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Katsara MA, Branicki W, Pośpiech E, Hysi P, Walsh S, Kayser M, Nothnagel M. Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits. Forensic Sci Int Genet 2020; 50:102412. [PMID: 33260052 DOI: 10.1016/j.fsigen.2020.102412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 09/09/2020] [Accepted: 10/25/2020] [Indexed: 11/26/2022]
Abstract
The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. We observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, our results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible.
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Affiliation(s)
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland; Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Pirro Hysi
- Department of Twin Research & Genetic Epidemiology, St Thomas Hospital, Campus, Kings College London (KCL), London, UK
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Michael Nothnagel
- Cologne Center for Genomics, University of Cologne, Cologne, Germany; University Hospital Cologne, Cologne, Germany.
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Weeks AL, Francis RW, Neri JICF, Costa NMC, Arrais NMR, Lassmann T, Blackwell JM, Jeronimo SMB. Reference exome data for a Northern Brazilian population. Sci Data 2020; 7:360. [PMID: 33087711 PMCID: PMC7578642 DOI: 10.1038/s41597-020-00703-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 08/14/2020] [Indexed: 11/09/2022] Open
Abstract
Exome sequencing is widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. There is not always adequate population-specific reference data to assist in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from Rio Grande do Nord in Brazil. Sequence data were processed using an 'intersect-then-combine' (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 185 (0.32%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil. (169 words).
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Affiliation(s)
- Alexia L Weeks
- Telethon Kids Institute, The University of Western Australia, Perth Children's Hospital, Western Australia, Perth, Australia
| | - Richard W Francis
- Telethon Kids Institute, The University of Western Australia, Perth Children's Hospital, Western Australia, Perth, Australia
| | - Joao I C F Neri
- Institute of Tropical Medicine of Rio Grande do Norte and Department of Biochemistry, Universidade Federal do Rio Grande do Norte, Natal, Rio de Grande do Norte, Natal, Brazil
| | - Nathaly M C Costa
- Institute of Tropical Medicine of Rio Grande do Norte and Department of Biochemistry, Universidade Federal do Rio Grande do Norte, Natal, Rio de Grande do Norte, Natal, Brazil
| | - Nivea M R Arrais
- Department of Pediatrics, Federal University of Rio Grande do Norte and Empresa Brasileira de Serviços Hospitalares, Natal, Brazil
| | - Timo Lassmann
- Telethon Kids Institute, The University of Western Australia, Perth Children's Hospital, Western Australia, Perth, Australia
| | - Jenefer M Blackwell
- Telethon Kids Institute, The University of Western Australia, Perth Children's Hospital, Western Australia, Perth, Australia.
| | - Selma M B Jeronimo
- Institute of Tropical Medicine of Rio Grande do Norte and Department of Biochemistry, Universidade Federal do Rio Grande do Norte, Natal, Rio de Grande do Norte, Natal, Brazil
- National Institute of Science and Technology of Tropical Diseases, Natal, RN, Brazil
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53
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Pretscher J, Ruebner M, Ekici AB, Rödl M, Huebner H, Schwitulla J, Titzmann A, Hartwig C, Beckmann MW, Fasching PA, Schneider MO, Schwenke E. Genetic variations in estrogen and progesterone pathway genes in preeclampsia patients and controls in Bavaria. Arch Gynecol Obstet 2020; 303:897-904. [PMID: 33000295 DOI: 10.1007/s00404-020-05812-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 09/18/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Hypertensive pregnancy disorders and preeclampsia are major causes of maternal and fetal morbidity and mortality worldwide. Many different organs are involved in the diseases' clinical phenotype. The underlying mechanism is still unknown, with a possible genetic component. This case-control study investigated effects on the risk of preeclampsia of genetic variations (single nucleotide polymorphisms, SNPs) in the estrogen and progesterone pathway genes. METHODS The study included 167 patients with preeclampsia and 115 healthy controls from the "Franconian Maternal Health Evaluation Studies" (FRAMES). All patients completed an epidemiological questionnaire, data from which were correlated with prospective data on pregnancy and labor. DNA was isolated from blood samples and genotyping was done by PCR. Variants in the aromatase gene CYP19A1 (rs10046, rs4646), progesterone receptor gene (rs1042838, rs10895068), and estrogen receptor-α gene (rs488133) were examined, and the genotype distribution in the two groups was analyzed statistically. RESULTS A significant difference in the distribution frequency of genotypes between preeclampsia patients and controls was identified in one of the five SNPs. For rs10895068 in the progesterone receptor gene, genotype G/A was significantly more frequent among cases than controls (P = 0.023). No significant differences between the two cohorts were found in the other SNPs. CONCLUSIONS This study showed a significant association between only one SNP in the progesterone receptor and preeclampsia. Other studies have also noted genetic aspects of preeclampsia. The underlying mechanism and causal relationship are not yet known, and further research is needed to explain the extent of genetic variations and the causal relationship in preeclampsia.
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Affiliation(s)
- Jutta Pretscher
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias Ruebner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Melanie Rödl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Judith Schwitulla
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Adriana Titzmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Charlotte Hartwig
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael O Schneider
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Eva Schwenke
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany.
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Reynolds LM, Dutta R, Seeds MC, Lake KN, Hallmark B, Mathias RA, Howard TD, Chilton FH. FADS genetic and metabolomic analyses identify the ∆5 desaturase (FADS1) step as a critical control point in the formation of biologically important lipids. Sci Rep 2020; 10:15873. [PMID: 32985521 PMCID: PMC7522985 DOI: 10.1038/s41598-020-71948-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 08/24/2020] [Indexed: 12/12/2022] Open
Abstract
Humans have undergone intense evolutionary selection to optimize their capacity to generate necessary quantities of long chain (LC-) polyunsaturated fatty acid (PUFA)-containing lipids. To better understand the impact of genetic variation within a locus of three FADS genes (FADS1, FADS2, and FADS3) on a diverse family of lipids, we examined the associations of 247 lipid metabolites (including four major classes of LC-PUFA-containing molecules and signaling molecules) with common and low-frequency genetic variants located within the FADS locus. Genetic variation in the FADS locus was strongly associated (p < 1.2 × 10–8) with 52 LC-PUFA-containing lipids and signaling molecules, including free fatty acids, phospholipids, lyso-phospholipids, and an endocannabinoid. Notably, the majority (80%) of FADS-associated lipids were not significantly associated with genetic variants outside of this FADS locus. These findings highlight the central role genetic variation at the FADS locus plays in regulating levels of physiologically critical LC-PUFA-containing lipids that participate in innate immunity, energy homeostasis, and brain development/function.
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Affiliation(s)
- Lindsay M Reynolds
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Rahul Dutta
- Department of Urology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Michael C Seeds
- Department of Internal Medicine/Molecular Medicine, and the Wake Forest Institute of Regenerative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Kirsten N Lake
- Department of Nutritional Sciences, University of Arizona, Tucson, AZ, 85719, USA
| | - Brian Hallmark
- The BIO5 Institute, University of Arizona, Tucson, AZ, 85719, USA
| | - Rasika A Mathias
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Timothy D Howard
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Floyd H Chilton
- Department of Nutritional Sciences, University of Arizona, Tucson, AZ, 85719, USA. .,The BIO5 Institute, University of Arizona, Tucson, AZ, 85719, USA.
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Cecelja M, Keehn L, Ye L, Spector TD, Hughes AD, Chowienczyk P. Genetic aetiology of blood pressure relates to aortic stiffness with bi-directional causality: evidence from heritability, blood pressure polymorphisms, and Mendelian randomization. Eur Heart J 2020; 41:3314-3322. [PMID: 32357239 PMCID: PMC7544538 DOI: 10.1093/eurheartj/ehaa238] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/08/2019] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
AIMS Haemodynamic determinants of blood pressure (BP) include cardiac output (CO), systemic vascular resistance (SVR), and arterial stiffness. We investigated the heritability of these phenotypes, their association with BP-related single-nucleotide polymorphisms (SNPs), and the causal association between BP and arterial stiffness. METHODS AND RESULTS We assessed BP, central BP components, and haemodynamic properties (during a single visit) including CO, SVR, and pulse wave velocity (PWV, measure of arterial stiffness) in 3531 (1934 monozygotic, 1586 dizygotic) female TwinsUK participants. Heritability was estimated using structural equation modelling. Association with 984 BP-associated SNP was examined using least absolute shrinkage and selection operator (LASSO) and generalized estimating equation regression. One and two-sample Mendelian randomization (MR) was used to estimate the causal direction between BP and arterial stiffness including data on 436 419 UK Biobank participants. We found high heritability for systolic and pulsatile components of BP (>50%) and PWV (65%) with overlapping genes accounting for >50% of their observed correlation. Environmental factors explained most of the variability of CO and SVR (>80%). Regression identified SNPs (n = 5) known to be associated with BP to also be associated with PWV. One-sample MR showed evidence of bi-directional causal association between BP and PWV in TwinsUK participants. Two-sample MR, confirmed a bi-directional causal effect of PWV on BP (inverse variance weighted (IVW) beta = 0.11, P < 0.02) and BP on arterial stiffness (IVW beta = 0.004, P < 0.0001). CONCLUSION The genetic basis of BP is mediated not only by genes regulating BP but also by genes that influence arterial stiffness. Mendelian randomization indicates a bi-directional causal association between BP and arterial stiffness.
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Affiliation(s)
- Marina Cecelja
- Cardiovascular Division, Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Louise Keehn
- Cardiovascular Division, Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Li Ye
- Cardiovascular Division, Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Alun D Hughes
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Sciences, University College London, 69 Chenies Mews, London W1T 7HA, UK
| | - Phil Chowienczyk
- Cardiovascular Division, Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
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Cahana I, Iraqi FA. Impact of host genetics on gut microbiome: Take-home lessons from human and mouse studies. Animal Model Exp Med 2020; 3:229-236. [PMID: 33024944 PMCID: PMC7529332 DOI: 10.1002/ame2.12134] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 08/23/2020] [Accepted: 08/23/2020] [Indexed: 12/19/2022] Open
Abstract
The intestinal microbiome has emerged as an important component involved in various diseases. Therefore, the interest in understanding the factors shaping its composition is growing. The gut microbiome, often defined as a complex trait, contains diverse components and its properties are determined by a combination of external and internal effects. Although much effort has been invested so far, it is still difficult to evaluate the extent to which human genetics shape the composition of the gut microbiota. However, in mouse studies, where the environmental factors are better controlled, the effect of the genetic background was significant. The purpose of this paper is to provide a current assessment of the role of human host genetics in shaping the gut microbiome composition. Despite the inconsistency of the reported results, it can be estimated that the genetic factor affects a portion of the microbiome. However, this effect is currently lower than the initial estimates, and it is difficult to separate the genetic influence from the environmental effect. Additionally, despite the differences between the microbial composition of humans and mice, results from mouse models can strengthen our knowledge of host genetics underlying the human gut microbial variation.
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Affiliation(s)
- Inbal Cahana
- Department of Human Microbiology and ImmunologySackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
| | - Fuad A. Iraqi
- Department of Human Microbiology and ImmunologySackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
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El-Sayed Moustafa JS, Jackson AU, Brotman SM, Guan L, Villicaña S, Roberts AL, Zito A, Bonnycastle L, Erdos MR, Narisu N, Stringham HM, Welch R, Yan T, Lakka T, Parker S, Tuomilehto J, Collins FS, Pajukanta P, Boehnke M, Koistinen HA, Laakso M, Falchi M, Bell JT, Scott LJ, Mohlke KL, Small KS. ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.08.11.20171108. [PMID: 32817962 PMCID: PMC7430606 DOI: 10.1101/2020.08.11.20171108] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
COVID-19 severity has varied widely, with demographic and cardio-metabolic factors increasing risk of severe reactions to SARS-CoV-2 infection, but the underlying mechanisms for this remain uncertain. We investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 ( ACE2 ), which has been shown to act as a receptor for SARS-CoV-2 cellular entry. In a meta-analysis of three independent studies including up to 1,471 participants, lower adipose tissue ACE2 expression was associated with adverse cardio-metabolic health indices including type 2 diabetes (T2D) and obesity status, higher serum fasting insulin and BMI, and lower serum HDL levels (P<5.32x10 -4 ). ACE2 expression levels were also associated with estimated proportions of cell types in adipose tissue; lower ACE2 expression was associated with a lower proportion of microvascular endothelial cells (P=4.25x10 -4 ) and higher macrophage proportion (P=2.74x10 -5 ), suggesting a link to inflammation. Despite an estimated heritability of 32%, we did not identify any proximal or distal genetic variants (eQTLs) associated with adipose tissue ACE2 expression. Our results demonstrate that at-risk individuals have lower background ACE2 levels in this highly relevant tissue. Further studies will be required to establish how this may contribute to increased COVID-19 severity.
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Affiliation(s)
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sarah M. Brotman
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Li Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Amy L. Roberts
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Antonino Zito
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115; USA
| | - Lori Bonnycastle
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael R. Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tingfen Yan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Timo Lakka
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Stephen Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Francis S. Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Päivi Pajukanta
- Department of Human Genetics and Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Heikki A. Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki and Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Laura J. Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
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Faber BG, Bredbenner TL, Baird D, Gregory J, Saunders F, Giuraniuc CV, Aspden RM, Lane NE, Orwoll E, Tobias JH. Subregional statistical shape modelling identifies lesser trochanter size as a possible risk factor for radiographic hip osteoarthritis, a cross-sectional analysis from the Osteoporotic Fractures in Men Study. Osteoarthritis Cartilage 2020; 28:1071-1078. [PMID: 32387760 PMCID: PMC7387228 DOI: 10.1016/j.joca.2020.04.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 03/17/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Statistical shape modelling (SSM) of hip dual-energy X-ray absorptiometry (DXA) scans has identified relationships between hip shape and radiographic hip OA (rHOA). We aimed to further elucidate shape characteristics related to rHOA by focusing on subregions identified from whole-hip shape models. METHOD SSM was applied to hip DXAs obtained in the Osteoporotic Fractures in Men Study. Whole-hip shape modes (HSMs) associated with rHOA were combined to form a composite at-risk-shape. Subsequently, subregional HSMs (cam-type and lesser trochanter modes) were built, and associations with rHOA were examined by logistic regression. Subregional HSMs were further characterised, by examining associations with 3D-HSMs derived from concurrent hip CT scans. RESULTS 4,098 participants were identified with hip DXAs and radiographs. Composite shapes from whole-hip HSMs revealed that lesser trochanter size and cam-type femoral head are related to rHOA. From sub-regional models, lesser trochanter mode (LTM)1 [OR 0.74; 95%CI 0.63.0.87] and cam-type mode (CTM)3 [OR 1.27; 1.13.1.42] were associated with rHOA, associations being similar to those for whole hip HSMs. 515 MrOS participants had hip DXAs and 3D-HSMs derived from hip CT scans. LTM1 was associated with 3D-HSMs that also represented a larger lesser trochanter [3D-HSM7 (beta (β)-0.23;-0.33,-0.14) and 3D-HSM9 (β0.36; 0.27.0.45)], and CTM3 with 3D-HSMs describing cam morphology [3D-HSM3 (β-0.16;-0.25,-0.07) and 3D-HSM6 (β 0.19; 0.10.0.28)]. CONCLUSION Subregional SSM of hip DXA scans suggested larger lesser trochanter and cam morphology underlie associations between overall hip shape and rHOA. 3D hip modelling suggests our subregional SSMs represent true anatomical variations in hip shape, warranting further investigation.
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Affiliation(s)
- B G Faber
- Medical Research Council Clinical Research Fellow, Musculoskeletal Research Unit, University of Bristol, Bristol, UK.
| | - T L Bredbenner
- Mechanical and Aerospace Engineering, University of Colorado Colorado Springs, Colorado, USA
| | - D Baird
- Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - J Gregory
- Centre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK
| | - F Saunders
- Centre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK
| | - C V Giuraniuc
- Centre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK
| | - R M Aspden
- Centre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK
| | - N E Lane
- Center for Musculoskeletal Health, U.C. Davis School of Medicine, Sacramento, CA 95817, USA
| | - E Orwoll
- Bone and Mineral Unit, Oregon Health and Sciences University, Portland, OR, USA
| | - J H Tobias
- Musculoskeletal Research Unit, University of Bristol, Bristol, UK
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Chennen K, Weber T, Lornage X, Kress A, Böhm J, Thompson J, Laporte J, Poch O. MISTIC: A prediction tool to reveal disease-relevant deleterious missense variants. PLoS One 2020; 15:e0236962. [PMID: 32735577 PMCID: PMC7394404 DOI: 10.1371/journal.pone.0236962] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022] Open
Abstract
The diffusion of next-generation sequencing technologies has revolutionized research and diagnosis in the field of rare Mendelian disorders, notably via whole-exome sequencing (WES). However, one of the main issues hampering achievement of a diagnosis via WES analyses is the extended list of variants of unknown significance (VUS), mostly composed of missense variants. Hence, improved solutions are needed to address the challenges of identifying potentially deleterious variants and ranking them in a prioritized short list. We present MISTIC (MISsense deleTeriousness predICtor), a new prediction tool based on an original combination of two complementary machine learning algorithms using a soft voting system that integrates 113 missense features, ranging from multi-ethnic minor allele frequencies and evolutionary conservation, to physiochemical and biochemical properties of amino acids. Our approach also uses training sets with a wide spectrum of variant profiles, including both high-confidence positive (deleterious) and negative (benign) variants. Compared to recent state-of-the-art prediction tools in various benchmark tests and independent evaluation scenarios, MISTIC exhibits the best and most consistent performance, notably with the highest AUC value (> 0.95). Importantly, MISTIC maintains its high performance in the specific case of discriminating deleterious variants from benign variants that are rare or population-specific. In a clinical context, MISTIC drastically reduces the list of VUS (<30%) and significantly improves the ranking of "causative" deleterious variants. Pre-computed MISTIC scores for all possible human missense variants are available at http://lbgi.fr/mistic.
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Affiliation(s)
- Kirsley Chennen
- Complex Systems and Translational Bioinformatics (CSTB), ICube laboratory – CNRS, Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U1258, CNRS UMR7104, University of Strasbourg, Illkirch, France
| | - Thomas Weber
- Complex Systems and Translational Bioinformatics (CSTB), ICube laboratory – CNRS, Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
| | - Xavière Lornage
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U1258, CNRS UMR7104, University of Strasbourg, Illkirch, France
| | - Arnaud Kress
- Complex Systems and Translational Bioinformatics (CSTB), ICube laboratory – CNRS, Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
| | - Johann Böhm
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U1258, CNRS UMR7104, University of Strasbourg, Illkirch, France
| | - Julie Thompson
- Complex Systems and Translational Bioinformatics (CSTB), ICube laboratory – CNRS, Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
| | - Jocelyn Laporte
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U1258, CNRS UMR7104, University of Strasbourg, Illkirch, France
| | - Olivier Poch
- Complex Systems and Translational Bioinformatics (CSTB), ICube laboratory – CNRS, Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
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60
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Desch KC, Ozel AB, Halvorsen M, Jacobi PM, Golden K, Underwood M, Germain M, Tregouet DA, Reitsma PH, Kearon C, Mokry L, Richards JB, Williams F, Li JZ, Goldstein D, Ginsburg D. Whole-exome sequencing identifies rare variants in STAB2 associated with venous thromboembolic disease. Blood 2020; 136:533-541. [PMID: 32457982 PMCID: PMC7393257 DOI: 10.1182/blood.2019004161] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Deep vein thrombosis and pulmonary embolism, collectively defined as venous thromboembolism (VTE), are the third leading cause of cardiovascular death in the United States. Common genetic variants conferring increased varying degrees of VTE risk have been identified by genome-wide association studies (GWAS). Rare mutations in the anticoagulant genes PROC, PROS1 and SERPINC1 result in perinatal lethal thrombosis in homozygotes and markedly increased VTE risk in heterozygotes. However, currently described VTE variants account for an insufficient portion of risk to be routinely used for clinical decision making. To identify new rare VTE risk variants, we performed a whole-exome study of 393 individuals with unprovoked VTE and 6114 controls. This study identified 4 genes harboring an excess number of rare damaging variants in patients with VTE: PROS1, STAB2, PROC, and SERPINC1. At STAB2, 7.8% of VTE cases and 2.4% of controls had a qualifying rare variant. In cell culture, VTE-associated variants of STAB2 had a reduced surface expression compared with reference STAB2. Common variants in STAB2 have been previously associated with plasma von Willebrand factor and coagulation factor VIII levels in GWAS, suggesting that haploinsufficiency of stabilin-2 may increase VTE risk through elevated levels of these procoagulants. In an independent cohort, we found higher von Willebrand factor levels and equivalent propeptide levels in individuals with rare STAB2 variants compared with controls. Taken together, this study demonstrates the utility of gene-based collapsing analyses to identify loci harboring an excess of rare variants with functional connections to a complex thrombotic disease.
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Affiliation(s)
| | - Ayse B Ozel
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - Matt Halvorsen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | | | | | - Marine Germain
- INSERM UMR_S 1219, Bordeaux Population Health Research Center, University of Bordeaux, Bordeaux, France
| | - David-Alexandre Tregouet
- INSERM UMR_S 1219, Bordeaux Population Health Research Center, University of Bordeaux, Bordeaux, France
| | - Pieter H Reitsma
- Einthoven Laboratory for Experimental Vascular and Regenerative Medicine, Leiden, The Netherlands
| | - Clive Kearon
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Lauren Mokry
- Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
| | - J Brent Richards
- Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
| | - Frances Williams
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, United Kingdom
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - David Goldstein
- Columbia University, Institute for Genomic Medicine, New York, NY; and
| | - David Ginsburg
- Department of Pediatrics and
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
- Department of Internal Medicine, Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI
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61
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Costello L, Dare J, Dontje M, Straker L. Informing retention in longitudinal cohort studies through a social marketing lens: Raine Study Generation 2 participants' perspectives on benefits and barriers to participation. BMC Med Res Methodol 2020; 20:202. [PMID: 32727373 PMCID: PMC7389450 DOI: 10.1186/s12874-020-01074-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 07/05/2020] [Indexed: 01/07/2023] Open
Abstract
Background Longitudinal cohort studies have made significant contributions to medical discoveries and provide the impetus for health interventions which reduce the risk of disease. Establishing and maintaining these cohorts is challenging and costly. While some attrition is unavoidable, maintaining a sufficient number of participants ensures that results remain representative and free from bias. Numerous studies have investigated ways to reduce attrition but few studies have sought to understand the experience of participants, and none have examined this through a social marketing framework. This first paper in a two part-series describes participants’ experiences according to: benefits, barriers, motivators and influencers. The second paper uses this understanding to address issues relating to the 4Ps (product, price, place, promotion) of social marketing. Methods Participants were recruited from the Raine Study, a pregnancy cohort study that has been running in Western Australia since 1989. Qualitative interviews were conducted with 29 active and inactive participants from the Generation 2 cohort, who were originally enrolled in the Raine Study at birth by their parents (Generation 1). ‘Active’ participants (n = 17) were defined as those who agreed to attend their 27 year follow-up, while ‘inactive’ (n = 12) participants were defined as those who had not attended either of the past two follow-ups (at 22 and 27 years). Results There were considerable differences between active and inactive participants, with active participants perceiving far more personal and collective benefits from their participation. Inactive participants described being constrained by structural barriers around work and life, whereas active participants were able to overcome them to attend follow-ups. Inactive participants also described the value of extrinsic incentives which might motivate their attendance, and active participants described the role of their parents as significant influencers in their propensity to remain in the study. Conclusions This paper provides rich descriptions of what participation in a long-running study means to participants. Use of a social marketing framework ensured that participants were constructed as ‘human consumers’ who are influenced by individual and broader social systems. Understanding participants in this way means that differentiated strategies can be tailored to enhance retention.
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Affiliation(s)
- Leesa Costello
- School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia.
| | - Julie Dare
- School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia
| | - Manon Dontje
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Leon Straker
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
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62
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Charng J, Simcoe M, Sanfilippo PG, Allingham RR, Hewitt AW, Hammond CJ, Mackey DA, Yazar S. Age-dependent regional retinal nerve fibre changes in SIX1/SIX6 polymorphism. Sci Rep 2020; 10:12485. [PMID: 32719476 PMCID: PMC7385166 DOI: 10.1038/s41598-020-69524-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/05/2020] [Indexed: 12/04/2022] Open
Abstract
SIX1/SIX6 polymorphism has been shown to be associated with glaucoma. Studies have also found that, in older adults, retinal nerve fibre layer (RNFL) thickness is significantly thinned with each copy of the risk allele in SIX1/SIX6. However, it is not known whether these genetic variants exert their effects in younger individuals. Comparing a healthy young adult with an older adult cohort (mean age 20 vs 63 years), both of Northern European descent, we found that there was no significant RNFL thinning in each copy of the risk alleles in SIX1/SIX6 in the eyes of younger individuals. The older cohort showed an unexpectedly thicker RNFL in the nasal sector with each copy of the risk allele for both the SIX1 (rs10483727) and SIX6 (rs33912345) variants. In the temporal sector, thinner RNFL was found with each copy of the risk allele in rs33912345 with a decrease trend observed in rs10483727. Our results suggest that SIX1/SIX6 gene variants exert their influence later in adult life.
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Affiliation(s)
- Jason Charng
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, 2 Verdun St, Perth, WA, 6009, Australia
| | - Mark Simcoe
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Paul G Sanfilippo
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, 2 Verdun St, Perth, WA, 6009, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, 3002, Australia
| | - R Rand Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, 3002, Australia.,School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Australia
| | - Chris J Hammond
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - David A Mackey
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, 2 Verdun St, Perth, WA, 6009, Australia
| | - Seyhan Yazar
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, 2 Verdun St, Perth, WA, 6009, Australia. .,Garvan Institute of Medical Research, Sydney, Australia.
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63
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Gilly A, Southam L, Suveges D, Kuchenbaecker K, Moore R, Melloni GEM, Hatzikotoulas K, Farmaki AE, Ritchie G, Schwartzentruber J, Danecek P, Kilian B, Pollard MO, Ge X, Tsafantakis E, Dedoussis G, Zeggini E. Very low-depth whole-genome sequencing in complex trait association studies. Bioinformatics 2020; 35:2555-2561. [PMID: 30576415 PMCID: PMC6662288 DOI: 10.1093/bioinformatics/bty1032] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 11/17/2018] [Accepted: 12/17/2018] [Indexed: 12/31/2022] Open
Abstract
Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arthur Gilly
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Daniel Suveges
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Karoline Kuchenbaecker
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Rachel Moore
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Giorgio E M Melloni
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Konstantinos Hatzikotoulas
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Graham Ritchie
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Jeremy Schwartzentruber
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Petr Danecek
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Britt Kilian
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Martin O Pollard
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Xiangyu Ge
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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64
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Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, Capdevila J, Hadjigeorgiou G, Davies R, Al Khatib H, Bonnett C, Ganesh S, Bakker E, Hart D, Mangino M, Merino J, Linenberg I, Wyatt P, Ordovas JM, Gardner CD, Delahanty LM, Chan AT, Segata N, Franks PW, Spector TD. Human postprandial responses to food and potential for precision nutrition. Nat Med 2020; 26:964-973. [PMID: 32528151 PMCID: PMC8265154 DOI: 10.1038/s41591-020-0934-0] [Citation(s) in RCA: 365] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 05/11/2020] [Indexed: 12/18/2022]
Abstract
Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.
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Affiliation(s)
- Sarah E Berry
- Department of Nutrition, King's College London, London, UK
| | - Ana M Valdes
- School of Medicine, University of Nottingham, Nottingham, UK.
- Nottingham NIHR Biomedical Research Centre, Nottingham, UK.
| | - David A Drew
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Mohsen Mazidi
- Department of Twins Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | | | | | - Haya Al Khatib
- Department of Nutrition, King's College London, London, UK
- Zoe Global Ltd, London, UK
| | | | | | | | - Deborah Hart
- Department of Twins Research & Genetic Epidemiology, King's College London, London, UK
| | - Massimo Mangino
- Department of Twins Research & Genetic Epidemiology, King's College London, London, UK
| | - Jordi Merino
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | | | | | - Jose M Ordovas
- JM-USDA-HNRCA at Tufts University, Boston, MA, USA
- IMDEA Food Institute, CEI UAM + CSIC, Madrid, Spain
| | | | - Linda M Delahanty
- Diabetes Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
| | - Paul W Franks
- Department of Twins Research & Genetic Epidemiology, King's College London, London, UK
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tim D Spector
- Department of Twins Research & Genetic Epidemiology, King's College London, London, UK.
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65
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Lamin A, Jarrar Z, Williams KM, Garg A, Basheer K, Sivaprasad S, Hammond CJ, Mahroo OA. Segmented Macular Layer Volumes from Spectral Domain Optical Coherence Tomography in 184 Adult Twins: Associations With Age and Heritability. Invest Ophthalmol Vis Sci 2020; 61:44. [PMID: 32446249 PMCID: PMC7405717 DOI: 10.1167/iovs.61.5.44] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To investigate segmented macular layer volumes from a healthy adult twin cohort (TwinsUK), exploring changes with age and heritability. Methods Macular spectral domain optical coherence tomography images were acquired from monozygotic (MZ) and dizygotic (DZ) twins in a cross-sectional study. The following layer volumes were derived for circles of 3 and 6 mm diameter around the foveal center, using automated segmentation software: retinal nerve fiber layer (RNFL), ganglion cell–inner plexiform layer (GCIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptors (PR), retinal pigment epithelium (RPE), and total retinal volume (TRV). Correlation coefficients (intereye; age; intrapair for MZ and DZ pairs) were quantified; heritability was estimated using structural equation modeling. Results Scans from 184 participants were included. Intereye correlation was highest for TRV and GCIPL. Negative correlations with age (for 3- or 6-mm areas, or both) were observed for TRV, RNFL, GCIPL, and INL. Positive correlations were observed for PR, RPE, and OPL. For all layers, intrapair correlation was greater for MZ than DZ pairs. Heritability estimates were highest (>80%) for TRV and GCIPL volume, and lowest for RPE volume. Conclusions Although TRV was negatively correlated with age, all layers did not show negative correlation. Some inner layers thinned with age, whereas some outer volumes increased (not the ONL). Reduced RPE phagocytic function with age and remodeling in the OPL could be contributing factors. Heritability estimates were highest for inner retinal layers (particularly GCIPL), and lowest for RPE volume.
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66
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Reference exome data for Australian Aboriginal populations to support health-based research. Sci Data 2020; 7:129. [PMID: 32350262 PMCID: PMC7190730 DOI: 10.1038/s41597-020-0463-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 03/24/2020] [Indexed: 11/29/2022] Open
Abstract
Whole exome sequencing (WES) is a popular and successful technology which is widely used in both research and clinical settings. However, there is a paucity of reference data for Aboriginal Australians to underpin the translation of health-based genomic research. Here we provide a catalogue of variants called after sequencing the exomes of 50 Aboriginal individuals from the Northern Territory (NT) of Australia and compare these to 72 previously published exomes from a Western Australian (WA) population of Martu origin. Sequence data for both NT and WA samples were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 289,829 variants were identified in at least one individual in the NT cohort and 248,374 variants in at least one individual in the WA cohort. Of these, 166,719 variants were present in both cohorts, whilst 123,110 variants were private to the NT cohort and 81,655 were private to the WA cohort. Our data set provides a useful reference point for genomic studies on Aboriginal Australians. Measurement(s) | Aboriginal Australian • DNA • sequence feature annotation | Technology Type(s) | Whole Exome Sequencing • DNA sequencing • sequence annotation | Factor Type(s) | ancestry • sex • age | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Northern Territory |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12040638
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67
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Evans DM, Moen GH, Hwang LD, Lawlor DA, Warrington NM. Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. Int J Epidemiol 2020; 48:861-875. [PMID: 30815700 PMCID: PMC6659380 DOI: 10.1093/ije/dyz019] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother-offspring pairs. METHODS We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. RESULTS We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. CONCLUSIONS We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.
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Affiliation(s)
- David M Evans
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gunn-Helen Moen
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Liang-Dar Hwang
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Debbie A Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
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Self-reported hearing loss questions provide a good measure for genetic studies: a polygenic risk score analysis from UK Biobank. Eur J Hum Genet 2020; 28:1056-1065. [PMID: 32203203 PMCID: PMC7382483 DOI: 10.1038/s41431-020-0603-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 01/20/2020] [Accepted: 02/18/2020] [Indexed: 02/07/2023] Open
Abstract
Age-related hearing impairment (ARHI) is very common in older adults and has major impact on quality of life. The heritability of ARHI has been estimated to be around 50%. The present study aimed to estimate heritability and environmental contributions to liability of ARHI and the extent to which a polygenic risk score (PRS) derived from a recent genome-wide association study of questionnaire items regarding hearing loss using the UK Biobank is predictive of hearing loss in other samples. We examined (1) a sample from TwinsUK who have had hearing ability measured by pure-tone audiogram and the speech-to-noise ratio test as well as questionnaire measures that are comparable with the UK Biobank questionnaire items and (2) European and non-European samples from the UK Biobank which were not part of the original GWAS. Results indicated that the questionnaire items were over 50% heritable in TwinsUK and comparable with the objective hearing measures. In addition, we found very high genetic correlation (0.30-0.84) between the questionnaire responses and objective hearing measures in the TwinsUK sample. Finally, PRS computed from weighted UK Biobank GWAS results were predictive of both questionnaire and objective measures of hearing loss in the TwinsUK sample, as well as questionnaire-measured hearing loss in Europeans but not non-European subpopulations. These results demonstrate the utility of questionnaire-based methods in genetic association studies of hearing loss in adults and highlight the differences in genetic predisposition to ARHI by ethnic background.
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Martin TC, Ilieva KM, Visconti A, Beaumont M, Kiddle SJ, Dobson RJB, Mangino M, Lim EM, Pezer M, Steves CJ, Bell JT, Wilson SG, Lauc G, Roederer M, Walsh JP, Spector TD, Karagiannis SN. Dysregulated Antibody, Natural Killer Cell and Immune Mediator Profiles in Autoimmune Thyroid Diseases. Cells 2020; 9:E665. [PMID: 32182948 PMCID: PMC7140647 DOI: 10.3390/cells9030665] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/04/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022] Open
Abstract
The pathogenesis of autoimmune thyroid diseases (AITD) is poorly understood and the association between different immune features and the germline variants involved in AITD are yet unclear. We previously observed systemic depletion of IgG core fucosylation and antennary α1,2 fucosylation in peripheral blood mononuclear cells in AITD, correlated with anti-thyroid peroxidase antibody (TPOAb) levels. Fucose depletion is known to potentiate strong antibody-mediated NK cell activation and enhanced target antigen-expressing cell killing. In autoimmunity, this may translate to autoantibody-mediated immune cell recruitment and attack of self-antigen expressing normal tissues. Hence, we investigated the crosstalk between immune cell traits, secreted proteins, genetic variants and the glycosylation patterns of serum IgG, in a multi-omic and cross-sectional study of 622 individuals from the TwinsUK cohort, 172 of whom were diagnosed with AITD. We observed associations between two genetic variants (rs505922 and rs687621), AITD status, the secretion of Desmoglein-2 protein, and the profile of two IgG N-glycan traits in AITD, but further studies need to be performed to better understand their crosstalk in AITD. On the other side, enhanced afucosylated IgG was positively associated with activatory CD335- CD314+ CD158b+ NK cell subsets. Increased levels of the apoptosis and inflammation markers Caspase-2 and Interleukin-1α positively associated with AITD. Two genetic variants associated with AITD, rs1521 and rs3094228, were also associated with altered expression of the thyrocyte-expressed ligands known to recognize the NK cell immunoreceptors CD314 and CD158b. Our analyses reveal a combination of heightened Fc-active IgG antibodies, effector cells, cytokines and apoptotic signals in AITD, and AITD genetic variants associated with altered expression of thyrocyte-expressed ligands to NK cell immunoreceptors. Together, TPOAb responses, dysregulated immune features, germline variants associated with immunoactivity profiles, are consistent with a positive autoreactive antibody-dependent NK cell-mediated immune response likely drawn to the thyroid gland in AITD.
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Affiliation(s)
- Tiphaine C. Martin
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
- School of Biomedical Sciences, University of Western Australia, Crawley, WA 6009, Australia
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristina M. Ilieva
- St John’s Institute of Dermatology, School of Basic & Medical Biosciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK; (K.M.I.); (S.N.K.)
- Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Cancer Centre, London SE1 9RT, UK
| | - Alessia Visconti
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
| | - Michelle Beaumont
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
| | - Steven J. Kiddle
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK; (S.J.K.); (R.J.B.D.)
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK; (S.J.K.); (R.J.B.D.)
- Health Data Research UK (HDR UK), London Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
- NIHR Biomedical Research Centre at Guy’s and St. Thomas’s NHS Foundation Trust, London SE1 9RT, UK
| | - Ee Mun Lim
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia; (E.M.L.); (J.P.W.)
- Medical School, The University of Western Australia, Crawley, WA 6009, Australia
- PathWest Laboratory Medicine, QEII Medical Centre, Nedlands, WA 6009, Australia
| | - Marija Pezer
- Genos, Glycoscience Research Laboratory, 10000 Zagreb, Croatia; (M.P.); (G.L.)
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
| | - Scott G. Wilson
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
- School of Biomedical Sciences, University of Western Australia, Crawley, WA 6009, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia; (E.M.L.); (J.P.W.)
| | - Gordan Lauc
- Genos, Glycoscience Research Laboratory, 10000 Zagreb, Croatia; (M.P.); (G.L.)
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
| | - Mario Roederer
- ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892, USA;
| | - John P. Walsh
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia; (E.M.L.); (J.P.W.)
- Medical School, The University of Western Australia, Crawley, WA 6009, Australia
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College, London SE1 7EH, UK; (A.V.); (M.B.); (M.M.); (C.J.S.); (J.T.B.); (S.G.W.); (T.D.S.)
| | - Sophia N. Karagiannis
- St John’s Institute of Dermatology, School of Basic & Medical Biosciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK; (K.M.I.); (S.N.K.)
- Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Cancer Centre, London SE1 9RT, UK
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Mora-Ortiz M, Ibraheim H, Hermangild Kottoor S, Bowyer RCE, Metrustry S, Sanderson J, Powell N, D. Spector T, S. Small K, Steves CJ. Introducing ExHiBITT – Exploring Host microBIome inTeractions in Twins –, a colon multiomic cohort study. Wellcome Open Res 2020. [DOI: 10.12688/wellcomeopenres.15632.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: The colon is populated by approximately 1012 microorganisms, but the relationships between this microbiome and the host health status are still not completely understood. Here, our objective is to present the cohort characteristics of ExHiBITT – Exploring Host microBIome inTeractions in Twins – including i) biomedical phenotypes, ii) environmental factors and ii) colonoscopic findings. Methods: Participants from the TwinsUK cohort were recruited to study the interactions between the microbiome and host adaptive immunity. In total, 205 monozygotic twins were recruited from the wider TwinsUK cohort. They completed health questionnaires, and provided saliva, blood, colon biopsies from three different locations, caecal fluid, and two faecal samples. Results: A significant proportion of this apparently normal cohort had colonic polyps (28%), which are of interest as potential precursors of colorectal cancer, and, as expected, the number of polyps found was significantly correlated with BMI and age. Hitherto undiagnosed diverticulosis was also not infrequently found during colonoscopy (26%) and was associated with changes in Hybrid Th1-17 cells in the colon. Twin proband co-occurrence rate for diverticulosis (82%) was much higher than for polyps (42%). Familial factors affecting morphology or tolerance may contribute to the ease of endoscopy, as both the time to reach the caecum and pain perceived were highly concordant (proband concordance: 85% and 56%, respectively). Conclusions: We found the expected positive relationship between BMI and colonoscopic anomalies such as diverticular disease and polyps in the whole population, but within twin pairs this association was reversed. This suggests that familial factors confound these associations. Host and microbial next generation sequencing and metabolomics of the samples collected are planned in this cohort.
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Klarić L, Tsepilov YA, Stanton CM, Mangino M, Sikka TT, Esko T, Pakhomov E, Salo P, Deelen J, McGurnaghan SJ, Keser T, Vučković F, Ugrina I, Krištić J, Gudelj I, Štambuk J, Plomp R, Pučić-Baković M, Pavić T, Vilaj M, Trbojević-Akmačić I, Drake C, Dobrinić P, Mlinarec J, Jelušić B, Richmond A, Timofeeva M, Grishchenko AK, Dmitrieva J, Bermingham ML, Sharapov SZ, Farrington SM, Theodoratou E, Uh HW, Beekman M, Slagboom EP, Louis E, Georges M, Wuhrer M, Colhoun HM, Dunlop MG, Perola M, Fischer K, Polasek O, Campbell H, Rudan I, Wilson JF, Zoldoš V, Vitart V, Spector T, Aulchenko YS, Lauc G, Hayward C. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases. SCIENCE ADVANCES 2020; 6:eaax0301. [PMID: 32128391 PMCID: PMC7030929 DOI: 10.1126/sciadv.aax0301] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 11/19/2019] [Indexed: 05/03/2023]
Abstract
Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.
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Affiliation(s)
- Lucija Klarić
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Yakov A. Tsepilov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Chloe M. Stanton
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Timo Tõnis Sikka
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Division of Endocrinology, Boston Children’s Hospital, Cambridge, MA, USA
| | - Eugene Pakhomov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Perttu Salo
- Genomics and Biomarkers Unit, Department of Health, National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Joris Deelen
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Stuart J. McGurnaghan
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Toma Keser
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | | | - Ivo Ugrina
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- University of Split, Faculty of Science, Split, Croatia
| | | | - Ivan Gudelj
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Jerko Štambuk
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Rosina Plomp
- Leiden University Medical Centre, Leiden, Netherlands
| | | | - Tamara Pavić
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Marija Vilaj
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | | | - Camilla Drake
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Paula Dobrinić
- Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Jelena Mlinarec
- Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Barbara Jelušić
- Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Anne Richmond
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Maria Timofeeva
- Colon Cancer Genetics Group, Cancer Research UK Edinburgh Centre and Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Alexander K. Grishchenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Mairead L. Bermingham
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sodbo Zh. Sharapov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
| | - Susan M. Farrington
- Colon Cancer Genetics Group, Cancer Research UK Edinburgh Centre and Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Hae-Won Uh
- Leiden University Medical Centre, Leiden, Netherlands
- Department of Biostatistics and Research Support, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Eline P. Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Edouard Louis
- CHU-Liège and Unit of Gastroenterology, GIGA-R and Faculty of Medicine, University of Liège, Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | | | - Helen M. Colhoun
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Department of Public Health, NHS Fife, Kirkcaldy, UK
| | - Malcolm G. Dunlop
- Colon Cancer Genetics Group, Cancer Research UK Edinburgh Centre and Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Markus Perola
- Genomics and Biomarkers Unit, Department of Health, National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Ozren Polasek
- Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia
- Gen-info, Zagreb, Croatia
- Psychiatric Hospital Sveti Ivan, Zagreb, Croatia
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - James F. Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Vlatka Zoldoš
- Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Yurii S. Aulchenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
- PolyOmica, Het Vlaggeschip 61, 5237 PA 's-Hertogenbosch, Netherlands
- Kurchatov Genomics Center, Institute of Cytology & Genetics, Novosibirsk, Russia
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Shomorony I, Cirulli ET, Huang L, Napier LA, Heister RR, Hicks M, Cohen IV, Yu HC, Swisher CL, Schenker-Ahmed NM, Li W, Nelson KE, Brar P, Kahn AM, Spector TD, Caskey CT, Venter JC, Karow DS, Kirkness EF, Shah N. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med 2020; 12:7. [PMID: 31924279 PMCID: PMC6953286 DOI: 10.1186/s13073-019-0705-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 12/12/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. METHODS We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. RESULTS Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. CONCLUSIONS Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment.
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Affiliation(s)
- Ilan Shomorony
- Human Longevity, Inc., San Diego, CA, 92121, USA
- Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61820, USA
| | | | - Lei Huang
- Human Longevity, Inc., San Diego, CA, 92121, USA
| | | | | | | | | | - Hung-Chun Yu
- Human Longevity, Inc., San Diego, CA, 92121, USA
| | | | | | - Weizhong Li
- Human Longevity, Inc., San Diego, CA, 92121, USA
- J. Craig Venter Institute, La Jolla, CA, 92037, USA
| | - Karen E Nelson
- Human Longevity, Inc., San Diego, CA, 92121, USA
- J. Craig Venter Institute, La Jolla, CA, 92037, USA
| | - Pamila Brar
- Human Longevity, Inc., San Diego, CA, 92121, USA
- J. Craig Venter Institute, La Jolla, CA, 92037, USA
| | - Andrew M Kahn
- Human Longevity, Inc., San Diego, CA, 92121, USA
- Division of Cardiovascular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - C Thomas Caskey
- Human Longevity, Inc., San Diego, CA, 92121, USA
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - J Craig Venter
- Human Longevity, Inc., San Diego, CA, 92121, USA
- J. Craig Venter Institute, La Jolla, CA, 92037, USA
| | | | - Ewen F Kirkness
- Human Longevity, Inc., San Diego, CA, 92121, USA
- J. Craig Venter Institute, La Jolla, CA, 92037, USA
| | - Naisha Shah
- Human Longevity, Inc., San Diego, CA, 92121, USA.
- J. Craig Venter Institute, La Jolla, CA, 92037, USA.
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Liu B, Montgomery SB. Identifying causal variants and genes using functional genomics in specialized cell types and contexts. Hum Genet 2020; 139:95-102. [PMID: 31317254 PMCID: PMC6942616 DOI: 10.1007/s00439-019-02044-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 07/03/2019] [Indexed: 02/06/2023]
Abstract
A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.
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Affiliation(s)
- Boxiang Liu
- Department of Biology, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University, Stanford, USA.
- Department of Genetics, Stanford University, Stanford, USA.
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74
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The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature 2019; 576:106-111. [PMID: 31802016 PMCID: PMC7054211 DOI: 10.1038/s41586-019-1793-z] [Citation(s) in RCA: 213] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 10/11/2019] [Indexed: 12/30/2022]
Abstract
The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world’s population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide. Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide.
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Heritability of skewed X-inactivation in female twins is tissue-specific and associated with age. Nat Commun 2019; 10:5339. [PMID: 31767861 PMCID: PMC6877649 DOI: 10.1038/s41467-019-13340-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 11/04/2019] [Indexed: 12/16/2022] Open
Abstract
Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes. Skewed XCI toward one parental X has been observed in several complex human traits, but the extent to which genetics and environment influence skewed XCI is largely unexplored. To address this, we quantify XCI-skew in multiple tissues and immune cell types in a twin cohort. Within an individual, XCI-skew differs between blood, fat and skin tissue, but is shared across immune cell types. XCI skew increases with age in blood, but not other tissues, and is associated with smoking. XCI-skew is increased in twins with Rheumatoid Arthritis compared to unaffected identical co-twins. XCI-skew is heritable in blood of females >55 years old (h2 = 0.34), but not in younger individuals or other tissues. This results in a Gene x Age interaction that shifts the functional dosage of all X-linked heterozygous loci in a tissue-restricted manner. Skewing of X chromosome inactivation (XCI) occurs when the silencing of one parental X chromosome is non-random. Here, Zito et al. report XCI patterns in lymphoblastoid cell lines, blood, subcutaneous adipose tissue samples and skin samples of monozygotic and dizygotic twins and find XCI skew to associate with tissue and age.
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Zaytseva OO, Freidin MB, Keser T, Štambuk J, Ugrina I, Šimurina M, Vilaj M, Štambuk T, Trbojević-Akmačić I, Pučić-Baković M, Lauc G, Williams FMK, Novokmet M. Heritability of Human Plasma N-Glycome. J Proteome Res 2019; 19:85-91. [DOI: 10.1021/acs.jproteome.9b00348] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Olga O. Zaytseva
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, Lambeth Palace Road, London SE1 7EH, U.K
| | - Toma Keser
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, Zagreb 10000, Croatia
| | - Jerko Štambuk
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
| | - Ivo Ugrina
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
- Faculty of Science, University of Split, Rud̵era Bošković 33, Split 21000, Croatia
| | - Mirna Šimurina
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, Zagreb 10000, Croatia
| | - Marija Vilaj
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
| | - Tamara Štambuk
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, Zagreb 10000, Croatia
| | | | - Maja Pučić-Baković
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
| | - Gordan Lauc
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, Zagreb 10000, Croatia
| | - Frances M. K. Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, Lambeth Palace Road, London SE1 7EH, U.K
| | - Mislav Novokmet
- Glycoscience Research Laboratory, Genos Ltd., Borongajska cesta 83H, Zagreb 10000, Croatia
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Abstract
The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.
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Affiliation(s)
- Marcus Seldin
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
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78
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Wells HRR, Freidin MB, Zainul Abidin FN, Payton A, Dawes P, Munro KJ, Morton CC, Moore DR, Dawson SJ, Williams FMK. GWAS Identifies 44 Independent Associated Genomic Loci for Self-Reported Adult Hearing Difficulty in UK Biobank. Am J Hum Genet 2019; 105:788-802. [PMID: 31564434 PMCID: PMC6817556 DOI: 10.1016/j.ajhg.2019.09.008] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 09/04/2019] [Indexed: 01/13/2023] Open
Abstract
Age-related hearing impairment (ARHI) is the most common sensory impairment in the aging population; a third of individuals are affected by disabling hearing loss by the age of 65. It causes social isolation and depression and has recently been identified as a risk factor for dementia. The genetic risk factors and underlying pathology of ARHI are largely unknown, meaning that targets for new therapies remain elusive, yet heritability estimates range between 35% and 55%. We performed genome-wide association studies (GWASs) for two self-reported hearing phenotypes, using more than 250,000 UK Biobank (UKBB) volunteers aged between 40 and 69 years. Forty-four independent genome-wide significant loci (p < 5E-08) were identified, considerably increasing the number of established trait loci. Thirty-four loci are novel associations with hearing loss of any form, and only one of the ten known hearing loci has a previously reported association with an ARHI-related trait. Gene sets from these loci are enriched in auditory processes such as synaptic activities, nervous system processes, inner ear morphology, and cognition, while genetic correlation analysis revealed strong positive correlations with multiple personality and psychological traits for the first time. Immunohistochemistry for protein localization in adult mouse cochlea implicate metabolic, sensory, and neuronal functions for NID2, CLRN2, and ARHGEF28. These results provide insight into the genetic landscape underlying ARHI, opening up novel therapeutic targets for further investigation. In a wider context, our study also highlights the viability of using self-report phenotypes for genetic discovery in very large samples when deep phenotyping is unavailable.
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Affiliation(s)
- Helena R R Wells
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London SE1 7EH, UK; UCL Ear Institute, University College London, London WC1X 8EE, UK
| | - Maxim B Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London SE1 7EH, UK
| | - Fatin N Zainul Abidin
- UCL Ear Institute, University College London, London WC1X 8EE, UK; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 7JE, UK
| | - Antony Payton
- Division of Informatics, Imaging & Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Piers Dawes
- Manchester Centre for Audiology and Deafness, The University of Manchester, Manchester M13 9PL, UK
| | - Kevin J Munro
- Manchester Centre for Audiology and Deafness, The University of Manchester, Manchester M13 9PL, UK; Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Cynthia C Morton
- Manchester Centre for Audiology and Deafness, The University of Manchester, Manchester M13 9PL, UK; Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; Departments of Obstetrics and Gynecology and of Pathology, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA 02115, USA
| | - David R Moore
- Manchester Centre for Audiology and Deafness, The University of Manchester, Manchester M13 9PL, UK; Cincinnati Children's Hospital Medical Centre, Department of Otolaryngology, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Sally J Dawson
- UCL Ear Institute, University College London, London WC1X 8EE, UK.
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London SE1 7EH, UK.
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Abstract
AbstractTwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.
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Abstract
Twin registries have developed as a valuable resource for the study of many aspects of disease and society over the years in many different countries. A number of these registries include large numbers of twins with data collected at varying information levels for twin cohorts over the past several decades. More recent expansion of twin datasets has allowed for the collection of genetic data, together with many other levels of 'omic' information along with multiple demographic, physiological, health outcomes and other measures typically used in epidemiologic research. Other twin data sources outside these registries reflect research interests in particular aspects of disease or specific phenotypic assessment. Twin registries have the potential to play a key role in many aspects of the artificial intelligence/machine learning-driven projects of the future and will continue to keep adapting to the changing research landscape.
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81
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Dissecting the role of the gut microbiota and diet on visceral fat mass accumulation. Sci Rep 2019; 9:9758. [PMID: 31278309 PMCID: PMC6611773 DOI: 10.1038/s41598-019-46193-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 06/24/2019] [Indexed: 12/11/2022] Open
Abstract
Both gut microbiota and diet have been shown to impact visceral fat mass (VFM), a major risk factor for cardiometabolic disease, but their relative contribution has not been well characterised. We aimed to estimate and separate the effect of gut microbiota composition from that of nutrient intake on VFM in 1760 older female twins. Through pairwise association analyses, we identified 93 operational taxonomic units (OTUs) and 10 nutrients independently linked to VFM (FDR < 5%). Conditional analyses revealed that the majority (87%) of the 93 VFM-associated OTUs remained significantly associated with VFM irrespective of nutrient intake correction. In contrast, we observed that the effect of fibre, magnesium, biotin and vitamin E on VFM was partially mediated by OTUs. Moreover, we estimated that OTUs were more accurate predictors of VFM than nutrients and accounted for a larger percentage of its variance. Our results suggest that while the role of certain nutrients on VFM appears to depend on gut microbiota composition, specific gut microbes may affect host adiposity regardless of dietary intake. The findings imply that the gut microbiota may have a greater contribution towards shaping host VFM than diet alone. Thus, microbial-based therapy should be prioritised for VFM reduction in overweight and obese subjects.
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82
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Suhre K, Trbojević-Akmačić I, Ugrina I, Mook-Kanamori DO, Spector T, Graumann J, Lauc G, Falchi M. Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome. Metabolites 2019; 9:metabo9070122. [PMID: 31247951 PMCID: PMC6681129 DOI: 10.3390/metabo9070122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/21/2019] [Accepted: 06/24/2019] [Indexed: 12/25/2022] Open
Abstract
Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders.
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Affiliation(s)
- Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO 24144, Doha, Qatar.
| | - Irena Trbojević-Akmačić
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Ivo Ugrina
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EHLondon, UK
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, D-61231 Bad Nauheim, Germany
| | - Gordan Lauc
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EHLondon, UK
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83
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Sharapov SZ, Tsepilov YA, Klaric L, Mangino M, Thareja G, Shadrina AS, Simurina M, Dagostino C, Dmitrieva J, Vilaj M, Vuckovic F, Pavic T, Stambuk J, Trbojevic-Akmacic I, Kristic J, Simunovic J, Momcilovic A, Campbell H, Doherty M, Dunlop MG, Farrington SM, Pucic-Bakovic M, Gieger C, Allegri M, Louis E, Georges M, Suhre K, Spector T, Williams FMK, Lauc G, Aulchenko YS. Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Hum Mol Genet 2019; 28:2062-2077. [PMID: 31163085 PMCID: PMC6664388 DOI: 10.1093/hmg/ddz054] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 01/10/2023] Open
Abstract
Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area.
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Affiliation(s)
- Sodbo Zh Sharapov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
| | - Yakov A Tsepilov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, UK
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Gaurav Thareja
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | | | - Mirna Simurina
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Concetta Dagostino
- Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, Italy
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Marija Vilaj
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Frano Vuckovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Tamara Pavic
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Jerko Stambuk
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | | | - Jasminka Kristic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Jelena Simunovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Ana Momcilovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Margaret Doherty
- Institute of Technology Sligo, Department of Life Sciences, Sligo, Ireland
- National Institute for Bioprocessing Research & Training, Dublin, Ireland
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Susan M Farrington
- Colon Cancer Genetics Group, MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK
| | - Maja Pucic-Bakovic
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
| | - Christian Gieger
- Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Centre Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg, Germany
| | - Massimo Allegri
- Pain Therapy Department, Policlinico Monza Hospital, Monza, Italy
| | - Edouard Louis
- CHU-Liège and Unit of Gastroenterology, GIGA-R and Faculty of Medicine, University of Liège, 1 Avenue de l’Hôpital, Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, St Thomas’ Campus, London, UK
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovacica 1, Zagreb, Croatia
| | - Yurii S Aulchenko
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, Russia
- Novosibirsk State University, 1, Pirogova str., Novosibirsk, Russia
- PolyOmica, Het Vlaggeschip 61, PA 's-Hertogenbosch, The Netherlands
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84
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Bocher O, Marenne G, Saint Pierre A, Ludwig TE, Guey S, Tournier-Lasserve E, Perdry H, Génin E. Rare variant association testing for multicategory phenotype. Genet Epidemiol 2019; 43:646-656. [PMID: 31087445 DOI: 10.1002/gepi.22210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 04/03/2019] [Accepted: 04/17/2019] [Indexed: 01/09/2023]
Abstract
Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context. To address this issue, we propose an extension of burden tests by using a multinomial regression model, which enables association tests between rare variants and multicategory phenotypes. We evaluated the type I error and the power of two burden tests, CAST and WSS, by simulating data under different scenarios. In the case of genetic heterogeneity between case subgroups, we showed an advantage of multinomial regression over logistic regression, which considers all the cases against the controls. We replicated these results on real data from Moyamoya disease where the burden tests performed better when cases were stratified according to age-of-onset. We implemented the functions for association tests in the R package "Ravages" available on Github.
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Affiliation(s)
- Ozvan Bocher
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
| | | | | | - Thomas E Ludwig
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France.,CHU Brest, Brest, France
| | - Stéphanie Guey
- Inserm UMR-S1161, Génétique et Physiopathologie des Maladies Cérébro-vasculaires, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Elisabeth Tournier-Lasserve
- Inserm UMR-S1161, Génétique et Physiopathologie des Maladies Cérébro-vasculaires, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Hervé Perdry
- CESP Inserm, U1018, UFR Médecine, Univ Paris-Sud, Université Paris-Saclay, Villejuif, France
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85
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Abbasian G, Lachance G, Yarand D, Hart D, Spector T, Steves C. Self-reported Anxiety Sensitivity Index in the TwinsUK cohort. Wellcome Open Res 2019. [DOI: 10.12688/wellcomeopenres.15050.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The anxiety sensitivity (AS) construct has received considerable attention in anxiety research and is considered to be a cognitive vulnerability factor for the study of anxiety related disorders. The Anxiety Sensitivity Index (ASI) is the most widely used instrument for the study of AS. The present Data Note provides an overview of all the 16-item ASI questionnaires filled and returned by the twins in the TwinsUK registry. This work does not provide any multidimensional or factor structure analysis of the responses provided. TwinsUK registry encompasses a wide range of clinical and self-reported data that can be used as confounding factors in the study of cognitive and mental health.
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86
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Freidin M, Kraatari M, Skarp S, Määttä J, Kettunen J, Niinimäki J, Karppinen J, Williams F, Männikkö M. Genome-wide meta-analysis identifies genetic locus on chromosome 9 associated with Modic changes. J Med Genet 2019; 56:420-426. [PMID: 30808802 DOI: 10.1136/jmedgenet-2018-105726] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 12/18/2018] [Accepted: 01/11/2019] [Indexed: 11/04/2022]
Abstract
BACKGROUND Low back pain (LBP) is a common disabling condition. Lumbar disc degeneration (LDD) may be a contributing factor for LBP. Modic change (MC), a distinct phenotype of LDD, is presented as a pathological bone marrow signal change adjacent to vertebral endplate on MRI. It is strongly associated with LBP and has heritability around 30%. Our objective was to identify genetic loci associated with MC using a genome-wide meta-analysis. METHODS Presence of MC was evaluated in lumbar MRI in the Northern Finland Birth Cohort 1966 (n=1182) and TwinsUK (n=647). Genome-wide association analyses were carried out using linear regression model. Inverse-variance weighting approach was used in the meta-analysis. RESULTS A locus associated with MC (p<5e-8) was found on chromosome 9 with the lead SNP rs1934268 in an intron of the PTPRD gene. It is located in the binding region of BCL11A, SPI1 and PBX3 transcription factors. The SNP was nominally associated with LBP in TwinsUK (p=0.001) but not associated in the UK Biobank (p=0.914). Suggestive signals (p<1e-5) were identified near XKR4, SCIN, MGMT, DLG2, ZNF184 and OPRK1. CONCLUSION PTPRD is a novel candidate gene for MC that may act via the development of cartilage or nervous system; further work is needed to define the mechanisms underlying the pathways leading to development of MC. This is the first genome-wide meta-analysis of MC, and the results pave the way for further studies on the genetic factors underlying the various features of spine degeneration and LBP.
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Affiliation(s)
- Maxim Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Minna Kraatari
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Sini Skarp
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Juhani Määttä
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Johannes Kettunen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.,National Institute for Health and Welfare, Helsinki, Finland
| | - Jaakko Niinimäki
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.,Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Jaro Karppinen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.,Finnish Institute of Occupational Health, Oulu, Finland
| | - Frances Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Minna Männikkö
- Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland
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87
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Cherny SS, Freidin MB, Williams FMK, Livshits G. The analysis of causal relationships between blood lipid levels and BMD. PLoS One 2019; 14:e0212464. [PMID: 30794634 PMCID: PMC6386286 DOI: 10.1371/journal.pone.0212464] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/01/2019] [Indexed: 12/11/2022] Open
Abstract
Bone mineral density (BMD) and lipid levels are two of the most extensively studied risk factors for common diseases of aging, such as cardiovascular disease (CVD) and osteoporosis (OP). These two risk factors are also correlated with each other, but little is known about the molecular mechanisms behind this correlation. Recent studies revealed that circulating levels of several metabolites involved in the biosynthesis of androsterone correlate significantly with BMD and have the capacity to affect cholesterol and lipids levels. A main aim of the present study was to investigate the hypothesis that androsterone-related metabolites could provide a link between CVD and OP, as a common cause of lipid levels and BMD. The present study employed data from the NIHR BRC TwinsUK BioResource, comprising 1909 and 1994 monozygotic and dizygotic twin pairs, respectively, to address the causal relationships among BMD and lipids, and their associated metabolites, using reciprocal causation twin modelling, as well as Mendelian randomization (MR) using large publicly-available GWAS datasets on lipids and BMD, in conjunction with TwinsUK metabolite data. While results involving the twin modelling and MR analyses with metabolites were unable to establish a causal link between metabolite levels and either lipids or BMD, MR analyses of BMD and lipids suggest that lipid levels have a causal impact on BMD, which is consistent with findings from clinical trials of lipid-lowering drugs, which have also increased BMD.
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Affiliation(s)
- Stacey S. Cherny
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Science, King’s College London, London, United Kingdom
| | - Frances M. K. Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Science, King’s College London, London, United Kingdom
| | - Gregory Livshits
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Twin Research and Genetic Epidemiology, School of Life Course Science, King’s College London, London, United Kingdom
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88
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Cirulli ET, Guo L, Leon Swisher C, Shah N, Huang L, Napier LA, Kirkness EF, Spector TD, Caskey CT, Thorens B, Venter JC, Telenti A. Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk. Cell Metab 2019; 29:488-500.e2. [PMID: 30318341 PMCID: PMC6370944 DOI: 10.1016/j.cmet.2018.09.022] [Citation(s) in RCA: 205] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 06/27/2018] [Accepted: 09/25/2018] [Indexed: 12/29/2022]
Abstract
Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.
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Affiliation(s)
| | | | | | - Naisha Shah
- Human Longevity, Inc., San Diego, CA 92121, USA
| | - Lei Huang
- Human Longevity, Inc., San Diego, CA 92121, USA
| | | | | | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - C Thomas Caskey
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | | | - Amalio Telenti
- The Scripps Research Institute, La Jolla, CA 92037, USA.
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89
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Abbasian G, Lachance G, Yarand D, Hart D, Spector T, Steves C. An overview of the TwinsUK cohort’s anxiety and depression assessment, using the self-reported Hospital Anxiety and Depression Scale. Wellcome Open Res 2019. [DOI: 10.12688/wellcomeopenres.14927.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Anxiety and depression are common mental health conditions that frequently coexist. The Hospital Anxiety and Depression (HADS) questionnaire, is a widely used questionnaire, both in the clinic and in research. HADS provides an assessment of anxiety and depression based on non-physical symptoms, hence it can be used in patients with other physical pain and symptoms. This resource provides an overview of all the HADS questionnaires completed by the twins in the TwinsUK registry. This includes both monozygotic and dizygotic twins, over a wide range of age and socioeconomic backgrounds. TwinsUK holds a wide range of phenotype and genotyping data that can be used as confounding factors in the study of mental health.
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90
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Beaumont RN, Warrington NM, Cavadino A, Tyrrell J, Nodzenski M, Horikoshi M, Geller F, Myhre R, Richmond RC, Paternoster L, Bradfield JP, Kreiner-Møller E, Huikari V, Metrustry S, Lunetta KL, Painter JN, Hottenga JJ, Allard C, Barton SJ, Espinosa A, Marsh JA, Potter C, Zhang G, Ang W, Berry DJ, Bouchard L, Das S, Hakonarson H, Heikkinen J, Helgeland Ø, Hocher B, Hofman A, Inskip HM, Jones SE, Kogevinas M, Lind PA, Marullo L, Medland SE, Murray A, Murray JC, Njølstad PR, Nohr EA, Reichetzeder C, Ring SM, Ruth KS, Santa-Marina L, Scholtens DM, Sebert S, Sengpiel V, Tuke MA, Vaudel M, Weedon MN, Willemsen G, Wood AR, Yaghootkar H, Muglia LJ, Bartels M, Relton CL, Pennell CE, Chatzi L, Estivill X, Holloway JW, Boomsma DI, Montgomery GW, Murabito JM, Spector TD, Power C, Järvelin MR, Bisgaard H, Grant SFA, Sørensen TIA, Jaddoe VW, Jacobsson B, Melbye M, McCarthy MI, Hattersley AT, Hayes MG, Frayling TM, Hivert MF, Felix JF, Hyppönen E, Lowe WL, Evans DM, Lawlor DA, Feenstra B, Freathy RM. Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Hum Mol Genet 2019; 27:742-756. [PMID: 29309628 PMCID: PMC5886200 DOI: 10.1093/hmg/ddx429] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 12/15/2017] [Indexed: 12/22/2022] Open
Abstract
Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother–child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10−8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
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Affiliation(s)
- Robin N Beaumont
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Nicole M Warrington
- Translational Research Institute, University of Queensland Diamantina Institute, Brisbane, QLD, Australia
| | - Alana Cavadino
- Centre for Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK.,European Centre for Environment and Human Health, University of Exeter, The Knowledge Spa, Truro TR1 3HD, UK
| | - Michael Nodzenski
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Momoko Horikoshi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Frank Geller
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Ronny Myhre
- Division of Epidemiology, Department of Genes and Environment, Norwegian Institute of Public Health, Oslo, Norway
| | - Rebecca C Richmond
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Lavinia Paternoster
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Jonathan P Bradfield
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Eskil Kreiner-Møller
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Danish Pediatric Asthma Center, Copenhagen University Hospital, Gentofte, Denmark
| | - Ville Huikari
- Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Sarah Metrustry
- Department of Twin Research, King's College London, St. Thomas' Hospital, London, UK
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Framingham Heart Study, Framingham, MA, USA
| | - Jodie N Painter
- QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Jouke-Jan Hottenga
- EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
| | - Catherine Allard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Sheila J Barton
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Ana Espinosa
- Pompeu Fabra University (UPF), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Julie A Marsh
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Australia
| | - Catherine Potter
- Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Ge Zhang
- Human Genetics Division, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, OH, USA.,March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, OH, USA
| | - Wei Ang
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Australia
| | - Diane J Berry
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada.,ECOGENE-21 and Lipid Clinic, Chicoutimi Hospital, Saguenay, QC, Canada.,Department of Biochemistry, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Shikta Das
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | | | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jani Heikkinen
- FIMM Institute for Molecular Medicine Finland, Helsinki University, Helsinki FI-00014, Finland
| | - Øyvind Helgeland
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway.,Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
| | - Berthold Hocher
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.,Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Hazel M Inskip
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Samuel E Jones
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Manolis Kogevinas
- Pompeu Fabra University (UPF), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Letizia Marullo
- Genetic Section, Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Anna Murray
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Jeffrey C Murray
- Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Pål R Njølstad
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway.,Department of Pediatrics, Haukeland University Hospital, Bergen 5021, Norway
| | - Ellen A Nohr
- Research Unit of Obstetrics & Gynecology, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Christoph Reichetzeder
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany.,Center for Cardiovascular Research, Charité, Berlin, Germany
| | - Susan M Ring
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Katherine S Ruth
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Loreto Santa-Marina
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Subdirección de Salud Pública y Adicciones de Gipuzkoa, Donostia/San Sebastián, Spain.,Instituto de Investigación Sanitaria BIODONOSTIA, Donostia/San Sebastián, Spain
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sylvain Sebert
- Institute of Health Sciences, University of Oulu, Oulu, Finland.,Department of Epidemiology and Biostatistics, School of Public Health, Medical Research Council-Health Protection Agency Centre for Environment and Health, Faculty of Medicine, Imperial College London, London, UK
| | - Verena Sengpiel
- Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahgrenska University Hospital, Gothenburg, Sweden
| | - Marcus A Tuke
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Marc Vaudel
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Michael N Weedon
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Gonneke Willemsen
- EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Hanieh Yaghootkar
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Louis J Muglia
- Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, OH, USA.,March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, OH, USA
| | - Meike Bartels
- EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Craig E Pennell
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Australia
| | - Leda Chatzi
- Department of Social Medicine, University of Crete, Crete, Greece
| | - Xavier Estivill
- Pompeu Fabra University (UPF), Barcelona, Spain.,ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - John W Holloway
- Human Development & Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Dorret I Boomsma
- EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
| | - Grant W Montgomery
- QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Joanne M Murabito
- Framingham Heart Study, Framingham, MA, USA.,Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Tim D Spector
- Department of Twin Research, King's College London, St. Thomas' Hospital, London, UK
| | - Christine Power
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Marjo-Ritta Järvelin
- Institute of Health Sciences, University of Oulu, Oulu, Finland.,Department of Epidemiology and Biostatistics, School of Public Health, Medical Research Council-Health Protection Agency Centre for Environment and Health, Faculty of Medicine, Imperial College London, London, UK.,Biocenter Oulu, University of Oulu, Oulu, Finland.,Unit of Primary Care, Oulu University Hospital, FI-90220 Oulu, 90029 OYS, Finland.,Department of Children and Young People and Families, National Institute for Health and Welfare, FI-90101 Oulu, Finland
| | - Hans Bisgaard
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Danish Pediatric Asthma Center, Copenhagen University Hospital, Gentofte, Denmark
| | - Struan F A Grant
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thorkild I A Sørensen
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vincent W Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Bo Jacobsson
- Division of Epidemiology, Department of Genes and Environment, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahgrenska University Hospital, Gothenburg, Sweden
| | - Mads Melbye
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.,Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.,Diabetes Center, Massachussetts General Hospital, Boston, MA, USA.,Department of Medicine, Universite de Sherbrooke, QC, Canada
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Elina Hyppönen
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Centre for School of Population Health Research, School of Health Sciences, and Sansom Institute, University of South Australia, Adelaide, Australia.,South Australian Health and Medical Research Institute, Adelaide, Australia
| | - William L Lowe
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - David M Evans
- Translational Research Institute, University of Queensland Diamantina Institute, Brisbane, QLD, Australia.,Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Debbie A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter EX2 5DW, UK.,Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
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91
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Definitive Zygosity Scores in the Peas in the Pod Questionnaire is a Sensitive and Accurate Assessment of the Zygosity of Adult Twins. Twin Res Hum Genet 2018; 21:146-154. [PMID: 29582724 DOI: 10.1017/thg.2018.9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Twin researchers face the challenge of accurately determining the zygosity of twins for research. As part of the annual questionnaire between 1999 and 2006, 8,307 twins from the TwinsUK registry were asked to complete five questions (independently from their co-twin) to ascertain their self-perceived zygosity during childhood on up to five separate occasions. This questionnaire is known as the 'peas in the pod' questionnaire (PPQ), but there is little evidence of its validation. Answers were scored and classified as monozygotic (MZ), dizygotic (DZ), or unknown zygosity (UZ) and were compared with 4,484 twins with genotyping data who had not been selected for zygosity. Of these, 3,859 individuals (46.5% of those who had a zygosity from PPQ) had zygosity classified by both the PPQ and genotyping. Of the 708 individual twins whose answers meant that they were consistently classed as MZ in the PPQ, 683 (96.5%) were MZ within the genotype data. Of the 945 individual twins consistently classed as DZ within questionnaire, 936 (99.0%) were DZ in the genotype data. Where both twins scored MZ consistently across multiple questionnaires, 99.6% were MZ on genotyping, 99.7% were DZ on genotyping if both twins consistently scored DZ. However, for the initial questionnaire, 88.6% of those scoring as MZ were genotypically MZ and 98.7% DZ. For twin pairs where both scored UZ, 94.7% were DZ. Using the PPQ on a single occasion provided a definitive classification of whether the twin was MZ or DZ with an overall accuracy of 86.9%, increasing to 97.9% when there was a consistent classification of zygosity across multiple questionnaires. This study has shown that the PPQ questionnaire is an excellent proxy indicator of zygosity in the absence of genotyping information.
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92
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Verdi S, Jackson MA, Beaumont M, Bowyer RCE, Bell JT, Spector TD, Steves CJ. An Investigation Into Physical Frailty as a Link Between the Gut Microbiome and Cognitive Health. Front Aging Neurosci 2018; 10:398. [PMID: 30564113 PMCID: PMC6288358 DOI: 10.3389/fnagi.2018.00398] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 11/19/2018] [Indexed: 12/26/2022] Open
Abstract
The preservation of cognitive abilities with aging is a priority both for individuals and nations given the aging populations of many countries. Recently the gut microbiome has been identified as a new territory to explore in relation to cognition. Experiments using rodents have identified a link between the gut microbiome and cognitive function, particularly that low microbial diversity leads to poor cognition function. Similar studies in humans could identify novel targets to encourage healthy cognition in an aging population. Here, we investigate the association of gut microbiota and cognitive function in a human cohort considering the influence of physical frailty. We analyzed 16S rRNA gene sequence data, derived from fecal samples obtained from 1,551 individuals over the age of 40. Cognitive data was collected using four cognitive tests: verbal fluency (n = 1,368), Deary-Liewald Reaction Time Test (DLRT; n = 873), Mini Mental State Examination (recall; n = 1,374) and Paired Associates Learning from the Cambridge Neuropsychological Test Automated Battery (CANTAB-PAL; n = 405). We use mixed effects models to identify associations with alpha diversity, operational taxonomic units (OTUs) and taxa and performed further analyses adjusting for physical frailty. We then repeated the analyses in a subset of individuals with dietary data, also excluding those using medications shown to influence gut microbiome composition. DLRT and verbal fluency were negatively associated with alpha diversity of the gut microbiota (False-Discovery Rate, FDR, p < 0.05). However, when considering frailty as a covariate, only associations between the DLRT and diversity measures remained. Repeating analyses excluding Proton pump inhibitor (PPI) and antibiotic users and accounting for diet, we similarly observe significant negative associations between the DLRT and alpha diversity measures and a further negative association between DLRT and the abundance of the order Burkholderiales that remains significant after adjusting for host frailty. This highlights the importance of considering concurrent differences in physical health in studies of cognitive performance and suggests that physical health has a relatively larger association with the gut microbiome. However, the frailty independent cognitive-gut microbiota associations that were observed might represent important targets for further research, with potential for use in diagnostic surveillance in cognitive aging and interventions to improve vitality.
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Affiliation(s)
- Serena Verdi
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
- Neuropsychopharmacology Unit, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, London, United Kingdom
| | - Matthew A. Jackson
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
| | - Michelle Beaumont
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Ruth C. E. Bowyer
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
- Clinical Age Research Unit, Department of Clinical Gerontology, King’s College Hospital, NHS Foundation Trust, London, United Kingdom
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93
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Endplate Defect Is Heritable, Associated With Low Back Pain and Triggers Intervertebral Disc Degeneration: A Longitudinal Study From TwinsUK. Spine (Phila Pa 1976) 2018; 43:1496-1501. [PMID: 29847371 DOI: 10.1097/brs.0000000000002721] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN Longitudinal study of spine magnetic resonance imaging (MRI) in a large-scale population-based study. OBJECTIVE To determine the order of appearance of degenerative change in vertebral bodies and intervertebral discs. We also sought to define the influence of endplate defect on low back pain (LBP) and to determine whether there is a genetic influence on endplate defect. SUMMARY OF BACKGROUND DATA Endplate defect is a magnetic resonance imaging trait, found to be associated with intervertebral disc degeneration. There is a lack of understanding regarding the mechanism underlying lumbar disc degeneration (LDD). Recent attention has shifted to vertebral endplate defects and their role in spine degeneration pathology. METHODS Individuals from the TwinsUK spine study having longitudinal T2-weighted lumbar MR scans at baseline (n = 996) and a decade later (n = 438) were included. LDD, vertebral endplate defect by calculating a total endplate score, and Modic change (MC) were assessed using standard techniques. Mixed-effects models were used to determine the association between the features of spine pathology, adjusted for covariates. Endplate defect heritability was estimated using variance component analysis. RESULTS Significant association was found between endplate defect, LDD, MRI features of LDD and MC was observed. Endplate defect was associated with severe disabling LBP (P ≤ 0.013) in multivariate analysis. An association between disc degeneration (DD) at baseline and MC at follow-up was shown at upper lumbar levels. Total endplate score was heritable with estimated additive genetic component A = 55.3% (95% CI 43.0-65.4). CONCLUSION Endplate defect, LDD, and MC are all independent risk factors for episodes of severe and disabling LBP. Longitudinal analysis showed DD is followed by MC. Endplate defect has significant heritability of 55%. However, whether endplate defect triggers DD or these pathological changes occur concurrently could not be conclusively determined. LEVEL OF EVIDENCE 2.
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94
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Herd P, Palloni A, Rey F, Dowd JB. Social and population health science approaches to understand the human microbiome. Nat Hum Behav 2018; 2:808-815. [PMID: 31457107 PMCID: PMC6711373 DOI: 10.1038/s41562-018-0452-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 09/12/2018] [Indexed: 12/31/2022]
Abstract
The microbiome is now considered our 'second genome' with potentially comparable importance to the genome in determining human health. There is, however, a relatively limited understanding of the broader environmental factors, particularly social conditions, that shape variation in human microbial communities. Fulfilling the promise of microbiome research - particularly the microbiome's potential for modification - will require collaboration between biologists and social and population scientists. For life scientists, the plasticity and adaptiveness of the microbiome calls for an agenda to understand the sensitivity of the microbiome to broader social environments already known to be powerful predictors of morbidity and mortality. For social and population scientists, attention to the microbiome may help answer nagging questions about the underlying biological mechanisms that link social conditions to health. We outline key substantive and methodological advances that can be made if collaborations between social and population health scientists and life scientists are strategically pursued.
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Affiliation(s)
- Pamela Herd
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA.
| | - Alberto Palloni
- Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA
| | - Federico Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jennifer B Dowd
- Department of Global Health and Social Medicine, Kings College London, London, UK
- CUNY Graduate School of Public Health and Health Policy, New York, NY, USA
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95
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Bonfiglio F, Henström M, Nag A, Hadizadeh F, Zheng T, Cenit MC, Tigchelaar E, Williams F, Reznichenko A, Ek WE, Rivera NV, Homuth G, Aghdassi AA, Kacprowski T, Männikkö M, Karhunen V, Bujanda L, Rafter J, Wijmenga C, Ronkainen J, Hysi P, Zhernakova A, D'Amato M. A GWAS meta-analysis from 5 population-based cohorts implicates ion channel genes in the pathogenesis of irritable bowel syndrome. Neurogastroenterol Motil 2018; 30:e13358. [PMID: 29673008 DOI: 10.1111/nmo.13358] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 03/23/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Irritable bowel syndrome (IBS) shows genetic predisposition, however, large-scale, powered gene mapping studies are lacking. We sought to exploit existing genetic (genotype) and epidemiological (questionnaire) data from a series of population-based cohorts for IBS genome-wide association studies (GWAS) and their meta-analysis. METHODS Based on questionnaire data compatible with Rome III Criteria, we identified a total of 1335 IBS cases and 9768 asymptomatic individuals from 5 independent European genotyped cohorts. Individual GWAS were carried out with sex-adjusted logistic regression under an additive model, followed by meta-analysis using the inverse variance method. Functional annotation of significant results was obtained via a computational pipeline exploiting ontology and interaction networks, and tissue-specific and gene set enrichment analyses. KEY RESULTS Suggestive GWAS signals (P ≤ 5.0 × 10-6 ) were detected for 7 genomic regions, harboring 64 gene candidates to affect IBS risk via functional or expression changes. Functional annotation of this gene set convincingly (best FDR-corrected P = 3.1 × 10-10 ) highlighted regulation of ion channel activity as the most plausible pathway affecting IBS risk. CONCLUSION & INFERENCES Our results confirm the feasibility of population-based studies for gene-discovery efforts in IBS, identify risk genes and loci to be prioritized in independent follow-ups, and pinpoint ion channels as important players and potential therapeutic targets warranting further investigation.
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Affiliation(s)
- F Bonfiglio
- Department of Gastrointestinal and Liver Diseases, Biodonostia Health Research Institute, Spain.,Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - M Henström
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - A Nag
- Department of Twin Research & Genetic Epidemiology, King's College London, London, England
| | - F Hadizadeh
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - T Zheng
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - M C Cenit
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - E Tigchelaar
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - F Williams
- Department of Twin Research & Genetic Epidemiology, King's College London, London, England
| | - A Reznichenko
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - W E Ek
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.,Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Uppsala, Sweden
| | - N V Rivera
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - G Homuth
- Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - A A Aghdassi
- Department of Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - T Kacprowski
- Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - M Männikkö
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - V Karhunen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland.,Oulu University Hospital, Oulu, Finland.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - L Bujanda
- Department of Gastrointestinal and Liver Diseases, Biodonostia Health Research Institute, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Universidad del País Vasco (UPV/EHU), San Sebastián, Spain
| | - J Rafter
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - C Wijmenga
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - J Ronkainen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland.,Primary Health Care Center, Tornio, Finland
| | - P Hysi
- Department of Ophthalmology, King's College London, St Thomas' Hospital Campus, London, UK
| | - A Zhernakova
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - M D'Amato
- Department of Gastrointestinal and Liver Diseases, Biodonostia Health Research Institute, Spain.,Unit of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,BioCruces Health Research Institute, Bilbao, Spain.,IKERBASQUE, Basque Science Foundation, Bilbao, Spain
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96
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Cuellar-Partida G, Williams KM, Yazar S, Guggenheim JA, Hewitt AW, Williams C, Wang JJ, Kho PF, Saw SM, Cheng CY, Wong TY, Aung T, Young TL, Tideman JWL, Jonas JB, Mitchell P, Wojciechowski R, Stambolian D, Hysi P, Hammond CJ, Mackey DA, Lucas RM, MacGregor S. Genetically low vitamin D concentrations and myopic refractive error: a Mendelian randomization study. Int J Epidemiol 2018; 46:1882-1890. [PMID: 28586461 DOI: 10.1093/ije/dyx068] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2017] [Indexed: 01/08/2023] Open
Abstract
Background Myopia prevalence has increased in the past 20 years, with many studies linking the increase to reduced time spent outdoors. A number of recent observational studies have shown an inverse association between vitamin D [25(OH)D] serum levels and myopia. However, in such studies it is difficult to separate the effects of time outdoors and vitamin D levels. In this work we use Mendelian randomization (MR) to assess if genetically determined 25(OH)D levels contribute to the degree of myopia. Methods We performed MR using results from a meta-analysis of refractive error (RE) genome-wide association study (GWAS) that included 37 382 and 8 376 adult participants of European and Asian ancestry, respectively, published by the Consortium for Refractive Error And Myopia (CREAM). We used single nucleotide polymorphisms (SNPs) in the DHCR7, CYP2R1, GC and CYP24A1 genes with known effects on 25(OH)D concentration as instrumental variables (IV). We estimated the effect of 25(OH)D on myopia level using a Wald-type ratio estimator based on the effect estimates from the CREAM GWAS. Results Using the combined effect attributed to the four SNPs, the estimate for the effect of 25(OH)D on refractive error was -0.02 [95% confidence interval (CI) -0.09, 0.04] dioptres (D) per 10 nmol/l increase in 25(OH)D concentration in Caucasians and 0.01 (95% CI -0.17, 0.19) D per 10 nmol/l increase in Asians. Conclusions The tight confidence intervals on our estimates suggest the true contribution of vitamin D levels to degree of myopia is very small and indistinguishable from zero. Previous findings from observational studies linking vitamin D levels to myopia were likely attributable to the effects of confounding by time spent outdoors.
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Affiliation(s)
- Gabriel Cuellar-Partida
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Katie M Williams
- Department of Ophthalmology.,Department of Ophthalmology and Twin Research, King's College London, London, UK
| | - Seyhan Yazar
- Lions Eye Institute, University of Western Australia, Perth, WA, Australia
| | | | - Alex W Hewitt
- Menzies Research Institute Tasmania, University of Tasmania, Hobart, Australia
| | - Cathy Williams
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jie Jin Wang
- Centre for Vision Research, Department of Ophthalmology and Westmead Institute for Medical Research, University of Sydney, Camperdown, NSW, Australia
| | - Pik-Fang Kho
- Department of Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD,Australia
| | - Seang Mei Saw
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Graduate Medical School.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Graduate Medical School.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Graduate Medical School.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Graduate Medical School.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Terri L Young
- Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - J Willem L Tideman
- Department of Ophthalmology and Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Capital University of Medical Science, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China.,Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Seegartenklinik Heidelberg, Germany
| | | | - Paul Mitchell
- Centre for Vision Research, Department of Ophthalmology and Westmead Institute for Medical Research, University of Sydney, Camperdown, NSW, Australia
| | | | - Dwight Stambolian
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA, USA
| | - Pirro Hysi
- Department of Ophthalmology and Twin Research, King's College London, London, UK
| | - Christopher J Hammond
- Department of Ophthalmology.,Department of Ophthalmology and Twin Research, King's College London, London, UK
| | - David A Mackey
- Lions Eye Institute, University of Western Australia, Perth, WA, Australia
| | - Robyn M Lucas
- National Centre for Epidemiology and Population Health, Research School of Population Health, The Australian National University, Canberra, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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97
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Effect of varying skin surface electrode position on electroretinogram responses recorded using a handheld stimulating and recording system. Doc Ophthalmol 2018; 137:79-86. [PMID: 30046929 PMCID: PMC6153519 DOI: 10.1007/s10633-018-9652-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 07/17/2018] [Indexed: 11/23/2022]
Abstract
Purpose A handheld device (the RETeval system, LKC Technologies) aims to increase the ease of electroretinogram (ERG) recording by using specially designed skin electrodes, rather than corneal electrodes. We explored effects of electrode position on response parameters recorded using this device. Methods Healthy adult twins were recruited from the TwinsUK cohort and underwent recording of light-adapted flicker ERGs (corresponding to international standard stimuli). In Group 1, skin electrodes were placed in a “comfortable” position, which was up to 20 mm below the lid margin. For subsequent participants (Group 2), the electrode was positioned 2 mm from the lid margin as recommended by the manufacturer. Amplitudes and peak times (averaged from both eyes) were compared between groups after age-matching and inclusion of only one twin per pair. Light-adapted flicker and flash ERGs were recorded for an additional 10 healthy subjects in two consecutive recording sessions: in the test eye, electrode position was varied from 2 to 10–20 mm below the lid margin between sessions; in the fellow (control) eye, the electrode was 2 mm below the lid margin throughout. Amplitudes and peak times (test eye normalised to control eye) were compared for the two sessions. Results Including one twin per pair, and age-matching yielded 28 individuals per group. Flicker ERG amplitudes were significantly lower for Group 1 than Group 2 participants (p = 0.0024). However, mean peak times did not differ between groups (p = 0.54). For the subjects in whom electrode position was changed between recording sessions, flash and flicker amplitudes were significantly lower when positioned further from the lid margin (p < 0.005), but peak times were similar (p > 0.5). Conclusions Moving the skin electrodes further from the lid margin significantly reduces response amplitudes, highlighting the importance of consistent electrode positioning. However, this does not significantly affect peak times. Thus, it may be feasible to adopt a more comfortable position in participants who cannot tolerate the recommended position if analysis is restricted to peak time parameters.
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98
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Jackson MA, Verdi S, Maxan ME, Shin CM, Zierer J, Bowyer RCE, Martin T, Williams FMK, Menni C, Bell JT, Spector TD, Steves CJ. Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nat Commun 2018; 9:2655. [PMID: 29985401 PMCID: PMC6037668 DOI: 10.1038/s41467-018-05184-7] [Citation(s) in RCA: 352] [Impact Index Per Article: 58.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 06/18/2018] [Indexed: 12/12/2022] Open
Abstract
The human gut microbiome has been associated with many health factors but variability between studies limits exploration of effects between them. Gut microbiota profiles are available for >2700 members of the deeply phenotyped TwinsUK cohort, providing a uniform platform for such comparisons. Here, we present gut microbiota association analyses for 38 common diseases and 51 medications within the cohort. We describe several novel associations, highlight associations common across multiple diseases, and determine which diseases and medications have the greatest association with the gut microbiota. These results provide a reference for future studies of the gut microbiome and its role in human health. The human gut microbiome has been associated with many health factors, but variability between studies limits exploration of these effects. Here, Jackson et al. analyse gut microbiota associations for 38 common diseases and 51 medications within >2700 members of the TwinsUK cohort.
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Affiliation(s)
- Matthew A Jackson
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK. .,Kennedy Institute of Rheumatology, University of Oxford, Oxford, OX3 7FY, UK.
| | - Serena Verdi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Maria-Emanuela Maxan
- Clinical Age Research Unit, King's College Hospital Foundation Trust, London, SE5 9RS, UK
| | - Cheol Min Shin
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea
| | - Jonas Zierer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Ruth C E Bowyer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Tiphaine Martin
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.,Department of Oncological Sciences, Tisch Institute of Cancer, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK. .,Clinical Age Research Unit, King's College Hospital Foundation Trust, London, SE5 9RS, UK.
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99
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Ostrom QT, Kinnersley B, Wrensch MR, Eckel-Passow JE, Armstrong G, Rice T, Chen Y, Wiencke JK, McCoy LS, Hansen HM, Amos CI, Bernstein JL, Claus EB, Il'yasova D, Johansen C, Lachance DH, Lai RK, Merrell RT, Olson SH, Sadetzki S, Schildkraut JM, Shete S, Rubin JB, Lathia JD, Berens ME, Andersson U, Rajaraman P, Chanock SJ, Linet MS, Wang Z, Yeager M, Houlston RS, Jenkins RB, Melin B, Bondy ML, Barnholtz-Sloan JS. Sex-specific glioma genome-wide association study identifies new risk locus at 3p21.31 in females, and finds sex-differences in risk at 8q24.21. Sci Rep 2018; 8:7352. [PMID: 29743610 PMCID: PMC5943590 DOI: 10.1038/s41598-018-24580-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 04/06/2018] [Indexed: 01/07/2023] Open
Abstract
Incidence of glioma is approximately 50% higher in males. Previous analyses have examined exposures related to sex hormones in women as potential protective factors for these tumors, with inconsistent results. Previous glioma genome-wide association studies (GWAS) have not stratified by sex. Potential sex-specific genetic effects were assessed in autosomal SNPs and sex chromosome variants for all glioma, GBM and non-GBM patients using data from four previous glioma GWAS. Datasets were analyzed using sex-stratified logistic regression models and combined using meta-analysis. There were 4,831 male cases, 5,216 male controls, 3,206 female cases and 5,470 female controls. A significant association was detected at rs11979158 (7p11.2) in males only. Association at rs55705857 (8q24.21) was stronger in females than in males. A large region on 3p21.31 was identified with significant association in females only. The identified differences in effect of risk variants do not fully explain the observed incidence difference in glioma by sex.
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Affiliation(s)
- Quinn T Ostrom
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Department of Population and Quantitative Heath Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Margaret R Wrensch
- Department of Neurological Surgery and Institute of Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Jeanette E Eckel-Passow
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, United States of America
| | - Georgina Armstrong
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Terri Rice
- Department of Neurological Surgery and Institute of Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Yanwen Chen
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - John K Wiencke
- Department of Neurological Surgery and Institute of Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Lucie S McCoy
- Department of Neurological Surgery and Institute of Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Helen M Hansen
- Department of Neurological Surgery and Institute of Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Elizabeth B Claus
- School of Public Health, Yale University, New Haven, Connecticut, United States of America
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Dora Il'yasova
- Department of Epidemiology and Biostatistics, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
- Cancer Control and Prevention Program, Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Christoffer Johansen
- Oncology clinic, Finsen Center, Rigshospitalet, Copenhagen, Denmark
- Survivorship Research Unit, The Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Daniel H Lachance
- Department of Neurology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Rose K Lai
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Ryan T Merrell
- Department of Neurology, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Siegal Sadetzki
- Cancer and Radiation Epidemiology Unit, Gertner Institute, Chaim Sheba Medical Center, Tel Hashomer, Israel
- Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Joellen M Schildkraut
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Sanjay Shete
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Joshua B Rubin
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Justin D Lathia
- Department of Stem Cell Biology and Regenerative Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America
| | - Michael E Berens
- Cancer and Cell Biology Division, The Translational Genomics Research Institute, Phoenix, Arizona, United States of America
| | - Ulrika Andersson
- Department of Radiation Sciences, Faculty of Medicine, Umeå University, Umeå, Sweden
| | - Preetha Rajaraman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, United States of America
| | - Martha S Linet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, United States of America
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, United States of America
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Beatrice Melin
- Department of Radiation Sciences, Faculty of Medicine, Umeå University, Umeå, Sweden
| | - Melissa L Bondy
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America.
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Munir S, Freidin MB, Brain S, Williams FMK. Association of Raynaud's phenomenon with a polymorphism in the NOS1 gene. PLoS One 2018; 13:e0196279. [PMID: 29698501 PMCID: PMC5919461 DOI: 10.1371/journal.pone.0196279] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 04/10/2018] [Indexed: 12/29/2022] Open
Abstract
Background Raynaud’s phenomenon (RP) describes the phenomenon of recurrent vasospasm of digital arteries, associated with skin colour changes: pallor, cyanosis and erythema. Twin studies have indicated a genetic predisposition for RP; however, the precise aetiology of RP remains unknown. It is thought that genetic variation in temperature-responsive or vasospastic genes might underlie RP so performed a candidate gene study in a large, population based sample. We assessed the association between RP and single nucleotide polymorphisms (SNPs) in the TRPA1, TRPM8, CALCA, CALCB and NOS1 genes. Methods Analysis included a total of 4276 individuals from the TwinsUK database. RP status had been determined using validated, self-administered questionnaires and was diagnosed in 640 individuals (17.6%). 66 tag SNPs across the candidate genes were tested for association with RP status using a linear regression model, accounting for covariates. Adjustment was made for multiple testing. RegulomeDB and GTEx databases were used to assess possible functional effects of the polymorphisms. Results Nominally significant associations between RP and four SNPs in NOS1 and one in CALCB were identified. After permutation testing, rs527590 SNP in NOS1 passed the significance threshold. RegulomeDB scores indicated an unlikely functional effect of this variant, while the survey of the GTEx database found the SNP and several variants in linkage disequilibrium to be cis-eQTLs in skin. Conclusion Results indicate that RP is associated with variation in gene NOS1. This finding may be related to the observation that the significant SNP in NOS1 is known to exhibit functional influence on the gene expression.
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Affiliation(s)
- Sabrina Munir
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Susan Brain
- Section of Vascular Biology & Inflammation, BHF Centre for Cardiovascular Excellence, School of Cardiovascular Medicine and Sciences, King’s College London, London, United Kingdom
| | - Frances M. K. Williams
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
- * E-mail:
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