Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses

Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value  = 1.27×10−32), PRODH with proline (P-value  = 1.11×10−19), SLC16A9 with carnitine level (P-value  = 4.81×10−14) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value  = 1.65×10−19) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value  = 1.26×10−8), KCNJ16 with 3-hydroxybutyrate (P-value  = 1.65×10−8) and 2p12 locus with valine (P-value  = 3.49×10−8). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution 1H-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits

The effect of mirabegron on energy expenditure and brown adipose tissue in healthy lean South Asian and Europid men

Aim: To compare the effects of cold exposure and the β3-adrenergic receptor agonist mirabegron on plasma lipids, energy expenditure and brown adipose tissue (BAT) activity in South Asians versus Europids. Materials and Methods: Ten lean Dutch South Asian (aged 18-30 years; body mass index [BMI] 18-25 kg/m2 ) and 10 age- and BMI-matched Europid men participated in a randomized, double-blinded, cross-over study consisting of three interventions: short-term (~ 2 hours) cold exposure, mirabegron (200 mg one dose p.o.) and placebo. Before and after each intervention, we performed lipidomic analysis in serum, assessed resting energy expenditure (REE) and skin temperature, and measured BAT fat fraction by magnetic resonance imaging. Results: In both ethnicities, cold exposure increased the levels of several serum lipid species, whereas mirabegron only increased free fatty acids. Cold exposure increased lipid oxidation in both ethnicities, while mirabegron increased lipid oxidation in Europids only. Cold exposure and mirabegron enhanced supraclavicular skin temperature in both ethnicities. Cold exposure decreased BAT fat fraction in both ethnicities. After the combination of data from both ethnicities, mirabegron decreased BAT fat fraction compared with placebo. Conclusions: In South Asians and Europids, cold exposure and mirabegron induced beneficial metabolic effects. When combining both ethnicities, cold exposure and mirabegron increased REE and lipid oxidation, coinciding with a higher supraclavicular skin temperature and lower BAT fat fraction.Diabetes Research Foundation Fellowship 2015.81.1808Netherlands CardioVascular Research Initiative: 'the Dutch Heart Foundation, Dutch Federation of University Medical Centers, the Netherlands Organisation for Health Research and Development and the Royal Netherlands Academy of Sciences' CVON2014-02 ENERGISE CVON2017-20 GENIUS-IIEuropean Union (EU) 602485European Research Council (NOMA-MRI) PCNR is an Established Investigator of the Netherlands Heart Foundation 2009T03

Estimation of Activity Related Energy Expenditure and Resting Metabolic Rate in Freely Moving Mice from Indirect Calorimetry Data

Physical activity (PA) is a main determinant of total energy expenditure (TEE) and has been suggested to play a key role in body weight regulation. However, thus far it has been challenging to determine what part of the expended energy is due to activity in freely moving subjects. We developed a computational method to estimate activity related energy expenditure (AEE) and resting metabolic rate (RMR) in mice from activity and indirect calorimetry data. The method is based on penalised spline regression and takes the time dependency of the RMR into account. In addition, estimates of AEE and RMR are corrected for the regression dilution bias that results from inaccurate PA measurements. We evaluated the performance of our method based on 500 simulated metabolic chamber datasets and compared it to that of conventional methods. It was found that for a sample time of 10 minutes the penalised spline model estimated the time-dependent RMR with 1.7 times higher accuracy than the Kalman filter and with 2.7 times higher accuracy than linear regression. We assessed the applicability of our method on experimental data in a case study involving high fat diet fed male and female C57Bl/6J mice. We found that TEE in male mice was higher due to a difference in RMR while AEE levels were similar in both groups, even though female mice were more active. Interestingly, the higher activity did not result in a difference in AEE because female mice had a lower caloric cost of activity, which was likely due to their lower body weight. In conclusion, TEE decomposition by means of penalised spline regression provides robust estimates of the time-dependent AEE and RMR and can be applied to data generated with generic metabolic chamber and indirect calorimetry set-ups

Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Background So far, more than 170 loci have been associated with circulating lipid levels through genomewide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels. Methods We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ~60 000 individuals in the discovery stage and ~90 000 samples in the replication stage. Results Our study resu

Practical aspects of estimating energy components in rodents

Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear and penalised spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and P-spline regression, except for the activity respiratory quotient. For low resolution data the basal metabolic rate and resting respiratory quotient could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R2 > 0.997; sample time of 9 min). In contrast, the thermic effect of food and activity related energy expenditure were more sensitive to a reduction in the sample rate (R2 > 0.97).In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results

Clustered Mendelian randomization analyses identify distinct and opposing pathways in the association between genetically influenced insulin-like growth factor-1 and type 2 diabetes mellitus

BACKGROUND: There is inconsistent evidence for the causal role of serum insulin-like growth factor-1 (IGF-1) concentration in the pathogenesis of human age-related diseases such as type 2 diabetes (T2D). Here, we investigated the association between IGF-1 and T2D using (clustered) Mendelian randomization (MR) analyses in the UK Biobank. METHODS: We conducted Cox proportional hazard analyses in 451 232 European-ancestry individuals of the UK Biobank (55.3% women, mean age at recruitment 56.6 years), among which 13 247 individuals developed type 2 diabetes during up to 12 years of follow-up. In addition, we conducted two-sample MR analyses based on independent single nucleotide polymorphisms (SNPs) associated with IGF-1. Given the heterogeneity between the MR effect estimates of individual instruments (P-value for Q statistic = 4.03e-145), we also conducted clustered MR analyses. Biological pathway analyses of the identified clusters were performed by over-representation analyses. RESULTS: In the Cox proportional hazard models, with IGF-1 concentrations stratified in quintiles, we observed that participants in the lowest quintile had the highest relative risk of type 2 diabetes [hazard ratio (HR): 1.31; 95% CI: 1.23-1.39). In contrast, in the two-sample MR analyses, higher genetically influenced IGF-1 was associated with a higher risk of type 2 diabetes. Based on the heterogeneous distribution of MR effect estimates of individual instruments, six clusters of genetically determined IGF-1 associated either with a lower or a higher risk of type 2 diabetes were identified. The main clusters in which a higher IGF-1 was associated with a lower risk of type 2 diabetes consisted of instruments mapping to genes in the growth hormone signalling pathway, whereas the main clusters in which a higher IGF-1 was associated with a higher risk of type 2 diabetes consisted of instruments mapping to genes in pathways related to amino acid metabolism and genomic integrity. CONCLUSIONS: The IGF-1-associated SNPs used as genetic instruments in MR analyses showed a heterogeneous distribution of MR effect estimates on the risk of type 2 diabetes. This was likely explained by differences in the underlying molecular pathways that increase IGF-1 concentration and differentially mediate the effects of IGF-1 on type 2 diabetes

Performance comparison of estimation methods.

<p>Estimated RMR time series by means of linear regression, TEE averaging for zero activity, Kalman filtering and penalised spline modelling are shown for a typical simulated (<b>A</b>) and experimental (<b>B</b>) dataset. The overall estimation accuracy in average RMR (<b>C</b>) and time-dependent RMR (<b>D</b>) was calculated based on 500 simulated datasets (expressed as Root Mean Square Error). For the estimation error in the average RMR the bias-variance decomposition was calculated to gain more insight in the origin of the error (<b>E</b>). Error bars indicate half the standard deviation.</p

Estimation of resting metabolic rate in experimental and simulated data.

<p>Mouse metabolic chamber datasets were simulated, consisting of the total energy expenditure (TEE), resting metabolic rate (RMR) and physical activity (PA) (<b>A</b>). The simulated data shows to be similar to the experimental data (<b>B</b>), exhibiting a diurnal rhythm in activity patterns and RMR, and high frequency time variations in the TEE that are due to PA. Simulated datasets were used to evaluate the accuracy of the estimated RMR time series (RMR est) by means of penalised spline regression. For details regarding the simulation procedure, see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036162#s4" target="_blank">Methods</a> section and Supplementary Text 4.</p