ABSTRACT
Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.
Subject(s)
Blood/metabolism , Genome-Wide Association Study , Blood Chemical Analysis , Cohort Studies , Humans , Metabolomics , Quantitative Trait Loci , Twins/geneticsABSTRACT
Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10-6), methionine (p-value = 9.2 × 10-5), tyrosine (p-value = 2.1 × 10-4), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10-4), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10-4), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10-4). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.
Subject(s)
Homocysteine/metabolism , Leukocytes/ultrastructure , Lipid Metabolism , Metabolomics/methods , Telomere , Adult , Aged , Cohort Studies , Female , Humans , Male , Middle Aged , Telomere ShorteningABSTRACT
BACKGROUND: Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression. RESULTS: We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting. CONCLUSIONS: Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.
Subject(s)
Alleles , Gene Expression/genetics , Genomic Imprinting/genetics , Haplotypes/genetics , Blood Chemical Analysis , Cell Line , Humans , Sequence Analysis, RNAABSTRACT
X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.
Subject(s)
Population/genetics , X Chromosome Inactivation , Calcium-Binding Proteins/genetics , Female , Humans , Intracellular Signaling Peptides and Proteins/genetics , Male , Membrane Glycoproteins/genetics , Netherlands , Polymorphism, Single Nucleotide , Receptors, Cytoplasmic and Nuclear/genetics , Receptors, Peptide/genetics , Septins/geneticsABSTRACT
Systemic sclerosis is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. We studied differentially expressed coding and non-coding genes in relation to systemic sclerosis pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from 14 early systemic sclerosis patients and six healthy individuals were sequenced with ion-torrent and analyzed using DEseq2. Overall, 4,901 genes with a fold change >1.5 and a false discovery rate <5% were detected in patients versus controls. Upregulated genes clustered in immunologic, cell adhesion, and keratin-related processes. Interestingly, 676 deregulated non-coding genes were detected, 257 of which were classified as antisense genes. Sense genes expressed opposite of these antisense genes were also deregulated in 42% of the observed sense-antisense gene pairs. The majority of the antisense genes had a similar effect sizes in an independent North American dataset with three genes (CTBP1-AS2, OTUD6B-AS1, and AGAP2-AS1) exceeding the study-wide Bonferroni-corrected P-value (PBonf < 0.0023, Pcombined = 1.1 × 10-9, 1.4 × 10-8, 1.7 × 10-6, respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of systemic sclerosis patients suggesting a novel class of genes involved in pathogenesis of systemic sclerosis.
Subject(s)
RNA, Long Noncoding/genetics , Scleroderma, Systemic/genetics , Skin/metabolism , Up-Regulation , Cells, Cultured , Humans , RNA, Long Noncoding/biosynthesis , Scleroderma, Systemic/metabolism , Scleroderma, Systemic/pathology , Skin/pathology , Transcription Factors , Transcriptional ActivationABSTRACT
BACKGROUND AND AIM: Previous studies have shown a relationship between schizophrenia and cannabis use. As both traits are substantially heritable, a shared genetic liability could explain the association. We use two recently developed genomics methods to investigate the genetic overlap between schizophrenia and cannabis use. METHODS: Firstly, polygenic risk scores for schizophrenia were created based on summary statistics from the largest schizophrenia genome-wide association (GWA) meta-analysis to date. We analysed the association between these schizophrenia polygenic scores and multiple cannabis use phenotypes (lifetime use, regular use, age at initiation, and quantity and frequency of use) in a sample of 6,931 individuals. Secondly, we applied LD-score regression to the GWA summary statistics of schizophrenia and lifetime cannabis use to calculate the genome-wide genetic correlation. RESULTS: Polygenic risk scores for schizophrenia were significantly (α<0.05) associated with five of the eight cannabis use phenotypes, including lifetime use, regular use, and quantity of use, with risk scores explaining up to 0.5% of the variance. Associations were not significant for age at initiation of use and two measures of frequency of use analyzed in lifetime users only, potentially because of reduced power due to a smaller sample size. The LD-score regression revealed a significant genetic correlation of rg=0.22 (SE=0.07, p=0.003) between schizophrenia and lifetime cannabis use. CONCLUSIONS: Common genetic variants underlying schizophrenia and lifetime cannabis use are partly overlapping. Individuals with a stronger genetic predisposition to schizophrenia are more likely to initiate cannabis use, use cannabis more regularly, and consume more cannabis over their lifetime.
Subject(s)
Marijuana Smoking/epidemiology , Marijuana Smoking/genetics , Multifactorial Inheritance/genetics , Schizophrenia/epidemiology , Schizophrenia/genetics , Adolescent , Adult , Aged , Aged, 80 and over , Diseases in Twins/epidemiology , Diseases in Twins/genetics , Female , Genetic Predisposition to Disease/epidemiology , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study/methods , Humans , Male , Middle Aged , Netherlands/epidemiology , Phenotype , Registries , Young AdultABSTRACT
An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
Subject(s)
Genome-Wide Association Study , HapMap Project , HumansABSTRACT
Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.
Subject(s)
Cardiovascular Diseases/genetics , Lipoprotein(a)/genetics , Metabolomics/methods , Adult , Aged , Cardiovascular Diseases/metabolism , Chromosome Mapping , Female , Genetic Predisposition to Disease , Genome-Wide Association Study , Humans , Lipoproteins, VLDL/metabolism , Magnetic Resonance Spectroscopy , Male , Mendelian Randomization Analysis , Middle Aged , Triglycerides/metabolism , Young AdultABSTRACT
Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. © 2016 Wiley Periodicals, Inc.
Subject(s)
Aggression/classification , Aggression/physiology , Biomarkers , Diagnostic and Statistical Manual of Mental Disorders , Humans , Mental Disorders , Metabolomics/methods , PsychiatryABSTRACT
OBJECTIVE: Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODS: We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTS: We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONS: Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.
Subject(s)
Cholesterol, LDL/metabolism , Diabetes Mellitus, Type 2/drug therapy , Hypoglycemic Agents/therapeutic use , Metformin/therapeutic use , Aged , Cross-Sectional Studies , Delta-5 Fatty Acid Desaturase , Diabetes Mellitus, Type 2/blood , Diabetes Mellitus, Type 2/prevention & control , Diabetic Angiopathies/prevention & control , Fasting/blood , Fatty Acid Desaturases/metabolism , Female , Genomics , Genotype , Humans , Lipid Metabolism/drug effects , Male , Metabolomics , Middle Aged , Risk FactorsABSTRACT
Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P < 1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N = 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.
Subject(s)
Blood/metabolism , Genome-Wide Association Study , Polymorphism, Single Nucleotide , HumansABSTRACT
BACKGROUND: Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods. METHODS: The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22,193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes. RESULTS: There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12-1.28, P = 1.9.10(-7)), heart failure (HR = 1.47, 95% CI, 1.35-1.60, P = 9.10(-19)) and ischaemic stroke (HR = 1.15, 95% CI, 1.06-1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (ß = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028-0.033, P = 3.10(-107)). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12-3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05-3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD. CONCLUSIONS: Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke.
Subject(s)
Adiposity/genetics , Cardiovascular Diseases/genetics , Polymorphism, Single Nucleotide/genetics , Body Mass Index , Cross-Sectional Studies , Female , Humans , Male , Mendelian Randomization Analysis , Middle Aged , Prospective StudiesABSTRACT
Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.
Subject(s)
Adiposity/physiology , Aging/physiology , Cardiovascular Diseases/metabolism , Blood Pressure , C-Reactive Protein/metabolism , Cholesterol, HDL/blood , Cholesterol, HDL/metabolism , Female , Humans , Insulin/blood , Interleukin-6 , Male , Middle Aged , Sex Factors , Triglycerides/blood , Triglycerides/metabolismABSTRACT
Metabolomics is the comprehensive study of metabolites, which are the substrates, intermediate, and end products of cellular metabolism. The heritability of the concentrations of circulating metabolites bears relevance for evaluating their suitability as biomarkers for disease. We report aspects of familial resemblance for the concentrations in human serum of more than 100 metabolites, measured using a targeted metabolomics platform. Age- and sex-corrected monozygotic twin correlations, midparent-offspring regression coefficients, and spouse correlations in subjects from two independent cohorts (Netherlands Twin Register and Leiden Longevity Study) were estimated for each metabolite. In the Netherlands Twin Register subjects, who were largely fasting, we found significant monozygotic twin correlations for 121 out of 123 metabolites. Heritability was confirmed by midparent-offspring regression. For most detected metabolites, the correlations between spouses were considerably lower than those between twins, indicating a contribution of genetic effects to familial resemblance. Remarkably high heritability was observed for free carnitine (monozygotic twin correlation 0.66), for the amino acids serine (monozygotic twin correlation 0.77) and threonine (monozygotic twin correlation 0.64), and for phosphatidylcholine acyl-alkyl C40:3 (monozygotic twin correlation 0.77). For octenoylcarnitine, a consistent point estimate of approximately 0.50 was found for the spouse correlations in the two cohorts as well as for the monozygotic twin correlation, suggesting that familiality for this metabolite is explained by shared environment. We conclude that for the majority of metabolites targeted by the used metabolomics platform, the familial resemblance of serum concentrations is largely genetic. Our results contribute to the knowledge of the heritability of fasting serum metabolite concentrations, which is relevant for biomarker research.
Subject(s)
Twins, Dizygotic , Twins, Monozygotic , Diseases in Twins/genetics , Environment , Humans , Netherlands , Twins, Dizygotic/genetics , Twins, Monozygotic/geneticsABSTRACT
BACKGROUND: The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach. METHODS AND FINDINGS: We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses. Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (nâ =â 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI-trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03-1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1-1.4; all p < 0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p < 0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (pâ = â0.001). CONCLUSIONS: We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes.
Subject(s)
Adiposity/genetics , Cardiovascular Diseases/genetics , Cardiovascular Diseases/metabolism , Mendelian Randomization Analysis , Quantitative Trait, Heritable , Alpha-Ketoglutarate-Dependent Dioxygenase FTO , Body Mass Index , Case-Control Studies , Confounding Factors, Epidemiologic , Genetic Association Studies , Humans , Meta-Analysis as Topic , Polymorphism, Single Nucleotide/genetics , Proteins/geneticsABSTRACT
Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
Subject(s)
Arrhythmias, Cardiac/genetics , Heart Rate/genetics , Animals , Arrhythmias, Cardiac/physiopathology , Gene Frequency , Genetic Loci , Genome-Wide Association Study , Heart Conduction System/physiopathology , Humans , Metabolic Networks and Pathways , Polymorphism, Single Nucleotide , Quantitative Trait LociABSTRACT
Over the past 25 years, the Adult Netherlands Twin Register (ANTR) has collected a wealth of information on physical and mental health, lifestyle, and personality in adolescents and adults. This article provides an overview of the sources of information available, the main research findings, and an outlook for the future. Between 1991 and 2012, longitudinal surveys were completed by twins, their parents, siblings, spouses, and offspring. Data are available for 33,957 participants, with most individuals having completed two or more surveys. Smaller projects provided in-depth phenotyping, including measurements of the autonomic nervous system, neurocognitive function, and brain imaging. For 46% of the ANTR participants, DNA samples are available and whole genome scans have been obtained in more than 11,000 individuals. These data have resulted in numerous studies on heritability, gene x environment interactions, and causality, as well as gene finding studies. In the future, these studies will continue with collection of additional phenotypes, such as metabolomic and telomere length data, and detailed genetic information provided by DNA and RNA sequencing. Record linkage to national registers will allow the study of morbidity and mortality, thus providing insight into the development of health, lifestyle, and behavior across the lifespan.
Subject(s)
Biological Specimen Banks , Biomedical Research , Diseases in Twins/genetics , Gene-Environment Interaction , Registries , Twins/genetics , Adolescent , Adult , Diseases in Twins/epidemiology , Female , Genetic Predisposition to Disease , Genome-Wide Association Study , Humans , Longitudinal Studies , Male , Netherlands/epidemiology , Young AdultABSTRACT
Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.
Subject(s)
Cluster Analysis , Lipids/blood , Lipids/genetics , Twins, Dizygotic/genetics , Twins, Monozygotic/genetics , Adolescent , C-Reactive Protein/genetics , C-Reactive Protein/metabolism , Female , Gene-Environment Interaction , Humans , Male , Models, Genetic , Netherlands , PedigreeABSTRACT
The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.