A meta-analysis approach with filtering for identifying gene-level gene-environment interactions

Gene-environment interactions in human health

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Decoding Cancer Risk: Understanding Gene-Environment Interactions in Cancer Development

While lifestyle choices or behavioral patterns remain the most significant factors influencing cancer risk, environmental exposure to certain chemicals, both manufactured and natural, may also contribute to an individual's likelihood of developing cancer. This interplay of factors, coupled with an aging demographic and shifting lifestyle patterns, has led to an increasing prevalence of cancer in recent years. This study examines the environmental and behavioral factors that contribute to anomalies in the immune system and increase the risk of developing cancer. Significant environmental and occupational factors include the contamination of air and water, exposure to radiation, contact with harmful microorganisms and pathogens, and workplace exposure to carcinogens such as asbestos, certain chemicals, and industrial pollutants. Behavioral factors, such as food, physical activity, stress, substance misuse, and sleep patterns, have a substantial impact on immunological function and the likelihood of developing cancer. For example, pollutants like benzene and arsenic can disrupt immune function and raise the risk of developing cancer. Similarly, lifestyle variables such as inactivity and poor nutrition have been linked to an increased risk of cancer. Long-term stress and substance abuse can also decrease immunological responses, increasing the risk of developing cancer. The review underlines the complexities of examining gene-environment interactions, as well as the importance of using several perspectives to fully comprehend these pathways. Future investigations should emphasize improved methodology and larger sample sizes. Public health campaigns should aim to reduce human exposure to cancer-causing compounds known as carcinogens while also encouraging the adoption of healthy behaviors and habits. Tailored preventive approaches that account for individual genetic vulnerabilities have the potential to improve cancer prevention and treatment.

Variance-component-based meta-analysis of gene-environment interactions for rare variants

Complex diseases are often caused by interplay between genetic and environmental factors. Existing gene-environment interaction (G × E) tests for rare variants largely focus on detecting gene-based G × E effects in a single study; thus, their statistical power is limited by the sample size of the study. Meta-analysis methods that synthesize summary statistics of G × E effects from multiple studies for rare variants are still limited. Based on variance component models, we propose four meta-analysis methods of testing G × E effects for rare variants: HOM-INT-FIX, HET-INT-FIX, HOM-INT-RAN, and HET-INT-RAN. Our methods consider homogeneous or heterogeneous G × E effects across studies and treat the main genetic effect as either fixed or random. Through simulations, we show that the empirical distributions of the four meta-statistics under the null hypothesis align with their expected theoretical distributions. When the interaction effect is homogeneous across studies, HOM-INT-FIX and HOM-INT-RAN have as much statistical power as a pooled analysis conducted on a single interaction test with individual-level data from all studies. When the interaction effect is heterogeneous across studies, HET-INT-FIX and HET-INT-RAN provide higher power than pooled analysis. Our methods are further validated via testing 12 candidate gene-age interactions in blood pressure traits using whole-exome sequencing data from UK Biobank.

Hereditary and breastfeeding factors are positively associated with the aetiology of mammary gland hyperplasia: a case-control study

BACKGROUND

Hyperplasia of mammary gland (HMG) has become a common disorder in women. A family history of breast cancer and female reproductive factors may work together to increase the risk of HMG. However, this specific relationship has not been fully characterized.

METHODS

A total of 1881 newly diagnosed HMG cases and 1900 controls were recruited from 2012 to 2017. Demographic characteristics including female reproductive factors and a family history of breast cancer were collected. A multi-analytic strategy combining unconditional logistic regression, multifactor dimensionality reduction (MDR) and crossover approaches were applied to systematically identify the interaction effect of family history of breast cancer and reproductive factors on HMG susceptibility.

RESULTS

In MDR analysis, high-order interactions among higher-level education, shorter breastfeeding duration and family history of breast cancer were identified (odds ratio [OR] 7.07 [95% confidence interval {CI} 6.08 to 8.22]). Similarly, in crossover analysis, HMG risk increased significantly for those with higher-level education (OR 36.39 [95% CI 11.47 to 115.45]), shorter duration of breastfeeding (OR 27.70 [95% CI 3.73 to 205.70]) and a family history of breast cancer.

CONCLUSION

Higher-level education, shorter breastfeeding duration and a family history of breast cancer may synergistically increase the risk of HMG.

Impact of Gene-Environment Interactions on Cancer Development

Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.