effect of genes on phenotypes. The purpose of the current study was to test for G × E at two speech sound disorder and reading disability linkage peaks using a sib-pair linkage design and continuous measures of socioeconomic status, home language/literacy environment, and number of ear infections. The interactions were tested using composite speech, language, and preliteracy phenotypes and previously identified linkage peaks on 6p22 and 15q21. Results showed five G × E at both the 6p22 and 15q21 locations across several phenotypes and environmental measures. Four of the five interactions were consistent with the bioecological model of G × E. Each of these four interactions involved environmental measures of the home language/literacy environment. The only interaction that was consistent with the diathesis–stress model was one involving the number of ear infections as the environmental risk variable. The direction of these interactions and possible interpretations are explored in the discussion.

Gene-environment interactions in sarcoidosis: challenge and opportunity

Susceptibility to most human diseases is polygenic, with complex interactions between functional polymorphisms of single genes governing disease incidence, phenotype, or both. In this context, the contribution of any discrete gene is generally modest for a single individual, but may confer substantial attributable risk on a population level. Environmental exposure can modify the effects of a polymorphism, either by providing a necessary substrate for development of human disease or because the effects of a given exposure modulate the effects of the gene. In several diseases, genetic polymorphisms have been shown to be context dependent, ie, the effects of a genetic variant are realized only in the setting of a relevant exposure. Because sarcoidosis susceptibility is dependent on both genetic and environmental modifiers, the study of gene-environment interactions may yield important pathogenetic information and will likely be crucial for uncovering the range of genetic susceptibility loci. The complexity of these relationships implies, however, that investigations of gene-environment interactions will require the study of large cohorts with carefully defined exposures and similar clinical phenotypes. A general principle is that the study of gene-environment interactions requires a sample size at least severalfold greater than for either factor alone. To date, the presence of environmental modifiers has been demonstrated for one sarcoidosis susceptibility locus, HLA-DQB1, in African-American families. This article reviews general considerations obtaining for the study of gene-environment interactions in sarcoidosis. It also describes the limited current understanding of the role of environmental influences on sarcoidosis susceptibility genes.

APOA5 gene variation modulates the effects of dietary fat intake on body mass index and obesity risk in the Framingham Heart Study

Diet is an important environmental factor interacting with our genes to modulate the likelihood of developing lipid disorders and, consequently, cardiovascular disease risk. Our objective was to study whether dietary intake modulates the association between APOA5 gene variation and body weight in a large population-based study. Specifically, we have examined the interaction between the APOA5-1131T>C and 56C>G (S19W) polymorphisms and the macronutrient intake (total fat, carbohydrate, and protein) in their relation to the body mass index (BMI) and obesity risk in 1,073 men and 1,207 women participating in the Framingham Offspring Study. We found a consistent and statistically significant interaction between the -1131T>C single-nucleotide polymorphism (SNP; but not the 56C>G) and total fat intake for BMI. This interaction was dose-dependent, and no statistically significant heterogeneity by gender was detected. In subjects homozygous for the -1131T major allele, BMI increased as total fat intake increased. Conversely, this increase was not present in carriers of the -1131C minor allele. Accordingly, we found significant interactions in determining obesity and overweight risks. APOA5-1131C minor allele carriers had a lower obesity risk (OR, 0.61, 95%; CI, 0.39-0.98; P = 0.032) and overweight risk (OR, 0.63, 95%; CI, 0.41-0.96; P = 0.031) compared with TT subjects in the high fat intake group (>or=30% of energy ) but not when fat intake was low (OR, 1.16, 95%; CI, 0.77-1.74; P = 0.47 and OR = 1.15, 95%; CI, 0.77-1.71; P = 0.48) for obesity and overweight, respectively). When specific fatty acid groups were analyzed, monounsaturated fatty acids showed the highest statistical significance for these interactions. In conclusion, the APOA5-1131T>C SNP, which is present in approximately 13% of this population, modulates the effect of fat intake on BMI and obesity risk in both men and women.

Gene-alcohol interactions in the metabolic syndrome

AIMS

Recent studies have reported that moderate alcohol consumption is associated with a lesser prevalence of the metabolic syndrome (MetS). However, this relationship is still confusing and the presence of gene-environment interactions has been suggested. Our aim is to summarize evidence for gene-alcohol interactions in the MetS.

DATA SYNTHESIS

Research in gene-alcohol interactions applied to MetS is very complex due to the difficulties surrounding the definition of phenotype, environment and genotype, as well as in estimating the influence of the social context. In the MetS there is a constellation of metabolic disturbances the definition of which is still changing. Thus, most studies that have reported on gene-alcohol interactions have done so by analyzing isolated components. Likewise, the definition of alcohol consumption is also complex given that apart from the amount of ethanol consumed, the type of drink, the frequency of consumption, etc., may be important. No less difficult is the definition of genotype as there are many candidate genes involved, including not only those relevant for each phenotype studied, but also those related with alcohol metabolism, as well as those related to alcohol intake.

CONCLUSIONS

Although various studies exist that show statistically significant interactions between alcohol consumption and MetS components, a greater integration of variables as well as greater homogeneity in definitions is required.

Social context in gene-environment interactions: retrospect and prospect

While many behavioral scientists believe that gene-environment (GE) interactions play an important and perhaps pervasive role in human development and aging, little attention has been devoted to a fundamental conceptual issue: What is it about social context that could alter gene expression? We draw on existing examples of GE interactions to formulate a typology that identifies a set of generic mechanisms by which E moderates G. Empirical studies suggest four ideal types: Social context can trigger a genetic diathesis, compensate for a genetic diathesis, act as a control to prevent behaviors for which there is a genetic predisposition, and enhance adaptation through proximal processes. This typology highlights several problems, however, with prior empirical research, which may explain, in part, why so few GE interactions have actually been observed. These problems include inattention to the dynamic nature of social experience, the manifold, often-intercorrelated dimensions of social context ("EE interactions"), mediators that link social context and the genotype, and analytic models that examine GE interactions as processes that characterize individual development. In turn, these insights call for the integration of life course sociology and behavioral genetics to foster ways of studying genes, context, and aging.