Effect of including environmental data in investigations of gene-disease associations in the presence of qualitative interactions

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions

BACKGROUND

Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci.

OBJECTIVES

This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification.

METHOD

For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error.

RESULTS

In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power.

CONCLUSION

SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.

Childhood adiposity, adult adiposity, and the ACE gene insertion/deletion polymorphism: evidence of gene-environment interaction effects on adult blood pressure and hypertension status in adulthood

BACKGROUND

Genetic variants may modify the associations of adiposity measures with blood pressure (BP) and hypertension. The insertion/deletion (I/D) polymorphism in the angiotensin-converting enzyme (ACE) gene is an attractive candidate.

AIMS

To examine interaction effects between I/D polymorphism and adiposity measures (BMI, waist circumference, waist-to-hip ratio, and skinfold thickness) during childhood and adulthood in relation to adult BP and hypertension.

METHODS

Data were available for 4835 participants from three prospective cohort studies. Multivariable linear regression models for adult SBP and DBP, and multivariable logistic regression models for hypertension were fit that included interaction effects between child or adult adiposity and I/D polymorphism.

RESULTS

Evidence for interaction effects on BP/hypertension were found across the three studies. Compared with childhood measures, the effect modification appeared to be more consistent when using adult adiposity. In particular, the adverse effects of greater adult waist circumference on increasing adult SBP and DBP appeared to be larger among carriers of ACE DD (or GG) [adjusted linear regression coefficients 0.26, 95% CI (0.21-0.31) and 0.28 (0.24-0.32) for SBP and DBP, respectively] and ID (or AG) genotypes [0.25 (0.21-0.29) and 0.25 (0.21-0.28), respectively], whereas those with II (or AA) genotypes had smaller effects [0.15 (0.09-0.21) and 0.19 (0.13-0.23)].

CONCLUSION

ACE genetic variation may modify the effect of adult adiposity on increasing BP and risk of hypertension in adulthood. Individuals with ACE DD (or GG) and/or ID (or AG) genotypes, compared with those with II (or AA) genotype, appear more vulnerable to the impact of excess adiposity.

The importance of gene-environment interactions in human obesity

The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.

The logistic regression model for gene-environment interactions using both case-parent trios and unrelated case-controls

One of the greatest challenges in genetic studies is the determination of gene-environment interactions due to underlying complications and inadequate statistical power. With the increased sample size gained by using case-parent trios and unrelated cases and controls, the performance may be much improved. Focusing on a dichotomous trait, a two-stage approach was previously proposed to deal with gene-environment interaction when utilizing mixed study samples. Theoretically, the two-stage association analysis uses likelihood functions such that the computational algorithms may not converge in the maximum likelihood estimation with small study samples. In an effort to avoid such convergence issues, we propose a logistic regression framework model, based on the combined haplotype relative risk (CHRR) method, which intuitively pools the case-parent trios and unrelated subjects in a two by two table. A positive feature of the logistic regression model is the effortless adjustment for either discrete or continuous covariates. According to computer simulations, under the circumstances in which the two-stage test converges in larger sample sizes, we discovered that the performances of the two tests were quite similar; the two-stage test is more powerful under the dominant and additive disease models, but the extended CHRR is more powerful under the recessive disease model.

Gene-environment interaction in autoimmune disease

Autoimmune disease manifests in numerous forms, but as a disease group is relatively common in the population. It is complex in aetiology, with genetic and environmental determinants. The involvement of gene variants in autoimmune disease is well established, and evidence for significant involvement of the environment in various disease forms is growing. These factors may act independently, or they may interact, with the effect of one factor influenced by the presence of another. Identifying combinations of genetic and environmental factors that interact in autoimmune disease has the capacity to more fully explain disease risk profile, and to uncover underlying molecular mechanisms contributing to disease pathogenesis. In turn, such knowledge is likely to contribute significantly to the development of personalised medicine, and targeted preventative approaches. In this review, we consider the current evidence for gene-environment (G-E) interaction in autoimmune disease. Large-scale G-E interaction research efforts, while well-justified, face significant practical and methodological challenges. However, it is clear from the evidence that has already been generated that knowledge on how genes and environment interact at a biological level will be crucial in fully understanding the processes that manifest as autoimmunity.

New directions in childhood obesity research: how a comprehensive biorepository will allow better prediction of outcomes

BACKGROUND

Childhood obesity is associated with the early development of diseases such as type 2 diabetes and cardiovascular disease. Unfortunately, to date, traditional methods of research have failed to identify effective prevention and treatment strategies, and large numbers of children and adolescents continue to be at high risk of developing weight-related disease.

AIM

To establish a unique 'biorepository' of data and biological samples from overweight and obese children, in order to investigate the complex 'gene × environment' interactions that govern disease risk.

METHODS

The 'Childhood Overweight BioRepository of Australia' collects baseline environmental, clinical and anthropometric data, alongside storage of blood samples for genetic, metabolic and hormonal profiles. Opportunities for longitudinal data collection have also been incorporated into the study design. National and international harmonization of data and sample collection will achieve required statistical power.

RESULTS

Ethical approval in the parent site has been obtained and early data indicate a high response rate among eligible participants (71%) with a high level of compliance for comprehensive data collection (range 56% to 97% for individual study components). Multi-site ethical approval is now underway.

CONCLUSIONS

In time, it is anticipated that this comprehensive approach to data collection will allow early identification of individuals most susceptible to disease, as well as facilitating refinement of prevention and treatment programs.