Analysis of metabolomics data from twin families

Metabolomics is the comprehensive analysis of small molecules involved in metabolism, on the basis of samples that have been obtained from organisms in a given physiological state. Data obtained from measurements of trait levels in twin families can be used to elucidate the importance of genetic and environmental variation for individual differences in trait levels. I describe the results of various analyses using metabolomics data from twin families. These data originated from analysis of blood plasma lipids by liquid chromatography-mass spectrometry, and from analysis of blood plasma and urine by proton nuclear magnetic resonance spectroscopy. Data analyses with a newly developed method, based on hierarchical clustering analysis of family members, suggested that shared genetic variation and shared environmental variation are important for similarities in blood plasma lipid profiles among individuals. Also, a method called __quantile equating__ was developed and applied that enables combination of semiquantitative metabolomics data sets originating from different measurement __blocks__. Univariate quantitative genetic analyses based on structural equation modeling revealed interesting differences in heritability among different metabolites. In multivariate analysis, relationships among genetic sources of phenotypic variation in different metabolites were investigated. These results bear relevance for the interpretation of the results from genome-wide association analyses.Netherlands Bioinformatics Centre (NBIC)UBL - phd migration 201

Radiofrequency ablation versus hepatic resection for hepatocellular carcinoma within the Milan criteria – A comparative study

AbstractBackgroundTo compare the results of radiofrequency ablation (RFA) with hepatic resection in the treatment of hepatocellular carcinoma (HCC) within the Milan criteria.MethodsA nonrandomized comparative study was performed with 111 consecutive patients who underwent laparoscopic RFA (n = 31) or curative hepatic resection (n = 80) for HCC within Milan criteria.ResultsProcedure related complications were less often and severe after RFA than resection (3.2% vs. 25%). There was no significant difference in hospital mortality (0% vs. 3.8%). Hospital stay was significantly shorter in the RFA group than in the resection group (mean, 3.8 vs. 6.8 days). The 1-, 3-, and 5-year disease-free survival rates for the RFA group and the resection group were 76%, 40%, 40% and 76%, 60%, 60%, respectively. Disease-free survival was significantly lower in the RFA group than in the resection group. The corresponding 1-, 3-, and 5-year overall survival rates for the RFA group and the resection group were 100%, 92%, 84%, and 92%, 75%, 71%, respectively. The overall survival for RFA and resection were not significantly different.ConclusionsOur result showed comparable overall survival between RFA and surgery, although RFA was associated with a significantly higher tumor recurrence rate. RFA had the advantages over surgical resection in being less invasive and having lower morbidity

Analysis of metabolomics data from twin families

Metabolomics is the comprehensive analysis of small molecules involved in metabolism, on the basis of samples that have been obtained from organisms in a given physiological state. Data obtained from measurements of trait levels in twin families can be used to elucidate the importance of genetic and environmental variation for individual differences in trait levels. I describe the results of various analyses using metabolomics data from twin families. These data originated from analysis of blood plasma lipids by liquid chromatography-mass spectrometry, and from analysis of blood plasma and urine by proton nuclear magnetic resonance spectroscopy. Data analyses with a newly developed method, based on hierarchical clustering analysis of family members, suggested that shared genetic variation and shared environmental variation are important for similarities in blood plasma lipid profiles among individuals. Also, a method called __quantile equating__ was developed and applied that enables combination of semiquantitative metabolomics data sets originating from different measurement __blocks__. Univariate quantitative genetic analyses based on structural equation modeling revealed interesting differences in heritability among different metabolites. In multivariate analysis, relationships among genetic sources of phenotypic variation in different metabolites were investigated. These results bear relevance for the interpretation of the results from genome-wide association analyses

Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

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. © 2013 Macmillan Publishers Limited. All rights reserved

Familial Resemblance for Serum Metabolite Concentrations

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

Skewed X-inactivation is common in the general female population

Contains fulltext : 202141.pdf (publisher's version ) (Open Access

The Adult Netherlands Twin Register: Twenty-five years of survey and biological data collection

Item does not contain fulltextOver 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.11 p