Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank.

Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP).

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes.

Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers.

We examined the associations of vegetarianism with metabolic biomarkers using traditional and genetic epidemiology. First, we addressed inconsistencies in self-reported vegetarianism among UK Biobank participants by utilizing data from two dietary surveys to find a cohort of strict European vegetarians (N = 2,312). Vegetarians were matched 1:4 with nonvegetarians for non-genetic association analyses, revealing significant effects of vegetarianism in 15 of 30 biomarkers.

Many roads to a gene-environment interaction.

Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

Motivation: statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.

Results: We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms.

Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis.

Background: Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D.

Methods: We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity.

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions.

Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin.

Hyperinsulinemia is a complex and heterogeneous phenotype that characterizes molecular alterations that precede the development of type 2 diabetes (T2D). It results from a complex combination of molecular processes, including insulin secretion and insulin sensitivity, that differ between individuals. To better understand the physiology of hyperinsulinemia and ultimately T2D, we implemented a genetic approach grouping fasting insulin (FI)-associated genetic variants based on their molecular and phenotypic similarities.

Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.

Objective: Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D.

Association between timing and consistency of physical activity and type 2 diabetes: a cohort study on participants of the UK Biobank.

Aims/hypothesis: We sought to quantify the relationship between morning, afternoon or evening physical activity and consistency (e.g. routine) and risk of type 2 diabetes.

Corrigendum: Genetic heritability as a tool to evaluate the precision of 24-hour recall dietary questionnaire variables in UK Biobank.

[This corrects the article DOI: 10.3389/fgene.2022.

Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Unlabelled: Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data.

Genetic heritability as a tool to evaluate the precision of 24-hour recall dietary questionnaire variables in UK Biobank.

A variety of statistical approaches in nutritional epidemiology have been developed to enhance the precision of dietary variables derived from longitudinal questionnaires. Correlation with biomarkers is often used to assess the relative validity of these different approaches, however, validated biomarkers do not always exist and are costly and laborious to collect. We present a novel high-throughput approach which utilizes the modest but importantly non-zero influence of genetic variation on variation in dietary intake to compare different statistical transformations of dietary variables.

High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

Aims/hypothesis: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways.

Methods: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations.

Metabolomic markers of glucose regulation after a lifestyle intervention in prediabetes.

Introduction: Disentangling the specific factors that regulate glycemia from prediabetes to normoglycemia could improve type 2 diabetes prevention strategies. Metabolomics provides substantial insights into the biological understanding of environmental factors such as diet. This study aimed to identify metabolomic markers of regression to normoglycemia in the context of a lifestyle intervention (LSI) in individuals with prediabetes.