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... Show moreMetabolomics 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 (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) 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. Show less
Spek, A. van der; Broer, L.; Draisma, H.H.M.; Pool, R.; Albrecht, E.; Beekman, M.; ... ; Gieger, C. 2019
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... Show moreX-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. Show less
Systemic sclerosis (SSc) is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. Here we studied differentially expressed... Show moreSystemic sclerosis (SSc) is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. Here we studied differentially expressed coding and non-coding genes in relation to SSc pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from fourteen early SSc patients and six healthy individuals were sequenced with ion-torrent and analysed using DEseq2. Overall, 4901 genes with a fold change >1.5 and a false discovery rate < 5% were detected in patients versus controls. Upregulated genes clustered in immunological, 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 ρ-value (PBonf<0.0023, Pcombined = 1.1x10-9, 1.4x10-8, 1.7x10-6, respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of SSc patients suggesting a novel class of genes involved in pathogenesis of SSc. Show less
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... Show moreMetabolomics 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. Show less
