A Genetic Epidemiologic Study of Lipids and Depressive Symptoms

__Abstract__ In 1719, Hensing published the first monograph on the chemical composition of the brain, in which he isolated phosphorus from cerebral tissue. His work is considered a cornerstone of modern neurochemistryl. One century later, Vauquelin demonstrated that the phosphorus was actually bound to fat in the brain2. Meanwhile, cholesterol, a marker for all lipoproteins, was first discovered in bile and in gallstones by Poulletier de Ia Salle in 17693 and then rediscovered in 1815 by Chevreul, who named it"cholesterine"4.1n 1823, Chevreul's work resulted in his masterpiece on lipids" Recherches chimiques sur /es corps gras d'origine animate" where he described for the first time several fatty acids (margaric, oleic, stearic, butyric and caproic acids), including isovaleric acid (he named it"acide phocenique"), the first branched-chain fatty acid to be isolated from the head oil of dolphins and from porpoise oi15. Ten years later, in 1833, Boudet found cholesterol in blood6• Finally, in 1884, Johann Ludwig Wilhelm Thudichum published another fundamental work"A treatise on the chemical constitution of the brain'; in which he described the presence of sphinosine in brain, but also the presence of a choline and sphingosine containing phospholipid without glycerol, which he named sphingomyelin. He additionally described the presence of cerebrosides and sulfatides in brain extracts and isolated cephalin (phosphatidylethanolamine) as a phospholipid distinct from lecithin (phosphatidylcholine)

A Combined Linkage and Exome Sequencing Analysis for Electrocardiogram Parameters in the Erasmus Rucphen Family Study

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 x 10(-4), minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait's genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca2+ levels (P = 3.3 x 10(-3)) and AMPK stimulated fatty acid oxidation in muscle (P = 4.1 x 10(-3)). Look-ups in bioinformatics resources showed that expression of FCRL2 is associated with ARHGAP24 and SETBP1 expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, FCRL2 emerges as a strong candidate gene for QT interval

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

Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.The Framingham Heart Study is funded by National Institutes of Health (NIH) contract N01-HC-25195. This study was made possible by a CRADA between BG Medicine, Inc., Boston University, and the NHLBI, and the laboratory work for this research was supported by the Division of Intramural Research of the National Heart, Lung, and Blood Institute (NHLBI). Analytical work was funded by the Division of Intramural Research of NHLBI as well as the Center for Information Technology, NIH, Bethesda, MD. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The PESA study is supported by a non-competitive unrestricted grant shared between the National Center for Cardiovascular Research Carlos III (CNIC) and the Bank of Santander. The PESA study is a noncommercial study independent of the health and pharmaceutical industry. The CNIC is supported by the Spanish Ministry of Science, Innovation and Universities, the Instituto de Salud Carlos III, and the proCNIC Foundation. The study was partially funded by a grant from AstraZeneca (TANSNIP project). JMO is supported by the US Department of Agriculture, under agreement no. 8050-51000-098-00D. MPO and MJ acknowledge an Institute of Health Carlos III grant (PI 17-00134). This research was in part funded by the Spanish Ministry of Economy and Competitiveness, Institute of Health Carlos III (PI14/00328), co-financed by FEDER funds from the European Union ('A way to built Europe'), and the Generalitat of Catalonia, Department of Health(SLT002/16/00250) and Department of Business and Knowledge(2017SGR696) to R.P. MJ is a Serra Hunter Fellow.S

A combined linkage and exome sequencing analysis for electrocardiogram parameters in the Erasmus Rucphen family study

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 × 10-4, minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait's genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca2+ levels (P = 3.3 × 10-3) and AMPK stimulated fatty acid oxidat