Genetic associations with lipoprotein subfractions provide information on their biological nature

The HuMet Repository: Watching human metabolism at work

Metabolism oscillates between catabolic and anabolic states depending on food intake, exercise, or stresses that change a multitude of metabolic pathways simultaneously. We present the HuMet Repository for exploring dynamic metabolic responses to oral glucose/lipid loads, mixed meals, 36-h fasting, exercise, and cold stress in healthy subjects. Metabolomics data from blood, urine, and breath of 15 young, healthy men at up to 56 time points are integrated and embedded within an interactive web application, enabling researchers with and without computational expertise to search, visualize, analyze, and contextualize the dynamic metabolite profiles of 2,656 metabolites acquired on multiple platforms. With examples, we demonstrate the utility of the resource for research into the dynamics of human metabolism, highlighting differences and similarities in systemic metabolic responses across challenges and the complementarity of metabolomics platforms. The repository, providing a reference for healthy metabolite changes to six standardized physiological challenges, is freely accessible through a web portal.

Glucokinase regulatory protein: a balancing act between glucose and lipid metabolism in NAFLD

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease worldwide, affected by both genetics and environment. Type 2 diabetes (T2D) stands as an independent environmental risk factor that precipitates the onset of hepatic steatosis and accelerates its progression to severe stages of liver damage. Furthermore, the coexistence of T2D and NAFLD magnifies the risk of cardiovascular disease synergistically. However, the association between genetic susceptibility and metabolic risk factors in NAFLD remains incompletely understood. The glucokinase regulator gene (GCKR), responsible for encoding the glucokinase regulatory protein (GKRP), acts as a regulator and protector of the glucose-metabolizing enzyme glucokinase (GK) in the liver. Two common variants (rs1260326 and rs780094) within the GCKR gene have been associated with a lower risk for T2D but a higher risk for NAFLD. Recent studies underscore that T2D presence significantly amplifies the effect of the GCKR gene, thereby increasing the risk of NASH and fibrosis in NAFLD patients. In this review, we focus on the critical roles of GKRP in T2D and NAFLD, drawing upon insights from genetic and biological studies. Notably, prior attempts at drug development targeting GK with glucokinase activators (GKAs) have shown potential risks of augmented plasma triglycerides or NAFLD. Conversely, overexpression of GKRP in diabetic rats improved glucose tolerance without causing NAFLD, suggesting the crucial regulatory role of GKRP in maintaining hepatic glucose and lipid metabolism balance. Collectively, this review sheds new light on the complex interaction between genes and environment in NAFLD, focusing on the GCKR gene. By integrating evidence from genetics, biology, and drug development, we reassess the therapeutic potential of targeting GK or GKRP for metabolic disease treatment. Emerging evidence suggests that selectively activating GK or enhancing GK-GKRP binding may represent a holistic strategy for restoring glucose and lipid metabolic balance.

The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

Genetic architecture of human plasma lipidome and its link to cardiovascular disease

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.

HDL in CKD-The Devil Is in the Detail

The picture of HDL cholesterol (HDL-C) as the "good" cholesterol has eroded. This is even more surprising because there exists strong evidence that HDL-C is associated with cardiovascular disease (CVD) in the general population as well as in patients with impairment of kidney function and/or progression of CKD. However, drugs that dramatically increase HDL-C have mostly failed to decrease CVD events. Furthermore, genetic studies took the same line, as genetic variants that have a pronounced influence on HDL-C concentrations did not show an association with cardiovascular risk. For many, this was not surprising, given that an HDL particle is highly complex and carries >80 proteins and several hundred lipid species. Simply measuring cholesterol might not reflect the variety of biologic effects of heterogeneous HDL particles. Therefore, functional studies and the involvement of HDL components in the reverse cholesterol transport, including the cholesterol efflux capacity, have become a further focus of study during recent years. As also observed for other aspects, CKD populations behave differently compared with non-CKD populations. Although clear disturbances have been observed for the "functionality" of HDL particles in patients with CKD, this did not necessarily translate into clear-cut associations with outcomes.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study

Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005<minor allele frequency [MAF]<0.05) or rare (MAF<0.005) variant, including Arg123His in LCAT. Gene-based associations (P<10-10) support a role for coding variants in LIPC and LIPG with lipoprotein subclass traits. 30 established lipid-associated loci had a stronger association for a subclass trait than any conventional trait. These novel association signals provide further insight into the molecular basis of dyslipidemia and the etiology of metabolic disorders.

High-Density Lipoprotein Subclasses, Coronary Artery Disease, and Cardiovascular Mortality

BACKGROUND

The inverse relationship between HDL cholesterol and cardiovascular mortality is weakened in coronary artery disease (CAD). We aimed to investigate the associations of HDL particle concentrations with cardiovascular mortality and the impact of CAD on these associations. We also sought to comparatively evaluate HDL cholesterol and HDL particle concentrations in predicting cardiovascular mortality.

METHODS

Total and subclass HDL particle concentrations were measured by nuclear magnetic resonance spectroscopy in 2290 participants of the LUdwigshafen RIsk and Cardiovascular Health study referred for coronary angiography. The participants were prospectively followed over a median (interquartile range) duration of 10.0 (6.1-10.6) years.

RESULTS

The mean (SD) age of the participants (1575 males, 715 females) was 62.9 (10.4) years; body mass index, 27.6 (4.1) kg/m²; HDL cholesterol, 39 (11) mg/dL [1 (0.29) mmol/L]; and total HDL particle concentration, 24.1 (5.8) μmol/L. Of the participants, 434 died from cardiovascular diseases. In multivariate analyses, tertiles of total HDL particle concentrations were inversely related to cardiovascular mortality (hazard ratio for third vs first tertile = 0.55, P < 0.001). This association was primarily mediated by small HDL particles (P < 0.001). Adding total or small HDL particle concentrations rather than HDL cholesterol to multivariate prediction models improved performance metrics for cardiovascular mortality. The presence of CAD had no impact on the associations between HDL particle concentrations and cardiovascular mortality.

CONCLUSIONS

High HDL particle concentration is consistently and independently of CAD associated with decreased cardiovascular mortality. Whether the inverse relationship between HDL particle concentration and cardiovascular mortality may be translated into novel therapies is under investigation.

Alternatives to Nuclear Overhauser Enhancement Spectroscopy Presat and Carr-Purcell-Meiboom-Gill Presat for NMR-Based Metabolomics

NMR metabolomics are primarily conducted with 1D nuclear Overhauser enhancement spectroscopy (NOESY) presat for water suppression and Carr-Purcell-Meiboom-Gill (CPMG) presat as a T2 filter to remove macromolecule signals. Others pulse sequences exist for these two objectives but are not often used in metabolomics studies, because they are less robust or unknown to the NMR metabolomics community. However, recent improvements on alternative pulse sequences provide attractive alternatives to 1D NOESY presat and CPMG presat. We focus this perspective on PURGE, a water suppression technique, and PROJECT presat, a T2 filter. These two pulse sequences, when optimized, performed at least on par with 1D NOESY presat and CPMG presat, if not better. These pulse sequences were tested on common samples for metabolomics, human plasma, and urine.

BCAT1 expression associates with ovarian cancer progression: possible implications in altered disease metabolism

Previously, we have identified the branched chain amino-acid transaminase 1 (BCAT1) gene as notably hypomethylated in low-malignant potential (LMP) and high-grade (HG) serous epithelial ovarian tumors, compared to normal ovarian tissues. Here we show that BCAT1 is strongly overexpressed in both LMP and HG serous epithelial ovarian tumors, which probably correlates with its hypomethylated status. Knockdown of the BCAT1 expression in epithelial ovarian cancer (EOC) cells led to sharp decrease of cell proliferation, migration and invasion and inhibited cell cycle progression. BCAT1 silencing was associated with the suppression of numerous genes and pathways known previously to be implicated in ovarian tumorigenesis, and the induction of some tumor suppressor genes (TSGs). Moreover, BCAT1 suppression resulted in downregulation of numerous genes implicated in lipid production and protein synthesis, suggesting its important role in controlling EOC metabolism. Further metabolomic analyses were indicative for significant depletion of most amino acids and different phospho- and sphingolipids following BCAT1 knockdown. Finally, BCAT1 suppression led to significantly prolonged survival time in xenograft model of advanced peritoneal EOC. Taken together, our findings provide new insights about the functional role of BCAT1 in ovarian carcinogenesis and identify this transaminase as a novel EOC biomarker and putative EOC therapeutic target.

Genetics of non-conventional lipoprotein fractions

Lipoprotein subclass measures associate with cardiometabolic disease risk. Currently the information that lipoproteins convey on disease risk over that of traditional demographic and lipid measures is minimal, and so their use is clinics is limited. However, lipoprotein subclass perturbations represent some of the earliest manifestations of metabolic dysfunction, and their etiology is partially distinct from lipids, so information on the genetic etiology of lipoproteins offers promise for improved risk prediction, and unique mechanistic insights into IR and atherosclerosis. Here, I review the genetic variants validated as associating with lipoprotein measures to date, and show that the majority of identified variants have functionality that is best understood as related to lipid measures. Until we focus on the genes as they relate to lipoprotein subclass production, we are limiting our understanding of biological mechanisms underlying cardiometabolic disease.

Differential Genetic Effects on Statin-Induced Changes Across Low-Density Lipoprotein-Related Measures

BACKGROUND

Statin therapy influences not only low-density lipoprotein (LDL) cholesterol levels but also LDL-related biomarkers, including non-high-density lipoprotein cholesterol (non-HDL-C), apolipoprotein B, total number of LDL particles, and mean LDL particle size. Recent studies have identified many genetic loci influencing circulating lipid levels and statin-induced LDL cholesterol reduction. However, it is unknown how these genetic variants influence statin-induced changes in LDL subfractions and non-HDL-C.

METHODS AND RESULTS

One hundred sixty candidate single-nucleotide polymorphisms for effects on circulating lipid levels or statin-induced LDL-cholesterol lowering were tested for association with response of LDL subfractions and non-HDL-C to rosuvastatin or placebo for 1 year among 7046 participants from the Justification for Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Of the 51 single-nucleotide polymorphisms associated with statin response for ≥ 1 of the LDL subfractions or non-HDL-C, 20 single-nucleotide polymorphisms could be clustered according to effects predominantly on LDL particle size, predominantly on LDL particle number, and on apolipoprotein B but not on LDL cholesterol or non-HDL-C.

CONCLUSIONS

These differential associations point to pathways of LDL response to statin therapy and possibly to mechanisms of statin-dependent cardiovascular disease risk reduction.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00239681.

Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes

This paper highlights the metabolomic roles in systems biology towards the elucidation of metabolic mechanisms in obesity and type 2 diabetes.

Formation of fenestrae in murine liver sinusoids depends on plasmalemma vesicle-associated protein and is required for lipoprotein passage

Liver sinusoidal endothelial cells (LSEC) are characterized by the presence of fenestrations that are not bridged by a diaphragm. The molecular mechanisms that control the formation of the fenestrations are largely unclear. Here we report that mice, which are deficient in plasmalemma vesicle-associated protein (PLVAP), develop a distinct phenotype that is caused by the lack of sinusoidal fenestrations. Fenestrations with a diaphragm were not observed in mouse LSEC at three weeks of age, but were present during embryonic life starting from embryonic day 12.5. PLVAP was expressed in LSEC of wild-type mice, but not in that of Plvap-deficient littermates. Plvap^-/- LSEC showed a pronounced and highly significant reduction in the number of fenestrations, a finding, which was seen both by transmission and scanning electron microscopy. The lack of fenestrations was associated with an impaired passage of macromolecules such as FITC-dextran and quantum dot nanoparticles from the sinusoidal lumen into Disse's space. Plvap-deficient mice suffered from a pronounced hyperlipoproteinemia as evidenced by milky plasma and the presence of lipid granules that occluded kidney and liver capillaries. By NMR spectroscopy of plasma, the nature of hyperlipoproteinemia was identified as massive accumulation of chylomicron remnants. Plasma levels of low density lipoproteins (LDL) were also significantly increased as were those of cholesterol and triglycerides. In contrast, plasma levels of high density lipoproteins (HDL), albumin and total protein were reduced. At around three weeks of life, Plvap-deficient livers developed extensive multivesicular steatosis, steatohepatitis, and fibrosis. PLVAP is critically required for the formation of fenestrations in LSEC. Lack of fenestrations caused by PLVAP deficiency substantially impairs the passage of chylomicron remnants between liver sinusoids and hepatocytes, and finally leads to liver damage.

Genomic determinants of triglyceride and cholesterol distribution into lipoprotein fractions in the rat

The plasma profile of major lipoprotein classes and its subdivision into particular fractions plays a crucial role in the pathogenesis of atherosclerosis and is a major predictor of coronary artery disease. Our aim was to identify genomic determinants of triglyceride and cholesterol distribution into lipoprotein fractions and lipoprotein particle sizes in the recombinant inbred rat set PXO, in which alleles of two rat models of the metabolic syndrome (SHR and PD inbred strains) segregate together with those from Brown Norway rat strain. Adult male rats of 15 PXO strains (n = 8–13/strain) and two progenitor strains SHR-Lx (n = 13) and BXH2/Cub (n = 18) were subjected to one-week of high-sucrose diet feeding. We performed association analyses of triglyceride (TG) and cholesterol (C) concentrations in 20 lipoprotein fractions and the size of major classes of lipoprotein particles utilizing 704 polymorphic microsatellite markers, the genome-wide significance was validated by 2,000 permutations per trait. Subsequent in silico focusing of the identified quantitative trait loci was completed using a map of over 20,000 single nucleotide polymorphisms. In most of the phenotypes we identified substantial gradient among the strains (e.g. VLDL-TG from 5.6 to 66.7 mg/dl). We have identified 14 loci (encompassing 1 to 65 genes) on rat chromosomes 3, 4, 7, 8, 11 and 12 showing suggestive or significant association to one or more of the studied traits. PXO strains carrying the SHR allele displayed significantly higher values of the linked traits except for LDL-TG and adiposity index. Cholesterol concentrations in large, medium and very small LDL particles were significantly associated to a haplotype block spanning part of a single gene, low density lipoprotein receptor-related protein 1B (Lrp1b). Using genome-wide association we have identified new genetic determinants of triglyceride and cholesterol distribution into lipoprotein fractions in the recombinant inbred panel of rat model strains.

Genetic risk scores associated with baseline lipoprotein subfraction concentrations do not associate with their responses to fenofibrate

Lipoprotein subclass concentrations are modifiable markers of cardiovascular disease risk. Fenofibrate is known to show beneficial effects on lipoprotein subclasses, but little is known about the role of genetics in mediating the responses of lipoprotein subclasses to fenofibrate. A recent genomewide association study (GWAS) associated several single nucleotide polymorphisms (SNPs) with lipoprotein measures, and validated these associations in two independent populations. We used this information to construct genetic risk scores (GRSs) for fasting lipoprotein measures at baseline (pre-fenofibrate), and aimed to examine whether these GRSs also associated with the responses of lipoproteins to fenofibrate. Fourteen lipoprotein subclass measures were assayed in 817 men and women before and after a three week fenofibrate trial. We set significance at a Bonferroni corrected alpha <0.05 (p < 0.004). Twelve subclass measures changed with fenofibrate administration (each p = 0.003 to <0.0001). Mixed linear models which controlled for age, sex, body mass index (BMI), smoking status, pedigree and study-center, revealed that GRSs were associated with eight baseline lipoprotein measures (p < 0.004), however no GRS was associated with fenofibrate response. These results suggest that the mechanisms for changes in lipoprotein subclass concentrations with fenofibrate treatment are not mediated by the genetic risk for fasting levels.

Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits

Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here, we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis, we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study for association with methylation of 457 004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome–metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style-dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (P = 3.9 × 10⁻²⁰ to 2.0 × 10⁻¹⁰⁸, r² = 0.036 to 0.221). Seven loci display CpG site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (P = 9.2 × 10⁻¹⁴ to 2.7 × 10⁻²⁷, r² = 0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3-beta,17-beta-diol disulfate. In these cases, the association between CpG-methylation and metabotype is likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggest that DNA methylation plays an important role in regulating human metabolism.

GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.

Nowadays, the availability of cheaper and accurate assays to quantify multiple (endo)phenotypes in large population cohorts allows multi-trait studies. However, these studies are limited by the lack of flexible models integrated with efficient computational tools for genome-wide multi SNPs-traits analyses. To overcome this problem, we propose a novel Bayesian analysis strategy and a new algorithmic implementation which exploits parallel processing architecture for fully multivariate modeling of groups of correlated phenotypes at the genome-wide scale. In addition to increased power of our algorithm over alternative Bayesian and well-established non-Bayesian multi-phenotype methods, we provide an application to a real case study of several blood lipid traits, and show how our method recovered most of the major associations and is better at refining multi-trait polygenic associations than alternative methods. We reveal and replicate in independent cohorts new associations with two phenotypic groups that were not detected by competing multivariate approaches and not noticed by a large meta-GWAS. We also discuss the applicability of the proposed method to large meta-analyses involving hundreds of thousands of individuals and to diverse genomic datasets where complex dependencies in the predictor space are present.

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in (1)H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.

Genome-wide association study indicates variants associated with insulin signaling and inflammation mediate lipoprotein responses to fenofibrate

OBJECTIVE

A shift towards overall larger very low-density lipoprotein (VLDL), and smaller low-density lipoprotein and high-density lipoprotein (HDL) diameters occurs in insulin resistance (IR), which reflects shifts in the distribution of the subfraction concentrations. Fenofibrate, indicated for hypertriglyceridemia, simultaneously reduces IR and shifts in lipoprotein diameter. Individual responses to fenofibrate vary, and we conducted a genome-wide association study to identify genetic differences that could contribute to such differences.

METHODS

Association analysis was conducted between single nucleotide polymorphisms (SNPs) on the Affymetrix 6.0 array and fasting particle diameter responses to a 12-week fenofibrate trial, in 817 related Caucasian participants of the Genetics of Lipid Lowering Drugs and Diet Network. Linear models were conducted, which adjusted for age, sex and study center as fixed effects, and pedigree as a random effect. The top three SNPs associated with each fraction were examined subsequently for associations with changes in subfraction concentrations.

RESULTS

SNPs in AHCYL2 and CD36 genes reached, or closely approached, genome-wide levels of significance with VLDL and HDL diameter responses to fenofibrate, respectively (P=4×10(-9) and 8×10(-8)). SNPs in AHCYL2 were associated with a decrease in the concentration of the large VLDL subfraction only (P=0.002). SNPs associated with HDL diameter change were not associated with a single subfraction concentration change (P>0.05) indicating small shifts across all subfractions.

CONCLUSION

We report novel associations between lipoprotein diameter responses to fenofibrate and the AHCYL2 and CD36 genes. Previous associations of these genes with IR emphasize the role of IR in mediating lipoprotein response to fenofibrate.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

Genome-wide association studies with metabolomics

Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.