Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers.

Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies

BACKGROUND

Diet is an important determinant of health and may moderate genetic susceptibility to obesity, but meta-analyses of available evidence are lacking.

OBJECTIVES

This study aimed to systematically review and meta-analyze evidence on the moderating effect of diet on genetic susceptibility to obesity, assessed with polygenic risk scores (PRS).

METHODS

A systematic search was conducted using MEDLINE, EMBASE, Web of Science, and the Cochrane Library to retrieve observational studies that examined PRS-diet interactions on obesity-related outcomes. Dietary exposures of interest included diet quality/dietary patterns and consumption of specific food and beverage groups. Random-effects meta-analyses were performed for pooled PRS- healthy eating index (HEI) interaction coefficients on body mass index (BMI) (on the basis of data from 4 cohort studies) and waist circumference (WC) (on the basis of data from 3 cohort studies).

RESULTS

Out of 36 retrieved studies, 78% were conducted among European samples. Twelve out of 21 articles examining dietary indices/patterns, and 16 out of 21 articles examining food/beverage groups observed some significant PRS-diet interactions. However, within many articles, findings are inconsistent when testing different combinations of obesity PRS-dietary factors and outcomes. Nevertheless, higher HEI scores and adherence to plant-based dietary patterns emerged as the more prominent diet quality/patterns that moderated genetic susceptibility to obesity, whereas higher consumption of fruits and vegetables, and lower consumption of fried foods and sugar-sweetened beverages emerged as individual food/beverage moderators. Results from the meta-analysis suggest that a higher HEI attenuates genetic susceptibility on BMI (pooled PRS∗HEI coefficient: -0.08; 95% confidence interval (CI): -0.15, 0.00; P = 0.0392) and WC (-0.37; 95% CI: -0.60, -0.15; P = 0.0013).

CONCLUSIONS

Current observational evidence suggests a moderating role of overall diet quality in polygenic risk of obesity. Future research should aim to identify genetic loci that interact with dietary exposures on anthropometric outcomes and conduct analyses among diverse ethnic groups.

TRIAL REGISTRATION NUMBER

This study was registered at the International Prospective Register of Systematic Reviews as CRD42022312289.

Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit

The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Removal of Exogenous Stimuli Reveals a Canalization of Circadian Physiology in a Vertically Migrating Copepod

Diel rhythms are observed across taxa and are important for maintaining synchrony between the environment and organismal physiology. A striking example of this is the diel vertical migration undertaken by zooplankton, some of which, such as the 5 mm-long copepod Pleuromamma xiphias (P. xiphias), migrate hundreds of meters daily between the surface ocean and deeper waters. Some of the molecular pathways that underlie the expressed phenotype at different stages of this migration are entrained by environmental variables (e.g., day length and food availability), while others are regulated by internal clocks. We identified a series of proteomic biomarkers that vary across ocean DVM and applied them to copepods incubated in 24 h of darkness to assess circadian control. The dark-incubated copepods shared some proteomic similarities to the ocean-caught copepods (i.e., increased abundance of carbohydrate metabolism proteins at night). Shipboard-incubated copepods demonstrated a clearer distinction between night and day proteomic profiles, and more proteins were differentially abundant than in the in situ copepods, even in the absence of the photoperiod and other environmental cues. This pattern suggests that there is a canalization of rhythmic diel physiology in P. xiphias that reflects likely circadian clock control over diverse molecular pathways.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity

Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities

In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

OBJECTIVE

Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity.

RESEARCH DESIGN AND METHODS

Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459).

RESULTS

The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations.

CONCLUSIONS

Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

Mendelian Randomization Indicates a Causal Role for Omega-3 Fatty Acids in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal system. Omega-3 (ω3) fatty acids are polyunsaturated fatty acids (PUFAs) that are largely obtained from diet and have been speculated to decrease the inflammatory response that is involved in IBD; however, the causality of this association has not been established. A two-sample Mendelian randomization (MR) was used to assess genetic associations between 249 circulating metabolites measured in the UK Biobank as exposures and IBD as the outcome. The genome-wide association study summary level data for metabolite measurements and IBD were derived from large European ancestry cohorts. We observed ω3 fatty acids as a significant protective association with IBD, with multiple modes of MR evidence replicated in three IBD summary genetic datasets. The instrumental variables that were involved in the causal association of ω3 fatty acids with IBD highlighted an intronic SNP, rs174564, in FADS2, a protein engaged in the first step of alpha-linolenic acid desaturation leading to anti-inflammatory EPA and thence DHA production. A low ratio of ω3 to ω6 fatty acids was observed to be a causal risk factor, particularly for Crohn's disease. ω3 fatty acid supplementation may provide anti-inflammatory responses that are required to attenuate inflammation that is involved in IBD.