DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >... Show moreDNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated. Show less
Background DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes... Show moreBackground DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. Results By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g.,NFKBIE,CDCA7(L), andNLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. Conclusion We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation. Show less
Insights into individual differences in gene expression and its heritability (h(2)) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52... Show moreInsights into individual differences in gene expression and its heritability (h(2)) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h(total)(2), composed of cis-heritability (h(cis)(2), the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h(res)(2), the residual variance explained by all other genome-wide variants). Mean h(total)(2) was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h(2) = 0.14, p = 6.15 x 10(-258)). Mean h(cis)(2) was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, rho = 0.76, p < 10(-308)) and with estimates from earlier RNA-Seq-based studies. Mean h(res)(2) was 0.20 and correlated with the beta of the corresponding trans-eQTL (rho = 0.04, p < 1.89 x 10(-3)) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 x 10(-15)), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h(2) estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies. Show less
In this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding... Show moreIn this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding regions. This hampers their interpretability, and has prompted the exploration of their effects on transcriptional regulation, a process that is crucial in the development of common and complex diseases. To do this, we have used a variety of omics data in a large collection of individuals from the general population. Using these data, we have investigated the local and distal effects of genetic variants on other molecular phenotypes, such as gene expression levels and DNA methylation levels of CpG sites, and the underlying mechanisms. This has resulted in a framework enabling the exploration of causal hypotheses about transcriptional regulation using genetics as a causal anchor. The approaches used in this thesis have yielded insight into transcriptional (dys)regulation and several underlying mechanisms. This will be helpful in better understanding how transcriptional regulation contributes to complex phenotypes related to health and disease, such as common diseases. Show less
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
Siemelink, M.A.; Laan, S.W. van der; Haitjema, S.; Koeverden, I.D. van; Schaap, J.; Wesseling, M.; ... ; Pasterkamp, G. 2018
