Lipid-induced transcriptomic changes in blood link to lipid metabolism and allergic response.

Immune cell function can be altered by lipids in circulation, a process potentially relevant to lipid-associated inflammatory diseases including atherosclerosis and rheumatoid arthritis. To gain further insight in the molecular changes involved, we here perform a transcriptome-wide association analysis of blood triglycerides, HDL cholesterol, and LDL cholesterol in 3229 individuals, followed by a systematic bidirectional Mendelian randomization analysis to assess the direction of effects and control for pleiotropy. Triglycerides are found to induce transcriptional changes in 55 genes and HDL cholesterol in 5 genes. The function and cell-specific expression pattern of these genes implies that triglycerides downregulate both cellular lipid metabolism and, unexpectedly, allergic response. Indeed, a Mendelian randomization approach based on GWAS summary statistics indicates that several of these genes, including interleukin-4 (IL4) and IgE receptors (FCER1A, MS4A2), affect the incidence of allergic diseases. Our findings highlight the interplay between triglycerides and immune cells in allergic disease.

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference.

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 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), and NLRC5, 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.

A characterization of cis- and trans-heritability of RNA-Seq-based gene expression.

Insights into individual differences in gene expression and its heritability (h2) 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 h2total, composed of cis-heritability (h2cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h2res, the residual variance explained by all other genome-wide variants). Mean h2total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h2 = 0.14, p = 6.15 × 10-258). Mean h2cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10-308) and with estimates from earlier RNA-Seq-based studies. Mean h2res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10-3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 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-h2 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Smoking is Associated to DNA Methylation in Atherosclerotic Carotid Lesions.

BACKGROUND: Tobacco smoking is a major risk factor for atherosclerotic disease and has been associated with DNA methylation (DNAm) changes in blood cells. However, whether smoking influences DNAm in the diseased vascular wall is unknown but may prove crucial in understanding the pathophysiology of atherosclerosis. In this study, we associated current tobacco smoking to epigenome-wide DNAm in atherosclerotic plaques from patients undergoing carotid endarterectomy. METHODS: DNAm at commonly methylated sites (cytosine-guanine nucleotide pairs separated by a phospho-group [CpGs]) was assessed in atherosclerotic plaque samples and peripheral blood samples from 485 carotid endarterectomy patients. We tested the association of current tobacco smoking with DNAm corrected for age and sex. To control for bias and inflation because of cellular heterogeneity, we applied a Bayesian method to estimate an empirical null distribution as implemented by the R package bacon. Replication of the smoking-associated methylated CpGs in atherosclerotic plaques was executed in the second sample of 190 carotid endarterectomy patients, and results were meta-analyzed using a fixed-effects model. RESULTS: Tobacco smoking was significantly associated to differential DNAm in atherosclerotic lesions of 4 CpGs (false discovery rate <0.05) mapped to 2 different genes ( AHRR, ITPK1) and 17 CpGs mapped to 8 genes and RNAs in blood. The strongest associations were found for CpGs mapped to the gene AHRR, a repressor of the aryl hydrocarbon receptor transcription factor involved in xenobiotic detoxification. One of these methylated CpGs were found to be regulated by local genetic variation. CONCLUSIONS: The risk factor tobacco smoking associates with DNAm at multiple loci in carotid atherosclerotic lesions. These observations support further investigation of the relationship between risk factors and epigenetic regulation in atherosclerotic disease.

Genome-wide identification of directed gene networks using large-scale population genomics data.

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10-10), among which transcription factors were overrepresented (Fisher's P = 3.3 × 10-7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.

omicsPrint: detection of data linkage errors in multiple omics studies.

Summary: OmicsPrint is a versatile method for the detection of data linkage errors in multiple omics studies encompassing genetic, transcriptome and/or methylome data. OmicsPrint evaluates data linkage within and between omics data types using genotype calls from SNP arrays, DNA- or RNA-sequencing data and includes an algorithm to infer genotypes from Illumina DNA methylation array data. The method uses classification to verify assumed relationships and detect any data linkage errors, e.g. arising from sample mix-ups and mislabeling. Graphical and text output is provided to inspect and resolve putative data linkage errors. If sufficient genotype calls are available, first degree family relations also are revealed which can be used to check parent-offspring relations or zygosity in twin studies. Availability and implementation: omicsPrint is available from BioConductor; http://bioconductor.org/packages/omicsPrint. Supplementary information: Supplementary data are available at Bioinformatics online.

A SNP panel for identification of DNA and RNA specimens.

BACKGROUND: SNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping. RESULTS: To determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy-Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10- 20 when assuming no family relations and 1.2 × 10- 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood. CONCLUSIONS: This first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals.

DNA methylation signatures of educational attainment.

Educational attainment is a key behavioural measure in studies of cognitive and physical health, and socioeconomic status. We measured DNA methylation at 410,746 CpGs (N = 4152) and identified 58 CpGs associated with educational attainment at loci characterized by pleiotropic functions shared with neuronal, immune and developmental processes. Associations overlapped with those for smoking behaviour, but remained after accounting for smoking at many CpGs: Effect sizes were on average 28% smaller and genome-wide significant at 11 CpGs after adjusting for smoking and were 62% smaller in never smokers. We examined sources and biological implications of education-related methylation differences, demonstrating correlations with maternal prenatal folate, smoking and air pollution signatures, and associations with gene expression in cis, dynamic methylation in foetal brain, and correlations between blood and brain. Our findings show that the methylome of lower-educated people resembles that of smokers beyond effects of their own smoking behaviour and shows traces of various other exposures.

IgG glycosylation and DNA methylation are interconnected with smoking.

BACKGROUND: Glycosylation is one of the most common post-translation modifications with large influences on protein structure and function. The effector function of immunoglobulin G (IgG) alters between pro- and anti-inflammatory, based on its glycosylation. IgG glycan synthesis is highly complex and dynamic. METHODS: With the use of two different analytical methods for assessing IgG glycosylation, we aim to elucidate the link between DNA methylation and glycosylation of IgG by means of epigenome-wide association studies. In total, 3000 individuals from 4 cohorts were analyzed. RESULTS: The overlap of the results from the two glycan measurement panels yielded DNA methylation of 7 CpG-sites on 5 genomic locations to be associated with IgG glycosylation: cg25189904 (chr.1, GNG12); cg05951221, cg21566642 and cg01940273 (chr.2, ALPPL2); cg05575921 (chr.5, AHRR); cg06126421 (6p21.33); and cg03636183 (chr.19, F2RL3). Mediation analyses with respect to smoking revealed that the effect of smoking on IgG glycosylation may be at least partially mediated via DNA methylation levels at these 7 CpG-sites. CONCLUSION: Our results suggest the presence of an indirect link between DNA methylation and IgG glycosylation that may in part capture environmental exposures. GENERAL SIGNIFICANCE: An epigenome-wide analysis conducted in four population-based cohorts revealed an association between DNA methylation and IgG glycosylation patterns. Presumably, DNA methylation mediates the effect of smoking on IgG glycosylation.

Genetically defined elevated homocysteine levels do not result in widespread changes of DNA methylation in leukocytes.

BACKGROUND: DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation. METHODS: Methylation levels were assessed using Illumina 450k arrays on 9,894 individuals of European ancestry from 12 cohort studies. Linear-mixed-models were used to study the association of additive MTHFR 677C>T and genetic-risk score (GRS) based on 18 homocysteine-associated SNPs, with genome-wide methylation. RESULTS: Meta-analysis revealed that the MTHFR 677C>T variant was associated with 35 CpG sites in cis, and the GRS showed association with 113 CpG sites near the homocysteine-associated variants. Genome-wide analysis revealed that the MTHFR 677C>T variant was associated with 1 trans-CpG (nearest gene ZNF184), while the GRS model showed association with 5 significant trans-CpGs annotated to nearest genes PTF1A, MRPL55, CTDSP2, CRYM and FKBP5. CONCLUSIONS: Our results do not show widespread changes in DNA-methylation across the genome, and therefore do not support the hypothesis that mildly elevated homocysteine is associated with widespread methylation changes in leukocytes.

Differentiation-Defective Human Induced Pluripotent Stem Cells Reveal Strengths and Limitations of the Teratoma Assay and In Vitro Pluripotency Assays.

The ability to form teratomas in vivo containing multiple somatic cell types is regarded as functional evidence of pluripotency for human pluripotent stem cells (hPSCs). Since the Teratoma assay is animal dependent, laborious, and only qualitative, the PluriTest and the hPSC ScoreCard assay have been developed as in vitro alternatives. Here we compared normal hPSCs, induced hPSCs (hiPSCs) with reactivated reprogramming transgenes, and human embryonal carcinoma cells (hECs) in these assays. While normal hPSCs gave rise to typical teratomas, the xenografts of the hECs and the hiPSCs with reactivated reprogramming transgenes were largely undifferentiated and malignant. The hPSC ScoreCard assay confirmed the line-specific differentiation propensities in vitro. However, when undifferentiated cells were analyzed by the PluriTest, only hECs were identified as abnormal whereas all other cell lines were indistinguishable and resembled normal hPSCs. Our results indicate that pluripotency assays are best selected on the basis of intended downstream applications.

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution.

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.

Molecular dissection of germline chromothripsis in a developmental context using patient-derived iPS cells.

BACKGROUND: Germline chromothripsis causes complex genomic rearrangements that are likely to affect multiple genes and their regulatory contexts. The contribution of individual rearrangements and affected genes to the phenotypes of patients with complex germline genomic rearrangements is generally unknown. METHODS: To dissect the impact of germline chromothripsis in a relevant developmental context, we performed trio-based RNA expression analysis on blood cells, induced pluripotent stem cells (iPSCs), and iPSC-derived neuronal cells from a patient with de novo germline chromothripsis and both healthy parents. In addition, Hi-C and 4C-seq experiments were performed to determine the effects of the genomic rearrangements on transcription regulation of genes in the proximity of the breakpoint junctions. RESULTS: Sixty-seven genes are located within 1 Mb of the complex chromothripsis rearrangements involving 17 breakpoints on four chromosomes. We find that three of these genes (FOXP1, DPYD, and TWIST1) are both associated with developmental disorders and differentially expressed in the patient. Interestingly, the effect on TWIST1 expression was exclusively detectable in the patient's iPSC-derived neuronal cells, stressing the need for studying developmental disorders in the biologically relevant context. Chromosome conformation capture analyses show that TWIST1 lost genomic interactions with several enhancers due to the chromothripsis event, which likely led to deregulation of TWIST1 expression and contributed to the patient's craniosynostosis phenotype. CONCLUSIONS: We demonstrate that a combination of patient-derived iPSC differentiation and trio-based molecular profiling is a powerful approach to improve the interpretation of pathogenic complex genomic rearrangements. Here we have applied this approach to identify misexpression of TWIST1, FOXP1, and DPYD as key contributors to the complex congenital phenotype resulting from germline chromothripsis rearrangements.

Identification of context-dependent expression quantitative trait loci in whole blood.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Disease variants alter transcription factor levels and methylation of their binding sites.

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms.

BACKGROUND: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. RESULTS: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related gain of methylation at CpG islands marked by PRC2 or a loss of methylation at enhancers. This distinct pattern extends to other tissues and multiple cancer types. Finally, genes associated with aVMPs in trans whose expression is variably upregulated with age (733 genes) play a key role in DNA repair and apoptosis, whereas downregulated aVMP-associated genes (121 genes) are mapped to defined pathways in cellular metabolism. CONCLUSIONS: Our results link age-related changes in DNA methylation to fundamental mechanisms that are thought to drive human ageing.

Blood lipids influence DNA methylation in circulating cells.

BACKGROUND: Cells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals. RESULTS: This analysis shows that differential methylation is the consequence of inter-individual variation in blood lipid levels and not vice versa. Specifically, we observe an effect of triglycerides on DNA methylation at three CpGs, of LDL cholesterol at one CpG, and of HDL cholesterol at two CpGs using multivariable Mendelian randomization. Using RNA-seq data available for a large subset of individuals (N = 2044), DNA methylation of these six CpGs is associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL cholesterol) and ABCG1 (for HDL cholesterol), which are all key regulators of lipid metabolism. CONCLUSIONS: Our analysis suggests a role for epigenetic priming in end-product feedback control of lipid metabolism and highlights Mendelian randomization as an effective tool to infer causal relationships in integrative genomics data.

Genetic and environmental influences interact with age and sex in shaping the human methylome.

The methylome is subject to genetic and environmental effects. Their impact may depend on sex and age, resulting in sex- and age-related physiological variation and disease susceptibility. Here we estimate the total heritability of DNA methylation levels in whole blood and estimate the variance explained by common single nucleotide polymorphisms at 411,169 sites in 2,603 individuals from twin families, to establish a catalogue of between-individual variation in DNA methylation. Heritability estimates vary across the genome (mean=19%) and interaction analyses reveal thousands of sites with sex-specific heritability as well as sites where the environmental variance increases with age. Integration with previously published data illustrates the impact of genome and environment across the lifespan at methylation sites associated with metabolic traits, smoking and ageing. These findings demonstrate that our catalogue holds valuable information on locations in the genome where methylation variation between people may reflect disease-relevant environmental exposures or genetic variation.

MethylAid: visual and interactive quality control of large Illumina 450k datasets.

UNLABELLED: The Illumina 450k array is a frequently used platform for large-scale genome-wide DNA methylation studies, i.e. epigenome-wide association studies. Currently, quality control of 450k data can be performed with Illumina's GenomeStudio and is part of a limited number 450k analysis pipelines. However, GenomeStudio cannot handle large-scale studies, and existing pipelines provide limited options for quality control and neither support interactive exploration by the user. To aid the detection of bad-quality samples in large-scale genome-wide DNA methylation studies as flexible and transparent as possible, we have developed MethylAid; a visual and interactive Web application using RStudio's shiny package. Bad-quality samples are detected using sample-dependent and sample-independent quality control probes present on the array and user-adjustable thresholds. In-depth exploration of bad-quality samples can be performed using several interactive diagnostic plots. Furthermore, plots can be annotated with user-provided metadata, for example, to identify outlying batches. Our new tool makes quality assessment of 450k array data interactive, flexible and efficient and is, therefore, expected to be useful for both data analysts and core facilities. AVAILABILITY AND IMPLEMENTATION: MethylAid is implemented as an R/Bioconductor package (www.bioconductor.org/packages/3.0/bioc/html/MethylAid.html). A demo application is available from shiny.bioexp.nl/MethylAid.

Extensive localization of long noncoding RNAs to the cytosol and mono- and polyribosomal complexes.

BACKGROUND: Long noncoding RNAs (lncRNAs) form an abundant class of transcripts, but the function of the majority of them remains elusive. While it has been shown that some lncRNAs are bound by ribosomes, it has also been convincingly demonstrated that these transcripts do not code for proteins. To obtain a comprehensive understanding of the extent to which lncRNAs bind ribosomes, we performed systematic RNA sequencing on ribosome-associated RNA pools obtained through ribosomal fractionation and compared the RNA content with nuclear and (non-ribosome bound) cytosolic RNA pools. RESULTS: The RNA composition of the subcellular fractions differs significantly from each other, but lncRNAs are found in all locations. A subset of specific lncRNAs is enriched in the nucleus but surprisingly the majority is enriched in the cytosol and in ribosomal fractions. The ribosomal enriched lncRNAs include H19 and TUG1. CONCLUSIONS: Most studies on lncRNAs have focused on the regulatory function of these transcripts in the nucleus. We demonstrate that only a minority of all lncRNAs are nuclear enriched. Our findings suggest that many lncRNAs may have a function in cytoplasmic processes, and in particular in ribosome complexes.