Genome-wide contribution of genotype by environment interaction to variation of diabetes-related traits

Exploring genetic diversity and population structure in Cinnamomum cassia (L.) J.Presl germplasm in China through phenotypic, chemical component, and molecular marker analyses

Cinnamomum cassia (L.) J.Presl, a tropical aromatic evergreen tree belonging to the Lauraceae family, is commonly used in traditional Chinese medicine. It is also a traditional spice used worldwide. However, little is currently known about the extent of the genetic variability and population structure of C. cassia. In this study, 71 individuals were collected from seven populations across two geographical provinces in China. Nine morphological features, three chemical components, and single nucleotide polymorphism (SNP) markers were used in an integrated study of C. cassia germplasm variations. Remarkable genetic variation exists in both phenotypic and chemical compositions, and certain traits, such as leaf length, leaf width, volatile oil content, and geographic distribution, are correlated with each other. One-year-old C. cassia seedling leaf length, leaf width, elevation, and volatile oil content were found to be the main contributors to diversity, according to principal component analysis (PCA). Three major groupings were identified by cluster analysis based on the phenotypic and volatile oil data. This was in line with the findings of related research using 1,387,213 SNP markers; crucially, they all demonstrated a substantial link with geographic origin. However, there was little similarity between the results of the two clusters. Analysis of molecular variance (AMOVA) revealed that the genetic diversity of C. Cassia populations was low, primarily among individuals within populations, accounting for 95.87% of the total. Shannon's information index (I) varied from 0.418 to 0.513, with a mean of 0.478 (Na=1.860, Ne =1.584, Ho =0.481, He =0.325, and PPB =86.04%). Genetic differentiation across populations was not significant because natural adaptation or extensive exchange of seeds among farmers between environments, thus maintaining the relationship. Following a population structure analysis using the ADMIXTURE software, 71 accessions were found to be clustered into three groups, with 38% of them being of the pure type, a finding that was further supported by PCA. Future breeding strategies and our understanding of the evolutionary relationships within the C. cassia population would benefit greatly from a thorough investigation of phenotypic, chemical, and molecular markers.

Multiset correlation and factor analysis enables exploration of multi-omics data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Genome-wide Association Studies Categorized by Class of Antihypertensive Drugs Reveal Complex Pathogenesis of Hypertension with Drug Resistance

Resistant hypertension is defined as uncontrolled blood pressure (BP) despite the use of three or more antihypertensive drugs of different classes. Although genetic factors may greatly contribute to hypertension with resistance to multiple drug classes, more than for general hypertension, its pathogenesis remains unknown. To reveal the genetic background of resistant hypertension, we categorized 32,239 patients whose data were obtained from the BioBank Japan Project, by prescription of 7 classes of antihypertensive drugs and performed genome-wide association studies (GWAS). Our GWAS identified four loci with significant association (P < 5 × 10-8 ): rs6445583 in CACNA1D and rs12308051 in the intergenic region on chromosome 12 for angiotensin II receptor blockers, rs35497065 in FOXA3 for calcium channel blockers, and rs11066280 in HECTD4 for αβ-blockers. Because these loci are known to be susceptibility loci for hypertension and/or BP, our results indicate that resistant hypertension is caused by a combination of excessive BP and drug resistance to each antihypertensive pharmacological class. Furthermore, to investigate the genetic difference between BP traits and the treatment effectiveness of antihypertensive drugs, we performed gene-set analysis and calculated the genetic correlation continuously. Most of the genetic factors were in common between BP traits and antihypertensive effectiveness, but it seems that the genetic architecture of the drug response to antihypertensive treatment is more complicated than BP traits. This corresponds to the well-known mosaic theory of hypertension. Our findings reveal the complex pathogenesis of hypertension with resistance to multiple classes of antihypertensive drugs.

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

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. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.

ARTICLE HIGHLIGHTS

We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

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.

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint < 5 × 10-8), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (Pint < 5 × 10-8). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (Pint = 2 × 10-6). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (Pint < 10-3). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Crebl2 regulates cell metabolism in muscle and liver cells

We previously identified Drosophila REPTOR and REPTOR-BP as transcription factors downstream of mTORC1 that play an important role in regulating organismal metabolism. We study here the mammalian ortholog of REPTOR-BP, Crebl2. We find that Crebl2 mediates part of the transcriptional induction caused by mTORC1 inhibition. In C2C12 myoblasts, Crebl2 knockdown leads to elevated glucose uptake, elevated glycolysis as observed by lactate secretion, and elevated triglyceride biosynthesis. In Hepa1-6 hepatoma cells, Crebl2 knockdown also leads to elevated triglyceride levels. In sum, this works identifies Crebl2 as a regulator of cellular metabolism that can link nutrient sensing via mTORC1 to the metabolic response of cells.

Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

Genome-Wide Meta-analysis of Gene-Environmental Interaction for Insulin Resistance Phenotypes and Breast Cancer Risk in Postmenopausal Women

Insulin resistance (IR)-related genetic variants are possibly associated with breast cancer, and the gene-phenotype-cancer association could be modified by lifestyle factors including obesity, physical inactivity, and high-fat diet. Using data from postmenopausal women, a population highly susceptible to obesity, IR, and increased risk of breast cancer, we implemented a genome-wide association study (GWAS) in two steps: (1) GWAS meta-analysis of gene-environmental (i.e., behavioral) interaction (G*E) for IR phenotypes (hyperglycemia, hyperinsulinemia, and homeostatic model assessment-insulin resistance) and (2) after the G*E GWAS meta-analysis, the identified SNPs were tested for their associations with breast cancer risk in overall or subgroup population, where the SNPs were identified at genome-wide significance. We found 58 loci (55 novel SNPs; 5 index SNPs and 6 SNPs, independent of each other) that are associated with IR phenotypes in women overall or women stratified by obesity, physical activity, and high-fat diet; among those 58 loci, 29 (26 new loci; 2 index SNPs and 2 SNPs, independently) were associated with postmenopausal breast cancer. Our study suggests that a number of newly identified SNPs may have their effects on glucose intolerance by interplaying with obesity and other lifestyle factors, and a substantial proportion of these SNPs' susceptibility can also interact with the lifestyle factors to ultimately influence breast cancer risk. These findings may contribute to improved prediction accuracy for cancer and suggest potential intervention strategies for those women carrying genetic risk that will reduce their breast cancer risk.

Epigenome wide association study of SNP-CpG interactions on changes in triglyceride levels after pharmaceutical intervention: a GAW20 analysis

In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphate-guanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes. In our analysis, we used both a genome-wide and candidate gene approach to investigate potential single-nucleotide polymorphism (SNP)–CpG interactions on changes in triglyceride levels. Although we were able to identify numerous loci of interest when using an exploratory significance threshold, we did not identify any significant interactions using a strict genome-wide significance threshold. We were also able to identify numerous loci using the candidate gene approach, in which we focused on 18 genes with prior evidence of association of triglyceride levels. In particular, we identified GALNT2 loci as containing potential CpG sites that moderate the impact of genetic polymorphisms on triglyceride levels. Further work is needed to provide clear guidance on analytic strategies for testing SNP–CpG interactions, although leveraging prior biological understanding may be needed to improve statistical power in data sets with smaller sample sizes.

Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale

BACKGROUND

Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results.

METHODS AND RESULTS

The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene-lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene-lifestyle or more generally gene-environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects.

CONCLUSIONS

The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene-lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice

Nutrigenetic research examines the effects of inter-individual differences in genotype on responses to nutrients and other food components, in the context of health and of nutrient requirements. A practical application of nutrigenetics is the use of personal genetic information to guide recommendations for dietary choices that are more efficacious at the individual or genetic subgroup level relative to generic dietary advice. Nutrigenetics is unregulated, with no defined standards, beyond some commercially adopted codes of practice. Only a few official nutrition-related professional bodies have embraced the subject, and, consequently, there is a lack of educational resources or guidance for implementation of the outcomes of nutrigenetic research. To avoid misuse and to protect the public, personalised nutrigenetic advice and information should be based on clear evidence of validity grounded in a careful and defensible interpretation of outcomes from nutrigenetic research studies. Evidence requirements are clearly stated and assessed within the context of state-of-the-art 'evidence-based nutrition'. We have developed and present here a draft framework that can be used to assess the strength of the evidence for scientific validity of nutrigenetic knowledge and whether 'actionable'. In addition, we propose that this framework be used as the basis for developing transparent and scientifically sound advice to the public based on nutrigenetic tests. We feel that although this area is still in its infancy, minimal guidelines are required. Though these guidelines are based on semi-quantitative data, they should stimulate debate on their utility. This framework will be revised biennially, as knowledge on the subject increases.

Genetic Risk Score of Nine Type 2 Diabetes Risk Variants that Interact with Erythrocyte Phospholipid Alpha-Linolenic Acid for Type 2 Diabetes in Chinese Hans: A Case-Control Study

Modulation of n-3 fatty acids on genetic susceptibility to type 2 diabetes (T2D) is still not clear. In a case-control study of 622 Chinese T2D patients and 293 healthy controls, a genetic risk score (GRS) was created based on nine T2D genetic variants. Logistic regression was used to examine the interaction of the GRS with erythrocyte phospholipid n-3 fatty acids for T2D risk. Every 1-unit (corresponding to 1 risk allele) increase in GRS was associated with 12% (Odds ratio (OR): 1.12; 95% confidence intervals (CI): 1.04–1.20) higher risk of T2D. Compared with the lowest quartile, participants had lower T2D risk in the 2nd (OR: 0.55; 95% CI: 0.36–0.84), 3rd (OR: 0.58; 95% CI: 0.38–0.88) and 4th (OR: 0.67; 95% CI: 0.44–1.03) quartile of alpha-linolenic acid (ALA) levels. Significant interaction (p-interaction = 0.029) of GRS with ALA for T2D risk was observed. Higher ALA levels were associated with lower T2D risk only among participants within the lowest GRS tertile, with ORs 0.51 (95% CI: 0.26–1.03), 0.44 (95% CI: 0.22–0.89) and 0.49 (95% CI: 0.25–0.96) for the 2nd, 3rd and 4th ALA quartile, compared with the 1st. This study suggests that higher erythrocyte ALA levels are inversely associated with T2D risk only among participants with low T2D genetic risk, with high genetic risk abolishing the ALA-T2D association.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.

Challenges of implementing personalized (precision) medicine: a focus on diabetes

The concept of personalized (precision) medicine (PM) emphasizes the scientific and technological innovations that enable the physician to tailor disease prediction, diagnosis and treatment to the individual patient, based on a personalized data-driven approach. The major challenge for the medical systems is to translate the molecular and genomic advances into clinical available means. Patients and healthcare providers, the pharmaceutical and diagnostic industries manifest a growing interest in PM. Multiple stakeholders need adaptation and re-engineering for successful clinical implementation of PM. Drawing primarily from the field of ‘diabetes’, this article will summarize the main challenges to implementation of PM into current medical practice and some of the approaches currently being implemented to overcome these challenges.

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.

Estimating Telomere Length Heritability in an Unrelated Sample of Adults: Is Heritability of Telomere Length Modified by Life Course Socioeconomic Status?

Telomere length (TL) is a widely used marker of biological aging and is associated with an increased risk of morbidity and mortality. Recently, there has been evidence for an association between TL and socioeconomic status (SES), particularly for measures of education and childhood SES. Individual differences in TL are also influenced by genetic factors, with heritability estimates from twin and sibling studies ranging from 34 to 82 percent. Yet the additive heritability of TL as a result of measured genetic variations and the extent to which heritability is modified by SES is still unknown. Data from the Health and Retirement Study, a nationally representative cohort of older adults (mean age 69 years), were used to provide the first estimates of molecular-based heritability of TL using genome-wide complex trait analysis (GCTA). We found that additive genetic variance contributed 28 percent (p = .012) of total phenotypic variance of TL in the European American sample (n = 3,290). Estimation using the GCTA and KING Robust relationship inference methods did not differ significantly in this sample. None of the variance from the gene-by-SES interactions examined contributed significantly to the total TL variance. Estimation of heritability and genetic interaction with SES in the African American sample (n = 442) was too unstable to provide reliable estimates.

Modulation of the Association between the PEPD Variant and the Risk of Type 2 Diabetes by n-3 Fatty Acids in Chinese Hans

BACKGROUND/AIMS

Type 2 diabetes (T2D) is modulated by the interactions between genetic and dietary factors. This study sought to examine whether the associations of genome-wide association study (GWAS)-identified genetic variants with T2D risk were modulated by n-3 fatty acids in Chinese Hans.

METHODS

Six hundred and twenty-two T2D patients and 293 healthy controls were recruited. Erythrocyte phospholipid fatty acids were measured by standard methods. Nine GWAS-identified T2D-related single-nucleotide polymorphisms (SNPs) were genotyped. These SNPs were all identified in GWAS of Asian populations with a high minor allele frequency (>0.2).

RESULTS

Among the 9 SNPs, only rs3786897 at PEPD (peptidase D) showed a significant interaction with n-3 fatty acids (p(interaction) after Bonferroni correction = 0.027). The rs3786897 A allele was associated with a higher risk of T2D [GA+AA vs. GG: odds ratio (OR) = 2.16, 95% confidence interval (CI) 1.32-3.55] when n-3 fatty acids were lower than the population median, but no significant association (GA+AA vs. GG: OR = 0.63, 95% CI 0.35-1.12) was observed when n-3 fatty acids were higher than the median.

CONCLUSIONS

The association between the PEPD genetic variant and the risk of T2D was modulated by n-3 fatty acids. Higher n-3 fatty acids may abolish the adverse effect of the risk allele at PEPD for T2D.

Genome-wide analysis of the heritability of amyotrophic lateral sclerosis

IMPORTANCE

Considerable advances have been made in our understanding of the genetics underlying amyotrophic lateral sclerosis (ALS). Nevertheless, for the majority of patients who receive a diagnosis of ALS, the role played by genetics is unclear. Further elucidation of the genetic architecture of this disease will help clarify the role of genetic variation in ALS populations.

OBJECTIVE

To estimate the relative importance of genetic factors in a complex disease such as ALS by accurately quantifying heritability using genome-wide data derived from genome-wide association studies.

DESIGN, SETTING, AND PARTICIPANTS

We applied the genome-wide complex trait analysis algorithm to 3 genome-wide association study data sets that were generated from ALS case-control cohorts of European ancestry to estimate the heritability of ALS. Cumulatively, these data sets contained genotype data from 1223 cases and 1591 controls that had been previously generated and are publically available on the National Center for Biotechnology Information database of genotypes and phenotypes website (http://www.ncbi.nlm.nih.gov/gap). The cohorts genotyped as part of these genome-wide association study efforts include the InCHIANTI (aging in the Chianti area) Study, the Piemonte and Valle d'Aosta Register for Amyotrophic Lateral Sclerosis, the National Institute of Neurological Disorders and Stroke Repository, and an ALS specialty clinic in Helsinki, Finland.

MAIN OUTCOMES AND MEASURES

A linear mixed model was used to account for all known single-nucleotide polymorphisms simultaneously and to quantify the phenotypic variance present in ostensibly outbred individuals. Variance measures were used to estimate heritability.

RESULTS

With our meta-analysis, which is based on genome-wide genotyping data, we estimated the overall heritability of ALS to be approximately 21.0% (95% CI, 17.1-24.9) (SE = 2.0%), indicating that additional genetic variation influencing risk of ALS loci remains to be identified. Furthermore, we identified 17 regions of the genome that display significantly high heritability estimates. Eleven of these regions represent novel candidate regions for ALS risk.

CONCLUSIONS AND RELEVANCE

We found the heritability of ALS to be significantly higher than previously reported. We also identified multiple, novel genomic regions that we hypothesize may contain causative risk variants that influence susceptibility to ALS.

Nutrigenetics and nutrigenomics insights into diabetes etiopathogenesis

Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, gene-diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools. In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, how epigenetic changes and micro RNAs (miRNAs) can alter cellular signaling in response to nutrients and the dietary interventions that may help to prevent the onset of DM.

CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits

Background

Genetic understanding of complex traits has developed immensely over the past decade but remains hampered by incomplete descriptions of contribution to phenotypic variance. Gene-environment (GxE) interactions are one of these contributors and in the guise of diet and physical activity are important modulators of cardiometabolic phenotypes and ensuing diseases.

Results

We mined the scientific literature to collect GxE interactions from 386 publications for blood lipids, glycemic traits, obesity anthropometrics, vascular measures, inflammation and metabolic syndrome, and introduce CardioGxE, a gene-environment interaction resource. We then analyzed the genes and SNPs supporting cardiometabolic GxEs in order to demonstrate utility of GxE SNPs and to discern characteristics of these important genetic variants. We were able to draw many observations from our extensive analysis of GxEs. 1) The CardioGxE SNPs showed little overlap with variants identified by main effect GWAS, indicating the importance of environmental interactions with genetic factors on cardiometabolic traits. 2) These GxE SNPs were enriched in adaptation to climatic and geographical features, with implications on energy homeostasis and response to physical activity. 3) Comparison to gene networks responding to plasma cholesterol-lowering or regression of atherosclerotic plaques showed that GxE genes have a greater role in those responses, particularly through high-energy diets and fat intake, than do GWAS-identified genes for the same traits. Other aspects of the CardioGxE dataset were explored.

Conclusions

Overall, we demonstrate that SNPs supporting cardiometabolic GxE interactions often exhibit transcriptional effects or are under positive selection. Still, not all such SNPs can be assigned potential functional or regulatory roles often because data are lacking in specific cell types or from treatments that approximate the environmental factor of the GxE. With research on metabolic related complex disease risk embarking on genome-wide GxE interaction tests, CardioGxE will be a useful resource.

METAINTER: meta-analysis of multiple regression models in genome-wide association studies

MOTIVATION

Meta-analysis of summary statistics is an essential approach to guarantee the success of genome-wide association studies (GWAS). Application of the fixed or random effects model to single-marker association tests is a standard practice. More complex methods of meta-analysis involving multiple parameters have not been used frequently, a gap that could be explained by the lack of a respective meta-analysis pipeline. Meta-analysis based on combining p-values can be applied to any association test. However, to be powerful, meta-analysis methods for high-dimensional models should incorporate additional information such as study-specific properties of parameter estimates, their effect directions, standard errors and covariance structure.

RESULTS

We modified 'method for the synthesis of linear regression slopes' recently proposed in the educational sciences to the case of multiple logistic regression, and implemented it in a meta-analysis tool called METAINTER. The software handles models with an arbitrary number of parameters, and can directly be applied to analyze the results of single-SNP tests, global haplotype tests, tests for and under gene-gene or gene-environment interaction. Via simulations for two-single nucleotide polymorphisms (SNP) models we have shown that the proposed meta-analysis method has correct type I error rate. Moreover, power estimates come close to that of the joint analysis of the entire sample. We conducted a real data analysis of six GWAS of type 2 diabetes, available from dbGaP (http://www.ncbi.nlm.nih.gov/gap). For each study, a genome-wide interaction analysis of all SNP pairs was performed by logistic regression tests. The results were then meta-analyzed with METAINTER.

AVAILABILITY

The software is freely available and distributed under the conditions specified on http://metainter.meb.uni-bonn.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-wide interaction of genotype by erythrocyte n-3 fatty acids contributes to phenotypic variance of diabetes-related traits

Background

Little is known about the interplay between n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level. The present study aimed to examine variance contributions of genotype by environment (GxE) interactions for different erythrocyte n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level in a non-Hispanic white population living in the U.S.A. (n = 820). A tool for Genome-wide Complex Trait Analysis (GCTA) was used to estimate the genome-wide GxE variance contribution of four diabetes-related traits: HOMA-Insulin Resistance (HOMA-IR), fasting plasma insulin, glucose and adiponectin. A GxE genome-wide association study (GWAS) was conducted to further elucidate the GCTA results. Replication was conducted in the participants of the Boston Puerto Rican Health Study (BPRHS) without diabetes (n = 716).

Results

In GOLDN, docosapentaenoic acid (DPA) contributed the most significant GxE variance to the total phenotypic variance of both HOMA-IR (26.5%, P-nominal = 0.034) and fasting insulin (24.3%, P-nominal = 0.042). The ratio of arachidonic acid to eicosapentaenoic acid + docosahexaenoic acid contributed the most significant GxE variance to the total variance of fasting glucose (27.0%, P-nominal = 0.023). GxE variance of the arachidonic acid/eicosapentaenoic acid ratio showed a marginally significant contribution to the adiponectin variance (16.0%, P-nominal = 0.058). None of the GCTA results were significant after Bonferroni correction (P < 0.001). For each trait, the GxE GWAS identified a far larger number of significant single-nucleotide polymorphisms (P-interaction ≤ 10E-5) for the significant E factor (significant GxE variance contributor) than a control E factor (non-significant GxE variance contributor). In the BPRHS, DPA contributed a marginally significant GxE variance to the phenotypic variance of HOMA-IR (12.9%, P-nominal = 0.068) and fasting insulin (18.0%, P-nominal = 0.033).

Conclusion

Erythrocyte n-3 fatty acids contributed a significant GxE variance to diabetes-related traits at the genome-wide level.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-781) contains supplementary material, which is available to authorized users.

Foodomics for healthy nutrition

PURPOSE OF REVIEW

To understand how the principles of foodomics could improve the assessment of the nutritional status and needs.

RECENT FINDINGS

The knowledge that metabolic pathways may be altered in individuals with genetic variants in the presence of certain dietary exposures offers great potential for personalized nutrition advice, and epigenetics and nutrigenetics have been used to assess the need and status of specific nutrients. MicroRNAs profiling and genome-wide association studies have also contributed. Since nutritional effects of complex diets emerge only if dietary assessments are validated, nutrimetabolomics offers the validation tools on the basis of food intake biomarkers.

SUMMARY

Apart from the provision, via a high-throughput approach, of objective measurable parameters to be used as biomarkers, a consensus must be reached on the definition of health and wellness. Health (and wellness) can be considered a position having specific coordinates in a multiple-dimension space, and many factors contribute to our movements in this space. Foodomics is the science aiming at studying, through the evaluation of different biomarkers, the entity and the direction of the movements across the healthy or unhealthy space, developing models that are able to explain how food components, food, diet and lifestyle can influence our trajectory toward the healthy condition. Only considering the 'health space' as a multidimensional one, we have the possibility of understanding the complex relationship linking nutrition and health, and of reaching healthier conditions by personalized balanced diets in a foodomics vision.

Opportunism: a panacea for implementation of whole-genome sequencing studies in nutrigenomics research?

Observational studies have consistently shown associations between mild deficiencies in folate and vitamin B12 with increased risk of a myriad of common diseases. These findings have invariably translated into null outcomes in intervention trials due in part to our ignorance of the specific genomic and environmental factors that underpin population variability in requirements to these B-vitamins. Although genome-wide association studies have shed initial light on the genetic architecture of variability in status of these vitamins, particularly vitamin B12, the causal mechanisms remain uncharacterised. A recent study by Grarup et al. (PLoS Genet 9(6):e1003530, 2013) used next-generation whole-genome sequencing to gain further insight into the genetic architecture of vitamin B12 and folate status in the general population. Their study represents the analysis of approximately ten times greater number of genetic variants and nearly four times the number of individuals compared to the largest previous GWAS study of these B-vitamins. In light of this, we purport that although the study may be viewed as the state of the art in the roadmap to personalised or precision nutrition, the lack of insight provided by the study serves as a cautionary reminder of the importance of study design, particularly when leveraging large-scale data, such as those from whole-genome sequences. We believe that the precedent set by such large-scale "proof of principle" type projects will wrongly enforce a negative outlook for nutrigenomics research and present alternative study designs, which although less opportunistic are far more likely to be informative and yield novel results.

Genotype-environment interactions for quantitative traits in Korea Associated Resource (KARE) cohorts

Background

Due to the lack of statistical power and confounding effects of population structure in human population data, genotype-environment interaction studies have not yielded promising results and have provided only limited knowledge for exploring how genotype and environmental factors interact to in their influence onto risk.

Results

We analyzed 49 human quantitative traits in 7,170 unrelated Korean individuals on 326,262 autosomal single nucleotide polymorphisms (SNPs) collected from the KARE (Korean Association Resource) project, and we estimated the statistically significant proportion of variance that could be explained by genotype-area interactions in the supra-iliac skinfold thickness trait (hGE2 = 0.269 and P = 0.00032), which is related to abdominal obesity. Data suggested that the genotypes could have different effects on the phenotype (supra-iliac skinfold thickness) in different environmental settings (rural vs. urban areas). We then defined the genotype groups of individuals with similar genetic profiles based on the additive genetic relationships among individuals using SNPs. We observed the norms of reaction, and the differential phenotypic response of a genotype to a change in environmental exposure. Interestingly, we also found that the gene clusters responsible for cell-cell and cell-extracellular matrix interactions were enriched significantly for genotype-area interaction.

Conclusions

This significant heritability estimate of genotype-environment interactions will lead to conceptual advances in our understanding of the mechanisms underlying genotype-environment interactions, and could be ultimately applied to personalized preventative treatments based on environmental exposures.

Untangling the interplay of genetic and metabolic influences on beta-cell function: Examples of potential therapeutic implications involving TCF7L2 and FFAR1

Deteriorating beta-cell function is a common feature of type 2 diabetes. In this review, we briefly address the regulation of beta-cell function, and discuss some of the main determinants of beta-cell failure. We will focus on the role of interactions between the genetic background and metabolic environment (insulin resistance, fuel supply and flux as well as metabolic signaling). We present data on the function of the strongest common diabetes risk variant, the single nucleotide polymorphism (SNP) rs7903146 in TCF7L2. As also mirrored by its interaction with glycemia on insulin secretion, this SNP in large part confers resistance against the incretin effect. Genetic influence on insulin secretion also interacts with free fatty acids, as evidenced by data on rs1573611 in FFAR1. Several medications marketed by now or currently under development for diabetes treatment engage these pathways, and therapeutic implications from these findings are soon to be expected.