IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score

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.

Leveraging GWAS: Path to Prevention?

Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7.

Heuristic Approach Uncovering Biological Significance of Gene-Lifestyle Interactions in Cardiometabolic Traits

BACKGROUND

Gene-lifestyle interaction studies using genome-wide association studies (GWAS) data contribute to a better understanding of individual responses to environmental exposures.

OBJECTIVES

Herein, we aimed at assessing the biological significance of overlapping genes reported in gene-lifestyle interaction studies in cardiometabolic health.

METHOD

A heuristic analysis of genes reporting significant interactions related to cardiometabolic traits was performed to determine the biological pathways common to the different traits.

RESULTS

A total of 873 genes were analyzed. Fine and condensed phenotypic solutions were obtained from overlapping genes common to more than one trait.

CONCLUSIONS

This study revealed significant metabolic pathways associated with the impact of gene-environment interactions on cardiometabolic risk. Graphical Abstract: Publicly available data in cloud-based repositories were used to perform enrichment analyses of genes previously described in GWAS studies that showed interaction with lifestyles. From the enriched pathways, cluster analysis was performed to group enriched metabolic disorders.

Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain

Chronic back pain (CBP) is a complex heritable trait and a major cause of disability worldwide. We developed and validated a genome-wide polygenic risk score (PRS) for CBP using a large-scale GWAS based on UK Biobank participants of European ancestry (N = 265,000). The PRS showed poor overall predictive ability (AUC = 0.56 and OR = 1.24 per SD, 95% CI: 1.22-1.26), but individuals from the 99th percentile of PRS distribution had a nearly two-fold increased risk of CBP (OR = 1.82, 95% CI: 1.60-2.06). We validated the PRS on an independent TwinsUK sample, obtaining a similar magnitude of effect. The PRS was significantly associated with various ICD-10 and OPCS-4 diagnostic codes, including chronic ischemic heart disease (OR = 1.1, p-value = 4.8 × 10^-15), obesity, metabolism-related traits, spine disorders, disc degeneration, and arthritis-related disorders. PRS and environment interaction analysis with twelve known CBP risk factors revealed no significant results, suggesting that the magnitude of G × E interactions with studied factors is small. The limited predictive ability of the PRS that we developed is likely explained by the complexity, heterogeneity, and polygenicity of CBP, for which sample sizes of a few hundred thousand are insufficient to estimate small genetic effects robustly.

Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation

PURPOSE OF REVIEW

This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits.

RECENT FINDINGS

Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.