Publications by authors named "Lude Franke"

217 Publications

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Sci Rep 2021 May 19;11(1):10606. Epub 2021 May 19.

Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.
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http://dx.doi.org/10.1038/s41598-021-89904-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134421PMC
May 2021

Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption.

Nat Commun 2021 05 14;12(1):2830. Epub 2021 May 14.

Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands.

Coffee and tea are extensively consumed beverages worldwide which have received considerable attention regarding health. Intake of these beverages is consistently linked to, among others, reduced risk of diabetes and liver diseases; however, the mechanisms of action remain elusive. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. Here we report the results from epigenome-wide association studies (EWAS) on coffee and tea consumption in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis of coffee consumption reveals 11 CpGs surpassing the epigenome-wide significance threshold (P-value <1.1×10), which annotated to the AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH genes. Among them, cg14476101 is significantly associated with expression of the PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells shows a correlation with expression levels of genes associated with circulating lipids, suggesting a role of PHGDH in hepatic-lipid metabolism. EWAS meta-analysis on tea consumption reveals no significant association, only two CpGs annotated to CACNA1A and PRDM16 genes show suggestive association (P-value <5.0×10). These findings indicate that coffee-associated changes in DNA methylation levels may explain the mechanism of action of coffee consumption in conferring risk of diseases.
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http://dx.doi.org/10.1038/s41467-021-22752-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121846PMC
May 2021

Lifelines COVID-19 cohort: investigating COVID-19 infection and its health and societal impacts in a Dutch population-based cohort.

BMJ Open 2021 03 17;11(3):e044474. Epub 2021 Mar 17.

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

Purpose: The Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort.

Participants: Participants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project. FINDINGS TO DATE: >71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.

Future Plans: Questionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.
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http://dx.doi.org/10.1136/bmjopen-2020-044474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7977080PMC
March 2021

Correction for both common and rare cell types in blood is important to identify genes that correlate with age.

BMC Genomics 2021 Mar 15;22(1):184. Epub 2021 Mar 15.

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

Background: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains.

Results: Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity.

Conclusions: We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.
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http://dx.doi.org/10.1186/s12864-020-07344-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958454PMC
March 2021

Translational insights from single-cell technologies across the cardiovascular disease continuum.

Trends Cardiovasc Med 2021 Mar 2. Epub 2021 Mar 2.

Department of Genetics, University of Groningen, University Medical Center Groningen, Oncode Institute, Groningen, the Netherlands; Department of Cardiology, University Medical Centre Utrecht, Utrecht, the Netherlands. Electronic address:

Cardiovascular disease is the leading cause of death worldwide. The societal health burden it represents can be reduced by taking preventive measures and developing more effective therapies. Reaching these goals, however, requires a better understanding of the pathophysiological processes leading to and occurring in the diseased heart. In the last 5 years, several biological advances applying single-cell technologies have enabled researchers to study cardiovascular diseases with unprecedented resolution. This has produced many new insights into how specific cell types change their gene expression level, activation status and potential cellular interactions with the development of cardiovascular disease, but a comprehensive overview of the clinical implications of these findings is lacking. In this review, we summarize and discuss these recent advances and the promise of single-cell technologies from a translational perspective across the cardiovascular disease continuum, covering both animal and human studies, and explore the future directions of the field.
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http://dx.doi.org/10.1016/j.tcm.2021.02.009DOI Listing
March 2021

Systematic Prioritization of Candidate Genes in Disease Loci Identifies as a Master Regulator of IFNγ Signaling in Celiac Disease.

Front Genet 2020 25;11:562434. Epub 2021 Jan 25.

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

Celiac disease (CeD) is a complex T cell-mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD. We used four different approaches-Mendelian randomization inverse variance weighting, COLOC, LD overlap, and DEPICT-to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signaling and T cell activation pathways. Fifty-one of these genes are targets of known drug compounds or likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene combinations that were affected by our CeD-prioritized genes in . Notably, 41 of these -mediated genes appear to be under control of one master regulator, (), and were found to be involved in interferon (IFN)γ signaling and MHC I antigen processing/presentation. Finally, we performed experiments in a human monocytic cell line that validated the role of as an immune regulator acting in . Our strategy confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and IFNγ signaling as well as with MHC I antigen processing, both major players of immune activation and CeD pathogenesis.
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http://dx.doi.org/10.3389/fgene.2020.562434DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868554PMC
January 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
February 2021

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

Mol Psychiatry 2021 Jan 8. 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
January 2021

Practical Barriers and Facilitators Experienced by Patients, Pharmacists and Physicians to the Implementation of Pharmacogenomic Screening in Dutch Outpatient Hospital Care-An Explorative Pilot Study.

J Pers Med 2020 Dec 21;10(4). Epub 2020 Dec 21.

Department of Genetics, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.

Pharmacogenomics (PGx) can provide optimized treatment to individual patients while potentially reducing healthcare costs. However, widespread implementation remains absent. We performed a pilot study of PGx screening in Dutch outpatient hospital care to identify the barriers and facilitators to implementation experienced by patients ( = 165), pharmacists ( = 58) and physicians ( = 21). Our results indeed suggest that the current practical experience of healthcare practitioners with PGx is limited, that proper education is necessary, that patients want to know the exact implications of the results, that healthcare practitioners heavily rely on their computer systems, that healthcare practitioners encounter practical problems in the systems used, and a new barrier was identified, namely that there is an unclear allocation of responsibilities between healthcare practitioners about who should discuss PGx with patients and apply PGx results in healthcare. We observed a positive attitude toward PGx among all the stakeholders in our study, and among patients, this was independent of the occurrence of drug-gene interactions during their treatment. Facilitators included the availability of and adherence to Dutch Pharmacogenetics Working Group guidelines. While clinical decision support (CDS) is available and valued in our medical center, the lack of availability of CDS may be an important barrier within Dutch healthcare in general.
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http://dx.doi.org/10.3390/jpm10040293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767378PMC
December 2020

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals.

Nat Metab 2020 10 16;2(10):1135-1148. Epub 2020 Oct 16.

SCALLOP consortium.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
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http://dx.doi.org/10.1038/s42255-020-00287-2DOI Listing
October 2020

An epigenome-wide association study identifies multiple DNA methylation markers of exposure to endocrine disruptors.

Environ Int 2020 11 9;144:106016. Epub 2020 Sep 9.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands; Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands; Genomics Coordination Centre, Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands. Electronic address:

Background: Exposure to environmental endocrine disrupting chemicals (EDCs) may play an important role in the epidemic of metabolic diseases. Epigenetic alterations may functionally link EDCs with gene expression and metabolic traits.

Objectives: We aimed to evaluate metabolic-related effects of the exposure to endocrine disruptors including five parabens, three bisphenols, and 13 metabolites of nine phthalates as measured in 24-hour urine on epigenome-wide DNA methylation.

Methods: A blood-based epigenome-wide association study was performed in 622 participants from the Lifelines DEEP cohort using Illumina Infinium HumanMethylation450 methylation data and EDC excretions in 24-hour urine. Out of the 21 EDCs, 13 compounds were detected in >75% of the samples and, together with bisphenol F, were included in these analyses. Furthermore, we explored the putative function of identified methylation markers and their correlations with metabolic traits.

Results: We found 20 differentially methylated cytosine-phosphate-guanines (CpGs) associated with 10 EDCs at suggestive p-value < 1 × 10, of which four, associated with MEHP and MEHHP, were genome-wide significant (Bonferroni-corrected p-value < 1.19 × 10). Nine out of 20 CpGs were significantly associated with at least one of the tested metabolic traits, such as fasting glucose, glycated hemoglobin, blood lipids, and/or blood pressure. 18 out of 20 EDC-associated CpGs were annotated to genes functionally related to metabolic syndrome, hypertension, obesity, type 2 diabetes, insulin resistance and glycemic traits.

Conclusions: The identified DNA methylation markers for exposure to the most common EDCs provide suggestive mechanism underlying the contributions of EDCs to metabolic health. Follow-up studies are needed to unravel the causality of EDC-induced methylation changes in metabolic alterations.
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http://dx.doi.org/10.1016/j.envint.2020.106016DOI Listing
November 2020

Lack of Association Between Genetic Variants at and Genes Involved in SARS-CoV-2 Infection and Human Quantitative Phenotypes.

Front Genet 2020 8;11:613. Epub 2020 Jun 8.

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

Coronavirus disease 2019 (COVID-19) shows a wide variation in expression and severity of symptoms, from very mild or no symptoms, to flu-like symptoms, and in more severe cases, to pneumonia, acute respiratory distress syndrome, and even death. Large differences in outcome have also been observed between males and females. The causes for this variability are likely to be multifactorial, and to include genetics. The SARS-CoV-2 virus responsible for the infection depends on two human genes: the human receptor angiotensin converting enzyme 2 () for cell invasion, and the serine protease for S protein priming. Genetic variation in these two genes may thus modulate an individual's genetic predisposition to infection and virus clearance. While genetic data on COVID-19 patients is being gathered, we carried out a phenome-wide association scan (PheWAS) to investigate the role of these genes in other human phenotypes in the general population. We examined 178 quantitative phenotypes including cytokines and cardio-metabolic biomarkers, as well as usage of 58 medications in 36,339 volunteers from the Lifelines population cohort, in relation to 1,273 genetic variants located in or near and . While none reached our threshold for significance, we observed several interesting suggestive associations. For example, single nucleotide polymorphisms (SNPs) near the genes were associated with thrombocytes count ( = 1.8 × 10). SNPs within the gene were associated with (1) the use of angiotensin II receptor blockers (ARBs) combination therapies ( = 5.7 × 10), an association that is significantly stronger in females ( = 0.01), and (2) with the use of non-steroid anti-inflammatory and antirheumatic products ( = 5.5 × 10). While these associations need to be confirmed in larger sample sizes, they suggest that these variants could play a role in diseases such as thrombocytopenia, hypertension, and chronic inflammation that are often observed in the more severe COVID-19 cases. Further investigation of these genetic variants in the context of COVID-19 is thus promising for better understanding of disease variability. Full results are available at https://covid19research.nl.
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http://dx.doi.org/10.3389/fgene.2020.00613DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295011PMC
June 2020

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

BMC Bioinformatics 2020 Jun 12;21(1):243. Epub 2020 Jun 12.

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

Background: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).

Results: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.

Conclusions: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).
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http://dx.doi.org/10.1186/s12859-020-03576-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291428PMC
June 2020

Identification, Heritability, and Relation With Gene Expression of Novel DNA Methylation Loci for Blood Pressure.

Hypertension 2020 07 10;76(1):195-205. Epub 2020 Jun 10.

Department of Endocrinology (B.H.R.W., J.V.v.V.-O.), University Medical Center Groningen, University of Groningen, The Netherlands.

We conducted an epigenome-wide association study meta-analysis on blood pressure (BP) in 4820 individuals of European and African ancestry aged 14 to 69. Genome-wide DNA methylation data from peripheral leukocytes were obtained using the Infinium Human Methylation 450k BeadChip. The epigenome-wide association study meta-analysis identified 39 BP-related CpG sites with <1×10. In silico replication in the CHARGE consortium of 17 010 individuals validated 16 of these CpG sites. Out of the 16 CpG sites, 13 showed novel association with BP. Conversely, out of the 126 CpG sites identified as being associated (<1×10) with BP in the CHARGE consortium, 21 were replicated in the current study. Methylation levels of all the 34 CpG sites that were cross-validated by the current study and the CHARGE consortium were heritable and 6 showed association with gene expression. Furthermore, 9 CpG sites also showed association with BP with <0.05 and consistent direction of the effect in the meta-analysis of the Finnish Twin Cohort (199 twin pairs and 4 singletons; 61% monozygous) and the Netherlands Twin Register (266 twin pairs and 62 singletons; 84% monozygous). Bivariate quantitative genetic modeling of the twin data showed that a majority of the phenotypic correlations between methylation levels of these CpG sites and BP could be explained by shared unique environmental rather than genetic factors, with 100% of the correlations of systolic BP with cg19693031 () and cg00716257 () determined by environmental effects acting on both systolic BP and methylation levels.
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http://dx.doi.org/10.1161/HYPERTENSIONAHA.120.14973DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295009PMC
July 2020

Predicted efficacy of a pharmacogenetic passport for inflammatory bowel disease.

Aliment Pharmacol Ther 2020 06 3;51(11):1105-1115. Epub 2020 May 3.

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

Background: High inter-individual variability in therapeutic response to drugs used in the management of Inflammatory Bowel Disease (IBD) leads to high morbidity and high costs. Genetic variants predictive of thiopurine-induced myelosuppression, thiopurine-induced pancreatitis and immunogenicity of Tumour Necrosis Factor alpha (TNFα) antagonists have been identified, but uptake of pre-treatment pharmacogenetic testing into clinical guidelines has been slow.

Aim: To explore the efficacy of a pharmacogenetic passport for IBD that includes multiple pharmacogenetic predictors of response.

Methods: Patients with IBD exposed to thiopurines and/or TNFα antagonists were retrospectively evaluated for the presence of thiopurine toxicity and/or immunogenicity of TNFα antagonists. All patients were genotyped using both whole-exome sequencing and the Illumina Global Screening Array. An in-house-developed computational pipeline translated genetic data into an IBD pharmacogenetic passport that predicted risks for thiopurine toxicity and immunogenicity of TNFα antagonists per patient. Using pharmacogenetic-guided treatment guidelines, we calculated clinical efficacy estimates for pharmacogenetic testing for IBD.

Results: Among 710 patients with IBD exposed to thiopurines and/or TNFα antagonists, 150 adverse drug responses occurred and our pharmacogenetic passport would have predicted 54 (36%) of these. Using a pharmacogenetic passport for IBD that includes genetic variants predictive of thiopurine-induced myelosuppression, thiopurine-induced pancreatitis, and immunogenicity of TNFα antagonists, 24 patients need to be genotyped to prevent one of these adverse drug responses.

Conclusions: This study highlights the clinical efficacy of a pharmacogenetic passport for IBD. Implementation of such a pharmacogenetic passport into clinical management of IBD may contribute to a reduction in adverse drug responses.
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http://dx.doi.org/10.1111/apt.15762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318341PMC
June 2020

Habitual dietary intake of IBD patients differs from population controls: a case-control study.

Eur J Nutr 2021 Feb 24;60(1):345-356. Epub 2020 Apr 24.

Department of Gastroenterology and Hepatology, University Medical Centre Groningen and University of Groningen, Hanzeplein 1, P.O. box 30.001, 9700RB, Groningen, The Netherlands.

Background: Since evidence-based dietary guidelines are lacking for IBD patients, they tend to follow "unguided" dietary habits; potentially leading to nutritional deficiencies and detrimental effects on disease course. Therefore, we compared dietary intake of IBD patients with controls.

Methods: Dietary intake of macronutrients and 25 food groups of 493 patients (207 UC, 286 CD), and 1291 controls was obtained via a food frequency questionnaire.

Results: 38.6% of patients in remission had protein intakes below the recommended 0.8 g/kg and 86.7% with active disease below the recommended 1.2 g/kg. Multinomial logistic regression, corrected for age, gender and BMI, showed that (compared to controls) UC patients consumed more meat and spreads, but less alcohol, breads, coffee and dairy; CD patients consumed more non-alcoholic drinks, potatoes, savoury snacks and sugar and sweets but less alcohol, dairy, nuts, pasta and prepared meals. Patients with active disease consumed more meat, soup and sugar and sweets but less alcohol, coffee, dairy, prepared meals and rice; patients in remission consumed more potatoes and spreads but less alcohol, breads, dairy, nuts, pasta and prepared meals.

Conclusions: Patients avoiding potentially favourable foods and gourmandizing potentially unfavourable foods are of concern. Special attention is needed for protein intake in the treatment of these patients.
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http://dx.doi.org/10.1007/s00394-020-02250-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867519PMC
February 2021

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response.

PLoS Pathog 2020 04 6;16(4):e1008408. Epub 2020 Apr 6.

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

Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection.
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http://dx.doi.org/10.1371/journal.ppat.1008408DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173933PMC
April 2020

Relationship between gut microbiota and circulating metabolites in population-based cohorts.

Nat Commun 2019 12 20;10(1):5813. Epub 2019 Dec 20.

Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.
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http://dx.doi.org/10.1038/s41467-019-13721-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925111PMC
December 2019

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies.

Genome Biol 2019 11 14;20(1):235. Epub 2019 Nov 14.

Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

Background: A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

Results: We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

Conclusions: Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.
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http://dx.doi.org/10.1186/s13059-019-1878-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857161PMC
November 2019

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

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

Eur J Hum Genet 2020 02 26;28(2):253-263. Epub 2019 Sep 26.

Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Insights into individual differences in gene expression and its heritability (h) 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, composed of cis-heritability (h, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h, the residual variance explained by all other genome-wide variants). Mean h 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 = 0.14, p = 6.15 × 10). Mean h was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10) and with estimates from earlier RNA-Seq-based studies. Mean h was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10), 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 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.
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http://dx.doi.org/10.1038/s41431-019-0511-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974598PMC
February 2020

High-throughput identification of human SNPs affecting regulatory element activity.

Nat Genet 2019 07 28;51(7):1160-1169. Epub 2019 Jun 28.

Division of Gene Regulation, Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.

Most of the millions of SNPs in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies (GWAS) have linked many of these SNPs to human traits or to gene expression levels, but rarely with sufficient resolution to identify the causal SNPs. Functional screens based on reporter assays have previously been of insufficient throughput to test the vast space of SNPs for possible effects on regulatory element activity. Here we leveraged the throughput and resolution of the survey of regulatory elements (SuRE) reporter technology to survey the effect of 5.9 million SNPs, including 57% of the known common SNPs, on enhancer and promoter activity. We identified more than 30,000 SNPs that alter the activity of putative regulatory elements, partially in a cell-type-specific manner. Integration of this dataset with GWAS results may help to pinpoint SNPs that underlie human traits.
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http://dx.doi.org/10.1038/s41588-019-0455-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609452PMC
July 2019

Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.

Nat Commun 2019 06 28;10(1):2837. Epub 2019 Jun 28.

University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 VB, Groningen, The Netherlands.

The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.
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http://dx.doi.org/10.1038/s41467-019-10649-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599066PMC
June 2019

RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting.

BMC Biol 2019 06 24;17(1):50. Epub 2019 Jun 24.

Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, the Netherlands.

Background: Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression.

Results: We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting.

Conclusions: Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.
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http://dx.doi.org/10.1186/s12915-019-0674-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589892PMC
June 2019

Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation.

Genetics 2019 07 22;212(3):905-918. Epub 2019 May 22.

School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332

Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary -eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of -eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50-60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary -eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.
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http://dx.doi.org/10.1534/genetics.119.302091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614888PMC
July 2019

Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine.

Neurology 2019 04 3;92(16):e1899-e1911. Epub 2019 Apr 3.

From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands.

Objective: To identify a plasma metabolomic biomarker signature for migraine.

Methods: Plasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a H-NMR-based metabolomics platform, to quantify 146 individual metabolites (e.g., lipids, fatty acids, and lipoproteins) and 79 metabolite ratios. Metabolite measures associated with migraine were obtained after single-metabolite logistic regression combined with a random-effects meta-analysis performed in a nonstratified and sex-stratified manner. Next, a global test analysis was performed to identify sets of related metabolites associated with migraine. The Holm procedure was applied to control the family-wise error rate at 5% in single-metabolite and global test analyses.

Results: Decreases in the level of apolipoprotein A1 (β -0.10; 95% confidence interval [CI] -0.16, -0.05; adjusted = 0.029) and free cholesterol to total lipid ratio present in small high-density lipoprotein subspecies (HDL) (β -0.10; 95% CI -0.15, -0.05; adjusted = 0.029) were associated with migraine status. In addition, only in male participants, a decreased level of omega-3 fatty acids (β -0.24; 95% CI -0.36, -0.12; adjusted = 0.033) was associated with migraine. Global test analysis further supported that HDL traits (but not other lipoproteins) were associated with migraine status.

Conclusions: Metabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs.
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http://dx.doi.org/10.1212/WNL.0000000000007313DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550500PMC
April 2019

The metabolic network coherence of human transcriptomes is associated with genetic variation at the cadherin 18 locus.

Hum Genet 2019 Apr 9;138(4):375-388. Epub 2019 Mar 9.

Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, 24105, Kiel, Germany.

Metabolic coherence (MC) is a network-based approach to dimensionality reduction that can be used, for example, to interpret the joint expression of genes linked to human metabolism. Computationally, the derivation of 'transcriptomic' MC involves mapping of an individual gene expression profile onto a gene-centric network derived beforehand from a metabolic network (currently Recon2), followed by the determination of the connectivity of a particular, profile-specific subnetwork. The biological significance of MC has been exemplified previously in the context of human inflammatory bowel disease, among others, but the genetic architecture of this quantitative cellular trait is still unclear. Therefore, we performed a genome-wide association study (GWAS) of MC in the 1000 Genomes/ GEUVADIS data (n = 457) and identified a solitary genome-wide significant association with single nucleotide polymorphisms (SNPs) in the intronic region of the cadherin 18 (CDH18) gene on chromosome 5 (lead SNP: rs11744487, p = 1.2 × 10). Cadherin 18 is a transmembrane protein involved in human neural development and cell-to-cell signaling. Notably, genetic variation at the CDH18 locus has been associated with metabolic syndrome-related traits before. Replication of our genome-wide significant GWAS result was successful in another population study from the Netherlands (BIOS, n = 2661; lead SNP), but failed in two additional studies (KORA, Germany, n = 711; GENOA, USA, n = 411). Besides sample size issues, we surmise that these discrepant findings may be attributable to technical differences. While 1000 Genomes/GEUVADIS and BIOS gene expression profiles were generated by RNA sequencing, the KORA and GENOA data were microarray-based. In addition to providing first evidence for a link between regional genetic variation and a metabolism-related characteristic of human transcriptomes, our findings highlight the benefit of adopting a systems biology-oriented approach to molecular data analysis.
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http://dx.doi.org/10.1007/s00439-019-01994-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6483969PMC
April 2019

Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases.

Nat Genet 2019 04 18;51(4):600-605. Epub 2019 Feb 18.

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.

Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.
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http://dx.doi.org/10.1038/s41588-019-0350-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441384PMC
April 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