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1. Comorbidities confound metabolomics studies of human disease

Author

Madis Jaagura, Jaanika Kronberg, Anu Reigo, Oliver Aasmets, Tiit Nikopensius, Urmo Võsa, Lorenzo Bomba, Estonian Biobank research team, Karol Estrada, Arthur Wuster, Tõnu Esko, and Elin Org

Subjects
Comorbidities, Metabolomics, Chronic disease, Risk factors, Electronic health records, Biobank, Medicine, Science
Abstract

Abstract The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC–MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

Published
2024
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2. Genetic determinants of plasma protein levels in the Estonian population

Author

Anette Kalnapenkis, Maarja Jõeloo, Kaido Lepik, Viktorija Kukuškina, Mart Kals, Kaur Alasoo, Estonian Biobank Research Team, Reedik Mägi, Tõnu Esko, and Urmo Võsa

Subjects
Medicine, Science
Abstract

Abstract The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.

Published
2024
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3. SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease

Author

Satu Strausz, Erik Abner, Grace Blacker, Sarah Galloway, Paige Hansen, Qingying Feng, Brandon T. Lee, Samuel E. Jones, Hele Haapaniemi, Sten Raak, George Ronald Nahass, Erin Sanders, FinnGen, Estonian Genome Centre, Estonian Biobank Research Team, Pilleriin Soodla, Urmo Võsa, Tõnu Esko, Nasa Sinnott-Armstrong, Irving L. Weissman, Mark Daly, Tuomas Aivelo, Michal Caspi Tal, and Hanna M. Ollila

Subjects
Science
Abstract

Abstract Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.

Published
2024
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4. OTTERS: a powerful TWAS framework leveraging summary-level reference data

Author

Qile Dai, Geyu Zhou, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis Battle, Alexander Teumer, Terho Lehtimäki, Olli T. Raitakari, Tõnu Esko, eQTLGen Consortium, Michael P. Epstein, and Jingjing Yang

Subjects
Science
Abstract

Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.

Published
2023
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5. Genetic analysis of over half a million people characterises C-reactive protein loci

Author

Saredo Said, Raha Pazoki, Ville Karhunen, Urmo Võsa, Symen Ligthart, Barbara Bodinier, Fotios Koskeridis, Paul Welsh, Behrooz Z. Alizadeh, Daniel I. Chasman, Naveed Sattar, Marc Chadeau-Hyam, Evangelos Evangelou, Marjo-Riitta Jarvelin, Paul Elliott, Ioanna Tzoulaki, and Abbas Dehghan

Subjects
Science
Abstract

Inflammation is associated with a variety of diseases. Here, the authors identify 266 genetic loci associated with C-reactive protein levels, a marker of inflammation, in >500,000 Europeans, along with associated pathways, clinical outcomes and potential causal associations with disease.

Published
2022
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6. Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations

Author

Maarja Lepamets, Chiara Auwerx, Margit Nõukas, Annique Claringbould, Eleonora Porcu, Mart Kals, Tuuli Jürgenson, Andrew Paul Morris, Urmo Võsa, Murielle Bochud, Silvia Stringhini, Cisca Wijmenga, Lude Franke, Hedi Peterson, Jaak Vilo, Kaido Lepik, Reedik Mägi, and Zoltán Kutalik

Subjects
anthropometric traits, copy-number variation, gene expression, methylation, multi-omics, PennCNV, Genetics, QH426-470
Abstract

Summary: Copy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (p

Published
2022
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7. Genetic regulation of spermine oxidase activity and cancer risk: a Mendelian randomization study

Author

João Fadista, Victor Yakimov, Urmo Võsa, Christine S. Hansen, Silva Kasela, Line Skotte, Frank Geller, Julie Courraud, Tõnu Esko, Viktorija Kukuškina, Alfonso Buil, Mads Melbye, Thomas M. Werge, David M. Hougaard, Lili Milani, Jonas Bybjerg-Grauholm, Arieh S. Cohen, and Bjarke Feenstra

Subjects
Medicine, Science
Abstract

Abstract Spermine oxidase (SMOX) catalyzes the oxidation of spermine to spermidine. Observational studies have reported SMOX as a source of reactive oxygen species associated with cancer, implying that inhibition of SMOX could be a target for chemoprevention. Here we test causality of SMOX levels with cancer risk using a Mendelian randomization analysis. We performed a GWAS of spermidine/spermine ratio to identify genetic variants associated with regulation of SMOX activity. Replication analysis was performed in two datasets of SMOX gene expression. We then did a Mendelian randomization analysis by testing the association between the SMOX genetic instrument and neuroblastoma, gastric, lung, breast, prostate, and colorectal cancers using GWAS summary statistics. GWAS of spermidine/spermine ratio identified SMOX locus (P = 1.34 × 10–49) explaining 32% of the variance. The lead SNP rs1741315 was also associated with SMOX gene expression in newborns (P = 8.48 × 10–28) and adults (P = 2.748 × 10–8) explaining 37% and 6% of the variance, respectively. Genetically determined SMOX activity was not associated with neuroblastoma, gastric, lung, breast, prostate nor colorectal cancer (P > 0.05). A PheWAS of rs1741315 did not reveal any relevant associations. Common genetic variation in the SMOX gene was strongly associated with SMOX activity in newborns, and less strongly in adults. Genetic down-regulation of SMOX was not significantly associated with lower odds of neuroblastoma, gastric, lung, breast, prostate and colorectal cancer. These results may inform studies of SMOX inhibition as a target for chemoprevention.

Published
2021
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8. Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Author

Raúl Aguirre-Gamboa, Niek de Klein, Jennifer di Tommaso, Annique Claringbould, Monique GP van der Wijst, Dylan de Vries, Harm Brugge, Roy Oelen, Urmo Võsa, Maria M. Zorro, Xiaojin Chu, Olivier B. Bakker, Zuzanna Borek, Isis Ricaño-Ponce, Patrick Deelen, Cheng-Jiang Xu, Morris Swertz, Iris Jonkers, Sebo Withoff, Irma Joosten, Serena Sanna, Vinod Kumar, Hans J. P. M. Koenen, Leo A. B. Joosten, Mihai G. Netea, Cisca Wijmenga, BIOS Consortium, Lude Franke, and Yang Li

Subjects
eQTL, Deconvolution, Cell types, Immune cells, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract 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 ).

Published
2020
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10. Systematic Prioritization of Candidate Genes in Disease Loci Identifies TRAFD1 as a Master Regulator of IFNγ Signaling in Celiac Disease

Author

Adriaan van der Graaf, Maria M. Zorro, Annique Claringbould, Urmo Võsa, Raúl Aguirre-Gamboa, Chan Li, Joram Mooiweer, Isis Ricaño-Ponce, Zuzanna Borek, Frits Koning, Yvonne Kooy-Winkelaar, Ludvig M. Sollid, Shuo-Wang Qiao, Vinod Kumar, Yang Li, Lude Franke, Sebo Withoff, Cisca Wijmenga, Serena Sanna, Iris Jonkers, and BIOS Consortium

Subjects
celiac disease, gene prioritization, expression quantitative trait locus (eQTL), TRAFD1, trans regulation, Genetics, QH426-470
Abstract

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 in silico 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 trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAF-type zinc finger domain containing 1 (TRAFD1), and were found to be involved in interferon (IFN)γ signaling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments in a human monocytic cell line that validated the role of TRAFD1 as an immune regulator acting in trans. 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.

Published
2021
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12. Metagenes Associated with Survival in Non-Small Cell Lung Cancer

Author

Egon Urgard, Tõnu Vooder, Urmo Võsa, Kristjan Välk, Mingming Liu, Cheng Luo, Fabian Hoti, Retlav Roosipuu, Tarmo Annilo, Jukka Laine, Christopher M. Frenz, Liqing Zhang, and Andres Metspalu

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Published
2011

13. Altered Gene Expression Associated with microRNA Binding Site Polymorphisms.

Author

Urmo Võsa, Tõnu Esko, Silva Kasela, and Tarmo Annilo

Subjects
Medicine, Science
Abstract

Allele-specific gene expression associated with genetic variation in regulatory regions can play an important role in the development of complex traits. We hypothesized that polymorphisms in microRNA (miRNA) response elements (MRE-SNPs) that either disrupt a miRNA binding site or create a new miRNA binding site can affect the allele-specific expression of target genes. By integrating public expression quantitative trait locus (eQTL) data, miRNA binding site predictions, small RNA sequencing, and Argonaute crosslinking immunoprecipitation (AGO-CLIP) datasets, we identified genetic variants that can affect gene expression by modulating miRNA binding efficiency. We also identified MRE-SNPs located in regions associated with complex traits, indicating possible causative mechanisms associated with these loci. The results of this study expand the current understanding of gene expression regulation and help to interpret the mechanisms underlying eQTL effects.

Published
2015
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14. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression.

Author

Vinod Kumar, Harm-Jan Westra, Juha Karjalainen, Daria V Zhernakova, Tõnu Esko, Barbara Hrdlickova, Rodrigo Almeida, Alexandra Zhernakova, Eva Reinmaa, Urmo Võsa, Marten H Hofker, Rudolf S N Fehrmann, Jingyuan Fu, Sebo Withoff, Andres Metspalu, Lude Franke, and Cisca Wijmenga

Subjects
Genetics, QH426-470
Abstract

Recently it has become clear that only a small percentage (7%) of disease-associated single nucleotide polymorphisms (SNPs) are located in protein-coding regions, while the remaining 93% are located in gene regulatory regions or in intergenic regions. Thus, the understanding of how genetic variations control the expression of non-coding RNAs (in a tissue-dependent manner) has far-reaching implications. We tested the association of SNPs with expression levels (eQTLs) of large intergenic non-coding RNAs (lincRNAs), using genome-wide gene expression and genotype data from five different tissues. We identified 112 cis-regulated lincRNAs, of which 45% could be replicated in an independent dataset. We observed that 75% of the SNPs affecting lincRNA expression (lincRNA cis-eQTLs) were specific to lincRNA alone and did not affect the expression of neighboring protein-coding genes. We show that this specific genotype-lincRNA expression correlation is tissue-dependent and that many of these lincRNA cis-eQTL SNPs are also associated with complex traits and diseases.

Published
2013
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15. Methylation markers of early-stage non-small cell lung cancer.

Author

Kaie Lokk, Tõnu Vooder, Raivo Kolde, Kristjan Välk, Urmo Võsa, Retlav Roosipuu, Lili Milani, Krista Fischer, Marina Koltsina, Egon Urgard, Tarmo Annilo, Andres Metspalu, and Neeme Tõnisson

Subjects
Medicine, Science
Abstract

Despite of intense research in early cancer detection, there is a lack of biomarkers for the reliable detection of malignant tumors, including non-small cell lung cancer (NSCLC). DNA methylation changes are common and relatively stable in various types of cancers, and may be used as diagnostic or prognostic biomarkers.We performed DNA methylation profiling of samples from 48 patients with stage I NSCLC and 18 matching cancer-free lung samples using microarrays that cover the promoter regions of more than 14,500 genes. We correlated DNA methylation changes with gene expression levels and performed survival analysis.We observed hypermethylation of 496 CpGs in 379 genes and hypomethylation of 373 CpGs in 335 genes in NSCLC. Compared to adenocarcinoma samples, squamous cell carcinoma samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes. 378 of 869 (43.5%) CpG sites discriminating the NSCLC and control samples showed an inverse correlation between CpG site methylation and gene expression levels. As a result of a survival analysis, we found 10 CpGs in 10 genes, in which the methylation level differs in different survival groups.We have identified a set of genes with altered methylation in NSCLC and found that a minority of them showed an inverse correlation with gene expression levels. We also found a set of genes that associated with the survival of the patients. These newly-identified marker candidates for the molecular screening of NSCLC will need further analysis in order to determine their clinical utility.

Published
2012
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17. Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Author

Maarja Jõeloo, Chiara Auwerx, and Urmo Võsa

Abstract

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0−10−90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

Published
2022

18. Loss of function variant in SMIM1 is associated with reduced energy expenditure and weight gain

Author

Mattia Frontini, Luca Stefanucci, Camous Moslemi, Ana Tome, Samuel Virtue, Nicholas Gleadall, Laura Watson, Jing Kwa, Frances Burden, Samantha Farrow, Ji Chen, Urmo Võsa, Keith Burling, Peter Baker, James Warner, Amy Frary, Karola Rehnstrom, Sofie Ashford, Jo Piper, Gail Biggs, Wendy Erber, Gary Hoffman, Nadia Schoenmakers, Christian Erikstrup, Klaus Rieneck, Morten Dziegiel, Henrik Ullum, Vian Azzu, Michele Vacca, Omer Ali Bayraktar, Antonio Vidal-Puig, Sisse Ostrowski, William Astle, Martin L. Olsson, Jill R. Storry, Ole Pedersen, Willem Ouwehand, Krishna Chatterjee, and Dragana Vuckovic

Abstract

Blood group antigens are the archetypal example of human genetic variation. Here, we characterised the functional metabolic consequences in individuals homozygous for a 17bp deletion in SMIM1 (rs566629828; minor allele frequency 0.0147) and thus lacking the protein defining the Vel blood group. Our analysis, in separate cohorts of SMIM1-/- individuals (UK Biobank, NHS Blood and Transplant, Danish Blood Donor Study, Copenhagen Hospital Biobank) and a mouse model, identified an increase in body weight accompanied by a range of metabolic differences, including dyslipidemia, changes in the leptin-adiponectin ratio, increased liver enzymes and lower total thyroid hormone levels. These changes in the metabolic state were at least in part due to a reduction in resting energy expenditure, as assessed during an in-depth clinical assessment of SMIM1-/- individuals. Additionally, electronic health records suggest that individuals lacking this 78-amino-acid type II transmembrane protein may be more prone to cerebral bleeds and thrombotic stroke.

Published
2022

19. Linking common and rare disease genetics through gene regulatory networks

Author

Henry Wiersma, Patrick Deelen, Olivier B. Bakker, Annique Claringbould, Sophie Mulcahy Symmons, Iris Jonkers, Urmo Võsa, Floranne Boulogne, Harm-Jan Westra, and Lude Franke

Subjects
Genetics, symbols.namesake, Gene regulatory network, Mendelian inheritance, symbols, Identification (biology), Genome-wide association study, Disease, Biology, Gene, Genetic association, Rare disease
Abstract

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding and exert small regulatory effects on downstream genes, but which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. Conversely, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. We demonstrate that common and rare genetic diseases can be linked by studying the gene regulatory networks impacted by common disease-associated variants. We implemented this in the ‘Downstreamer’ method and applied it to 44 GWAS traits and find that predicted downstream “key genes” are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. We find that 82% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Finally, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve identification of these highly connected genes for common traits and diseases.

Published
2021

20. Long-range regulatory effects of Neandertal DNA in modern humans

Author

Michael Dannemann, Urmo Võsa, Bayazit Yunusbayev, and Danat Yermakovich

Abstract

The admixture between modern humans and Neandertals has resulted in ∼2% of the genomes of present-day non-Africans being composed of Neandertal DNA. Association studies have shown that introgressed DNA significantly influences phenotypic variation in people today and that several of the phenotype-associated archaic variants had links to expression regulation as well. In general, introgressed DNA has been demonstrated to significantly affect the transcriptomic landscape in people today. However, little is known about how much of that impact is mediated through long-range regulatory effects that have been shown to explain ∼20% of expression variation.Here we identified 60 transcription factors (TFs) with their top cis-eQTL SNP being of Neandertal ancestry in GTEx and predicted long-range Neandertal DNA-induced regulatory effects by screening for the predicted target genes of those TFs. We show that genes in regions devoid of Neandertal DNA are enriched among the target genes of some of these TFs. Furthermore, archaic cis-eQTLs for these TFs included multiple candidates for local adaptation and have associations with various immune traits, schizophrenia, blood cell type composition and anthropometric measures. Finally, we show that our results can be replicated in empirical trans-eQTLs with Neandertal variants.Our results suggest that the regulatory reach of Neandertal DNA goes beyond the 40% of genomic sequence that it still covers in present-day non-Africans and that via this mechanism Neandertal DNA additionally influences the phenotypic variation in people today.

Published
2021

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

Author

Mahmoud Elansary, Knut Krohn, Eleonora Porcu, Julia Dmitrieva, Michel Georges, Harm-Jan Westra, Isabel Alves, Matthias Nauck, Jan H. Veldink, Joost Verlouw, Anette Kalnapenkis, Silva Kasela, Alex W. Hewitt, Roy Oelen, Willem H. Ouwehand, Frank Beutner, Ilkka Seppälä, Yukihide Momozawa, Samuli Ripatti, Brenda W.J.H. Penninx, Patrick Deelen, Michael Stumvoll, Jenny van Dongen, Jonathan K. Pritchard, Roman Kreuzhuber, Marie-Julie Favé, Bernett Lee, Hailang Mei, Biao Zeng, Philip Awadalla, Shuang Li, Kate Downes, Gibran Hemani, Urko M. Marigorta, Anke Tönjes, Morris Swertz, Robert Warmerdam, Joseph E. Powell, Mika Kähönen, Urmo Võsa, Brandon L. Pierce, Benjamin P. Fairfax, Anand Kumar Andiappan, Bastiaan T. Heijmans, Martina Müller-Nurasyid, Sven Bergmann, Katharina Schramm, Hanieh Yaghootkar, Sina Rüeger, Monique G. P. van der Wijst, Lude Franke, Ting Qi, Rick Jansen, Greg Gibson, Cisca Wijmenga, Marc Jan Bonder, Yungil Kim, Viktorija Kukushkina, Johannes Kettunen, Joachim Thiery, Peter A C 't Hoen, Zoltán Kutalik, Jian Yang, Dylan H. de Vries, Olaf Rötzschke, Maarten van Iterson, Peter Kovacs, Peter M. Visscher, Wibowo Arindrarto, Oliver Stegle, Natalia Pervjakova, Julian C. Knight, Tõnu Esko, Annique Claringbould, Lili Milani, Patrick F. Sullivan, Habibul Ahsan, Timothy M. Frayling, Lin Tong, Uwe Völker, Reyhan Sönmez Flitman, Eline Slagboom, Dorret I. Boomsma, Holger Prokisch, Michel G. Nivard, Mawusse Agbessi, Joyce B. J. van Meurs, Alexis Battle, Futao Zhang, Emmanouil T. Dermitzakis, Morris A. Swertz, Grant W. Montgomery, Terho Lehtimäki, Coen D.A. Stehouwer, Jaanika Kronberg, Holger Kirsten, Olli T. Raitakari, Sina A. Gharib, Bruce M. Psaty, Seyhan Yazar, Markus Loeffler, Harm Brugge, Jose Alquicira Hernandez, Mark W. Christiansen, Andrew A. Brown, Markus Perola, Markus Scholz, Ashis Saha, Alexander Teumer, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Digital Health, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), BIOS Consortium, i2QTL Consortium, 't Hoen, PAC, van Meurs, J., van Dongen, J., van Iterson, M., Swertz, M.A., Jan Bonder, M., Biological Psychology, APH - Personalized Medicine, APH - Methodology, Internal Medicine, Interne Geneeskunde, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), and RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome

Subjects
DISORDER, Multifactorial Inheritance, Quantitative Trait Loci, Genome-wide association study, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Gene Expression Regulation/genetics, DISEASE, LINKS, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, All institutes and research themes of the Radboud University Medical Center, RELEVANCE, Blood Proteins/genetics, Gene expression, Genetics, Humans, GENOME-WIDE ASSOCIATION, Polymorphism, Gene, Multifactorial Inheritance/genetics, 030304 developmental biology, Regulation of gene expression, RISK, 0303 health sciences, ARCHITECTURE, Blood Proteins, Transcriptome/genetics, Polymorphism, Single Nucleotide/genetics, Phenotype, SERINE BIOSYNTHESIS, HUMAN TRANSCRIPTOME, DEFICIENCY, Gene Expression Regulation, Expression quantitative trait loci, genome-wide association studies, gene expression, gene regulation, Quantitative Trait Loci/genetics, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], Single Nucleotide/genetics, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

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.Analyses of expression profiles from whole blood of 31,684 individuals identify cis-expression quantitative trait loci (eQTL) effects for 88% of genes and trans-eQTL effects for 37% of trait-associated variants.

Published
2021

23. Mapping genetic determinants of 184 circulating proteins in 26,494 individuals to connect proteins and diseases

Author

Erin Macdonald-Dunlop, Lucija Klarić, Lasse Folkersen, Paul R.H.J. Timmers, Stefan Gustafsson, Jing Hua Zhao, Niclas Eriksson, Anne Richmond, Stefan Enroth, Niklas Mattsson-Carlgren, Daria V. Zhernakova, Anette Kalnapenkis, Martin Magnusson, Eleanor Wheeler, Shih-Jen Hwang, Yan Chen, Andrew P Morris, Bram Prins, Urmo Võsa, Nicholas J. Wareham, John Danesh, Johan Sundstrom, Bruna Gigante, Damiano Baldassarre, Rona J. Strawbridge, Harry Campbell, Ulf Gyllensten, Chen Yao, Daniela Zanetti, Themistocles L. Assimes, Per Eriksson, Daniel Levy, Claudia Langenberg, J. Gustav Smith, Tõnu Esko, Jingyuan Fu, Oskar Hansson, Åsa Johansson, Caroline Hayward, Lars Wallentin, Agneta Siegbahn, Lars Lind, Adam S. Butterworth, Karl Michaëlsson, James E. Peters, Anders Mälarstig, Peter K. Joshi, and James F. Wilson

Subjects
Genetics, medicine.medical_specialty, Genetic epidemiology, Expression quantitative trait loci, DNA methylation, medicine, Medical genetics, Disease, Quantitative trait locus, Biology, medicine.disease, Inflammatory bowel disease, Genetic architecture
Abstract

We performed the largest genome-wide meta-analysis (GWAMA) (Max N=26,494) of the levels of 184 cardiovascular-related plasma protein levels to date and reported 592 independent loci (pQTL) associated with the level of at least one protein (1308 significant associations, median 6 per protein). We estimated that only between 8-37% of testable pQTL overlap with established expression quantitative trait loci (eQTL) using multiple methods, while 132 out of 1064 lead variants show evidence for transcription factor binding, and found that 75% of our pQTL are known DNA methylation QTL. We highlight the variation in genetic architecture between proteins and that proteins share genetic architecture with cardiometabolic complex traits. Using cis-instrument Mendelian randomisation (MR), we infer causal relationships for 11 proteins, recapitulating the previously reported relationship between PCSK9 and LDL cholesterol, replicating previous pQTL MR findings and discovering 16 causal relationships between protein levels and disease. Our MR results highlight IL2-RA as a candidate for drug repurposing for Crohn’s Disease as well as 2 novel therapeutic targets: IL-27 (Crohn’s disease) and TNFRSF14 (Inflammatory bowel disease, Multiple sclerosis and Ulcerative colitis). We have demonstrated the discoveries possible using our pQTL and highlight the potential of this work as a resource for genetic epidemiology.

Published
2021

24. Genetic regulation of spermine oxidase activity and cancer risk: a Mendelian randomization study

Author

Line Skotte, Victor Yakimov, Urmo Võsa, Tõnu Esko, Frank Geller, Bjarke Feenstra, Silva Kasela, Julie Courraud, Lili Milani, Arieh S. Cohen, Thomas Werge, Viktorija Kukuškina, David M. Hougaard, João Fadista, Christine Søholm Hansen, Mads Melbye, Jonas Bybjerg-Grauholm, Alfonso Buil, and Institute for Molecular Medicine Finland

Subjects
Spermine, Genome-wide association study, VARIANTS, chemistry.chemical_compound, 0302 clinical medicine, Neoplasms, Cancer, 0303 health sciences, Oxidoreductases Acting on CH-NH Group Donors, Multidisciplinary, ADENOMA RECURRENCE, Medical genetics, Mendelian Randomization Analysis, 3. Good health, GENOTYPE, Gene Expression Regulation, Neoplastic, Phenotype, 030220 oncology & carcinogenesis, Medicine, Adult, Spermine oxidase, POLYAMINE METABOLISM, SUSCEPTIBILITY LOCI, Science, 3122 Cancers, Quantitative Trait Loci, Biology, Polymorphism, Single Nucleotide, Article, Gene Expression Regulation, Enzymologic, 03 medical and health sciences, Mendelian randomization, medicine, Genetics, SNP, Humans, Genetic Predisposition to Disease, 030304 developmental biology, Genetic association, Genetic association study, ORNITHINE-DECARBOXYLASE ACTIVITY, Infant, Newborn, medicine.disease, Computational biology and bioinformatics, ASSOCIATION ANALYSIS, chemistry, Cancer research, 3111 Biomedicine
Abstract

Spermine oxidase (SMOX) catalyzes the oxidation of spermine to spermidine. Observational studies have reported SMOX as a source of reactive oxygen species associated with cancer, implying that inhibition of SMOX could be a target for chemoprevention. Here we test causality of SMOX levels with cancer risk using a Mendelian randomization analysis. We performed a GWAS of spermidine/spermine ratio to identify genetic variants associated with regulation of SMOX activity. Replication analysis was performed in two datasets of SMOX gene expression. We then did a Mendelian randomization analysis by testing the association between the SMOX genetic instrument and neuroblastoma, gastric, lung, breast, prostate, and colorectal cancers using GWAS summary statistics. GWAS of spermidine/spermine ratio identified SMOX locus (P = 1.34 × 10–49) explaining 32% of the variance. The lead SNP rs1741315 was also associated with SMOX gene expression in newborns (P = 8.48 × 10–28) and adults (P = 2.748 × 10–8) explaining 37% and 6% of the variance, respectively. Genetically determined SMOX activity was not associated with neuroblastoma, gastric, lung, breast, prostate nor colorectal cancer (P > 0.05). A PheWAS of rs1741315 did not reveal any relevant associations. Common genetic variation in the SMOX gene was strongly associated with SMOX activity in newborns, and less strongly in adults. Genetic down-regulation of SMOX was not significantly associated with lower odds of neuroblastoma, gastric, lung, breast, prostate and colorectal cancer. These results may inform studies of SMOX inhibition as a target for chemoprevention.

Published
2021

25. GWAS meta-analysis and gene expression data link reproductive tract development, immune response and cellular proliferation/apoptosis with cervical cancer and clarify overlap with other cervical phenotypes

Author

Maarja Lepamets, Mariann Koel, Reedik Mägi, Triin Laisk, Priit Palta, Mark J. Daly, Susanna Lemmelä, Hannele Laivuori, and Urmo Võsa

Subjects
Cervical cancer, 0303 health sciences, Candidate gene, Cervical ectropion, Cancer, Genome-wide association study, Biology, Bioinformatics, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Dysplasia, 030220 oncology & carcinogenesis, Genetic predisposition, medicine, Allele, 030304 developmental biology
Abstract

Background The uterine cervix has an important role in female reproductive health, but not much is known about the genetic determinants of cervical biology and pathology. Genome-wide association studies (GWAS) with increasing sample sizes have reported preliminary genetic associations for cervical cancer. However, GWAS is only the first step in mapping the genetic susceptibility and thus, the underlying biology in cervical cancer and other cervical phenotypes is still not entirely understood. Here, we use data from large biobanks to characterise the genetics of cervical phenotypes (including cervical cancer) and leverage latest computational methods and gene expression data to refine the association signals for cervical cancer. Methods Using Estonian Biobank and FinnGen data, we characterise the genetic signals associated with cervical ectropion (10,162 cases/151,347 controls), cervicitis (19,285/185,708) and cervical dysplasia (14,694/150,563). We present the results from the largest trans-ethnic GWAS meta-analysis of cervical cancer, including up to 9,229 cases and 490,304 controls from Estonian Biobank, the FinnGen study, the UK Biobank and Biobank Japan. We combine GWAS results with gene expression data and chromatin regulatory annotations in HeLa cervical carcinoma cells to propose the most likely candidate genes and causal variants for every locus associated with cervical cancer. We further dissect the HLA association with cervical pathology using imputed data on alleles and amino acid polymorphisms. Results We report a single associated locus on 2q13 for both cervical ectropion (rs3748916, p=5.1 × 10−16) and cervicitis (rs1049137, p=3.9 × 10−10), and five signals for cervical dysplasia - HLA-B and HLA-DQA1 (rs1053726, p=9.1 × 10−9; rs36214159, 1.6 × 10−22), DAPL1 (rs12611652, p=3.2 × 10−9), PAX8 (rs1049137, p=6.4 × 10−9), and CLPTM1L (rs6866294, p=2.1 × 10−9). We identify five loci associated with cervical cancer in the trans-ethnic meta-analysis: LINC00339/CDC42 (rs2268177, p= 3.1 × 10−8), PAX8/PAX8-AS1 (rs4849177, p=9.4 × 10−15), CLPTM1L (rs27069, p=1.3 × 10−14), GSDMB (rs12603332, p=1.2 × 10−9), and HLA-DRB1/HLA-DQA1 (rs35508382, p=1.0 × 10−39). Joint analysis of dysplasia and cancer datasets revealed an association on chromosome 19 (rs425787, p=3.5 × 10−8), near CD70. Discussion Our results map PAX8/PAX8-AS1, LINC00339, CDC42, CLPTM1L, HLA-DRB1, HLA-B, and GSDMB as the most likely candidate genes for cervical cancer, which provides novel insight into cervical cancer pathogenesis and supports the role of genes involved in reproductive tract development, immune response, and cellular proliferation/apoptosis. We further show that PAX8/PAX8-AS1 has a central role in cervical biology and pathology, as it was associated with all analysed phenotypes. The detailed characterisation of association signals, together with mapping of causal variants and genes offers valuable leads for further functional studies.

Published
2021

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

Author

Urmo Võsa, Alexander Kurilshikov, Marije Oosting, Leo A. B. Joosten, Natalie R. van Zuydam, Daisy Jonkers, Jingyuan Fu, Serena Sanna, Zlatan Mujagic, Mark I. McCarthy, Ad A.M. Masclee, Lude Franke, Alexandra Zhernakova, Mihai G. Netea, Cisca Wijmenga, Anubha Mahajan, Arnau Vich Vila, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Center for Liver, Digestive and Metabolic Diseases (CLDM), Stem Cell Aging Leukemia and Lymphoma (SALL), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), Interne Geneeskunde, MUMC+: MA Maag Darm Lever (9), and RS: NUTRIM - R2 - Liver and digestive health

Subjects
lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Genome-wide association study, Butyrate, Type 2 diabetes, Biology, HOST GENETICS, GLUCOSE, 03 medical and health sciences, 0302 clinical medicine, BUTYRATE, Diabetes mellitus, GENETIC-VARIANTS, Mendelian randomization, Genetics, medicine, GENOME-WIDE ASSOCIATION, Feces, 030304 developmental biology, Genetic association, chemistry.chemical_classification, 0303 health sciences, Fatty acid, COMMON VARIANTS, CYTOKINE PRODUCTION, medicine.disease, PROPIONATE, MODEL, chemistry, MENDELIAN RANDOMIZATION, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 202884.pdf (Publisher’s version ) (Closed access) 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(1). 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(2), 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(3), 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 x 10(-5)), 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.

Published
2019

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

Author

Marc-Thorsten Hütt, Holger Prokisch, Sharon L.R. Kardia, Hansjörg Baurecht, Lude Franke, Michael Krawczak, Piotr Nyczka, Amke Caliebe, Jennifer A. Smith, Silke Szymczak, Carolin Knecht, Urmo Võsa, Konstantin Strauch, Christian Gieger, Stephan Weidinger, Annette Peters, Annique Claringbould, Sandra Freitag-Wolf, Philip Rosenstiel, Kristina Schlicht, Wei Zhao, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
Male, SUSCEPTIBILITY LOCI, Quantitative Trait Loci, PROTEIN, Metabolic network, Locus (genetics), Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Quantitative trait locus, Biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, DISEASE, Cohort Studies, 03 medical and health sciences, ADIPONECTIN, Genetic variation, Genetics, Humans, Gene Regulatory Networks, RECONSTRUCTION, GENOME-WIDE ASSOCIATION, 1000 Genomes Project, Genetics (clinical), Original Investigation, 030304 developmental biology, 0303 health sciences, IDENTIFICATION, 030305 genetics & heredity, KORA, Cadherins, Genetic architecture, Metabolism, Genetic Loci, OBESITY, Female, Transcriptome, Metabolic Networks and Pathways, Genome-Wide Association Study
Abstract

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− 8). 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. Electronic supplementary material The online version of this article (10.1007/s00439-019-01994-x) contains supplementary material, which is available to authorized users.

Published
2019

28. Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19

Author

Barbara Thorand, Lars Lind, Anne Richmond, Artemis Papadaki, Erola Pairo-Castineira, Oskar Hansson, Andreas Göteson, Dirk S. Paul, Nicola Pirastu, Eleanor Wheeler, Stefan Enroth, Chen Yao, Karsten Suhre, John Danesh, Daniel Levy, Lucija Klaric, Tõnu Esko, Jingyuan Fu, Shih-Jen Hwang, Daniela Zanetti, Themistocles L. Assimes, James F. Wilson, Niclas Eriksson, Nicholas J. Wareham, Karl Michaëlsson, Caroline Hayward, Stefan Gustafsson, Daria V. Zhernakova, Anders Mälarstig, Peter K. Joshi, Martin Magnusson, Christian Gieger, John R. Petrie, Claudia Langenberg, Rona J Strawbridge, Christian Herder, James E. Peters, Åsa K. Hedman, Erin Macdonald-Dunlop, Arianna Landini, Bram Prins, Mikael Landén, J Kenneth Baillie, Niklas Mattsson-Carlgren, Harry Campbell, Johan Sundström, Charles Kooperberg, Jack Gisby, Yan Chen, Leonid Padyukov, Lars Wallentin, Paul R. H. J. Timmers, Alexander P. Reiner, Adam S. Butterworth, Agneta Siegbahn, Lasse Folkersen, Alex Tokolyi, Bruna Gigante, J. Gustav Smith, Mark Walker, Urmo Võsa, Ulf Gyllensten, Marisa D Muckian, Åsa Johansson, Anette Kalnapenkis, Andrew P. Morris, Elodie Persyn, and Jing Hua Zhao

Subjects
Exon, Mediator, Apoptosis, Alternative splicing, Immunology, Locus (genetics), macromolecular substances, Biology, Quantitative trait locus, Receptor, Cytokine receptor, Article
Abstract

Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at theFASlocus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.

Published
2021

29. TU34. ASSOCIATION BETWEEN DEPRESSION DIAGNOSIS, POLYGENIC RISK FOR DEPRESSION AND ANTIHYPERTENSIVE MEDICATION NON-PERSISTENCE

Author

Urmo Võsa, Jana Lass, Kristi Krebs, Kelli Lehto, Hanna Kariis, Tuuli Jürgenson, Aet Saar, Lili Milani, and Silva Kasela

Subjects
Pharmacology, Persistence (psychology), medicine.medical_specialty, business.industry, Psychiatry and Mental health, Neurology, Internal medicine, Medicine, Pharmacology (medical), Polygenic risk score, Neurology (clinical), Association (psychology), business, Biological Psychiatry, Depression (differential diagnoses), Antihypertensive medication
Published
2021

30. TU5. PHENOME-WIDE ASSOCIATION STUDY BETWEEN ADHD POLYGENIC RISK AND ELECTRONIC HEALTH RECORD ICD-10 DIAGNOSIS CODES IN THE ESTONIAN BIOBANK

Author

Kelli Lehto, Elis Haan, Kristi Krebs, and Urmo Võsa

Subjects
Pharmacology, medicine.medical_specialty, business.industry, ICD-10, Phenome, Biobank, Estonian, language.human_language, Psychiatry and Mental health, Neurology, Electronic health record, Family medicine, medicine, language, Pharmacology (medical), Polygenic risk score, Neurology (clinical), Diagnosis code, Association (psychology), business, Biological Psychiatry
Published
2021

31. Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a master regulator of IFN gamma signaling in Celiac disease

Author

Adriaan van der Graaf, Maria M. Zorro, Annique Claringbould, Urmo Võsa, Raúl Aguirre-Gamboa, Chan Li, Joram Mooiweer, Isis Ricaño-Ponce, Zuzanna Borek, Frits Koning, Yvonne Kooy-Winkelaar, Ludvig M. Sollid, Shuo-Wang Qiao, Vinod Kumar, Yang Li, Lude Franke, Sebo Withoff, Cisca Wijmenga, Serena Sanna, Iris Jonkers, BIOS Consortium, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), and Molecular Neuroscience and Ageing Research (MOLAR)

Subjects
Candidate gene, gene prioritization, lcsh:QH426-470, In silico, T cell, NF-KAPPA-B, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Regulator, TRAFD1, Computational biology, VARIANTS, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, 0302 clinical medicine, Immune system, INTRAEPITHELIAL LYMPHOCYTES, MHC class I, Genetics, medicine, trans regulation, MULTIPLE COMMON, expression quantitative trait locus (eQTL), Genetics (clinical), Original Research, 030304 developmental biology, 0303 health sciences, biology, Antigen processing, TRANS-EQTLS, fungi, CYTOKINES, ASSOCIATION, Acquired immune system, SUGGESTS, lcsh:Genetics, medicine.anatomical_structure, CELLS, biology.protein, Molecular Medicine, 030217 neurology & neurosurgery, celiac disease, RESPONSES
Abstract

BackgroundCeliac 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.ResultsWe used four different in silico 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 signalling and T cell activation pathways. 51 of these genes are targets of known drug compounds and 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 trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAFD1, and were found to be involved in IFNγ signalling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments that validated the role of TRAFD1 as an immune regulator acting in trans.ConclusionsOur strategy has confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and the IFNγ signalling and MHC I antigen processing pathways, both major players of immune activation and CeD pathogenesis.

Published
2021

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

Author

Jordana T. Bell, Sara Vieira-Silva, Torben Hansen, Andrew D. Paterson, Nicholas J. Timpson, Robert C. Kaplan, Vincent W. V. Jaddoe, Fabian Frost, Gwen Falony, Zachary D. Wallen, Mauro D'Amato, David A. Hughes, Jakob Stokholm, Lude Franke, Matthew A. Jackson, Marc Jan Bonder, Gonneke Willemsen, Dmitrii Borisevich, Hocheol Shin, Kyoko Watanabe, Annique Claringbould, Joanna Szopinska-Tokov, Markus M. Lerch, Caroline I. Le Roy, Markku Laakso, Eran Segal, Daisy Jonkers, Jee-Young Moon, Williams Turpin, Susanna Walter, Xingrong Liu, Lars Agréus, Tim D. Spector, Malte C. Rühlemann, Raul Y. Tito, Daniel Keszthelyi, Gareth E. Davies, Shiraz A. Shah, Ad A.M. Masclee, Carolina Medina-Gomez, Kreete Lüll, Robert D. Burk, Elin Org, Matthias Laudes, Cornelia M. van Duijn, Myriam Fornage, Urmo Võsa, Oluf Pedersen, Lenore J. Launer, Kaitlin H Wade, Juan Antonio Raygoza Garay, Rob Knight, Elad Barkan, Daria V. Zhernakova, Michael Boehnke, Frank Ulrich Weiss, Andre Franke, Jonathan Thorsen, Henry Völzke, Jingyuan Fu, Fernando Rivadeneira, Daphna Rothschild, Robert Kraaij, Katie A. Meyer, Ayse Demirkan, Claire J. Steves, Stefan Weiss, Cisca Wijmenga, Hans Bisgaard, Qi Qibin, Corinna Bang, Rodrigo Bacigalupe, Haydeh Payami, Han-Na Kim, Liesbeth Duijts, Alexander Kurilshikov, Serena Sanna, Kenneth Croitoru, Jeroen Raes, Djawad Radjabzadeh, Eco J. C. de Geus, Jun Wang, Richard G. IJzerman, Harm-Jan Westra, Uwe Völker, Torben Jørgensen, Dorret I. Boomsma, Omer Weissbrod, Alexandra Zhernakova, Alejandro Arias Vasquez, Casey T. Finnicum, Hyung Lae Kim, Marie Joossens, Tue H. Hansen, Henriette A. Moll, Anna Andreasson, Larbi Bedrani, Zlatan Mujagic, Adriaan van der Graaf, Søren J. Sørensen, Aldons J. Lusis, Wolfgang Lieb, Georg Homuth, André G. Uitterlinden, Biological Psychology, APH - Mental Health, APH - Methodology, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, APH - Health Behaviors & Chronic Diseases, Interne Geneeskunde, RS: NUTRIM - R2 - Liver and digestive health, MUMC+: MA Maag Darm Lever (9), RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Internal Medicine, Epidemiology, Pediatrics, Erasmus MC other, Center for Liver, Digestive and Metabolic Diseases (CLDM), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Translational Immunology Groningen (TRIGR), Internal medicine, ACS - Diabetes & metabolism, and Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
Male, Linkage disequilibrium, Genome-wide association study, Genome-wide association studies, Medical and Health Sciences, Oral and gastrointestinal, Linkage Disequilibrium, Cohort Studies, 0302 clinical medicine, RNA, Ribosomal, 16S, 2.1 Biological and endogenous factors, Aetiology, Child, Lactase, Genetics & Heredity, 2. Zero hunger, Genetics, 0303 health sciences, Biological Sciences, Child, Preschool, MENDELIAN RANDOMIZATION, Female, Life Sciences & Biomedicine, Biotechnology, Adult, 16S, Adolescent, Quantitative Trait Loci, Locus (genetics), Biology, Quantitative trait locus, GENOTYPE IMPUTATION, Microbiology, metagenome, Article, diseases, 03 medical and health sciences, Clinical Research, Genetic variation, Mendelian randomization, Humans, Microbiome, Preschool, Nutrition, 030304 developmental biology, Ribosomal, ecosystem, ENVIRONMENT, Science & Technology, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], IDENTIFICATION, Human Genome, Genetic Variation, Mendelian Randomization Analysis, FRAMEWORK, Gastrointestinal Microbiome, Metabolism, Metagenomics, genome-wide association, RNA, Bifidobacterium, Digestive Diseases, 030217 neurology & neurosurgery, Developmental Biology, Genome-Wide Association Study
Abstract

Contains fulltext : 231681.pdf (Publisher’s version ) (Closed access) 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

Published
2021

33. A cross-disorder dosage sensitivity map of the human genome

Author

Ryan L. Collins, Joseph T. Glessner, Eleonora Porcu, Maarja Lepamets, Rhonda Brandon, Christopher Lauricella, Lide Han, Theodore Morley, Lisa-Marie Niestroj, Jacob Ulirsch, Selin Everett, Daniel P. Howrigan, Philip M. Boone, Jack Fu, Konrad J. Karczewski, Georgios Kellaris, Chelsea Lowther, Diane Lucente, Kiana Mohajeri, Margit Nõukas, Xander Nuttle, Kaitlin E. Samocha, Mi Trinh, Farid Ullah, Urmo Võsa, Matthew E. Hurles, Swaroop Aradhya, Erica E. Davis, Hilary Finucane, James F. Gusella, Aura Janze, Nicholas Katsanis, Ludmila Matyakhina, Benjamin M. Neale, David Sanders, Stephanie Warren, Jennelle C. Hodge, Dennis Lal, Douglas M. Ruderfer, Jeanne Meck, Reedik Mägi, Tõnu Esko, Alexandre Reymond, Zoltán Kutalik, Hakon Hakonarson, Shamil Sunyaev, Harrison Brand, Michael E. Talkowski, Andres Metspalu, Mari Nelis, and Lili Milani

Subjects
DNA Copy Number Variations, Genome, Human, Gene Dosage, Humans, Haploinsufficiency, General Biochemistry, Genetics and Molecular Biology
Abstract

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplopTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Published
2020

34. Mendelian randomization study of spermine oxidase and cancer risk

Author

João Fadista, Christine Søholm Hansen, Frank Geller, Victor Yakimov, Alfonso Buil, Lili Milani, David M. Hougaard, Line Skotte, Mads Melbye, Jonas Bybjerg-Grauholm, Bjarke Feenstra, Thomas Werge, Tõnu Esko, Viktorija Kukuškina, Arieh S. Cohen, Urmo Võsa, Julie Courraud, and Silva Kasela

Subjects
Oncology, 0303 health sciences, medicine.medical_specialty, Colorectal cancer, business.industry, Confounding, Genome-wide association study, Mendelian Randomization Analysis, Quantitative trait locus, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Internal medicine, Genetic variation, Mendelian randomization, medicine, SNP, business, 030304 developmental biology
Abstract

ImportanceSpermine oxidase (SMOX) catalyzes the oxidation of spermine to spermidine. Observational studies have reported SMOX as a source of induced reactive oxygen species associated with cancer, implying that inhibition of SMOX could be a target for chemoprevention.ObjectiveTo test the causality of SMOX levels with cancer risk using a Mendelian randomization analysis.Design, Setting, and ParticipantsWe performed a GWAS of spermidine/spermine ratio, from blood of 534 infants, to identify genetic variants associated with regulation of SMOX activity. In two additional data sets of 262 newborns and 508 adults, we quantified SMOX gene expression using RNA-sequencing and performed expression and methylation QTL lookups. We then did a Mendelian randomization analysis by testing the association between the SMOX genetic instrument and various cancer types using GWAS summary statistics.Main Outcomes and MeasuresNeuroblastoma, gastric, lung, breast, prostate, and colorectal cancers.ResultsThe GWAS of spermidine/spermine ratio identified a genome-wide significant locus (P=1.34×10−49) explaining 32% of the variance. The lead SNP rs1741315 was also associated with SMOX gene expression in newborns (P=8.48×10−28) and adults (P=2.748×10−8) explaining 37% and 6% of the variance, respectively. rs1741315 was not associated with neuroblastoma (OR=0.95; 95% CI:0.88, 1.03; P=0.18), gastric (OR=0.99; 95% CI:0.95, 1.03; P=0.54), lung (OR=0.97; 95% CI:0.94, 1.00; P=0.08), breast (OR=0.99; 95% CI:0.96, 1.02; P=0.47), prostate (OR=0.98; 95% CI:0.96, 1.00; P=0.05) nor colorectal cancer (OR=1.03; 95% CI:0.99, 1.07; P=0.10). A PheWAS of rs1741315 did not reveal any associations with risk factors of the cancers tested.Conclusions and RelevanceGenetic variation in the SMOX gene was strongly associated with SMOX activity in newborns, and less strongly in adults. Genetic down-regulation of SMOX was not significantly associated with lower odds of neuroblastoma, gastric, lung, breast, prostate and colorectal cancer. Further studies are needed to understand the effect of SMOX inhibition in relation to cancer risk.ARTICLE SUMMARYQuestionIs SMOX causally associated with risk of cancer?FindingsIn this Mendelian randomization study, genetically lower levels of SMOX were not associated with decreased risk of neuroblastoma, gastric, lung, breast, prostate and colorectal cancer.MeaningThese findings do not support a causal association between SMOX activity and risk of cancer, suggesting that ongoing efforts to identify SMOX inhibitors for chemoprevention may not be successful.STRENGHTS AND LIMITATIONS-Previous studies which examined SMOX activity and cancer risk were susceptible to recall bias, confounding and reverse causation, none of which are concerns of this Mendelian randomization study.-Our genetic instrument explained a sizeable proportion of the variance of SMOX activity-We used summary statistics from the largest meta-analyses of primary cancer GWAS to date.-Elevated SMOX levels in cancer could also be due to environmental factors not captured by genetics.-Our genetic instrument was developed based on normal range SMOX activity data, thus additional genetic variants might play a role in aberrant expression of this enzyme.

Published
2020

35. Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

Author

Anke R. Hammerschlag, Enda M. Byrne, Meike Bartels, Naomi R. Wray, Christel M. Middeldorp, Mawussé Agbessi, Habibul Ahsan, Isabel Alves, Anand Andiappan, Wibowo Arindrarto, Philip Awadalla, Alexis Battle, Frank Beutner, Marc Jan Bonder, Dorret I. Boomsma, Mark Christiansen, Annique Claringbould, Patrick Deelen, Tõnu Esko, Marie-Julie Favé, Lude Franke, Timothy Frayling, Sina A. Gharib, Gregory Gibson, Bastiaan T. Heijmans, Gibran Hemani, Rick Jansen, Mika Kähönen, Anette Kalnapenkis, Silva Kasela, Johannes Kettunen, Yungil Kim, Holger Kirsten, Peter Kovacs, Knut Krohn, Jaanika Kronberg-Guzman, Viktorija Kukushkina, Zoltan Kutalik, Bernett Lee, Terho Lehtimäki, Markus Loeffler, Urko M. Marigorta, Hailang Mei, Lili Milani, Grant W. Montgomery, Martina Müller-Nurasyid, Matthias Nauck, Michel Nivard, Brenda Penninx, Markus Perola, Natalia Pervjakova, Brandon L. Pierce, Joseph Powell, Holger Prokisch, Bruce M. Psaty, Olli T. Raitakari, Samuli Ripatti, Olaf Rotzschke, Sina Rüeger, Ashis Saha, Markus Scholz, Katharina Schramm, Ilkka Seppälä, Eline P. Slagboom, Coen D.A. Stehouwer, Michael Stumvoll, Patrick Sullivan, Peter A.C. ‘t Hoen, Alexander Teumer, Joachim Thiery, Lin Tong, Anke Tönjes, Jenny van Dongen, Maarten van Iterson, Joyce van Meurs, Jan H. Veldink, Joost Verlouw, Peter M. Visscher, Uwe Völker, Urmo Võsa, Harm-Jan Westra, Cisca Wijmenga, Hanieh Yaghootkar, Jian Yang, Biao Zeng, Futao Zhang, Aaron Isaacs, René Pool, Jouke J. Hottenga, Marleen MJ. van Greevenbroek, Carla J.H. van der Kallen, Casper G. Schalkwijk, Sasha Zhernakova, Ettje F. Tigchelaar, P. Eline Slagboom, Marian Beekman, Joris Deelen, Diana van Heemst, Leonard H. van den Berg, Cornelia M. van Duijn, Bert A. Hofman, André G. Uitterlinden, P. Mila Jhamai, Michael Verbiest, H.Eka D. Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Michiel van Galen, Jan Bot, Daria V. Zhernakova, Peter van ‘t Hof, Irene Nooren, Matthijs Moed, Martijn Vermaat, René Luijk, Freerk van Dijk, Szymon M. Kielbasa, Morris A. Swertz, Erik. W. van Zwet, Peter-Bram ‘t Hoen, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), Urology, Medical Informatics, Department of Marketing Management, Epidemiology, Internal Medicine, Dermatology, Ophthalmology, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, APH - Mental Health, VU University medical center, Clinical chemistry, Human genetics, APH - Digital Health, APH - Methodology, Biological Psychology, APH - Personalized Medicine, and APH - Health Behaviors & Chronic Diseases

Subjects
0301 basic medicine, Candidate gene, mQTL, Autism Spectrum Disorder, Gene Expression, Genome-wide association study, Biology, Quantitative trait locus, VARIANTS, eQTL, 03 medical and health sciences, 0302 clinical medicine, Mendelian randomization, mental disorders, Humans, GWAS, BRAIN, Biological Psychiatry, Genetic association, Genetics, Regulation of gene expression, Pleiotropy, HERITABILITY, dNaM, DNA Methylation, SMR, Fetal brain, PREVALENCE, 030104 developmental biology, Attention Deficit Disorder with Hyperactivity, DNA methylation, 17Q21.31, Psychiatric disorders, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

BACKGROUND: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.METHODS: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.RESULTS: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.CONCLUSIONS: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

Published
2020

36. Small RNA expression and miRNA modification dynamics in human oocytes and early embryos

Author

Timo Tuuri, Pauliina Paloviita, Juha S. Tapanainen, Kaarel Krjutškov, Urmo Võsa, Priit Paluoja, Sanna Vuoristo, Christel Hydén-Granskog, Dawit A. Yohannes, and Juha Kere

Subjects
Resource, Small interfering RNA, Small RNA, Zygote, 03 medical and health sciences, 0302 clinical medicine, Transcription (biology), Gene expression, microRNA, Genetics, medicine, Humans, Genetics (clinical), 030304 developmental biology, 0303 health sciences, biology, Sequence Analysis, RNA, Oocyte, Embryo, Mammalian, Phenotype, Cell biology, MicroRNAs, medicine.anatomical_structure, biology.protein, Oocytes, 030217 neurology & neurosurgery, Dicer
Abstract

Small noncoding RNAs (sRNAs) play important roles during the oocyte-to-embryo transition (OET), when the maternal phenotype is reprogrammed and the embryo genome is gradually activated. The transcriptional program driving early human development has been studied with the focus mainly on protein-coding RNAs, and expression dynamics of sRNAs remain largely unexplored. We profiled sRNAs in human oocytes and early embryos using an RNA-sequencing (RNA-seq) method suitable for low inputs of material. We show that OET in humans is temporally coupled with the transition from predominant expression of oocyte short piRNAs (os-piRNAs) in oocytes, to activation of microRNA (miRNA) expression in cleavage stage embryos. Additionally, 3′ mono- and oligoadenylation of miRNAs is markedly increased in zygotes. We hypothesize that this may modulate the function or stability of maternal miRNAs, some of which are retained throughout the first cell divisions in embryos. This study is the first of its kind elucidating the dynamics of sRNA expression and miRNA modification along a continuous trajectory of early human development and provides a valuable data set for in-depth interpretative analyses.

Published
2020

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

Author

Vincent W. V. Jaddoe, Myriam Fornage, Lenore J. Launer, Nicholas J. Timpson, Gwen Falony, Xingrong Liu, Mauro D'Amato, Robert C. Kaplan, Daphna Rothschild, Qi Qibin, Jordana T. Bell, Sara Vieira-Silva, Anna Andreasson, Dmitrii Borisevich, Caroline I. Le Roy, Torben Hansen, Eran Segal, Stefan Weiss, Hocheol Shin, Markus M. Lerch, Larbi Bedrani, André G. Uitterlinden, Serena Sanna, Uwe Völker, Urmo Võsa, Jonathan Thorsen, Matthias Laudes, Henry Völzke, Liesbeth Duijts, Kenneth Croitoru, Adriaan van der Graaf, Juan Antonio Raygoza Garay, Jeroen Raes, Dorret I. Boomsma, Daisy Jonkers, Jee-Young Moon, Søren J. Sørensen, Harm-Jan Westra, David A. Hughes, Tue H. Hansen, Andrew D. Paterson, Aldons J. Lusis, Daniel Keszthelyi, Gareth E. Davies, Wolfgang Lieb, Omer Weissbrod, Georg Homuth, Alexandra Zhernakova, Matthew A. Jackson, Robert D. Burk, Elin Org, Katie A. Meyer, Lars Agréus, Susanna Walter, Malte C. Rühlemann, Ad A.M. Masclee, Torben Jørgensen, Zachary D. Wallen, Kaitlin H Wade, Casey T. Finnicum, Andre Franke, Han-Na Kim, Alexander Kurilshikov, Rob Knight, Elad Barkan, Cornelia M. van Duijn, Hyung Lae Kim, Kyoko Watanabe, Annique Claringbould, Alejandro Arias Vasquez, Joanna Szopinska-Tokov, Richard G. IJzerman, Michael Boehnke, Lude Franke, Zlatan Mujagic, Marie Joossens, Marc Jan Bonder, Tim D. Spector, Frank Ulrich Weiss, Raul Y. Tito, Fernando Rivadeneira, Robert Kraaij, Williams Turpin, Eco J. C. de Geus, Henriette A. Moll, Jun Wang, Jingyuan Fu, Oluf Pedersen, Ayse Demirkan, Fabian Frost, Claire J. Steves, Gonneke Willemsen, Shiraz A. Shah, Markku Laakso, Haydeh Payami, Djawad Radjabzadeh, Daria V. Zhernakova, Hans Bisgaard, Cisca Wijmenga, Corinna Bang, Rodrigo Bacigalupe, Carolina Medina-Gomez, Kreete Lüll, and Jakob Stokholm

Subjects
Genetics, Human gut, Host (biology), Genotype, Genetic variation, Host factors, Genome-wide association study, Microbiome, Biology, Feces
Abstract

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 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28×10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95×10−10−8) 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 has causal effects in ulcerative colitis and rheumatoid arthritis.

Published
2020

38. Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Author

Sebo Withoff, Cisca Wijmenga, Xiaojin Chu, Patrick Deelen, Roy Oelen, Irma Joosten, Olivier B. Bakker, Annique Claringbould, Yang Li, Mihai G. Netea, Jennifer di Tommaso, Iris Jonkers, Vinod Kumar, Morris A. Swertz, Monique G. P. van der Wijst, Maria M. Zorro, Zuzanna Borek, Serena Sanna, Isis Ricaño-Ponce, Dylan H. de Vries, Lude Franke, Hans J. P. M. Koenen, Harm Brugge, Cheng-Jian Xu, Raul Aguirre-Gamboa, Niek de Klein, Urmo Võsa, Leo A. B. Joosten, HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany., Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Molecular Neuroscience and Ageing Research (MOLAR), and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
Cell type, Population, Cell, Quantitative Trait Loci, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Context (language use), Computational biology, Deconvolution, FORMAT, Biology, Quantitative trait locus, lcsh:Computer applications to medicine. Medical informatics, eQTL, Biochemistry, Whole-Body Counting, 03 medical and health sciences, 0302 clinical medicine, Structural Biology, DRIVERS, medicine, Humans, Allele, education, Molecular Biology, lcsh:QH301-705.5, 030304 developmental biology, Whole blood, 0303 health sciences, education.field_of_study, Applied Mathematics, Methodology Article, Immune cells, ASSOCIATION, Cell types, Computer Science Applications, 3. Good health, medicine.anatomical_structure, lcsh:Biology (General), Expression quantitative trait loci, SURVIVAL, lcsh:R858-859.7, DNA microarray, 030217 neurology & neurosurgery, Inflammatory diseases Radboud Institute for Molecular Life Sciences [Radboudumc 5], Genome-Wide Association Study
Abstract

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).

Published
2020

39. Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Author

Tõnu Esko, Lars Wallentin, Andrew D. Bretherick, Adam S. Butterworth, Jesper R. Gådin, Anders Hamsten, Anders Mälarstig, Gunnar Engström, Johan Ärnlöv, Andres Ingason, Céline Fernandez, Jian Yang, Peter M. Nilsson, George Davey-Smith, Weidong Zhang, Agneta Siegbahn, Lasse Folkersen, Thibaud Boutin, Michael V. Holmes, Lars Lind, Julie Lee, Erik Ingelsson, James F. Wilson, Olle Melander, Eric B. Fauman, Lude Franke, Amira Quazi, John Danesh, Ozren Polasek, Niclas Ericsson, Sarah E. Bergen, Mika Ala-Korpela, Andrew J. Schork, Marketa Sjögren, Seung Hoan Choi, Jan Nilsson, Åsa K Hedman, Cecilia M. Lindgren, Bram P. Prins, Daria V. Zhernakova, Yang Wu, Karl Michaëlsson, James E. Peters, Andrew P. Morris, Marju Orho-Melander, Stefan Enroth, Reedik Mägi, Stefan Gustafsson, Mikael Landén, Martin Magnusson, Qin Wang, Steven A. Lubitz, Johan Sundström, Federico De Masi, Jingyuan Fu, Rasmus Wernersson, Thomas Werge, Praveen Surendran, Tom Palmer, Paul W. Franks, Caroline Hayward, Daniel Hvidberg Hansen, Ulf Gyllensten, Vilmantas Giedraitis, Ljubica Perisic Matic, Urmo Võsa, Åsa Johansson, Alexandra Zhernakova, Bianca E. Suur, Karen Page, Gustav Smith, Erik Pålsson, Ting Qi, Harm-Jan Westra, Michael W. Nagle, Erin Macdonald-Dunlop, Annique Claringbould, Peter K. Joshi, Sölve Elmståhl, Anettne Kalnapenkis, Yan Chen, Chris Haley, and Jeremy D. Gale

Subjects
Drug, 0303 health sciences, media_common.quotation_subject, Drug target, Disease, Computational biology, 030204 cardiovascular system & hematology, Biology, Quantitative trait locus, Precision medicine, 3. Good health, 03 medical and health sciences, Human health, 0302 clinical medicine, Mendelian randomization, Gene, 030304 developmental biology, media_common
Abstract

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. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping usingtrans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

Published
2020
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40. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Author

Tom Palmer, Lars Lind, George Davey Smith, Ting Qi, Michael W. Nagle, Paul W. Franks, Bram P. Prins, Julie Lee, Jingyuan Fu, Niclas Eriksson, Peter K. Joshi, Chris Haley, Ljubica Perisic Matic, Jeremy D. Gale, Mika Ala-Korpela, Michael V. Holmes, Urmo Võsa, Adam S. Butterworth, Eric B. Fauman, Anette Kalnapenkis, Reedik Mägi, Åsa Johansson, James F. Wilson, Mikael Landén, Gunnar Engström, Johan Ärnlöv, Anders Hamsten, Ozren Polasek, Andres Ingason, Andrew J. Schork, Agneta Siegbahn, Lasse Folkersen, Qin Wang, Andrew P. Morris, Johan Sundström, Daria V. Zhernakova, Olle Melander, Erik Ingelsson, Federico De Masi, Lude Franke, James E. Peters, Alexandra Zhernakova, Seung Hoan Choi, Rasmus Wernersson, Thibaud Boutin, Karl Michaëlsson, Stefan Gustafsson, Bianca E. Suur, Karen Page, Yang Wu, Caroline Hayward, Marketa Sjögren, Cecilia M. Lindgren, Stefan Enroth, Tõnu Esko, Amira Quazi, John Danesh, Anders Mälarstig, Daniel Hvidberg Hansen, Åsa K Hedman, Jan Nilsson, Ulf Gyllensten, Vilmantas Giedraitis, J. Gustav Smith, Martin Magnusson, Marju Orho-Melander, Steven A. Lubitz, Erin Macdonald-Dunlop, Thomas Werge, Praveen Surendran, Yan Chen, Céline Fernandez, Weidong Zhang, Lars Wallentin, Andrew D. Bretherick, Jian Yang, Peter M. Nilsson, Jesper R. Gådin, Annique Claringbould, Sölve Elmståhl, Sarah E Bergen, Harm-Jan Westra, Erik Pålsson, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Center for Liver, Digestive and Metabolic Diseases (CLDM), Stem Cell Aging Leukemia and Lymphoma (SALL), United Kingdom Research and Innovation, Folkersen, Lasse [0000-0003-0708-9530], Hansen, Daniel Hvidberg [0000-0003-3285-605X], Wu, Yang [0000-0002-0128-7280], Eriksson, Niclas [0000-0002-2152-4343], Bretherick, Andrew D [0000-0001-9258-3140], Enroth, Stefan [0000-0002-5056-9137], Lee, Julie [0000-0001-6090-6718], Ala-Korpela, Mika [0000-0001-5905-1206], Claringbould, Annique [0000-0002-9201-6557], Davey Smith, George [0000-0002-1407-8314], Fauman, Eric [0000-0002-9739-0249], Fernandez, Celine [0000-0003-1290-4982], Franke, Lude [0000-0002-5159-8802], Franks, Paul W [0000-0002-0520-7604], Giedraitis, Vilmantas [0000-0003-3423-2021], Haley, Chris [0000-0002-9811-0210], Johansson, Åsa [0000-0002-2915-4498], Lubitz, Steven [0000-0002-9599-4866], Palmer, Tom [0000-0003-4655-4511], Macdonald-Dunlop, Erin [0000-0001-6569-6086], Magnusson, Martin [0000-0003-1710-5936], Michaelsson, Karl [0000-0003-2815-1217], Nagle, Michael W [0000-0002-4677-7582], Nilsson, Peter M [0000-0002-5652-8459], Nilsson, Jan [0000-0002-9752-7479], Prins, Bram [0000-0001-5774-034X], Sundström, Johan [0000-0003-2247-8454], Werge, Thomas [0000-0003-1829-0766], Westra, Harm-Jan [0000-0001-7038-567X], Fu, Jingyuan [0000-0001-5578-1236], Esko, Tõnu [0000-0003-1982-6569], Hayward, Caroline [0000-0002-9405-9550], Landen, Mikael [0000-0002-4496-6451], Butterworth, Adam S [0000-0002-6915-9015], Holmes, Michael V [0000-0001-6617-0879], Ingelsson, Erik [0000-0003-2256-6972], Mälarstig, Anders [0000-0003-2608-1358], Apollo - University of Cambridge Repository, and 30387078 - Magnusson, P. Martin

Subjects
Proteomics, Proteome, Endocrinology, Diabetes and Metabolism, Asthma, ATP Binding Cassette Transporter 1, Cardiovascular System, Chromosome Mapping, Drug Delivery Systems, Gene Knockdown Techniques, Genome-Wide Association Study, Genomics, Humans, Inflammatory Bowel Diseases, Interleukin-1 Receptor-Like 1 Protein, Intracellular Signaling Peptides and Proteins, Linkage Disequilibrium, Mendelian Randomization Analysis, Protein-Serine-Threonine Kinases, Quantitative Trait Loci, Receptors, CCR2, Receptors, CCR5, Genome-wide association study, 030204 cardiovascular system & hematology, Chemokine receptor, 0302 clinical medicine, RECEPTOR ANTAGONIST, GWAS, Cardiac and Cardiovascular Systems, 0303 health sciences, Molecular medicine, 3. Good health, Medical genetics, Medical Genetics, Life Sciences & Biomedicine, medicine.medical_specialty, Computational biology, Biology, Protein Serine-Threonine Kinases, Article, 03 medical and health sciences, Endocrinology & Metabolism, Physiology (medical), Mendelian randomization, Internal Medicine, medicine, 030304 developmental biology, Science & Technology, Cell Biology, GENE, ANTIBODY
Abstract

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.

Published
2020
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41. Differences in local population history at the finest level: the case of the Estonian population

Author

Marten Seppel, Rodrigo Flores, Mart Kals, Lili Milani, Luca Pagani, Lauri Saag, Georgi Hudjashov, Francesco Montinaro, Andres Metspalu, Daniel Lawson, Davide Marnetto, Flora Jay, Vasili Pankratov, Reedik Mägi, Mait Metspalu, Urmo Võsa, Cristian Taccioli, Märt Möls, Anto Aasa, Alena Kushniarevich, Tõnu Esko, University of Tartu, Massey University, Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Bioinformatique (LRI) (BioInfo - LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), Università degli Studi di Padova = University of Padua (Unipd), University of Bristol [Bristol], CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), and Universita degli Studi di Padova

Subjects
Estonia, Evolution, Molecular, Human Migration, Humans, Population, Selection, Genetic, Pedigree, Polymorphism, Genetic, Demographic history, Evolution, Evolutionary biology, Article, 03 medical and health sciences, 0302 clinical medicine, Effective population size, Genetic, Genetics, Genetic variation, Polymorphism, education, Allele frequency, Selection, Genetics (clinical), Selection (genetic algorithm), 030304 developmental biology, 0303 health sciences, education.field_of_study, Natural selection, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Molecular, demographic history, Estonian, language.human_language, Geography, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Genetic structure, language, 030217 neurology & neurosurgery
Abstract

Several recent studies detected fine-scale genetic structure in human populations. Hence, groups conventionally treated as single populations harbour significant variation in terms of allele frequencies and patterns of haplotype sharing. It has been shown that these findings should be considered when performing studies of genetic associations and natural selection, especially when dealing with polygenic phenotypes. However, there is little understanding of the practical effects of such genetic structure on demography reconstructions and selection scans when focusing on recent population history. Here we tested the impact of population structure on such inferences using high-coverage (~30×) genome sequences of 2305 Estonians. We show that different regions of Estonia differ in both effective population size dynamics and signatures of natural selection. By analyzing identity-by-descent segments we also reveal that some Estonian regions exhibit evidence of a bottleneck 10–15 generations ago reflecting sequential episodes of wars, plague and famine, although this signal is virtually undetected when treating Estonia as a single population. Besides that, we provide a framework for relating effective population size estimated from genetic data to actual census size and validate it on the Estonian population. This approach may be widely used both to cross-check estimates based on historical sources as well as to get insight into times and/or regions with no other information available. Our results suggest that the history of human populations within the last few millennia can be highly region specific and cannot be properly studied without taking local genetic structure into account.

Published
2020

42. Optimizing bone morphogenic protein 4-mediated human embryonic stem cell differentiation into trophoblast-like cells using fibroblast growth factor 2 and transforming growth factor-β/activin/nodal signalling inhibition

Author

Elisabet Einarsdottir, Juha Kere, Timo Tuuri, Juha S. Tapanainen, Shintaro Katayama, Viljar Jaks, Ulf-Håkan Stenman, Urmo Võsa, Sulev Ingerpuu, Mariann Koel, Kaarel Krjutškov, Karolina Lundin, Andres Salumets, Research Programme for Molecular Neurology, Research Programs Unit, Juha Kere / Principal Investigator, Clinicum, Department of Obstetrics and Gynecology, Department of Clinical Chemistry and Hematology, Medicum, Reproductive Disease Modeling, and HUS Gynecology and Obstetrics

Subjects
0301 basic medicine, FGF2, Cellular differentiation, Human Embryonic Stem Cells, Cell Culture Techniques, Nodal signaling, Bone Morphogenetic Protein 4, Fibroblast growth factor, 0302 clinical medicine, 3123 Gynaecology and paediatrics, Transforming Growth Factor beta, Cells, Cultured, reproductive and urinary physiology, 030219 obstetrics & reproductive medicine, biology, Obstetrics and Gynecology, Cell Differentiation, SWITCHES, bone morphogenic protein 4, Activins, Trophoblasts, 3. Good health, Cell biology, EXTRAVILLOUS TROPHOBLAST, embryonic structures, Fibroblast Growth Factor 2, Signal Transduction, EXPRESSION, medicine.medical_specialty, placenta, Nodal Protein, Down-Regulation, BMP4, human embryonic stem cell, 03 medical and health sciences, Internal medicine, medicine, Humans, Activin type 2 receptors, Transforming growth factor beta, trophoblast cells, Embryonic stem cell, 030104 developmental biology, Endocrinology, Reproductive Medicine, Cell culture, transforming growth factor-beta, biology.protein, Developmental Biology, Transforming growth factor
Abstract

Several studies have demonstrated that human embryonic stem cells [hESC] can be differentiated into trophoblast-like cells if exposed to bone morphogenic protein 4 [BMP4] and/or inhibitors of fibroblast growth factor 2 [FGF2] and the transforming growth factor beta [TGF-beta]/activin/nodal signalling pathways. The goal of this study was to investigate how the inhibitors of these pathways improve the efficiency of hESC differentiation when compared with basic BMP4 treatment. RNA sequencing was used to analyse the effects of all possible inhibitor combinations on the differentiation of hESC into trophoblast-like cells over 12 days. Genes differentially expressed compared with untreated cells were identified at seven time points. Additionally, expression of total human chorionic gonadotrophin [HCG] and its hyperglycosylated form [HCG-H] were determined by immunoassay from cell culture media. We showed that FGF2 inhibition with BMP4 activation up-regulates syncytiotrophoblast-specific genes [CGA, CGB and LGALS16], induces several molecular pathways involved in embryo implantation and triggers HCG-H production. In contrast, inhibition of the TGF-beta/activin/nodal pathway decreases the ability of hESC to form trophoblast-like cells. Information about the conditions needed for hESC differentiation toward trophoblast-like cells helps us to find an optimal model for studying the early development of human trophoblasts in normal and in complicated pregnancy. (C) 2017 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Published
2017

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

Author

Lude Franke, Harmen J. Bussemaker, Federico Comoglio, Joris van Arensbergen, Vincent FitzPatrick, Marcel de Haas, Urmo Võsa, Robin H. van der Weide, Elzo de Wit, Michiel Vermeulen, Bas van Steensel, Marijke P. Baltissen, Ludo Pagie, Hans Teunissen, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
PROMOTER, DATABASE, DNA-BINDING, Quantitative Trait Loci, Genome-wide association study, Single-nucleotide polymorphism, Computational biology, Quantitative trait locus, Biology, VARIANTS, Genome, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, Genetic Predisposition to Disease, Regulatory Elements, Transcriptional, GENOME-WIDE ASSOCIATION, CHROMATIN STATES, Enhancer, 030304 developmental biology, Genetic association, 0303 health sciences, Genome, Human, Proteomics and Chromatin Biology, COMPLEX TRAITS, High-Throughput Nucleotide Sequencing, GENETIC-VARIATION, Hep G2 Cells, Phenotype, READ ALIGNMENT, TRANSCRIPTION FACTOR-BINDING, Human genome, K562 Cells, 030217 neurology & neurosurgery, Genome-Wide Association Study, Transcription Factors
Abstract

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.

Published
2019

44. Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation

Author

Biao Zeng, Joseph E. Powell, Jian Yang, Lude Franke, Grant W. Montgomery, Andres Metspalu, Arshed A. Quyyumi, Kenneth L. Brigham, Tõnu Esko, Annique Claringbould, Luke R. Lloyd-Jones, Youssef Idaghdour, Urmo Võsa, Peter M. Visscher, Greg Gibson, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
Multifactorial Inheritance, Microarray, conditional association, Quantitative Trait Loci, LOCI, Genome-wide association study, Single-nucleotide polymorphism, ANNOTATION DATA, Biology, Quantitative trait locus, Investigations, colocalization, Linkage Disequilibrium, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, 030304 developmental biology, GENE-EXPRESSION, SNPS, Regulation of gene expression, 0303 health sciences, ARCHITECTURE, IDENTIFICATION, HERITABILITY, Gene Expression Profiling, Colocalization, PolyQTL, Phenotype, ALIGNMENT, Expression quantitative trait loci, fine mapping, gene regulation, 030217 neurology & neurosurgery, Algorithms, INFLAMMATORY-BOWEL-DISEASE, Genome-Wide Association Study
Abstract

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 cis-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 cis-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

Published
2019

45. Author response: Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

Author

Uwe Völker, Ninon Mounier, J. M. van Dongen, Martina Müller-Nurasyid, Peter K. Joshi, David W. Clark, Jaanika Kronberg-Guzman, Holger Kirsten, Viktorija Kukushkina, Xia Shen, Patrick Deelen, Mawusse Agbessi, Alexis Battle, Susan M. Ring, Marie-Julie Favé, Anke Tönjes, Tõnu Esko, Z. Kutalik, Alexander Teumer, Biao Zeng, Michael Stumvoll, Katharina Schramm, Natalia Pervjakova, Greg Gibson, Hanieh Yaghootkar, Yungil Kim, Annique Claringbould, Patrick F. Sullivan, Habibul Ahsan, Lin Tong, Markus Scholz, Philip Awadalla, Mark W. Christiansen, Brenda W.J.H. Penninx, Ashis Saha, Urmo Võsa, Urko M. Marigorta, D.I. Boomsma, Peter Kovacs, Isabel Alves, Gibran Hemani, Knut Krohn, Jarno Kettunen, Krista Fischer, Anand Kumar Andiappan, James F. Wilson, Holger Prokisch, Timothy M. Frayling, Joost Verlouw, Anette Kalnapenkis, Markus Perola, Andrew D. Bretherick, Jian Yang, Peter M. Visscher, Markus Loeffler, Joseph E. Powell, Lude Franke, Marc Jan Bonder, Zoltán Kutalik, Silva Kasela, Kristi Läll, Sina A. Gharib, Paul R. H. J. Timmers, Lili Milani, Olli T. Raitakari, T Esko, Zheng Ning, Ilkka Seppälä, Andres Metspalu, Bernett Lee, Michel G. Nivard, Matthias Nauck, Brandon L. Pierce, Joachim Thiery, J.B. van Meurs, Sina Rüeger, Olaf Rötzschke, Samuli Ripatti, Mika Kähönen, Bruce M. Psaty, Rick Jansen, Futao Zhang, Terho Lehtimäki, and Xiao Feng

Subjects
Evolutionary biology, Genomics, Biology
Published
2018

46. Systematic identification of human SNPs affecting regulatory element activity

Author

Urmo Võsa, Hans Teunissen, Vincent FitzPatrick, Federico Comoglio, Harmen J. Bussemaker, Marijke P. Baltissen, Robin H. van der Weide, Ludo Pagie, Elzo de Wit, Bas van Steensel, Joris van Arensbergen, Lude Franke, Marcel de Haas, and Michiel Vermeulen

Subjects
Regulation of gene expression, 0303 health sciences, Single-nucleotide polymorphism, Genomics, Computational biology, Biology, 03 medical and health sciences, 0302 clinical medicine, Promoter activity, Identification (biology), Human genome, Enhancer, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Most of the millions of single-nucleotide polymorphisms (SNPs) in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies 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 enhancer and promoter activity. Here, we have leveraged the throughput of the SuRE reporter technology to survey a total of 5.9 million SNPs, including 57% of the known common SNPs. We identified more than 30 thousand SNPs that alter the activity of putative regulatory elements, often in a cell-type specific manner. These data indicate that a large proportion of human non-coding SNPs may affect gene regulation. Integration of these SuRE data with genome-wide association studies may help pinpoint SNPs that underlie human traits.

Published
2018

47. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author

Symen Ligthart, Ahmad Vaez, Urmo Võsa, Maria G. Stathopoulou, Paul S. de Vries, Bram P. Prins, Peter J. Van der Most, Toshiko Tanaka, Elnaz Naderi, Lynda M. Rose, Ying Wu, Robert Karlsson, Maja Barbalic, Honghuang Lin, René Pool, Gu Zhu, Aurélien Macé, Carlo Sidore, Stella Trompet, Massimo Mangino, Maria Sabater-Lleal, John P. Kemp, Ali Abbasi, Tim Kacprowski, Niek Verweij, Albert V. Smith, Tao Huang, Carola Marzi, Mary F. Feitosa, Kurt K. Lohman, Marcus E. Kleber, Yuri Milaneschi, Christian Mueller, Mahmudul Huq, Efthymia Vlachopoulou, Leo-Pekka Lyytikäinen, Christopher Oldmeadow, Joris Deelen, Markus Perola, Jing Hua Zhao, Bjarke Feenstra, Marzyeh Amini, Jari Lahti, Katharina E. Schraut, Myriam Fornage, Bhoom Suktitipat, Wei-Min Chen, Xiaohui Li, Teresa Nutile, Giovanni Malerba, Jian’an Luan, Tom Bak, Nicholas Schork, Fabiola Del Greco M., Elisabeth Thiering, Anubha Mahajan, Riccardo E. Marioni, Evelin Mihailov, Joel Eriksson, Ayse Bilge Ozel, Weihua Zhang, Maria Nethander, Yu-Ching Cheng, Stella Aslibekyan, Wei Ang, Ilaria Gandin, Loïc Yengo, Laura Portas, Charles Kooperberg, Edith Hofer, Kumar B. Rajan, Claudia Schurmann, Wouter den Hollander, Tarunveer S. Ahluwalia, Jing Zhao, Harmen H.M. Draisma, Ian Ford, Nicholas Timpson, Alexander Teumer, Hongyan Huang, Simone Wahl, YongMei Liu, Jie Huang, Hae-Won Uh, Frank Geller, Peter K. Joshi, Lisa R. Yanek, Elisabetta Trabetti, Benjamin Lehne, Diego Vozzi, Marie Verbanck, Ginevra Biino, Yasaman Saba, Ingrid Meulenbelt, Jeff R. O’Connell, Markku Laakso, Franco Giulianini, Patrik K.E. Magnusson, Christie M. Ballantyne, Jouke Jan Hottenga, Grant W. Montgomery, Fernando Rivadineira, Rico Rueedi, Maristella Steri, Karl-Heinz Herzig, David J. Stott, Cristina Menni, Mattias Frånberg, Beate St. Pourcain, Stephan B. Felix, Tune H. Pers, Stephan J.L. Bakker, Peter Kraft, Annette Peters, Dhananjay Vaidya, Graciela Delgado, Johannes H. Smit, Vera Großmann, Juha Sinisalo, Ilkka Seppälä, Stephen R. Williams, Elizabeth G. Holliday, Matthijs Moed, Claudia Langenberg, Katri Räikkönen, Jingzhong Ding, Harry Campbell, Michele M. Sale, Yii-Der I. Chen, Alan L. James, Daniela Ruggiero, Nicole Soranzo, Catharina A. Hartman, Erin N. Smith, Gerald S. Berenson, Christian Fuchsberger, Dena Hernandez, Carla M.T. Tiesler, Vilmantas Giedraitis, David Liewald, Krista Fischer, Dan Mellström, Anders Larsson, Yunmei Wang, William R. Scott, Matthias Lorentzon, John Beilby, Kathleen A. Ryan, Craig E. Pennell, Dragana Vuckovic, Beverly Balkau, Maria Pina Concas, Reinhold Schmidt, Carlos F. Mendes de Leon, Erwin P. Bottinger, Margreet Kloppenburg, Lavinia Paternoster, Michael Boehnke, A.W. Musk, Gonneke Willemsen, David M. Evans, Pamela A.F. Madden, Mika Kähönen, Zoltán Kutalik, Magdalena Zoledziewska, Ville Karhunen, Stephen B. Kritchevsky, Naveed Sattar, Genevieve Lachance, Robert Clarke, Tamara B. Harris, Olli T. Raitakari, John R. Attia, Diana van Heemst, Eero Kajantie, Rossella Sorice, Giovanni Gambaro, Robert A. Scott, Andrew A. Hicks, Luigi Ferrucci, Marie Standl, Cecilia M. Lindgren, John M. Starr, Magnus Karlsson, Lars Lind, Jun Z. Li, John C. Chambers, Trevor A. Mori, Eco J.C.N. de Geus, Andrew C. Heath, Nicholas G. Martin, Juha Auvinen, Brendan M. Buckley, Anton J.M. de Craen, Melanie Waldenberger, Konstantin Strauch, Thomas Meitinger, Rodney J. Scott, Mark McEvoy, Marian Beekman, Cristina Bombieri, Paul M. Ridker, Karen L. Mohlke, Nancy L. Pedersen, Alanna C. Morrison, Dorret I. Boomsma, John B. Whitfield, David P. Strachan, Albert Hofman, Peter Vollenweider, Francesco Cucca, Marjo-Riitta Jarvelin, J. Wouter Jukema, Tim D. Spector, Anders Hamsten, Tanja Zeller, André G. Uitterlinden, Matthias Nauck, Vilmundur Gudnason, Lu Qi, Harald Grallert, Ingrid B. Borecki, Jerome I. Rotter, Winfried März, Philipp S. Wild, Marja-Liisa Lokki, Michael Boyle, Veikko Salomaa, Mads Melbye, Johan G. Eriksson, James F. Wilson, Brenda W.J.H. Penninx, Diane M. Becker, Bradford B. Worrall, Greg Gibson, Ronald M. Krauss, Marina Ciullo, Gianluigi Zaza, Nicholas J. Wareham, Albertine J. Oldehinkel, Lyle J. Palmer, Sarah S. Murray, Peter P. Pramstaller, Stefania Bandinelli, Joachim Heinrich, Erik Ingelsson, Ian J. Deary, Reedik Mägi, Liesbeth Vandenput, Pim van der Harst, Karl C. Desch, Jaspal S. Kooner, Claes Ohlsson, Caroline Hayward, Terho Lehtimäki, Alan R. Shuldiner, Donna K. Arnett, Lawrence J. Beilin, Antonietta Robino, Philippe Froguel, Mario Pirastu, Tine Jess, Wolfgang Koenig, Ruth J.F. Loos, Denis A. Evans, Helena Schmidt, George Davey Smith, P. Eline Slagboom, Gudny Eiriksdottir, Andrew P. Morris, Bruce M. Psaty, Russell P. Tracy, Ilja M. Nolte, Eric Boerwinkle, Sophie Visvikis-Siest, Alex P. Reiner, Myron Gross, Joshua C. Bis, Lude Franke, Oscar H. Franco, Emelia J. Benjamin, Daniel I. Chasman, Josée Dupuis, Harold Snieder, Abbas Dehghan, Behrooz Z. Alizadeh, H. Marike Boezen, Gerjan Navis, Marianne Rots, Morris Swertz, Bruce H.R. Wolffenbuttel, Cisca Wijmenga, Emelia Benjamin, Tarunveer Singh Ahluwalia, James Meigs, Russell Tracy, Josh Bis, Nathan Pankratz, Alex Rainer, James G. Wilson, Josee Dupuis, Bram Prins, Urmo Vaso, Maria Stathopoulou, Terho Lehtimaki, Yalda Jamshidi, Sophie Siest, Andre G. Uitterlinden, Mohammadreza Abdollahi, Renate Schnabel, Ursula M. Schick, Aldi Kraja, Yi-Hsiang Hsu, Daniel S. Tylee, Alyson Zwicker, Rudolf Uher, George Davey-Smith, Andrew Hicks, Cornelia M. van Duijn, Cavin Ward-Caviness, J. Rotter, Ken Rice, Leslie Lange, Eco de Geus, Kari Matti Makela, David Stacey, Johan Eriksson, Tim M. Frayling, Eline P. Slagboom, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University Medical Center Groningen [Groningen] (UMCG), University of Isfahan, University of Tartu, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), The University of Texas Health Science Center at Houston (UTHealth), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Brigham and Women's Hospital [Boston], University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Split, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Process & Energy Laboratory, Delft University of Technology (TU Delft), Grand Lyon : communauté urbaine de Lyon, Interuniversity Cardiology Institute Netherlands, Department of Twin Research and Genetic Epidemiology, King's College London, London, Huazhong University of Science and Technology [Wuhan] (HUST), Division of Statistical Genomics, Washington University School of Medicine, Department of Psychiatry, VU University Medical Center [Amsterdam], Institut fuer Theoretische Physik (Institut fuer Theoretische Physik), Universität Heidelberg [Heidelberg] = Heidelberg University, Molecular Epidemiology, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Mahidol University [Bangkok], Northwest A and F University, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Institute of Genetics and Biophysics, CNR, Naples, Università degli studi di Verona = University of Verona (UNIVR), Department of Molecular Medicine [Scripps Research Institute], The Scripps Research Institute [La Jolla, San Diego], Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], University of Maryland School of Medicine, University of Maryland System, Institut National de l'Environnement Industriel et des Risques (INERIS), Institute of Pop. Genetics, CNR, Sassari, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, IT University of Copenhagen (ITU), Robertson Centre for Biostatistics, University of Glasgow, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, King‘s College London, Jinan University [Guangzhou], Institute of Oceanology [China], School Medicine, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Shardna life science Pula Cagliari, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Medstar Research Institute, Department of Cardiology, Ernst-Moritz-Arndt University, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Department of Epidemiology, Harvard School of Public Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Metacohorts Consortium, INEOS Technologies (SWITZERLAND), MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, University of Edinburgh, School of Population Health [Crawley, Western Australia], The University of Western Australia (UWA), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), The Scripps Translational Science Institute and Scripps Health, Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, Centre for Population Health Sciences, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Luleå University of Technology (LUT), Recherche en épidémiologie et biostatistique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Austrian Institute of Technology [Vienna] (AIT), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Rheumatology and Clinical Epidemiology, Leiden University Medical Center (LUMC), Department of Rheumatology and Clinical Epidemiology [Leiden University Medical Center] (LUMC), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Virginia, Tampere University Hospital, Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Department of Pathological Biochemistry, Royal Infirmary, Oxford University, University of Oxford, University of Newcastle [Callaghan, Australia] (UoN), Department of neurology, Institute of Metabolic Science, MRC, The Wellcome Trust Centre for Human Genetics [Oxford], Uppsala Universitet [Uppsala], QIMR Berghofer Medical Research Institute, Institute of Genetic Epidemiology [Neuherberg, Germany], Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Schizophrenia Research Institute [Sydney], Department of Genetics, University of North Carolina System (UNC)-University of North Carolina System (UNC), Vrije Universiteit Brussel (VUB), Population Health Sciences and Education, St George's University of London, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Institute of Health Sciences and Biocenter Oulu, University of Oulu, Medizinische Klinik und Poliklinik, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Institute of Clinical Chemistry and Laboratory Medicine, Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Departments of Epidemiology and Nutrition, Institute of Epidemiology [Neuherberg] (EPI), Medical University Graz, Transplantation Laboratory [Helsinki], Haartman Institute [Helsinki], Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], CLinical Psychology, Genetics and Pathology, Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Center For Narcolepsy, Stanford University, Centre for Bone and Arthritis Research, University of Gothenburg (GU)-Institute of Medicine, MRC Human Gentics Unit, Inst Genet and Mol Med, Western General Hospital, Edinburgh, University of Maryland School of Medicine [Baltimore, MD, USA], Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Department of Physics [Stockholm], Stockholm University, University of Bristol [Bristol], Universiteit Leiden, Department of Epidemiology, University of Washington, University of Washington [Seattle], Department of Epidemiology [Rotterdam], University of Groningen [Groningen], Dutch Initiative on Crohn and Colitis (ICC), Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Physiology and Biophysics [Jackson, MS, USA], University of Southern Mississippi (USM), Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Faculty of Medicine and Life Sciences [Tampere], University of Tampere [Finland], German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), MRC Centre for Neuropsychiatric Genetics and Genomics, Medical Research Council-Cardiff University, Department of Social Medicine, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Medicine [Aurora, CO, USA], University of Colorado [Denver], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Mathematical Institute [Oxford] (MI), Institute of Psychiatry, Psychology & Neuroscience, King's College London, LifeLines Cohort Study, CHARGE Inflammation Working Group, Ligthart, S., Vaez, A., Vosa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., Van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., Wu, Y., Karlsson, R., Barbalic, M., Lin, H., Pool, R., Zhu, G., Mace, A., Sidore, C., Trompet, S., Mangino, M., Sabater-Lleal, M., Kemp, J. P., Abbasi, A., Kacprowski, T., Verweij, N., Smith, A. V., Huang, T., Marzi, C., Feitosa, M. F., Lohman, K. K., Kleber, M. E., Milaneschi, Y., Mueller, C., Huq, M., Vlachopoulou, E., Lyytikainen, L. -P., Oldmeadow, C., Deelen, J., Perola, M., Zhao, J. H., Feenstra, B., Alizadeh, B. Z., Boezen, H. M., Franke, L., van der Harst, P., Navis, G., Rots, M., Snieder, H., Swertz, M., Wolffenbuttel, B. H. R., Wijmenga, C., Amini, M., Benjamin, E., Chasman, D. I., Dehghan, A., Ahluwalia, T. S., Meigs, J., Tracy, R., Bis, J., Eiriksdottir, G., Pankratz, N., Gross, M., Rainer, A., Wilson, J. G., Psaty, B. M., Dupuis, J., Prins, B., Vaso, U., Stathopoulou, M., Lehtimaki, T., Koenig, W., Jamshidi, Y., Siest, S., Uitterlinden, A. G., Abdollahi, M., Schnabel, R., Schick, U. M., Nolte, I. M., Kraja, A., Hsu, Y. -H., Tylee, D. S., Zwicker, A., Uher, R., Davey-Smith, G., Morrison, A. C., Hicks, A., van Duijn, C. M., Ward-Caviness, C., Boerwinkle, E., Rotter, J., Rice, K., Lange, L., de Geus, E., Morris, A. P., Makela, K. M., Stacey, D., Eriksson, J., Frayling, T. M., Slagboom, E. P., Lahti, J., Schraut, K. E., Fornage, M., Suktitipat, B., Chen, W. -M., Li, X., Nutile, T., Malerba, G., Luan, J., Bak, T., Schork, N., Del Greco, M. F., Thiering, E., Mahajan, A., Marioni, R. E., Mihailov, E., Ozel, A. B., Zhang, W., Nethander, M., Cheng, Y. -C., Aslibekyan, S., Ang, W., Gandin, I., Yengo, L., Portas, L., Kooperberg, C., Hofer, E., Rajan, K. B., Schurmann, C., den Hollander, W., Zhao, J., Draisma, H. H. M., Ford, I., Timpson, N., Teumer, A., Huang, H., Wahl, S., Liu, Y., Huang, J., Uh, H. -W., Geller, F., Joshi, P. K., Yanek, L. R., Trabetti, E., Lehne, B., Vozzi, D., Verbanck, M., Biino, G., Saba, Y., Meulenbelt, I., O'Connell, J. R., Laakso, M., Giulianini, F., Magnusson, P. K. E., Ballantyne, C. M., Hottenga, J. J., Montgomery, G. W., Rivadineira, F., Rueedi, R., Steri, M., Herzig, K. -H., Stott, D. J., Menni, C., Franberg, M., S, t. Pourcain B., Felix, S. B., Pers, T. H., Bakker, S. J. L., Kraft, P., Peters, A., Vaidya, D., Delgado, G., Smit, J. H., Grossmann, V., Sinisalo, J., Seppala, I., Williams, S. R., Holliday, E. G., Moed, M., Langenberg, C., Raikkonen, K., Ding, J., Campbell, H., Sale, M. M., Chen, Y. -D. I., James, A. L., Ruggiero, D., Soranzo, N., Hartman, C. A., Smith, E. N., Berenson, G. S., Fuchsberger, C., Hernandez, D., Tiesler, C. M. T., Giedraitis, V., Liewald, D., Fischer, K., Mellstrom, D., Larsson, A., Wang, Y., Scott, W. R., Lorentzon, M., Beilby, J., Ryan, K. A., Pennell, C. E., Vuckovic, D., Balkau, B., Concas, M. P., Schmidt, R., Mendes de Leon, C. F., Bottinger, E. P., Kloppenburg, M., Paternoster, L., Boehnke, M., Musk, A. W., Willemsen, G., Evans, D. M., Madden, P. A. F., Kahonen, M., Kutalik, Z., Zoledziewska, M., Karhunen, V., Kritchevsky, S. B., Sattar, N., Lachance, G., Clarke, R., Harris, T. B., Raitakari, O. T., Attia, J. R., van Heemst, D., Kajantie, E., Sorice, R., Gambaro, G., Scott, R. A., Hicks, A. A., Ferrucci, L., Standl, M., Lindgren, C. M., Starr, J. M., Karlsson, M., Lind, L., Li, J. Z., Chambers, J. C., Mori, T. A., de Geus, E. J. C. N., Heath, A. C., Martin, N. G., Auvinen, J., Buckley, B. M., de Craen, A. J. M., Waldenberger, M., Strauch, K., Meitinger, T., Scott, R. J., Mcevoy, M., Beekman, M., Bombieri, C., Ridker, P. M., Mohlke, K. L., Pedersen, N. L., Boomsma, D. I., Whitfield, J. B., Strachan, D. P., Hofman, A., Vollenweider, P., Cucca, F., Jarvelin, M. -R., Jukema, J. W., Spector, T. D., Hamsten, A., Zeller, T., Nauck, M., Gudnason, V., Qi, L., Grallert, H., Borecki, I. B., Rotter, J. I., Marz, W., Wild, P. S., Lokki, M. -L., Boyle, M., Salomaa, V., Melbye, M., Eriksson, J. G., Wilson, J. F., Penninx, B. W. J. H., Becker, D. M., Worrall, B. B., Gibson, G., Krauss, R. M., Ciullo, M., Zaza, G., Wareham, N. J., Oldehinkel, A. J., Palmer, L. J., Murray, S. S., Pramstaller, P. P., Bandinelli, S., Heinrich, J., Ingelsson, E., Deary, I. J., Magi, R., Vandenput, L., Desch, K. C., Kooner, J. S., Ohlsson, C., Hayward, C., Shuldiner, A. R., Arnett, D. K., Beilin, L. J., Robino, A., Froguel, P., Pirastu, M., Jess, T., Loos, R. J. F., Evans, D. A., Schmidt, H., Slagboom, P. E., Tracy, R. P., Visvikis-Siest, S., Reiner, A. P., Bis, J. C., Franco, O. H., Benjamin, E. J., AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Graduate School, Epidemiology, Internal Medicine, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), VU University medical center, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, APH - Digital Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Universität Heidelberg [Heidelberg], University of Verona (UNIVR), Department of Molecular and Experimental Medicine, The Scripps Research Institute, The Scripps Research Institute, Université Grenoble Alpes - UFR Sciences de l'Homme et de la Société (UGA UFR SHS), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IT University of Copenhagen, Technical University of Denmark [Lyngby] (DTU), Consiglio Nazionale delle Ricerche (CNR), University of Virginia [Charlottesville], Université de Lausanne (UNIL), University of Oxford [Oxford], University of Newcastle [Australia] (UoN), Centre d'économie industrielle i3 (CERNA i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Helmholtz-Zentrum München (HZM), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Universitätsmedizin der Johannes-Gutenberg Universität Mainz, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Cardiff University-Medical Research Council, University of California-University of California, and DE CARVALHO, Philippe

Subjects
0301 basic medicine, Male, Netherlands Twin Register (NTR), Bipolar Disorder, LD SCORE REGRESSION, [SDV]Life Sciences [q-bio], Genome-wide association study, [SDV.GEN] Life Sciences [q-bio]/Genetics, Body Mass Index, inflammatory disorder, 80 and over, WIDE ASSOCIATION, EPIDEMIOLOGY, ta318, International HapMap Project, Child, Genetics (clinical), 2. Zero hunger, Genetics, Genetics & Heredity, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, C-reactive proteingenome-wide association studyinflammationMendelian randomizationinflammatory disordersDEPICTcoronary artery diseaseschizophreniasystem biology, system biology, DEPICT, Mendelian Randomization Analysis, 11 Medical And Health Sciences, Middle Aged, C-reactive protein, coronary artery disease, genome-wide association study, inflammation, inflammatory disorders, Mendelian randomization, schizophrenia, Adolescent, Adult, Aged, Biomarkers, C-Reactive Protein, Female, Genetic Loci, Genome-Wide Association Study, Humans, Inflammation, Liver, Metabolic Networks and Pathways, Schizophrenia, Young Adult, 3. Good health, [SDV] Life Sciences [q-bio], Medical genetics, Biomarker (medicine), Life Sciences & Biomedicine, Human, medicine.medical_specialty, CHARGE Inflammation Working Group, Biology, IMMUNITY, ta3111, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, CORONARY-HEART-DISEASE, Mendelian Randomization Analysi, 1000 Genomes Project, METAANALYSIS, Genetic association, [SDV.GEN]Life Sciences [q-bio]/Genetics, Science & Technology, ta1184, Metabolic Networks and Pathway, Biomarker, INSTRUMENTS, 06 Biological Sciences, 030104 developmental biology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, LifeLines Cohort Study
Abstract

International audience; C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.Copyright © 2018 American Society of Human Genetics. All rights reserved.

Published
2018

48. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Author

Dmitreva J, van Iterson M, Brenda W.J.H. Penninx, Reyhan Sonmez, Michel Georges, Tõnu Esko, Matthias Nauck, Eline Slagboom, Bruce M. Psaty, Urko M. Marigorta, Michel G. Nivard, C Wijmenga, Markus Perola, Joost Verlouw, Jaanika Kronberg-Guzman, Bastiaan T. Heijmans, van Dongen J, Martina Müller-Nurasyid, Yukihide Momozawa, Silva Kasela, Joseph E. Powell, Sina Rüeger, Brandon L. Pierce, Sven Bergmann, van Meurs J, Lude Franke, Uwe Völker, Futao Zhang, Marc Jan Bonder, Z. Kutalik, Frank Beutner, Ilkka Seppälä, Jarno Kettunen, Morris A. Swertz, Harm-Jan Westra, Jan H. Veldink, Hailiang Mei, Joachim Thiery, Jian Yang, Anand Kumar Andiappan, Grant W. Montgomery, Wibowo Arindrarto, Julian C. Knight, Markus Scholz, Mark W. Christiansen, Alexander Teumer, Bernett Lee, Patrick Deelen, Willem H. Ouwehand, Roman Kreuzhuber, Ashis Saha, Andrew A. Brown, Marie-Julie Favé, Eleonora Porcu, Mawusse Agbessi, Samuli Ripatti, Terho Lehtimäki, Jonathan K. Pritchard, Lili Milani, Alexis Battle, Holger Kirsten, Sina A. Gharib, Natalia Pervjakova, Annique Claringbould, Patrick F. Sullivan, Habibul Ahsan, Katharina Schramm, Hanieh Yaghootkar, Oliver Stegle, D.I. Boomsma, Anette Kalnapenkis, Mika Kähönen, Harm Brugge, Lin Tong, Biao Zeng, Holger Prokisch, Kate Downes, Rick Jansen, Peter M. Visscher, Benjamin P. Fairfax, Shuang Li, Urmo Võsa, Emmanouil T. Dermitzakis, Michael Stumvoll, Markus Loeffler, Alvaes I, Rotzchke O, Philip Awadalla, Coen D.A. Stehouwer, Olli T. Raitakari, Gibran Hemani, Peter Kovacs, Peter A C 't Hoen, Timothy M. Frayling, Anke Tönjes, Greg Gibson, Yungil Kim, Kukushkina, Mahmoud Elansary, and Knut Krohn

Subjects
Genetics, 0303 health sciences, Genome-wide association study, Genomics, Quantitative trait locus, Biology, Genetic architecture, 03 medical and health sciences, 0302 clinical medicine, Expression quantitative trait loci, Trait, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.To identify these effects, we performedcis-andtrans-expressionquantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.We observed thatcis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to usecis-eQTLs to pinpoint causal genes within susceptibility loci.In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

Published
2018

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

Author

Serena, Sanna, Natalie R, van Zuydam, Anubha, Mahajan, Alexander, Kurilshikov, Arnau, Vich Vila, Urmo, Võsa, Zlatan, Mujagic, Ad A M, Masclee, Daisy M A E, Jonkers, Marije, Oosting, Leo A B, Joosten, Mihai G, Netea, Lude, Franke, Alexandra, Zhernakova, Jingyuan, Fu, Cisca, Wijmenga, and Mark I, McCarthy

Subjects
Adult, Aged, 80 and over, Male, Adolescent, Genotype, Glucose Tolerance Test, Middle Aged, Fatty Acids, Volatile, Gastrointestinal Microbiome, Young Adult, Diabetes Mellitus, Type 2, Metabolic Diseases, Humans, Female, Aged, Netherlands
Abstract

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

Published
2018

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

Author

Andrew Paterson, Jakob Stokholm, Mauro D'Amato, Eco De Geus, Urmo Võsa, Carolina Medina-Gomez, Malte Rühlemann, Serena Sanna, Jeroen Raes, Jingyuan Fu, Marc Jan Bonder, Claire Steves, Zachary Wallen, Chuan He, Jordana Bell, Frank Ulrich Weiss, Casey Finnicum, Tim Kacprowski, Nicholas Timpson, Joanna Szopinska-Tokov, Williams Turpin, Jonathan Thorsen, Matthew Jackson-Wood, Leon Eyrich Jessen, David Hughes, Elin Org, Hamdi Mbarek, Markus M. Lerch, Gonneke Willemsen, Haydeh Payami, Internal medicine, ACS - Diabetes & metabolism, AGEM - Endocrinology, metabolism and nutrition, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Stem Cell Aging Leukemia and Lymphoma (SALL), Internal Medicine, Epidemiology, Erasmus MC other, APH - Methodology, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Biological Psychology, APH - Health Behaviors & Chronic Diseases, and APH - Personalized Medicine

Subjects
Netherlands Twin Register (NTR), 0301 basic medicine, Microbiology (medical), GENETICS, 030106 microbiology, Genome-wide association study, Computational biology, Gut flora, Microbiology, lcsh:Microbial ecology, Cohort Studies, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, SDG 17 - Partnerships for the Goals, Microbiome Announcement, RNA, Ribosomal, 16S, WIDE ASSOCIATION, Humans, Microbiome, Genome-wide association studies (GWAS), GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Genetic association, Gut microbiome, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], biology, Bacteria, Genome, Human, Gastrointestinal Microbiome, Human microbiome, Genetic Variation, biology.organism_classification, 3. Good health, Meta-analysis, 030104 developmental biology, Research Programm of Donders Centre for Neuroscience, lcsh:QR100-130, Human genome, Genome-Wide Association Study
Abstract

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. Electronic supplementary material The online version of this article (10.1186/s40168-018-0479-3) contains supplementary material, which is available to authorized users.

Published
2018

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