Publications by authors named "Marc Jan Bonder"

55 Publications

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Nat Genet 2021 09 2;53(9):1300-1310. Epub 2021 Sep 2.

Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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http://dx.doi.org/10.1038/s41588-021-00913-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432599PMC
September 2021

Optimizing expression quantitative trait locus mapping workflows for single-cell studies.

Genome Biol 2021 06 24;22(1):188. Epub 2021 Jun 24.

Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Background: Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease.

Results: While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches.

Conclusion: We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
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http://dx.doi.org/10.1186/s13059-021-02407-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223300PMC
June 2021

Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.

Nat Genet 2021 03 4;53(3):313-321. Epub 2021 Mar 4.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.
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http://dx.doi.org/10.1038/s41588-021-00800-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944648PMC
March 2021

Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation.

Nat Genet 2021 03 4;53(3):304-312. Epub 2021 Mar 4.

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.

Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.
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http://dx.doi.org/10.1038/s41588-021-00801-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610897PMC
March 2021

Haplotype-resolved diverse human genomes and integrated analysis of structural variation.

Science 2021 04 25;372(6537). Epub 2021 Feb 25.

New York Genome Center, New York, NY 10013, USA.

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.
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http://dx.doi.org/10.1126/science.abf7117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026704PMC
April 2021

Large-scale association analyses identify host factors influencing human gut microbiome composition.

Nat Genet 2021 02 18;53(2):156-165. Epub 2021 Jan 18.

Department of Twin Research & Genetic Epidemiology, King's College London, London, UK.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10 < P < 5 × 10) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.
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http://dx.doi.org/10.1038/s41588-020-00763-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515199PMC
February 2021

DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan.

Mol Psychiatry 2021 06 8;26(6):2148-2162. Epub 2021 Jan 8.

Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland.

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.
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http://dx.doi.org/10.1038/s41380-020-00987-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263810PMC
June 2021

Population-scale proteome variation in human induced pluripotent stem cells.

Elife 2020 08 10;9. Epub 2020 Aug 10.

Centre for Gene Regulation & Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom.

Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.
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http://dx.doi.org/10.7554/eLife.57390DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447446PMC
August 2020

Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats.

Nat Commun 2020 06 10;11(1):2928. Epub 2020 Jun 10.

Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.

Structural variants (SVs) and short tandem repeats (STRs) are important sources of genetic diversity but are not routinely analyzed in genetic studies because they are difficult to accurately identify and genotype. Because SVs and STRs range in size and type, it is necessary to apply multiple algorithms that incorporate different types of evidence from sequencing data and employ complex filtering strategies to discover a comprehensive set of high-quality and reproducible variants. Here we assemble a set of 719 deep whole genome sequencing (WGS) samples (mean 42×) from 477 distinct individuals which we use to discover and genotype a wide spectrum of SV and STR variants using five algorithms. We use 177 unique pairs of genetic replicates to identify factors that affect variant call reproducibility and develop a systematic filtering strategy to create of one of the most complete and well characterized maps of SVs and STRs to date.
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http://dx.doi.org/10.1038/s41467-020-16481-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287045PMC
June 2020

Properties of structural variants and short tandem repeats associated with gene expression and complex traits.

Nat Commun 2020 06 10;11(1):2927. Epub 2020 Jun 10.

Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we identify genomic features of SV classes and STRs that are associated with gene expression and complex traits, including their locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We identify a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and show that they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that are associated with gene expression and human traits.
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http://dx.doi.org/10.1038/s41467-020-16482-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286898PMC
June 2020

Publisher Correction: Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression.

Nat Commun 2020 Mar 23;11(1):1572. Epub 2020 Mar 23.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-15098-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089972PMC
March 2020

Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes.

Nat Methods 2020 04 16;17(4):414-421. Epub 2020 Mar 16.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.

Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino (https://github.com/single-cell-genetics/cardelino), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin.
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http://dx.doi.org/10.1038/s41592-020-0766-3DOI Listing
April 2020

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression.

Nat Commun 2020 02 10;11(1):810. Epub 2020 Feb 10.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK.

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.
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http://dx.doi.org/10.1038/s41467-020-14457-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010688PMC
February 2020

Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease.

Elife 2019 11 20;8. Epub 2019 Nov 20.

Institute for Genomic Medicine, University of California, San Diego, San Diego, United States.

The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.
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http://dx.doi.org/10.7554/eLife.48476DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904215PMC
November 2019

Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr mice on a high-fat, high-cholesterol diet.

Sci Rep 2019 10 18;9(1):14956. Epub 2019 Oct 18.

Departments of Molecular Genetics and Medical Microbiology, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands.

While the link between diet-induced changes in gut microbiota and lipid metabolism in metabolic syndrome (MetS) has been established, the contribution of host genetics is rather unexplored. As several findings suggested a role for the lysosomal lipid transporter Niemann-Pick type C1 (NPC1) in macrophages during MetS, we here explored whether a hematopoietic Npc1 mutation, induced via bone marrow transplantation, influences gut microbiota composition in low-density lipoprotein receptor knockout (Ldlr) mice fed a high-fat, high-cholesterol (HFC) diet for 12 weeks. Ldlr mice fed a HFC diet mimic a human plasma lipoprotein profile and show features of MetS, providing a model to explore the role of host genetics on gut microbiota under MetS conditions. Fecal samples were used to profile the microbial composition by 16 s ribosomal RNA gene sequencing. The hematopoietic Npc1 mutation shifted the gut microbiota composition and increased microbial richness and diversity. Variations in plasma lipid levels correlated with microbial diversity and richness as well as with several bacterial genera. This study suggests that host genetic influences on lipid metabolism affect the gut microbiome under MetS conditions. Future research investigating the role of host genetics on gut microbiota might therefore lead to identification of diagnostic and therapeutic targets for MetS.
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http://dx.doi.org/10.1038/s41598-019-51525-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802207PMC
October 2019

Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells.

Nat Commun 2019 09 25;10(1):4361. Epub 2019 Sep 25.

Epigenetics Programme, Babraham Institute, Cambridge, CB22 3AT, UK.

Age-related tissue alterations have been associated with a decline in stem cell number and function. Although increased cell-to-cell variability in transcription or epigenetic marks has been proposed to be a major hallmark of ageing, little is known about the molecular diversity of stem cells during ageing. Here we present a single cell multi-omics study of mouse muscle stem cells, combining single-cell transcriptome and DNA methylome profiling. Aged cells show a global increase of uncoordinated transcriptional heterogeneity biased towards genes regulating cell-niche interactions. We find context-dependent alterations of DNA methylation in aged stem cells. Importantly, promoters with increased methylation heterogeneity are associated with increased transcriptional heterogeneity of the genes they drive. These results indicate that epigenetic drift, by accumulation of stochastic DNA methylation changes in promoters, is associated with the degradation of coherent transcriptional networks during stem cell ageing. Furthermore, our observations also shed light on the mechanisms underlying the DNA methylation clock.
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http://dx.doi.org/10.1038/s41467-019-12293-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761124PMC
September 2019

Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1.

Genome Biol 2019 08 14;20(1):146. Epub 2019 Aug 14.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.

Background: Epigenetic clocks are mathematical models that predict the biological age of an individual using DNA methylation data and have emerged in the last few years as the most accurate biomarkers of the aging process. However, little is known about the molecular mechanisms that control the rate of such clocks. Here, we have examined the human epigenetic clock in patients with a variety of developmental disorders, harboring mutations in proteins of the epigenetic machinery.

Results: Using the Horvath epigenetic clock, we perform an unbiased screen for epigenetic age acceleration in the blood of these patients. We demonstrate that loss-of-function mutations in the H3K36 histone methyltransferase NSD1, which cause Sotos syndrome, substantially accelerate epigenetic aging. Furthermore, we show that the normal aging process and Sotos syndrome share methylation changes and the genomic context in which they occur. Finally, we found that the Horvath clock CpG sites are characterized by a higher Shannon methylation entropy when compared with the rest of the genome, which is dramatically decreased in Sotos syndrome patients.

Conclusions: These results suggest that the H3K36 methylation machinery is a key component of the epigenetic maintenance system in humans, which controls the rate of epigenetic aging, and this role seems to be conserved in model organisms. Our observations provide novel insights into the mechanisms behind the epigenetic aging clock and we expect will shed light on the different processes that erode the human epigenetic landscape during aging.
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http://dx.doi.org/10.1186/s13059-019-1753-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693144PMC
August 2019

Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity.

Genome Biol 2019 02 11;20(1):30. Epub 2019 Feb 11.

European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.

Background: Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic features, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remains poorly understood.

Results: We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results show that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on genomic features as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons. These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.

Conclusions: Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.
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http://dx.doi.org/10.1186/s13059-019-1644-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371455PMC
February 2019

DNA methylation signatures of educational attainment.

NPJ Sci Learn 2018 23;3. Epub 2018 Mar 23.

1Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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 ( = 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.
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http://dx.doi.org/10.1038/s41539-018-0020-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220239PMC
March 2018

Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome.

Sci Transl Med 2018 12;10(472)

University of Groningen and University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, Netherlands.

Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases.
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http://dx.doi.org/10.1126/scitranslmed.aap8914DOI Listing
December 2018

A linear mixed-model approach to study multivariate gene-environment interactions.

Nat Genet 2019 01 26;51(1):180-186. Epub 2018 Nov 26.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.

Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
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http://dx.doi.org/10.1038/s41588-018-0271-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354905PMC
January 2019

Analysis of 1135 gut metagenomes identifies sex-specific resistome profiles.

Gut Microbes 2019 29;10(3):358-366. Epub 2018 Oct 29.

a Department of Genetics , University of Groningen, University Medical Center Groningen , Groningen , the Netherlands.

Several gastrointestinal diseases show a sex imbalance, although the underlying (patho)physiological mechanisms behind this are not well understood. The gut microbiome may be involved in this process, forming a complex interaction with host immune system, sex hormones, medication and other environmental factors. Here we performed sex-specific analyses of fecal microbiota composition in 1135 individuals from a population-based cohort. The overall gut microbiome composition of females and males was significantly different (p = 0.001), with females showing a greater microbial diversity (p = 0.009). After correcting for the effects of intrinsic factors, smoking, diet and medications, female hormonal factors such as the use of oral contraceptives and undergoing an ovariectomy were associated with microbial species and pathways. Females had a higher richness of antibiotic-resistance genes, with the most notable being resistance to the gene family. The higher abundance of resistance genes is consistent with the greater prescription of the Macrolide-Lincosamide-Streptogramin classes of antibiotics to females. Furthermore, we observed an increased resistance to aminoglycosides in females with self-reported irritable bowel syndrome. These results throw light upon the effects of common medications that are differentially prescribed between sexes and highlight the importance of sex-specific analysis when studying the gut microbiome and resistome.
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http://dx.doi.org/10.1080/19490976.2018.1528822DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546312PMC
December 2019

Author Correction: Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome.

Nat Genet 2018 12;50(12):1752

Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

In the version of this paper originally published, there was a typographical error. In the Discussion, the sentence "In line with this, Ep-CAM-deficient mice exhibited increased intestinal permeability and decreased ion transport, which may contribute to CVD susceptibility risk" originally read iron instead of ion transport. This error has been corrected in the HTML, PDF and print versions of the article.
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http://dx.doi.org/10.1038/s41588-018-0275-9DOI Listing
December 2018

Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome.

Nat Genet 2018 11 24;50(11):1524-1532. Epub 2018 Sep 24.

Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Despite a growing body of evidence, the role of the gut microbiome in cardiovascular diseases is still unclear. Here, we present a systems-genome-wide and metagenome-wide association study on plasma concentrations of 92 cardiovascular-disease-related proteins in the population cohort LifeLines-DEEP. We identified genetic components for 73 proteins and microbial associations for 41 proteins, of which 31 were associated to both. The genetic and microbial factors identified mostly exert additive effects and collectively explain up to 76.6% of inter-individual variation (17.5% on average). Genetics contribute most to concentrations of immune-related proteins, while the gut microbiome contributes most to proteins involved in metabolism and intestinal health. We found several host-microbe interactions that impact proteins involved in epithelial function, lipid metabolism, and central nervous system function. This study provides important evidence for a joint genetic and microbial effect in cardiovascular disease and provides directions for future applications in personalized medicine.
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http://dx.doi.org/10.1038/s41588-018-0224-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241851PMC
November 2018

Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative.

Microbiome 2018 06 8;6(1):101. Epub 2018 Jun 8.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Background: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture.

Results: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories.

Conclusion: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.
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http://dx.doi.org/10.1186/s40168-018-0479-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992867PMC
June 2018

A locus at 7p14.3 predisposes to refractory celiac disease progression from celiac disease.

Eur J Gastroenterol Hepatol 2018 08;30(8):828-837

Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen.

Background: Approximately 5% of patients with celiac disease (CeD) do not respond to a gluten-free diet and progress to refractory celiac disease (RCD), a severe progression that is characterized by infiltration of intraepithelial T lymphocytes. Patients with RCD type II (RCDII) show clonal expansions of intraepithelial T lymphocytes that result in a poor prognosis and a high mortality rate through development of aggressive enteropathy-associated T-cell lymphoma. It is not known whether genetic variations play a role in severe progression of CeD to RCDII.

Patients And Methods: We performed the first genome-wide association study to identify the causal genes for RCDII and the molecular pathways perturbed in RCDII. The genome-wide association study was performed in 38 Dutch patients with RCDII, and the 15 independent top-associated single nucleotide polymorphism (SNP) variants (P<5×10) were replicated in 56 independent French and Dutch patients with RCDII.

Results: After replication, SNP rs2041570 on chromosome 7 was significantly associated with progression to RCDII (P=2.37×10, odds ratio=2.36) but not with CeD susceptibility. SNP rs2041570 risk allele A was associated with lower levels of FAM188B expression in blood and small intestinal biopsies. Stratification of RCDII biopsies based on rs2041570 genotype showed differential expression of innate immune and antibacterial genes that are expressed in Paneth cells.

Conclusion: We have identified a novel SNP associated with the severe progression of CeD to RCDII. Our data suggest that genetic susceptibility to CeD might be distinct from the progression to RCDII and suggest a role for Paneth cells in RCDII progression.
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http://dx.doi.org/10.1097/MEG.0000000000001168DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373482PMC
August 2018

DNA methylation in childhood asthma: an epigenome-wide meta-analysis.

Lancet Respir Med 2018 05 26;6(5):379-388. Epub 2018 Feb 26.

ISGlobal, Centre for Research in Environmental Epidemiology, the Barcelona Institute of Science and Technology, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.

Background: DNA methylation profiles associated with childhood asthma might provide novel insights into disease pathogenesis. We did an epigenome-wide association study to assess methylation profiles associated with childhood asthma.

Methods: We did a large-scale epigenome-wide association study (EWAS) within the Mechanisms of the Development of ALLergy (MeDALL) project. We examined epigenome-wide methylation using Illumina Infinium Human Methylation450 BeadChips (450K) in whole blood in 207 children with asthma and 610 controls at age 4-5 years, and 185 children with asthma and 546 controls at age 8 years using a cross-sectional case-control design. After identification of differentially methylated CpG sites in the discovery analysis, we did a validation study in children (4-16 years; 247 cases and 2949 controls) from six additional European cohorts and meta-analysed the results. We next investigated whether replicated CpG sites in cord blood predict later asthma in 1316 children. We subsequently investigated cell-type-specific methylation of the identified CpG sites in eosinophils and respiratory epithelial cells and their related gene-expression signatures. We studied cell-type specificity of the asthma association of the replicated CpG sites in 455 respiratory epithelial cell samples, collected by nasal brushing of 16-year-old children as well as in DNA isolated from blood eosinophils (16 with asthma, eight controls [age 2-56 years]) and compared this with whole-blood DNA samples of 74 individuals with asthma and 93 controls (age 1-79 years). Whole-blood transcriptional profiles associated with replicated CpG sites were annotated using RNA-seq data of subsets of peripheral blood mononuclear cells sorted by fluorescence-activated cell sorting.

Findings: 27 methylated CpG sites were identified in the discovery analysis. 14 of these CpG sites were replicated and passed genome-wide significance (p<1·14 × 10) after meta-analysis. Consistently lower methylation levels were observed at all associated loci across childhood from age 4 to 16 years in participants with asthma, but not in cord blood at birth. All 14 CpG sites were significantly associated with asthma in the second replication study using whole-blood DNA, and were strongly associated with asthma in purified eosinophils. Whole-blood transcriptional signatures associated with these CpG sites indicated increased activation of eosinophils, effector and memory CD8 T cells and natural killer cells, and reduced number of naive T cells. Five of the 14 CpG sites were associated with asthma in respiratory epithelial cells, indicating cross-tissue epigenetic effects.

Interpretation: Reduced whole-blood DNA methylation at 14 CpG sites acquired after birth was strongly associated with childhood asthma. These CpG sites and their associated transcriptional profiles indicate activation of eosinophils and cytotoxic T cells in childhood asthma. Our findings merit further investigations of the role of epigenetics in a clinical context.

Funding: EU and the Seventh Framework Programme (the MeDALL project).
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http://dx.doi.org/10.1016/S2213-2600(18)30052-3DOI Listing
May 2018

Detection of stable community structures within gut microbiota co-occurrence networks from different human populations.

PeerJ 2018 7;6:e4303. Epub 2018 Feb 7.

Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom.

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.
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http://dx.doi.org/10.7717/peerj.4303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807925PMC
February 2018

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

PLoS One 2017 30;12(10):e0182472. Epub 2017 Oct 30.

Framingham Heart Study, Framingham, MA, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182472PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662081PMC
November 2017

Multi-tissue DNA methylation age predictor in mouse.

Genome Biol 2017 04 11;18(1):68. Epub 2017 Apr 11.

Epigenetics Programme, The Babraham Institute, Cambridge, CB22 3AT, UK.

Background: DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse.

Results: We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet.

Conclusions: Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.
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http://dx.doi.org/10.1186/s13059-017-1203-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389178PMC
April 2017
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