A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk

BACKGROUND

Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions.

METHODS

In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk.

RESULTS

A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk.

CONCLUSIONS

These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

Selection scan in Native Americans of Mexico identifies FADS2 rs174616: Evidence of gene-diet interactions affecting lipid levels and Delta-6-desaturase activity

Searching for positive selection signals across genomes has identified functional genetic variants responding to environmental change. In Native Americans of Mexico, we used the fixation index (Fst) and population branch statistic (PBS) to identify SNPs suggesting positive selection. The 103 most differentiated SNPs were tested for associations with metabolic traits, the most significant association was FADS2/rs174616 with body mass index (BMI). This variant lies within a linkage disequilibrium (LD) block independent of previously reported FADS selection signals and has not been clearly associated with metabolic phenotypes. We tested this variant in two independent cohorts with cardiometabolic data. In the Genetics of Atherosclerotic Disease (GEA) cohort, the derived allele (T) was associated with increased BMI, lower LDL-C levels and a decreased risk of subclinical atherosclerosis in women. Significant gene-diet interactions affected lipid, apolipoprotein and adiponectin levels with differences according to sex, involving mainly total and complex dietary carbohydrate%. In the Genotype-related Effects of PUFA trial, the derived allele was associated with lower Δ-6 desaturase activity and erythrocyte membrane dihomo-gamma-linolenic acid (DGLA) levels, and with increased Δ-5 desaturase activity and eicosapentaenoic acid levels. This variant interacted with dietary carbohydrate% affecting Δ-6 desaturase activity. Notably, the relationship of DGLA and other erythrocyte membrane LC-PUFA indices with HOMA-IR differed according to rs174616 genotype, which has implications regarding how these indices should be interpreted. In conclusion, this observational study identified rs174616 as a signal suggesting selection in an independent linkage disequilibrium block, was associated with cardiometabolic and erythrocyte measurements of LC-PUFA in two independent Mexican cohorts and showed significant gene-diet interactions.

Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability

Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.

Preliminary study of identified novel susceptibility loci for HAPE risk in a Chinese male Han population

High altitude pulmonary edema (HAPE) is a life-threatening form of non-cardiogenic pulmonary edema. In recent years, association studies have become the main method for identifying HAPE genetic loci. A genome-wide association study (GWAS) of HAPE risk-associated loci was performed in Chinese male Han individuals (164 HAPE cases and 189 healthy controls) by the Precision Medicine Diversity Array Chip with 2,771,835 loci (Applied Biosystems Axiom™). Eight overlapping candidate loci in CCNG2, RP11-445O3.2, NUPL1 and WWOX were finally selected. In silico functional analyses displayed the PPI network, functional enrichment and signal pathways related to CCNG2, NUPL1, WWOX and NRXN1. This study provides data supplements for HAPE susceptibility gene loci and new insights into HAPE susceptibility.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

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.

The selection landscape and genetic legacy of ancient Eurasians

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

The Relationship between Changes in MYBPC3 Single-Nucleotide Polymorphism-Associated Metabolites and Elite Athletes' Adaptive Cardiac Function

Athletic performance is a multifactorial trait influenced by a complex interaction of environmental and genetic factors. Over the last decades, understanding and improving elite athletes' endurance and performance has become a real challenge for scientists. Significant tools include but are not limited to the development of molecular methods for talent identification, personalized exercise training, dietary requirements, prevention of exercise-related diseases, as well as the recognition of the structure and function of the genome in elite athletes. Investigating the genetic markers and phenotypes has become critical for elite endurance surveillance. The identification of genetic variants contributing to a predisposition for excellence in certain types of athletic activities has been difficult despite the relatively high genetic inheritance of athlete status. Metabolomics can potentially represent a useful approach for gaining a thorough understanding of various physiological states and for clarifying disorders caused by strength-endurance physical exercise. Based on a previous GWAS study, this manuscript aims to discuss the association of specific single-nucleotide polymorphisms (SNPs) located in the MYBPC3 gene encoding for cardiac MyBP-C protein with endurance athlete status. MYBPC3 is linked to elite athlete heart remodeling during or after exercise, but it could also be linked to the phenotype of cardiac hypertrophy (HCM). To make the distinction between both phenotypes, specific metabolites that are influenced by variants in the MYBPC3 gene are analyzed in relation to elite athletic performance and HCM. These include theophylline, ursodeoxycholate, quinate, and decanoyl-carnitine. According to the analysis of effect size, theophylline, quinate, and decanoyl carnitine increase with endurance while decreasing with cardiovascular disease, whereas ursodeoxycholate increases with cardiovascular disease. In conclusion, and based on our metabolomics data, the specific effects on athletic performance for each MYBPC3 SNP-associated metabolite are discussed.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study

PURPOSE

While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown.

METHODS

Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity.

RESULTS

In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant.

CONCLUSION

We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.

The Associations of Maternal Health Characteristics, Newborn Metabolite Concentrations, and Child Body Mass Index among US Children in the ECHO Program

We aimed first to assess associations between maternal health characteristics and newborn metabolite concentrations and second to assess associations between metabolites associated with maternal health characteristics and child body mass index (BMI). This study included 3492 infants enrolled in three birth cohorts with linked newborn screening metabolic data. Maternal health characteristics were ascertained from questionnaires, birth certificates, and medical records. Child BMI was ascertained from medical records and study visits. We used multivariate analysis of variance, followed by multivariable linear/proportional odds regression, to determine maternal health characteristic-newborn metabolite associations. Significant associations were found in discovery and replication cohorts of higher pre-pregnancy BMI with increased C0 and higher maternal age at delivery with increased C2 (C0: discovery: aβ 0.05 [95% CI 0.03, 0.07]; replication: aβ 0.04 [95% CI 0.006, 0.06]; C2: discovery: aβ 0.04 [95% CI 0.003, 0.08]; replication: aβ 0.04 [95% CI 0.02, 0.07]). Social Vulnerability Index, insurance, and residence were also associated with metabolite concentrations in a discovery cohort. Associations between metabolites associated with maternal health characteristics and child BMI were modified from 1-3 years (interaction: p < 0.05). These findings may provide insights on potential biologic pathways through which maternal health characteristics may impact fetal metabolic programming and child growth patterns.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

Longitudinal profiling of metabolic ageing trends in two population cohorts of young adults

BACKGROUND

Quantification of metabolic changes over the human life course is essential to understanding ageing processes. Yet longitudinal metabolomics data are rare and long gaps between visits can introduce biases that mask true trends. We introduce new ways to process quantitative time-series population data and elucidate metabolic ageing trends in two large cohorts.

METHODS

Eligible participants included 1672 individuals from the Cardiovascular Risk in Young Finns Study and 3117 from the Northern Finland Birth Cohort 1966. Up to three time points (ages 24-49 years) were analysed by nuclear magnetic resonance metabolomics and clinical biochemistry (236 measures). Temporal trends were quantified as median change per decade. Sample quality was verified by consistency of shared biomarkers between metabolomics and clinical assays. Batch effects between visits were mitigated by a new algorithm introduced in this report. The results below satisfy multiple testing threshold of P < 0.0006.

RESULTS

Women gained more weight than men (+6.5% vs +5.0%) but showed milder metabolic changes overall. Temporal sex differences were observed for C-reactive protein (women +5.1%, men +21.1%), glycine (women +5.2%, men +1.9%) and phenylalanine (women +0.6%, men +3.5%). In 566 individuals with ≥+3% weight gain vs 561 with weight change ≤-3%, divergent patterns were observed for insulin (+24% vs -10%), very-low-density-lipoprotein triglycerides (+32% vs -6%), high-density-lipoprotein2 cholesterol (-6.5% vs +4.7%), isoleucine (+5.7% vs -6.0%) and C-reactive protein (+25% vs -22%).

CONCLUSION

We report absolute and proportional trends for 236 metabolic measures as new reference material for overall age-associated and specific weight-driven changes in real-world populations.

Pleiotropic Effects of APOB Variants on Lipid Profiles, Metabolic Syndrome, and the Risk of Diabetes Mellitus

Apolipoprotein B (ApoB) plays a crucial role in lipid and lipoprotein metabolism. The effects of APOB locus variants on lipid profiles, metabolic syndrome, and the risk of diabetes mellitus (DM) in Asian populations are unclear. We included 1478 Taiwan Biobank participants with whole-genome sequence (WGS) data and 115,088 TWB participants with Axiom genome-wide CHB array data and subjected them to genotype-phenotype analyses using APOB locus variants. Five APOB nonsynonymous mutations, including Asian-specific rs144467873 and rs13306194 variants, were selected from participants with the WGS data. Using a combination of regional association studies, a linkage disequilibrium map, and multivariate analysis, we revealed that the APOB locus variants rs144467873, rs13306194, and rs1367117 were independently associated with total, low-density lipoprotein (LDL), and non-high-density lipoprotein (non-HDL) cholesterol levels; rs1318006 was associated with HDL cholesterol levels; rs13306194 and rs35131127 were associated with serum triglyceride levels; rs144467873, rs13306194, rs56213756, and rs679899 were associated with remnant cholesterol levels; and rs144467873 and rs4665709 were associated with metabolic syndrome. Mendelian randomization (MR) analyses conducted using weighted genetic risk scores from three or two LDL-cholesterol-level-associated APOB variants revealed significant association with prevalent DM (p = 0.0029 and 8.2 × 10^-5, respectively), which became insignificant after adjustment for LDL-C levels. In conclusion, these results indicate that common and rare APOB variants are independently associated with various lipid levels and metabolic syndrome in Taiwanese individuals. MR analyses supported APOB variants associated with the risk of DM through their associations with LDL cholesterol levels.

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome-microbiome control. Additionally, interaction effects, where a metabolite-microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers

BACKGROUND

Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.

METHODS

We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.

RESULTS

As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.

CONCLUSIONS

We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.

FUNDING

This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers

Background

Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.

Methods

We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.

Results

As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.

Conclusions

We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.

Funding

This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Ancestry-related distribution of Runs of homozygosity and functional variants in Qatari population

Background

Describing how genetic history shapes the pattern of medically relevant variants could improve the understanding of how specific loci interact with each other and affect diseases and traits prevalence. The Qatari population is characterized by a complex history of admixture and substructure, and the study of its population genomic features would provide valuable insights into the genetic landscape of functional variants. Here, we analyzed the genomic variation of 186 newly-genotyped healthy individuals from the Qatari peninsula.

Results

We discovered an intricate genetic structure using ancestry related analyses. In particular, the presence of three different clusters, Cluster 1, Cluster 2 and Cluster 3 (with Near Eastern, South Asian and African ancestry, respectively), was detected with an additional fourth one (Cluster 4) with East Asian ancestry. These subpopulations show differences in the distribution of runs of homozygosity (ROH) and admixture events in the past, ranging from 40 to 5 generations ago. This complex genetic history led to a peculiar pattern of functional markers under positive selection, differentiated in shared signals and private signals. Interestingly we found several signatures of shared selection on SNPs in the FADS2 gene, hinting at a possible common evolutionary link to dietary intake. Among the private signals, we found enrichment for markers associated with HDL and LDL for Cluster 1(Near Eastern ancestry) and Cluster 3 (South Asian ancestry) and height and blood traits for Cluster 2 (African ancestry).

The differences in genetic history among these populations also resulted in the different frequency distribution of putative loss of function variants. For example, homozygous carriers for rs2884737, a variant linked to an anticoagulant drug (warfarin) response, are mainly represented by individuals with predominant Bedouin ancestry (risk allele frequency G at 0.48).

Conclusions

We provided a detailed catalogue of the different ancestral pattern in the Qatari population highlighting differences and similarities in the distribution of selected variants and putative loss of functions. Finally, these results would provide useful guidance for assessing genetic risk factors linked to consanguinity and genetic ancestry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01087-1.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

Preliminary Study of Genome-Wide Association Identified Novel Susceptibility Genes for Hemorheological Indexes in a Chinese Population

Background

Genome-wide association studies for various hemorheological characteristics have not been reported. We aimed to identify genetic loci associated with hemorheological indexes in a cohort of healthy Chinese Han individuals.

Methods

Genotyping was performed using Applied Biosystems Axiom™ Precision Medicine Diversity Array in 838 individuals, and 6,423,076 single nucleotide polymorphisms were available for genotyping. The relations were examined in an additive genetic model using mixed linear regression and combined with identical by descent matrix.

Results

We identified 38 genetic loci (p < 5 × 10^-6) related to hemorheological traits. In which, LOC102724502-OLIG2 rs28371438 was related to the levels of nd30 (p = 8.58 × 10^-07), nd300 (p = 1.89 × 10^-06), erythrocyte rigidity (p = 1.29 × 10^-06), assigned viscosity (p = 6.20 × 10^-08) and whole blood high cut relative (p = 7.30 × 10^-08). The association of STK32B rs4689231 for nd30 (p = 3.85 × 10^-06) and nd300 (p = 2.94 × 10^-06) and GTSCR1-LINC01541 rs11661911 for erythrocyte rigidity (p = 9.93 × 10^-09) and whole blood high cut relative (p = 2.09 × 10^-07) was found. USP25-MIR99AHG rs1297329 was associated with erythrocyte rigidity (p = 1.81 × 10^-06) and erythrocyte deformation (p = 1.14 × 10^-06). Moreover, the association of TMEM232-SLC25A46 rs3985087 and LINC00470-METTL4 rs9966987 for fibrinogen (p = 1.31 × 10^-06 and p = 4.29 × 10^-07) and plasma viscosity (p = 1.01 × 10^-06 and p = 4.59 × 10^-07) was found.

Conclusion

These findings may represent biological candidates for hemorheological indexes and contribute to hemorheological study.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk

BACKGROUND

The etiology of colorectal cancer is not fully understood.

METHODS

Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.

RESULTS

Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.

CONCLUSIONS

This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.

IMPACT

The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

Diabetes risk loci-associated pathways are shared across metabolic tissues

Aims/hypothesis

Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants.

Methods

In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms.

Results

One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated.

Conclusion

Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08587-5.

Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system

Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β = -0.055, pcorrected = 0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2, which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N = 3360, β = 0.377, p = 3.12 × 10-11, R2 = 2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.

Metabolomic signatures for the longitudinal reduction of muscle strength over 10 years

Background

Skeletal muscles are essential components of the neuromuscular skeletal system that have an integral role in the structure and function of the synovial joints which are often affected by osteoarthritis (OA). The aim of this study was to identify the baseline metabolomic signatures for the longitudinal reduction of muscle strength over 10 years in the well-established community-based Tasmanian Older Adult Cohort (TASOAC).

Methods

Study participants were 50–79 year old individuals from the TASOAC. Hand grip, knee extension, and leg strength were measured at baseline, 2.6-, 5-, and 10-year follow-up points. Fasting serum samples were collected at 2.6-year follow-up point, and metabolomic profiling was performed using the TMIC Prime Metabolomics Profiling Assay. Generalized linear mixed effects model was used to identify metabolites that were associated with the reduction in muscle strength over 10 years after controlling for age, sex, and BMI. Significance level was defined at α=0.0004 after correction of multiple testing of 129 metabolites with Bonferroni method. Further, a genome-wide association study (GWAS) analysis was performed to explore if genetic factors account for the association between the identified metabolomic markers and the longitudinal reduction of muscle strength over 10 years.

Results

A total of 409 older adults (50% of them females) were included. The mean age was 60.93±6.50 years, and mean BMI was 27.12±4.18 kg/m² at baseline. Muscle strength declined by 0.09 psi, 0.02 kg, and 2.57 kg per year for hand grip, knee extension, and leg strength, respectively. Among the 143 metabolites measured, 129 passed the quality checks and were included in the analysis. We found that the elevated blood level of asymmetric dimethylarginine (ADMA) was associated with the reduction in hand grip (p=0.0003) and knee extension strength (p=0.008) over 10 years. GWAS analysis found that a SNP rs1125718 adjacent to WISP1gene was associated with ADMA levels (p=4.39*10^-8). Further, we found that the increased serum concentration of uric acid was significantly associated with the decline in leg strength over 10 years (p=0.0001).

Conclusion

Our results demonstrated that elevated serum ADMA and uric acid at baseline were associated with age-dependent muscle strength reduction. They might be novel targets to prevent muscle strength loss over time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00286-9.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Genetic overlap and causality between blood metabolites and migraine

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

Preliminary study of genome-wide association identified novel susceptibility genes for thyroid-related hormones in Chinese population

BACKGROUND

Thyroid hormones are critical regulators of metabolism, development and growth in mammals. However, the genetic association of thyroid-related hormones in the Chinese Han population is not fully understood.

OBJECTIVE

We aimed to identify the genetic loci associated with circulating thyroid-related hormones concentrations in the healthy Chinese Han population.

METHODS

Genotyping was performed in 124 individuals using Applied Biosystems™ Axiom™ PMDA, and 796,288 single nucleotide polymorphisms (SNPs) were available for the GWAS analysis. For replication, eleven SNPs were selected as candidate loci for genotyping by Agena MassARRAY platform in additional samples (313 subjects). The values of p < 5 × 10^- 6 suggest a suggestively significant genome-wide association with circulating thyroid-related hormones concentrations.

RESULTS

We identified that rs11178277 (PTPRB, p = 4.88 × 10^- 07) and rs7320337 (LMO7DN-KCTD12, p = 1.22 × 10^- 06) were associated with serum FT3 level. Three SNPs (rs4850041 in LOC105373394-LINC01249: p = 3.55 × 10^- 06, rs6867291 in LINC02208: p = 2.40 × 10^- 06 and rs79508321 in WWOX: p = 3.35 × 10^- 06) were related to circulating T3 level. Rs12474167 (LOC105373394-LINC01249, p = 1.65 × 10^- 06) and rs1864553 (IWS1, p = 2.00 × 10^- 06) were associated with circulating T4 concentration. The association with TGA concentration was for rs17163542 in DISP1 (p = 3.46 × 10^- 06) and rs12601151 in NOG-C17orf67 (p = 2.72 × 10^- 07). Two genome-level significant SNPs (rs2114707 in LINC01314, p = 1.69 × 10^- 06 and rs12601151, p = 1.41 × 10^- 07) associated with serum TMA concentration were identified. Moreover, rs6083269 (CST1-CST2, p = 3.36 × 10^- 06) was a significant locus for circulating TSH level. In replication, rs12601151 in NOG-C17orf67 was still associated with serum TGA level (p = 0.012).

CONCLUSIONS

The GWAS reported 11 new suggestively significant loci associated with circulating thyroid-related hormones levels among the Chinese Han population. These findings represented suggestively biological candidates for circulating thyroid-related hormones levels and provided new insights into the mechanisms of regulating serum TGA concentration.

ANGPTL3 Variants Associate with Lower Levels of Irisin and C-Peptide in a Cohort of Arab Individuals

ANGPTL3 is an important regulator of lipid metabolism. Its inhibition in people with hypercholesteremia reduces plasma lipid levels dramatically. Genome-wide association studies have associated ANGPTL3 variants with lipid traits. Irisin, an exercise-modulated protein, has been associated with lipid metabolism. Intracellular accumulation of lipids impairs insulin action and contributes to metabolic disorders. In this study, we evaluate the impact of ANGPTL3 variants on levels of irisin and markers associated with lipid metabolism and insulin resistance. ANGPTL3 rs1748197 and rs12130333 variants were genotyped in a cohort of 278 Arab individuals from Kuwait. Levels of irisin and other metabolic markers were measured by ELISA. Significance of association signals was assessed using Bonferroni-corrected p-values and empirical p-values. The study variants were significantly associated with low levels of c-peptide and irisin. Levels of c-peptide and irisin were mediated by interaction between carrier genotypes (GA + AA) at rs1748197 and measures of IL13 and TG, respectively. While levels of c-peptide and IL13 were directly correlated in individuals with the reference genotype, they were inversely correlated in individuals with the carrier genotype. Irisin correlated positively with TG and was strong in individuals with carrier genotypes. These observations illustrate ANGPTL3 as a potential link connecting lipid metabolism, insulin resistance and cardioprotection.

The lipidome in nonalcoholic fatty liver disease: actionable targets

Nonalcoholic fatty liver disease (NAFLD) has become the most prevalent chronic liver disease. Recent technological advances, combined with OMICs experiments and explorations involving different biological samples, have uncovered vital aspects of NAFLD biology. In this review, we summarize recent work by our group and others that expands what is known about the role of lipidome in NAFLD pathogenesis. We discuss how pathway and enrichment analyses were performed by integrating a list of query metabolites derived from text-mining existing NAFLD-lipidomics studies, resulting in the identification of nine Kyoto Encyclopedia of Genes and Genomes dysregulated pathways, including biosynthesis of unsaturated fatty acids, butanoate metabolism, synthesis and degradation of ketone bodies, sphingolipid, arachidonic acid and pyruvate metabolism, and numerous nonsteroidal antiinflammatory drug pathways predicted from The Small Molecule Pathway Database. We also summarize an integrated pathway-level analysis of genes and lipid-related metabolites associated with NAFLD, which shows overrepresentation of signal transduction, selenium micronutrient network, Class A/1Rhodopsin-like receptors and G protein-coupled receptor ligand binding, and G protein-coupled receptor downstream signaling. Generated gene-metabolite-disease interaction networks indicate that NAFLD and arterial hypertension are interlinked by molecular signatures. Finally, we discuss how mining pathways and associations among metabolites, lipids, genes, and proteins can be exploited to infer networks and potential pharmacological targets and how lipidomic studies may provide insight into the interrelationships among metabolite clusters that modify NAFLD biology, genetic susceptibility, diet, and the gut microbiome.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.

CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.

Elucidation of disease etiology by trans-layer omics analysis

To date, genome-wide association studies (GWASs) have successfully identified thousands of associations between genetic polymorphisms and human traits. However, the pathways between the associated genotype and phenotype are often poorly understood. The transcriptome, proteome, and metabolome, the omics, are positioned along the pathway and can provide useful information to translate from genotype to phenotype. This review shows useful data resources for connecting each omics and describes how they are combined into a cohesive analysis. Quantitative trait loci (QTL) are useful information for connecting the genome and other omics. QTL represent how much genetic variants have effects on other omics and give us clues to how GWAS risk SNPs affect biological mechanisms. Integration of each omics provides a robust analytical framework for estimating disease causality, discovering drug targets, and identifying disease-associated tissues. Technological advances and the rise of consortia and biobanks have facilitated the analyses of unprecedented data, improving both the quality and quantity of research. Proficient management of these valuable datasets allows discovering novel insights into the genetic background and etiology of complex human diseases and contributing to personalized medicine.

A cross-platform approach identifies genetic regulators of human metabolism and health

In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10^-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.

Characterizing the effect of background selection on the polygenicity of brain-related traits

BACKGROUND

Genome-wide association studies (GWAS) have demonstrated that psychopathology phenotypes are affected by many risk alleles with small effect (polygenicity). It is unclear how ubiquitously evolutionary pressures influence the genetic architecture of these traits.

METHODS

We partitioned SNP heritability to assess the contribution of background (BGS) and positive selection, Neanderthal local ancestry, functional significance, and genotype networks in 75 brain-related traits (8411 ≤ N ≤ 1,131,181, mean N = 205,289). We applied binary annotations by dichotomizing each measure based on top 2%, 1%, and 0.5% of all scores genome-wide. Effect size distribution features were calculated using GENESIS. We tested the relationship between effect size distribution descriptive statistics and natural selection. In a subset of traits, we explore the inclusion of diagnostic heterogeneity (e.g., number of diagnostic combinations and total symptoms) in the tested relationship.

RESULTS

SNP-heritability was enriched (false discovery rate q < 0.05) for loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and loss-of-function (LoF)-intolerant regions (67 phenotypes). These effects were strongest in GWAS of schizophrenia (1.90-fold BGS, 1.16-fold genic, and 1.92-fold LoF), educational attainment (1.86-fold BGS, 1.12-fold genic, and 1.79-fold LoF), and cognitive performance (2.29-fold BGS, 1.12-fold genic, and 1.79-fold LoF). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β = 4.39 × 10-5, p = 1.43 × 10-5, model r2 = 0.548). Considering the number of DSM-5 diagnostic combinations per psychiatric disorder improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2 = 0.661).

CONCLUSIONS

Brain-related phenotypes with larger variance in risk locus effect sizes are associated with loci under BGS. We show exploratory results suggesting that diagnostic complexity may also contribute to the increased polygenicity of psychiatric disorders.

Urinary metabolite quantitative trait loci in children and their interaction with dietary factors

Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.

Genomic Heritability and Genome-Wide Association Studies of Plasma Metabolites in Crossbred Beef Cattle

Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.

Leptin alters energy intake and fat mass but not energy expenditure in lean subjects

Based on studies in mice, leptin was expected to decrease body weight in obese individuals. However, the majority of the obese are hyperleptinemic and do not respond to leptin treatment, suggesting the presence of leptin tolerance and questioning the role of leptin as regulator of energy balance in humans. We thus performed detailed novel measurements and analyses of samples and data from our clinical trials biobank to investigate leptin effects on mechanisms of weight regulation in lean normo- and mildly hypo-leptinemic individuals without genetic disorders. We demonstrate that short-term leptin administration alters food intake during refeeding after fasting, whereas long-term leptin treatment reduces fat mass and body weight, and transiently alters circulating free fatty acids in lean mildly hypoleptinemic individuals. Leptin levels before treatment initiation and leptin dose do not predict the observed weight loss in lean individuals suggesting a saturable effect of leptin. In contrast to data from animal studies, leptin treatment does not affect energy expenditure, lipid utilization, SNS activity, heart rate, blood pressure or lean body mass.

Leptin treatment is effective to reduce body weight in animal models, but patients with obesity and associated hyperleptinemia do not respond well to leptin therapy. Here the authors report a retrospective analysis of four clinical trials in normo- and mildly hypoleptinemic individuals and show that leptin therapy alters food intake in the short term and reduces weight and fat mass in the long term without effects on energy expenditure.