751
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Välimäki N, Kuisma H, Pasanen A, Heikinheimo O, Sjöberg J, Bützow R, Sarvilinna N, Heinonen HR, Tolvanen J, Bramante S, Tanskanen T, Auvinen J, Uimari O, Alkodsi A, Lehtonen R, Kaasinen E, Palin K, Aaltonen LA. Genetic predisposition to uterine leiomyoma is determined by loci for genitourinary development and genome stability. eLife 2018; 7:37110. [PMID: 30226466 PMCID: PMC6203434 DOI: 10.7554/elife.37110] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 09/17/2018] [Indexed: 02/06/2023] Open
Abstract
Uterine leiomyomas (ULs) are benign tumors that are a major burden to women’s health. A genome-wide association study on 15,453 UL cases and 392,628 controls was performed, followed by replication of the genomic risk in six cohorts. Effects of the risk alleles were evaluated in view of molecular and clinical characteristics. 22 loci displayed a genome-wide significant association. The likely predisposition genes could be grouped to two biological processes. Genes involved in genome stability were represented by TERT, TERC, OBFC1 - highlighting the role of telomere maintenance - TP53 and ATM. Genes involved in genitourinary development, WNT4, WT1, SALL1, MED12, ESR1, GREB1, FOXO1, DMRT1 and uterine stem cell marker antigen CD44, formed another strong subgroup. The combined risk contributed by the 22 loci was associated with MED12 mutation-positive tumors. The findings link genes for uterine development and genetic stability to leiomyomagenesis, and in part explain the more frequent occurrence of UL in women of African origin. Fibroids – also known as uterine leiomyomas, or myomas – are a very common form of benign tumor that grows in the muscle wall of the uterus. As many as 70% of women develop fibroids in their lifetime. About a fifth of women report symptoms including severe pain, heavy bleeding during periods and complications in pregnancy. In the United States, the cost of treating fibroids is estimated to be $34 billion each year. Despite the prevalence of fibroids in women, there are few treatments available. Drugs to target them have limited effect and often an invasive procedure such as surgery is needed to remove the tumors. However, a better understanding of the genetics of fibroids could lead to a way to develop better treatment options. Välimäki, Kuisma et al. used a genome-wide association study to seek out DNA variations that are more common in people with fibroids. Using data from the UK Biobank, the genomes of over 15,000 women with fibroids were analyzed against a control population of over 392,000 individuals. The analysis revealed 22 regions of the genome that were associated with fibroids. These regions included genes that may well contribute to fibroid development, such as the gene TP53, which influences the stability of the genome, and ESR1, which codes for a receptor for estrogen – a hormone known to play a role in the growth of fibroids. Variation in a set of genes known to control development of the female reproductive organs was also identified in women with fibroids. The findings are the result of the largest genome-wide association study on fibroids, revealing a set of genes that could influence the development of fibroids. Studying these genes could lead to more effective drug development to treat fibroids. Revealing this group of genes could also help to identify women at high risk of developing fibroids and help to prevent or manage the condition.
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Affiliation(s)
- Niko Välimäki
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Heli Kuisma
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Annukka Pasanen
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Oskari Heikinheimo
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jari Sjöberg
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ralf Bützow
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nanna Sarvilinna
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.,Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Institute of Biomedicine, Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland
| | - Hanna-Riikka Heinonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Jaana Tolvanen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Simona Bramante
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Tomas Tanskanen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Juha Auvinen
- Northern Finland Birth Cohorts' Project Center, Faculty of Medicine, University of Oulu, Oulu, Finland.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Outi Uimari
- Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Amjad Alkodsi
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Rainer Lehtonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Eevi Kaasinen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.,Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Kimmo Palin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Lauri A Aaltonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
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752
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Lumley T, Brody J, Peloso G, Morrison A, Rice K. FastSKAT: Sequence kernel association tests for very large sets of markers. Genet Epidemiol 2018; 42:516-527. [PMID: 29932245 PMCID: PMC6129408 DOI: 10.1002/gepi.22136] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/30/2018] [Accepted: 05/10/2018] [Indexed: 11/06/2022]
Abstract
The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n × n matrix, for n subjects) has computational complexity proportional to n3 . As SKAT is often used when n > 10 4 , this step becomes a major bottleneck in its use in practice. We therefore propose fastSKAT, a new computationally inexpensive but accurate approximations to the tail probabilities, in which the k largest eigenvalues of a weighted genotype covariance matrix or the largest singular values of a weighted genotype matrix are extracted, and a single term based on the Satterthwaite approximation is used for the remaining eigenvalues. While the method is not particularly sensitive to the choice of k, we also describe how to choose its value, and show how fastSKAT can automatically alert users to the rare cases where the choice may affect results. As well as providing faster implementation of SKAT, the new method also enables entirely new applications of SKAT that were not possible before; we give examples grouping variants by topologically associating domains, and comparing chromosome-wide association by class of histone marker.
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Affiliation(s)
| | - Jennifer Brody
- Cardiovascular Health Research Unit, University of Washington
| | - Gina Peloso
- Department of Biostatistics, Boston University
| | | | - Kenneth Rice
- Department of Biostatistics, University of Washington
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753
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Ferguson A, Lyall LM, Ward J, Strawbridge RJ, Cullen B, Graham N, Niedzwiedz CL, Johnston KJA, MacKay D, Biello SM, Pell JP, Cavanagh J, McIntosh AM, Doherty A, Bailey MES, Lyall DM, Wyse CA, Smith DJ. Genome-Wide Association Study of Circadian Rhythmicity in 71,500 UK Biobank Participants and Polygenic Association with Mood Instability. EBioMedicine 2018; 35:279-287. [PMID: 30120083 PMCID: PMC6154782 DOI: 10.1016/j.ebiom.2018.08.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 08/03/2018] [Accepted: 08/03/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Circadian rhythms are fundamental to health and are particularly important for mental wellbeing. Disrupted rhythms of rest and activity are recognised as risk factors for major depressive disorder and bipolar disorder. METHODS We conducted a genome-wide association study (GWAS) of low relative amplitude (RA), an objective measure of rest-activity cycles derived from the accelerometer data of 71,500 UK Biobank participants. Polygenic risk scores (PRS) for low RA were used to investigate potential associations with psychiatric phenotypes. OUTCOMES Two independent genetic loci were associated with low RA, within genomic regions for Neurofascin (NFASC) and Solute Carrier Family 25 Member 17 (SLC25A17). A secondary GWAS of RA as a continuous measure identified a locus within Meis Homeobox 1 (MEIS1). There were no significant genetic correlations between low RA and any of the psychiatric phenotypes assessed. However, PRS for low RA was significantly associated with mood instability across multiple PRS thresholds (at PRS threshold 0·05: OR = 1·02, 95% CI = 1·01-1·02, p = 9·6 × 10-5), and with major depressive disorder (at PRS threshold 0·1: OR = 1·03, 95% CI = 1·01-1·05, p = 0·025) and neuroticism (at PRS threshold 0·5: Beta = 0·02, 95% CI = 0·007-0·04, p = 0·021). INTERPRETATION Overall, our findings contribute new knowledge on the complex genetic architecture of circadian rhythmicity and suggest a putative biological link between disrupted circadian function and mood disorder phenotypes, particularly mood instability, but also major depressive disorder and neuroticism. FUNDING Medical Research Council (MR/K501335/1).
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Affiliation(s)
- Amy Ferguson
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK.
| | - Laura M Lyall
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Joey Ward
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Rona J Strawbridge
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK; Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Breda Cullen
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Nicholas Graham
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | | | | | - Daniel MacKay
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Stephany M Biello
- Institute of Neuroscience and Psychology, University of Glasgow, Scotland, UK
| | - Jill P Pell
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Jonathan Cavanagh
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Scotland, UK
| | - Aiden Doherty
- Big Data Institute, Nuffield Department of Population Health, BHF Centre of Research Excellence, University of Oxford, Oxford, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Mark E S Bailey
- School of Life Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Scotland, UK
| | - Donald M Lyall
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK
| | - Cathy A Wyse
- Department of Molecular and Cellular Therapeutics, Irish Centre for Vascular Biology, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland
| | - Daniel J Smith
- Institute of Health & Wellbeing, University of Glasgow, Scotland, UK.
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754
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Colodro-Conde L, Couvy-Duchesne B, Whitfield JB, Streit F, Gordon S, Kemper KE, Yengo L, Zheng Z, Trzaskowski M, de Zeeuw EL, Nivard MG, Das M, Neale RE, MacGregor S, Olsen CM, Whiteman DC, Boomsma DI, Yang J, Rietschel M, McGrath JJ, Medland SE, Martin NG. Association Between Population Density and Genetic Risk for Schizophrenia. JAMA Psychiatry 2018; 75:901-910. [PMID: 29936532 PMCID: PMC6142911 DOI: 10.1001/jamapsychiatry.2018.1581] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 05/04/2018] [Indexed: 12/13/2022]
Abstract
Importance Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas. Objective To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk. Design, Setting, and Participants Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and mendelian randomization (MR) were included. The study was conducted between 2016 and 2018. Exposures Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models. Main Outcomes and Measures Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested. Results The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia (r2 = 0.12%; P = 5.69 × 10-5), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P = 3.7 × 10-7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations. Conclusions and Relevance The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions.
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Affiliation(s)
| | - Baptiste Couvy-Duchesne
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | | | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Scott Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Eveline L. de Zeeuw
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Michel G. Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marjolijn Das
- Statistics Netherlands, The Hague, the Netherlands
- Centre for BOLD Cities, Leiden-Delft-Erasmus University, Rotterdam, the Netherlands
| | - Rachel E. Neale
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | | | | | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jian Yang
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - John J. McGrath
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
- Queensland Institute of Medical Research, The Park Centre for Mental Health, Wacol, Australia
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
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755
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A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci. Genetics 2018; 210:499-515. [PMID: 30108127 DOI: 10.1534/genetics.118.301479] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 08/08/2018] [Indexed: 12/31/2022] Open
Abstract
Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10-8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene-age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice.
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756
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Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet 2018; 50:1335-1341. [PMID: 30104761 PMCID: PMC6119127 DOI: 10.1038/s41588-018-0184-y] [Citation(s) in RCA: 656] [Impact Index Per Article: 109.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 06/21/2018] [Indexed: 12/13/2022]
Abstract
In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational cost, and hence is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.
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757
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Zhang Y, Qi G, Park JH, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat Genet 2018; 50:1318-1326. [DOI: 10.1038/s41588-018-0193-x] [Citation(s) in RCA: 158] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 05/18/2018] [Indexed: 12/23/2022]
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758
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Chen JA, Chen Z, Won H, Huang AY, Lowe JK, Wojta K, Yokoyama JS, Bensimon G, Leigh PN, Payan C, Shatunov A, Jones AR, Lewis CM, Deloukas P, Amouyel P, Tzourio C, Dartigues JF, Ludolph A, Boxer AL, Bronstein JM, Al-Chalabi A, Geschwind DH, Coppola G. Joint genome-wide association study of progressive supranuclear palsy identifies novel susceptibility loci and genetic correlation to neurodegenerative diseases. Mol Neurodegener 2018; 13:41. [PMID: 30089514 PMCID: PMC6083608 DOI: 10.1186/s13024-018-0270-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 06/29/2018] [Indexed: 11/21/2022] Open
Abstract
Background Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease for which the genetic contribution is incompletely understood. Methods We conducted a joint analysis of 5,523,934 imputed SNPs in two newly-genotyped progressive supranuclear palsy cohorts, primarily derived from two clinical trials (Allon davunetide and NNIPPS riluzole trials in PSP) and a previously published genome-wide association study (GWAS), in total comprising 1646 cases and 10,662 controls of European ancestry. Results We identified 5 associated loci at a genome-wide significance threshold P < 5 × 10− 8, including replication of 3 loci from previous studies and 2 novel loci at 6p21.1 and 12p12.1 (near RUNX2 and SLCO1A2, respectively). At the 17q21.31 locus, stepwise regression analysis confirmed the presence of multiple independent loci (localized near MAPT and KANSL1). An additional 4 loci were highly suggestive of association (P < 1 × 10− 6). We analyzed the genetic correlation with multiple neurodegenerative diseases, and found that PSP had shared polygenic heritability with Parkinson’s disease and amyotrophic lateral sclerosis. Conclusions In total, we identified 6 additional significant or suggestive SNP associations with PSP, and discovered genetic overlap with other neurodegenerative diseases. These findings clarify the pathogenesis and genetic architecture of PSP. Electronic supplementary material The online version of this article (10.1186/s13024-018-0270-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jason A Chen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA
| | - Zhongbo Chen
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King's College London, London, SE5 9RX, UK
| | - Hyejung Won
- Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Alden Y Huang
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA
| | - Jennifer K Lowe
- Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Kevin Wojta
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, 90095, USA
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, 94158, USA
| | - Gilbert Bensimon
- BESPIM, CHU-Nîmes, Nîmes, France.,Dept Pharmacologie Clinique, Pitié-Salpêtrière Hospital, AP-PH, Paris, France.,Pharmacology UPMC-Paris VI, Universite Paris-Sorbonne, Paris, France
| | - P Nigel Leigh
- Trafford Centre for Biomedical Research, Brighton and Sussex Medical School, University of Sussex, Falmer, Brighton, UK
| | - Christine Payan
- BESPIM, CHU-Nîmes, Nîmes, France.,Dept Pharmacologie Clinique, Pitié-Salpêtrière Hospital, AP-PH, Paris, France
| | - Aleksey Shatunov
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King's College London, London, SE5 9RX, UK
| | - Ashley R Jones
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King's College London, London, SE5 9RX, UK
| | - Cathryn M Lewis
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, and Department of Medical and Molecular Genetics, King's College London, London, SE5 8AF, UK
| | - Panagiotis Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Philippe Amouyel
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Risk Factor and molecular determinants of aging diseases, Labex-Distalz, F-59000, Lille, France
| | - Christophe Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, F-33000 Bordeaux, France
| | - Jean-Francois Dartigues
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, F-33000 Bordeaux, France
| | - Albert Ludolph
- Department of Neurology, University of Ulm, Oberer Eselsberg, Ulm, Germany
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, 94158, USA
| | - Jeff M Bronstein
- Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Ammar Al-Chalabi
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King's College London, London, SE5 9RX, UK
| | - Daniel H Geschwind
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA.,Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Giovanni Coppola
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA. .,Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. .,Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, 90095, USA. .,Departments of Psychiatry and Neurology, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E Young Dr. South, Gonda Bldg, Rm 1524, Los Angeles, CA, 90095, USA.
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759
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Tucci S, Vohr SH, McCoy RC, Vernot B, Robinson MR, Barbieri C, Nelson BJ, Fu W, Purnomo GA, Sudoyo H, Eichler EE, Barbujani G, Visscher PM, Akey JM, Green RE. Evolutionary history and adaptation of a human pygmy population of Flores Island, Indonesia. Science 2018; 361:511-516. [PMID: 30072539 PMCID: PMC6709593 DOI: 10.1126/science.aar8486] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 06/22/2018] [Indexed: 12/21/2022]
Abstract
Flores Island, Indonesia, was inhabited by the small-bodied hominin species Homo floresiensis, which has an unknown evolutionary relationship to modern humans. This island is also home to an extant human pygmy population. Here we describe genome-scale single-nucleotide polymorphism data and whole-genome sequences from a contemporary human pygmy population living on Flores near the cave where H. floresiensis was found. The genomes of Flores pygmies reveal a complex history of admixture with Denisovans and Neanderthals but no evidence for gene flow with other archaic hominins. Modern individuals bear the signatures of recent positive selection encompassing the FADS (fatty acid desaturase) gene cluster, likely related to diet, and polygenic selection acting on standing variation that contributed to their short-stature phenotype. Thus, multiple independent instances of hominin insular dwarfism occurred on Flores.
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Affiliation(s)
- Serena Tucci
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
- Department of Life Sciences and Biotechnologies, University of Ferrara, Ferrara, Italy
| | - Samuel H Vohr
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Rajiv C McCoy
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Benjamin Vernot
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Matthew R Robinson
- Department of Computational Biology, Génopode, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Génopode, Quatier Sorge, Lausanne, Switzerland
| | - Chiara Barbieri
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Switzerland
| | - Brad J Nelson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Wenqing Fu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Gludhug A Purnomo
- Genome Diversity and Diseases Laboratory, Eijkman Institute for Molecular Biology, Jakarta, Indonesia
| | - Herawati Sudoyo
- Genome Diversity and Diseases Laboratory, Eijkman Institute for Molecular Biology, Jakarta, Indonesia
- Department of Medical Biology, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Guido Barbujani
- Department of Life Sciences and Biotechnologies, University of Ferrara, Ferrara, Italy
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Joshua M Akey
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Richard E Green
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
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760
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Wedow R, Zacher M, Huibregtse BM, Harris KM, Domingue BW, Boardman JD. Education, Smoking, and Cohort Change: Forwarding a Multidimensional Theory of the Environmental Moderation of Genetic Effects. AMERICAN SOCIOLOGICAL REVIEW 2018; 83:802-832. [PMID: 31534265 PMCID: PMC6750804 DOI: 10.1177/0003122418785368] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We introduce a genetic correlation by environment interaction model [(rG)xE] which allows for social environmental moderation of the genetic relationship between two traits. To empirically demonstrate the significance of the (rG)xE perspective, we use genome wide information from respondents of the Health and Retirement Study (HRS; n = 8,181; birth years 1920-1959) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; n = 4,347; birth years 1974-1983) to examine whether the genetic correlation (rG) between education and smoking has increased over historical time. Genetic correlation estimates (rGHRS = -0.357; rGAdd Health = -0.729) support this hypothesis. Using polygenic scores for educational attainment, we show that this is not due to latent indicators of intellectual capacity, and we highlight the importance of education itself as an explanation of the increasing genetic correlation. Analyses based on contextual variation the milieus of the Add Health respondents corroborate key elements of the birth cohort analyses. We argue that the increasing overlap with respect to genes associated with educational attainment and smoking is a fundamentally social process involving complex process of selection based on observable behaviors that may be linked to genotype.
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Affiliation(s)
- Robbee Wedow
- Department of Sociology, University of Colorado, Boulder, Colorado
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
- Social Science Genetic Association Consortium (SSGAC)
- Direct correspondence to Robbee Wedow, Institute of Behavioral Science University of Colorado Boulder, 1440 15th Street, Boulder, CO 80302,
| | - Meghan Zacher
- Social Science Genetic Association Consortium (SSGAC)
- Department of Sociology, Harvard University, Cambridge, Massachusetts
| | - Brooke M. Huibregtse
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
- Department of Psychology, University of Colorado, Boulder, Colorado
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Benjamin W. Domingue
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Graduate School of Education, Stanford University, Stanford, California
| | - Jason D. Boardman
- Department of Sociology, University of Colorado, Boulder, Colorado
- Health and Society Program and Population Program, Institute of Behavioral Science, University of Colorado, Boulder, Colorado
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
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761
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Wang H, Liu X, Xiao Y, Xu M, Xing EP. Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model. Methods 2018; 145:33-40. [PMID: 29705210 PMCID: PMC6108326 DOI: 10.1016/j.ymeth.2018.04.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/14/2018] [Accepted: 04/23/2018] [Indexed: 01/27/2023] Open
Abstract
Genome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.
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Affiliation(s)
- Haohan Wang
- Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiang Liu
- School of Information and Communication Engineering, Beijing Univ. of Posts & Telecoms, Beijing, China
| | - Yunpeng Xiao
- Chongqing Engineering laboratory of Internet and Information Security, Chongqing Univ. of Posts & Telecoms, Chongqing, China
| | - Ming Xu
- Research Institute of Information Technology, Tsinghua University, Beijing, China
| | - Eric P. Xing
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
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762
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Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet 2018; 50:1234-1239. [PMID: 30061737 DOI: 10.1038/s41588-018-0171-3] [Citation(s) in RCA: 472] [Impact Index Per Article: 78.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 06/01/2018] [Indexed: 02/07/2023]
Abstract
To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of >1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans (GATA4, MYH6, NKX2-5, PITX2, TBX5)1, or near genes important for striated muscle function and integrity (for example, CFL2, MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and functional enrichment analyses also suggested that many of the putative atrial fibrillation genes act via cardiac structural remodeling, potentially in the form of an 'atrial cardiomyopathy'2, either during fetal heart development or as a response to stress in the adult heart.
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763
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Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma. Nat Genet 2018; 50:1067-1071. [PMID: 30054594 DOI: 10.1038/s41588-018-0176-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 06/13/2018] [Indexed: 12/14/2022]
Abstract
Intraocular pressure (IOP) is currently the sole modifiable risk factor for primary open-angle glaucoma (POAG), one of the leading causes of blindness worldwide1. Both IOP and POAG are highly heritable2. We report a combined analysis of participants from the UK Biobank (n = 103,914) and previously published data from the International Glaucoma Genetic Consortium (n = 29,578)3,4 that identified 101 statistically independent genome-wide-significant SNPs for IOP, 85 of which have not been previously reported4-12. We examined these SNPs in 11,018 glaucoma cases and 126,069 controls, and 53 SNPs showed evidence of association. Gene-based tests implicated an additional 22 independent genes associated with IOP. We derived an allele score based on the IOP loci and loci influencing optic nerve head morphology. In 1,734 people with advanced glaucoma and 2,938 controls, participants in the top decile of the allele score were at increased risk (odds ratio (OR) = 5.6; 95% confidence interval (CI): 4.1-7.6) of glaucoma relative to the bottom decile.
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764
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Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, Yengo L, Lloyd-Jones LR, Sidorenko J, Wu Y, McRae AF, Visscher PM, Zeng J, Yang J. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 2018; 9:2941. [PMID: 30054458 PMCID: PMC6063971 DOI: 10.1038/s41467-018-04951-w] [Citation(s) in RCA: 462] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 06/05/2018] [Indexed: 02/06/2023] Open
Abstract
Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.
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Affiliation(s)
- Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Luke R Lloyd-Jones
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China.
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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765
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Kim SK. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PLoS One 2018; 13:e0200785. [PMID: 30048462 PMCID: PMC6062019 DOI: 10.1371/journal.pone.0200785] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 07/03/2018] [Indexed: 01/03/2023] Open
Abstract
Low bone mineral density (BMD) leads to osteoporosis, and is a risk factor for bone fractures, including stress fractures. Using data from UK Biobank, a genome-wide association study identified 1,362 independent SNPs that clustered into 899 loci of which 613 are new. These data were used to train a genetic algorithm using 22,886 SNPs as predictors and showing a correlation with heel bone mineral density of 0.415. Combining this genetic algorithm with height, weight, age and sex resulted in a correlation with heel bone mineral density of 0.496. Individuals with low scores (2.2% of total) showed a change in BMD of -1.16 T-score units, an increase in risk for osteoporosis of 17.4 fold and an increase in risk for fracture of 1.87 fold. Genetic predictors could assist in the identification of individuals at risk for osteoporosis or fractures.
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Affiliation(s)
- Stuart K. Kim
- Department of Developmental Biology, Stanford University Medical Center, Stanford, California, United States of America
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766
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Awany D, Allali I, Chimusa ER. Tantalizing dilemma in risk prediction from disease scoring statistics. Brief Funct Genomics 2018; 18:211-219. [PMID: 30605512 PMCID: PMC6609536 DOI: 10.1093/bfgp/ely040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 08/17/2018] [Accepted: 11/29/2018] [Indexed: 02/01/2023] Open
Abstract
Over the past decade, human host genome-wide association studies (GWASs) have contributed greatly to our understanding of the impact of host genetics on phenotypes. Recently, the microbiome has been recognized as a complex trait in host genetic variation, leading to microbiome GWAS (mGWASs). For these, many different statistical methods and software tools have been developed for association mapping. Applications of these methods and tools have revealed several important findings; however, the establishment of causal factors and the direction of causality in the interactive role between human genetic polymorphisms, the microbiome and the host phenotypes are still a huge challenge. Here, we review disease scoring approaches in host and mGWAS and their underlying statistical methods and tools. We highlight the challenges in pinpointing the genetic-associated causal factors in host and mGWAS and discuss the role of multi-omic approach in disease scoring statistics that may provide a better understanding of human phenotypic variation by enabling further system biological experiment to establish causality.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
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767
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Prins BP, Mead TJ, Brody JA, Sveinbjornsson G, Ntalla I, Bihlmeyer NA, van den Berg M, Bork-Jensen J, Cappellani S, Van Duijvenboden S, Klena NT, Gabriel GC, Liu X, Gulec C, Grarup N, Haessler J, Hall LM, Iorio A, Isaacs A, Li-Gao R, Lin H, Liu CT, Lyytikäinen LP, Marten J, Mei H, Müller-Nurasyid M, Orini M, Padmanabhan S, Radmanesh F, Ramirez J, Robino A, Schwartz M, van Setten J, Smith AV, Verweij N, Warren HR, Weiss S, Alonso A, Arnar DO, Bots ML, de Boer RA, Dominiczak AF, Eijgelsheim M, Ellinor PT, Guo X, Felix SB, Harris TB, Hayward C, Heckbert SR, Huang PL, Jukema JW, Kähönen M, Kors JA, Lambiase PD, Launer LJ, Li M, Linneberg A, Nelson CP, Pedersen O, Perez M, Peters A, Polasek O, Psaty BM, Raitakari OT, Rice KM, Rotter JI, Sinner MF, Soliman EZ, Spector TD, Strauch K, Thorsteinsdottir U, Tinker A, Trompet S, Uitterlinden A, Vaartjes I, van der Meer P, Völker U, Völzke H, Waldenberger M, Wilson JG, Xie Z, Asselbergs FW, Dörr M, van Duijn CM, Gasparini P, Gudbjartsson DF, Gudnason V, Hansen T, Kääb S, Kanters JK, Kooperberg C, Lehtimäki T, Lin HJ, Lubitz SA, Mook-Kanamori DO, Conti FJ, Newton-Cheh CH, Rosand J, Rudan I, Samani NJ, Sinagra G, Smith BH, Holm H, Stricker BH, Ulivi S, Sotoodehnia N, Apte SS, van der Harst P, Stefansson K, Munroe PB, Arking DE, Lo CW, Jamshidi Y. Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6. Genome Biol 2018; 19:87. [PMID: 30012220 PMCID: PMC6048820 DOI: 10.1186/s13059-018-1457-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/23/2018] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. RESULTS Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. CONCLUSIONS Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.
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Affiliation(s)
- Bram P Prins
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St George's University of London, London, SW17 0RE, UK
- Department of Public Health and Primary Care, MRC/BHF Cardiovascular Epidemiology Unit, University of Cambridge, Strangeways Research Laboratory, Worts' Causeway, Cambridge, CB1 8RN, UK
| | - Timothy J Mead
- Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, Cleveland, OH, 44195, USA
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
| | | | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Nathan A Bihlmeyer
- Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Marten van den Berg
- Department of Medical Informatics Erasmus MC - University Medical Center, P.O. Box 2040, Rotterdam, 3000, CA, The Netherlands
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Stefania Cappellani
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", 34137, Trieste, Italy
| | - Stefan Van Duijvenboden
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK
| | - Nikolai T Klena
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - George C Gabriel
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Xiaoqin Liu
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Cagri Gulec
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Leanne M Hall
- Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
- Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Annamaria Iorio
- Cardiovascular Department, Ospedali Riuniti and University of Trieste, 34100, Trieste, Italy
| | - Aaron Isaacs
- CARIM School for Cardiovascular Diseases, Maastricht Center for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Universiteitssingel 60, Maastricht, 6229 ER, The Netherlands
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
| | - Honghuang Lin
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Ching-Ti Liu
- Biostatistics Department, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, 33520, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, 33014, Tampere, Finland
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Hao Mei
- Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Munich, Germany
- German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
| | - Michele Orini
- Mechanical Engineering Department, University College London, London, WC1E 6BT, UK
- Barts Heart Centre, St Bartholomews Hospital, London, EC1A 7BE, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF GCRC, Glasgow, G12 8TA, UK
| | - Farid Radmanesh
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Julia Ramirez
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Antonietta Robino
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", 34137, Trieste, Italy
| | - Molly Schwartz
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Jessica van Setten
- Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, the Netherlands
| | - Albert V Smith
- Icelandic Heart Association, 201, Kopavogur, Iceland
- Department of Cardiology, Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Niek Verweij
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Cardiovascular Research Center and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, 2114.0, USA
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, 17475, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475, Greifswald, Germany
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - David O Arnar
- deCODE genetics/Amgen, Inc., 101, Reykjavik, Iceland
- Department of Medicine, Landspitali University Hospital, 101, Reykjavik, Iceland
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rudolf A de Boer
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anna F Dominiczak
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Mark Eijgelsheim
- Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, 02114, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Division of Genomic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Stephan B Felix
- DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475, Greifswald, Germany
- Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Susan R Heckbert
- Cardiovascular Health Research Unit and the Department of Epidemiology, University of Washington, Seattle, WA, 98101, USA
| | - Paul L Huang
- Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, 02114, USA
| | - J W Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
- Interuniversity Cardiology Institute of The Netherlands, Utrecht, The Netherlands
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, 33521, Tampere, Finland
- Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, 33014, Tampere, Finland
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK
- Barts Heart Centre, St Bartholomews Hospital, London, EC1A 7BE, UK
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Man Li
- Division of Nephrology & Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT, 84109, USA
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, 2600, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200.0, Copenhagen, Denmark
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
- Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Marco Perez
- Division of Cardiovascular Medicine, Stanford University, Stanford, CA, 94305, USA
| | - Annette Peters
- German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, 98101, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20521, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20014, Turku, Finland
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences and Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Moritz F Sinner
- Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Munich, Germany
- German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Inc., 101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
| | - André Uitterlinden
- Human Genotyping Facility Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Peter van der Meer
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, 17475, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475, Greifswald, Germany
| | - Henry Völzke
- DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Melanie Waldenberger
- German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Research unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Zhijun Xie
- TCM Clinical Basis Institute, Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, China
| | - Folkert W Asselbergs
- Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, the Netherlands
- Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, UK
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475, Greifswald, Germany
- Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Paolo Gasparini
- Department of Medical, Surgical and Health Sciences, University of Trieste, 34100, Trieste, Italy
- Division of Experimental Genetics, Sidra Medical and Research Center, Doha, Qatar
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., 101, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, 101, Reykjavik, Iceland
| | - Vilmundur Gudnason
- Icelandic Heart Association, 201, Kopavogur, Iceland
- Department of Cardiology, Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Stefan Kääb
- Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Munich, Germany
- German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
| | - Jørgen K Kanters
- Laboratory of Experimental Cardiology, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, 33520, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, 33014, Tampere, Finland
| | - Henry J Lin
- The Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Steven A Lubitz
- Division of Genomic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
| | - Francesco J Conti
- Dubowitz Neuromuscular Centre, Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK
| | - Christopher H Newton-Cheh
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Center for Human Genetic Research and Cardiovascular Research Center, Harvard Medical School and Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jonathan Rosand
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
- Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Gianfranco Sinagra
- Cardiovascular Department, Ospedali Riuniti and University of Trieste, 34100, Trieste, Italy
| | - Blair H Smith
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., 101, Reykjavik, Iceland
| | - Bruno H Stricker
- Department of Epidemiology Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
| | - Sheila Ulivi
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", 34137, Trieste, Italy
| | - Nona Sotoodehnia
- Division of Cardiology, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, 98101, USA
| | - Suneel S Apte
- Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, Cleveland, OH, 44195, USA
| | - Pim van der Harst
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., 101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Cecilia W Lo
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Yalda Jamshidi
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St George's University of London, London, SW17 0RE, UK.
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St George's University of London, London, UK.
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768
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Elucidating the genetic basis of social interaction and isolation. Nat Commun 2018; 9:2457. [PMID: 29970889 PMCID: PMC6030100 DOI: 10.1038/s41467-018-04930-1] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 06/04/2018] [Indexed: 12/03/2022] Open
Abstract
The negative impacts of social isolation and loneliness on health are well documented. However, little is known about their possible biological determinants. In up to 452,302 UK Biobank study participants, we perform genome-wide association study analyses for loneliness and regular participation in social activities. We identify 15 genomic loci (P < 5 × 10−8) for loneliness, and demonstrate a likely causal association between adiposity and increased susceptibility to loneliness and depressive symptoms. Further loci were identified for regular attendance at a sports club or gym (N = 6 loci), pub or social club (N = 13) or religious group (N = 18). Across these traits there was strong enrichment for genes expressed in brain regions that control emotional expression and behaviour. We demonstrate aetiological mechanisms specific to each trait, in addition to identifying loci that are pleiotropic across multiple complex traits. Further study of these traits may identify novel modifiable risk factors associated with social withdrawal and isolation. Little is known about the genetic determinants of social isolation and loneliness despite their well-established importance for health. Here, using multi-trait GWAS, Day et al. identify 15 genomic loci for loneliness and further show a bidirectional causal relationship between BMI and loneliness by MR.
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769
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Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet 2018; 50:956-967. [PMID: 29955180 PMCID: PMC6248311 DOI: 10.1038/s41588-018-0154-4] [Citation(s) in RCA: 284] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 05/08/2018] [Indexed: 12/27/2022]
Abstract
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
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Affiliation(s)
- Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Clare Hall, University of Cambridge, Cambridge, UK.
| | - Ayellet V Segrè
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Ophthalmology and Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
| | - Martijn van de Bunt
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hualin S Xi
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Farhad Hormozdiari
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Anuar Konkashbaev
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eske M Derks
- Translational Neurogenomics Group, QIMR Berghofer, Brisbane, Queensland, Australia
| | - François Aguet
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jie Quan
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Dan L Nicolae
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Statistics, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Manolis Kellis
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gad Getz
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kristin G Ardlie
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
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770
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Abstract
Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method—capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al. bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.
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Affiliation(s)
- Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Steven Gazal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Armin P Schoech
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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771
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Loh PR, Genovese G, Handsaker RE, Finucane HK, Reshef YA, Palamara PF, Birmann BM, Talkowski ME, Bakhoum SF, McCarroll SA, Price AL. Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. Nature 2018; 559:350-355. [PMID: 29995854 PMCID: PMC6054542 DOI: 10.1038/s41586-018-0321-x] [Citation(s) in RCA: 233] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 05/16/2018] [Indexed: 02/06/2023]
Abstract
The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6-9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3-TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5-50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.
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Affiliation(s)
- Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Robert E Handsaker
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Schmidt Fellows Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir A Reshef
- Department of Computer Science, Harvard University, Cambridge, MA, USA
| | | | - Brenda M Birmann
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael E Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Samuel F Bakhoum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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772
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Ye M, Zhang P, Nie L. Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.01.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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773
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Guo Z, Chen D, Röder MS, Ganal MW, Schnurbusch T. Genetic dissection of pre-anthesis sub-phase durations during the reproductive spike development of wheat. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2018; 95:909-918. [PMID: 29906301 DOI: 10.1111/tpj.13998] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/09/2018] [Accepted: 04/17/2018] [Indexed: 05/18/2023]
Abstract
Flowering time is an important factor affecting grain yield in wheat. In this study, we divided reproductive spike development into eight sub-phases. These sub-phases have the potential to be delicately manipulated to increase grain yield. We measured 36 traits with regard to sub-phase durations, determined three grain yield-related traits in eight field environments and mapped 15 696 single nucleotide polymorphism (SNP, based on 90k Infinium chip and 35k Affymetrix chip) markers in 210 wheat genotypes. Phenotypic and genetic associations between grain yield traits and sub-phase durations showed significant consistency (Mantel test; r = 0.5377, P < 0.001). The shared quantitative trait loci (QTLs) revealed by the genome-wide association study suggested a close association between grain yield and sub-phase duration, which may be attributed to effects on spikelet initiation/spikelet number (double ridge to terminal spikelet stage, DR-TS) and assimilate accumulation (green anther to anthesis stage, GA-AN). Moreover, we observed that the photoperiod-sensitivity allele at the Ppd-D1 locus on chromosome 2D markedly extended all sub-phase durations, which may contribute to its positive effects on grain yield traits. The dwarfing allele at the Rht-D1 (chromosome 4D) locus altered the sub-phase duration and displayed positive effects on grain yield traits. Data for 30 selected genotypes (from among the original 210 genotypes) in the field displayed a close association with that from the greenhouse. Most importantly, this study demonstrated specific connections to grain yield in narrower time windows (i.e. the eight sub-phases), rather than the entire stem elongation phase as a whole.
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Affiliation(s)
- Zifeng Guo
- Independent Heisenberg Research Group Plant Architecture, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, 06466, Germany
| | - Dijun Chen
- Department of Breeding Research, Research Group Image Analysis, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, 06466, Germany
| | - Marion S Röder
- Research Group Gene and Genome Mapping, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, 06466, Germany
| | - Martin W Ganal
- TraitGenetics GmbH, Stadt Seeland, Gatersleben, 06466, OT, Germany
| | - Thorsten Schnurbusch
- Independent Heisenberg Research Group Plant Architecture, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, 06466, Germany
- Faculty of Natural Sciences III, Institute of Agricultural and Nutritional Sciences, Martin-Luther-University, Halle-Wittenberg, Halle, 06099, Germany
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774
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Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, Zhu Z, Kemper K, Yengo L, Zheng Z, Marioni RE, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun 2018; 9:2282. [PMID: 29891976 PMCID: PMC5995828 DOI: 10.1038/s41467-018-04558-1] [Citation(s) in RCA: 213] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 05/10/2018] [Indexed: 01/01/2023] Open
Abstract
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ([Formula: see text] for cis-eQTLs and [Formula: see text] for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.
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Affiliation(s)
- Ting Qi
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Kathryn Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China
| | | | - Riccardo E Marioni
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian J Deary
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia. .,The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China.
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775
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Peterson RE, Cai N, Dahl AW, Bigdeli TB, Edwards AC, Webb BT, Bacanu SA, Zaitlen N, Flint J, Kendler KS. Molecular Genetic Analysis Subdivided by Adversity Exposure Suggests Etiologic Heterogeneity in Major Depression. Am J Psychiatry 2018; 175:545-554. [PMID: 29495898 PMCID: PMC5988935 DOI: 10.1176/appi.ajp.2017.17060621] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The extent to which major depression is the outcome of a single biological mechanism or represents a final common pathway of multiple disease processes remains uncertain. Genetic approaches can potentially identify etiologic heterogeneity in major depression by classifying patients on the basis of their experience of major adverse events. METHOD Data are from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology (CONVERGE) project, a study of Han Chinese women with recurrent major depression aimed at identifying genetic risk factors for major depression in a rigorously ascertained cohort carefully assessed for key environmental risk factors (N=9,599). To detect etiologic heterogeneity, genome-wide association studies, heritability analyses, and gene-by-environment interaction analyses were performed. RESULTS Genome-wide association studies stratified by exposure to adversity revealed three novel loci associated with major depression only in study participants with no history of adversity. Significant gene-by-environment interactions were seen between adversity and genotype at all three loci, and 13.2% of major depression liability can be attributed to genome-wide interaction with adversity exposure. The genetic risk in major depression for participants who reported major adverse life events (27%) was partially shared with that in participants who did not (73%; genetic correlation=+0.64). Together with results from simulation studies, these findings suggest etiologic heterogeneity within major depression as a function of environmental exposures. CONCLUSIONS The genetic contributions to major depression may differ between women with and those without major adverse life events. These results have implications for the molecular dissection of major depression and other complex psychiatric and biomedical diseases.
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Affiliation(s)
- Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Na Cai
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, CB10 1SA Hinxton, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK
| | - Andy W. Dahl
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Tim B. Bigdeli
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- State University of New York Downstate Medical Center, Brooklyn, New York
| | - Alexis C. Edwards
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Bradley T. Webb
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Noah Zaitlen
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California
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776
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Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, Alexander GE, Chen Z, Going SB. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond) 2018; 42:1161-1176. [PMID: 29899525 PMCID: PMC6195860 DOI: 10.1038/s41366-018-0120-3] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 04/03/2018] [Accepted: 04/30/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND/OBJECTIVES Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual. SUBJECTS/METHODS We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA). RESULTS We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10-9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer's risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10-5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. CONCLUSIONS These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.
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Affiliation(s)
- Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
| | | | - Jennifer Bea
- Department of Medicine, University of Arizona, Tucson, AZ, USA
- Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA
| | - David O Garcia
- Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | | | - Lawrence J Mandarino
- Center for Disparities in Diabetes, Obesity and Metabolism, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Arizona, Tucson, AZ, USA
| | - Gene E Alexander
- Departments of Psychology and Psychiatry, Neuroscience and Physiological Sciences Interdisciplinary Programs, BIO5 Institute, and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Zhao Chen
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Scott B Going
- Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA
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777
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Tedja MS, Wojciechowski R, Hysi PG, Eriksson N, Furlotte NA, Verhoeven VJ, Iglesias AI, Meester-Smoor MA, Tompson SW, Fan Q, Khawaja AP, Cheng CY, Höhn R, Yamashiro K, Wenocur A, Grazal C, Haller T, Metspalu A, Wedenoja J, Jonas JB, Wang YX, Xie J, Mitchell P, Foster PJ, Klein BE, Klein R, Paterson AD, Hosseini SM, Shah RL, Williams C, Teo YY, Tham YC, Gupta P, Zhao W, Shi Y, Saw WY, Tai ES, Sim XL, Huffman JE, Polašek O, Hayward C, Bencic G, Rudan I, Wilson JF, Joshi PK, Tsujikawa A, Matsuda F, Whisenhunt KN, Zeller T, van der Spek PJ, Haak R, Meijers-Heijboer H, van Leeuwen EM, Iyengar SK, Lass JH, Hofman A, Rivadeneira F, Uitterlinden AG, Vingerling JR, Lehtimäki T, Raitakari OT, Biino G, Concas MP, Schwantes-An TH, Igo RP, Cuellar-Partida G, Martin NG, Craig JE, Gharahkhani P, Williams KM, Nag A, Rahi JS, Cumberland PM, Delcourt C, Bellenguez C, Ried JS, Bergen AA, Meitinger T, Gieger C, Wong TY, Hewitt AW, Mackey DA, Simpson CL, Pfeiffer N, Pärssinen O, Baird PN, Vitart V, Amin N, van Duijn CM, Bailey-Wilson JE, Young TL, Saw SM, Stambolian D, MacGregor S, Guggenheim JA, Tung JY, Hammond CJ, Klaver CC. Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nat Genet 2018; 50:834-848. [PMID: 29808027 PMCID: PMC5980758 DOI: 10.1038/s41588-018-0127-7] [Citation(s) in RCA: 193] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 03/26/2018] [Indexed: 12/18/2022]
Abstract
Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants increased the number of established independent signals from 37 to 161 and showed high genetic correlation between Europeans and Asians (>0.78). Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and also identified functional contributions to refractive-error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes implicate novel mechanisms such as rod-and-cone bipolar synaptic neurotransmission, anterior-segment morphology and angiogenesis. Thirty-one loci resided in or near regions transcribing small RNAs, thus suggesting a role for post-transcriptional regulation. Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade and delineate potential pathobiological molecular drivers.
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Affiliation(s)
- Milly S. Tedja
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Robert Wojciechowski
- Department of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Wilmer Eye Institute, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
| | - Pirro G. Hysi
- Section of Academic Ophthalmology, School of Life Course Sciences, King’s College London, London, UK
| | | | | | - Virginie J.M. Verhoeven
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Adriana I. Iglesias
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Magda A. Meester-Smoor
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stuart W. Tompson
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Qiao Fan
- Centre for Quantitative Medicine, DUKE-National University of Singapore, Singapore
| | - Anthony P. Khawaja
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Ching-Yu Cheng
- Centre for Quantitative Medicine, DUKE-National University of Singapore, Singapore
- Ocular Epidemiology Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - René Höhn
- Department of Ophthalmology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
- Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany
| | - Kenji Yamashiro
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Adam Wenocur
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Clare Grazal
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Toomas Haller
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Juho Wedenoja
- Department of Ophthalmology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jost B. Jonas
- Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Mannheim, Germany
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jing Xie
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Paul Mitchell
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Paul J. Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Barbara E.K. Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Andrew D. Paterson
- Program in Genetics and Genome Biology, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada
| | - S. Mohsen Hosseini
- Program in Genetics and Genome Biology, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada
| | - Rupal L. Shah
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, UK
| | - Cathy Williams
- Department of Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Yik Ying Teo
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
- Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore
| | - Yih Chung Tham
- Ocular Epidemiology Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Preeti Gupta
- Department of Health Service Research, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Wanting Zhao
- Centre for Quantitative Medicine, DUKE-National University of Singapore, Singapore
- Statistics Support Platform, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yuan Shi
- Statistics Support Platform, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Woei-Yuh Saw
- Life Sciences Institute, National University of Singapore, Singapore
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore
| | - Xue Ling Sim
- Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore
| | - Jennifer E. Huffman
- MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ozren Polašek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Goran Bencic
- Department of Ophthalmology, Sisters of Mercy University Hospital, Zagreb, Croatia
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - James F. Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | | | | | | | - Peter K. Joshi
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Akitaka Tsujikawa
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kristina N. Whisenhunt
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Tanja Zeller
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | | | - Roxanna Haak
- Department of Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Hanne Meijers-Heijboer
- Department of Clinical Genetics, Academic Medical Center, Amsterdam, The Netherlands
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Elisabeth M. van Leeuwen
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Sudha K. Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University and University Hospitals Eye Institute, Cleveland, Ohio, USA
- Department of Genetics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jonathan H. Lass
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University and University Hospitals Eye Institute, Cleveland, Ohio, USA
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Harvard T.HChan School of Public Health, Boston, Massachusetts, USA
- Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, the Hague, the Netherlands
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, the Hague, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, the Hague, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Terho Lehtimäki
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere
- Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere, Tampere, Finland
| | - Olli T. Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Sassari, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Tae-Hwi Schwantes-An
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Robert P. Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | | | - Nicholas G. Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Jamie E. Craig
- Department of Ophthalmology, Flinders University, Adelaide, Australia
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Katie M. Williams
- Section of Academic Ophthalmology, School of Life Course Sciences, King’s College London, London, UK
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Jugnoo S. Rahi
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Ulverscroft Vision Research Group, University College London, London, UK
| | | | - Cécile Delcourt
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, team LEHA, UMR 1219, F-33000 Bordeaux, France
| | - Céline Bellenguez
- Institut Pasteur de Lille, Lille, France
- Inserm, U1167, RID-AGE - Risk factors and molecular determinants of aging-related diseases, Lille, France
- Université de Lille, U1167 - Excellence Laboratory LabEx DISTALZ, Lille, France
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Arthur A. Bergen
- Department of Clinical Genetics, Academic Medical Center, Amsterdam, The Netherlands
- Department of Ophthalmology, Academic Medical Center, Amsterdam, The Netherlands
- The Netherlands Institute for Neurosciences (NIN-KNAW), Amsterdam, The Netherlands
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Tien Yin Wong
- Academic Medicine Research Institute, Singapore
- Retino Center, Singapore National Eye Centre, Singapore, Singapore
| | - Alex W. Hewitt
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Ophthalmology, Menzies Institute of Medical Research, University of Tasmania, Hobart, Australia
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia
| | - David A. Mackey
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Ophthalmology, Menzies Institute of Medical Research, University of Tasmania, Hobart, Australia
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia
| | - Claire L. Simpson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, Memphis, Tenessee
| | - Norbert Pfeiffer
- Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany
| | - Olavi Pärssinen
- Department of Ophthalmology, Central Hospital of Central Finland, Jyväskylä, Finland
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Paul N. Baird
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Terri L. Young
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Seang-Mei Saw
- Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore
- Myopia Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Dwight Stambolian
- Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | | | - Christopher J. Hammond
- Section of Academic Ophthalmology, School of Life Course Sciences, King’s College London, London, UK
| | - Caroline C.W. Klaver
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
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778
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Khawaja AP, Cooke Bailey JN, Wareham NJ, Scott RA, Simcoe M, Igo RP, Song YE, Wojciechowski R, Cheng CY, Khaw PT, Pasquale LR, Haines JL, Foster PJ, Wiggs JL, Hammond CJ, Hysi PG. Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma. Nat Genet 2018; 50:778-782. [PMID: 29785010 PMCID: PMC5985943 DOI: 10.1038/s41588-018-0126-8] [Citation(s) in RCA: 169] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 03/27/2018] [Indexed: 01/10/2023]
Abstract
Glaucoma is the leading cause of irreversible blindness globally 1 . Despite its gravity, the disease is frequently undiagnosed in the community 2 . Raised intraocular pressure (IOP) is the most important risk factor for primary open-angle glaucoma (POAG)3,4. Here we present a meta-analysis of 139,555 European participants, which identified 112 genomic loci associated with IOP, 68 of which are novel. These loci suggest a strong role for angiopoietin-receptor tyrosine kinase signaling, lipid metabolism, mitochondrial function and developmental processes underlying risk for elevated IOP. In addition, 48 of these loci were nominally associated with glaucoma in an independent cohort, 14 of which were significant at a Bonferroni-corrected threshold. Regression-based glaucoma-prediction models had an area under the receiver operating characteristic curve (AUROC) of 0.76 in US NEIGHBORHOOD study participants and 0.74 in independent glaucoma cases from the UK Biobank. Genetic-prediction models for POAG offer an opportunity to target screening and timely therapy to individuals most at risk.
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Affiliation(s)
- Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jessica N Cooke Bailey
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Mark Simcoe
- Department of Ophthalmology, King's College London, St. Thomas' Hospital, London, UK
- Department of Twin Research & Genetic Epidemiology, King's College London, St. Thomas' Hospital, London, UK
| | - Robert P Igo
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Robert Wojciechowski
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Johns Hopkins Wilmer Eye Institute, Baltimore, MD, USA
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye-ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Peng T Khaw
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Louis R Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paul J Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, UK
| | - Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA.
| | - Chris J Hammond
- Department of Ophthalmology, King's College London, St. Thomas' Hospital, London, UK.
| | - Pirro G Hysi
- Department of Ophthalmology, King's College London, St. Thomas' Hospital, London, UK.
- Department of Twin Research & Genetic Epidemiology, King's College London, St. Thomas' Hospital, London, UK.
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779
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Ramírez J, Duijvenboden SV, Ntalla I, Mifsud B, Warren HR, Tzanis E, Orini M, Tinker A, Lambiase PD, Munroe PB. Thirty loci identified for heart rate response to exercise and recovery implicate autonomic nervous system. Nat Commun 2018; 9:1947. [PMID: 29769521 PMCID: PMC5955978 DOI: 10.1038/s41467-018-04148-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/06/2018] [Indexed: 12/25/2022] Open
Abstract
Impaired capacity to increase heart rate (HR) during exercise (ΔHRex), and a reduced rate of recovery post-exercise (ΔHRrec) are associated with higher cardiovascular mortality rates. Currently, the genetic basis of both phenotypes remains to be elucidated. We conduct genome-wide association studies (GWASs) for ΔHRex and ΔHRrec in ~40,000 individuals, followed by replication in ~27,000 independent samples, all from UK Biobank. Six and seven single-nucleotide polymorphisms for ΔHRex and ΔHRrec, respectively, formally replicate. In a full data set GWAS, eight further loci for ΔHRex and nine for ΔHRrec are genome-wide significant (P ≤ 5 × 10−8). In total, 30 loci are discovered, 8 being common across traits. Processes of neural development and modulation of adrenergic activity by the autonomic nervous system are enriched in these results. Our findings reinforce current understanding of HR response to exercise and recovery and could guide future studies evaluating its contribution to cardiovascular risk prediction. Genome-wide association studies have identified multiple loci for resting heart rate (HR) but the genetic factors associated with HR increase during and HR recovery after exercise are less well studied. Here, the authors examine both traits in a two-stage GWAS design in up to 67,257 individuals from UK Biobank.
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Affiliation(s)
- Julia Ramírez
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK
| | - Stefan van Duijvenboden
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK
| | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Borbala Mifsud
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Evan Tzanis
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Michele Orini
- Barts Heart Centre, St Bartholomews Hospital, London, EC1A 7BE, UK.,Mechanical Engineering Department, University College London, London, WC1E 6BT, UK
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK. .,Barts Heart Centre, St Bartholomews Hospital, London, EC1A 7BE, UK.
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK. .,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
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780
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Clifton EAD, Perry JRB, Imamura F, Lotta LA, Brage S, Forouhi NG, Griffin SJ, Wareham NJ, Ong KK, Day FR. Genome-wide association study for risk taking propensity indicates shared pathways with body mass index. Commun Biol 2018; 1:36. [PMID: 30271922 PMCID: PMC6123697 DOI: 10.1038/s42003-018-0042-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 03/14/2018] [Indexed: 01/08/2023] Open
Abstract
Risk-taking propensity is a trait of significant public health relevance but few specific genetic factors are known. Here we perform a genome-wide association study of self-reported risk-taking propensity among 436,236 white European UK Biobank study participants. We identify genome-wide associations at 26 loci (P < 5 × 10-8), 24 of which are novel, implicating genes enriched in the GABA and GABA receptor pathways. Modelling the relationship between risk-taking propensity and body mass index (BMI) using Mendelian randomisation shows a positive association (0.25 approximate SDs of BMI (SE: 0.06); P = 6.7 × 10-5). The impact of individual SNPs is heterogeneous, indicating a complex relationship arising from multiple shared pathways. We identify positive genetic correlations between risk-taking and waist-hip ratio, childhood obesity, ever smoking, attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia, alongside a negative correlation with women's age at first birth. These findings highlight that behavioural pathways involved in risk-taking propensity may play a role in obesity, smoking and psychiatric disorders.
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Affiliation(s)
- Emma A D Clifton
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK.
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Simon J Griffin
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK.
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781
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Maier RM, Visscher PM, Robinson MR, Wray NR. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med 2018; 48:1055-1067. [PMID: 28847336 PMCID: PMC6088780 DOI: 10.1017/s0033291717002318] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 07/16/2017] [Accepted: 07/18/2017] [Indexed: 01/09/2023]
Abstract
The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.
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Affiliation(s)
- R. M. Maier
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - P. M. Visscher
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - M. R. Robinson
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - N. R. Wray
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
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782
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Wolford BN, Willer CJ, Surakka I. Electronic health records: the next wave of complex disease genetics. Hum Mol Genet 2018; 27:R14-R21. [PMID: 29547983 PMCID: PMC5946915 DOI: 10.1093/hmg/ddy081] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 02/28/2018] [Accepted: 03/02/2018] [Indexed: 12/29/2022] Open
Abstract
The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. We enumerate the many existing biobanks with EHRs linked to genetic data, many of which are available to researchers via application and contain sample sizes >50 000. We also discuss the computational and statistical considerations for analysis of such large datasets including mixed models, phenotype curation and cloud computing. Finally, we demonstrate how genome-wide association studies and phenome-wide association studies have identified novel genetic findings for complex diseases, specifically cardiometabolic traits. As more researchers employ innovative hypotheses and analysis approaches to study EHR-linked biobanks, we anticipate a richer understanding of the genetic etiology of complex diseases.
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Affiliation(s)
- Brooke N Wolford
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Center for Statistical Genetics, Ann Arbor, MI, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Center for Statistical Genetics, Ann Arbor, MI, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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783
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Derkach A, Zhang H, Chatterjee N. Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies. Bioinformatics 2018; 34:1506-1513. [PMID: 29194474 PMCID: PMC5925788 DOI: 10.1093/bioinformatics/btx770] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 10/02/2017] [Accepted: 11/27/2017] [Indexed: 12/18/2022] Open
Abstract
Motivation Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus. Results We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using these simplified power calculations, we develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from genome-wide association studies with rare variants. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power. Availability and implementation A shiny application that allows a variety of Power Analysis of GEnetic AssociatioN Tests (PAGEANT), in R is made publicly available at https://andrewhaoyu.shinyapps.io/PAGEANT/. Contact nilanjan@jhu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andriy Derkach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Haoyu Zhang
- Department of Biostatistics, Bloomberg School of Public Health, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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784
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Wang H, Aragam B, Xing EP. Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. Methods 2018; 145:2-9. [PMID: 29705212 DOI: 10.1016/j.ymeth.2018.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/14/2018] [Accepted: 04/23/2018] [Indexed: 10/17/2022] Open
Abstract
A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.
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Affiliation(s)
- Haohan Wang
- Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Bryon Aragam
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Eric P Xing
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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785
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Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data. Proc Natl Acad Sci U S A 2018; 115:E4970-E4979. [PMID: 29686100 PMCID: PMC5984483 DOI: 10.1073/pnas.1707388115] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We propose genetic instrumental variable (GIV) regression—a method that controls for pleiotropic effects of genes on two variables. GIV regression is broadly applicable to study outcomes for which polygenic scores from large-scale genome-wide association studies are available. We explore the performance of GIV regression in the presence of pleiotropy across a range of scenarios and find that it yields more accurate estimates than alternative approaches such as ordinary least-squares regression or Mendelian randomization. When GIV regression is combined with proper controls for purely environmental sources of bias (e.g., using control variables and sibling fixed effects), it improves our understanding of the causal relationships between genetically correlated variables. Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.
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786
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St Pourcain B, Eaves LJ, Ring SM, Fisher SE, Medland S, Evans DM, Davey Smith G. Developmental Changes Within the Genetic Architecture of Social Communication Behavior: A Multivariate Study of Genetic Variance in Unrelated Individuals. Biol Psychiatry 2018; 83:598-606. [PMID: 29100628 PMCID: PMC5855319 DOI: 10.1016/j.biopsych.2017.09.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 08/01/2017] [Accepted: 09/17/2017] [Indexed: 12/04/2022]
Abstract
BACKGROUND Recent analyses of trait-disorder overlap suggest that psychiatric dimensions may relate to distinct sets of genes that exert maximum influence during different periods of development. This includes analyses of social communication difficulties that share, depending on their developmental stage, stronger genetic links with either autism spectrum disorder or schizophrenia. We developed a multivariate analysis framework in unrelated individuals to model directly the developmental profile of genetic influences contributing to complex traits, such as social communication difficulties, during an approximately 10-year period spanning childhood and adolescence. METHODS Longitudinally assessed quantitative social communication problems (N ≤ 5551) were studied in participants from a United Kingdom birth cohort (Avon Longitudinal Study of Parents and Children; age range, 8-17 years). Using standardized measures, genetic architectures were investigated with novel multivariate genetic-relationship-matrix structural equation models incorporating whole-genome genotyping information. Analogous to twin research, genetic-relationship-matrix structural equation models included Cholesky decomposition, common pathway, and independent pathway models. RESULTS A two-factor Cholesky decomposition model described the data best. One genetic factor was common to Social Communication Disorder Checklist measures across development; the other accounted for independent variation at 11 years and later, consistent with distinct developmental profiles in trait-disorder overlap. Importantly, genetic factors operating at 8 years explained only approximately 50% of genetic variation at 17 years. CONCLUSIONS Using latent factor models, we identified developmental changes in the genetic architecture of social communication difficulties that enhance the understanding of autism spectrum disorder- and schizophrenia-related dimensions. More generally, genetic-relationship-matrix structural equation models present a framework for modeling shared genetic etiologies between phenotypes and can provide prior information with respect to patterns and continuity of trait-disorder overlap.
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Affiliation(s)
- Beate St Pourcain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.
| | - Lindon J Eaves
- Department of Human and Molecular Genetics, Institute for Psychiatric and Behavioral Genetics, Commonwealth University School of Medicine, Richmond, Virginia
| | - Susan M Ring
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Sarah Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Queensland, Australia
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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787
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Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM. Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio. Genetics 2018; 208:1397-1408. [PMID: 29429966 PMCID: PMC5887138 DOI: 10.1534/genetics.117.300360] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 01/25/2018] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.
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Affiliation(s)
- Luke R Lloyd-Jones
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
| | - Matthew R Robinson
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
- Department of Computational Biology, University of Lausanne, CH-1015, Switzerland
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
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788
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Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, Kvale MN, Kwok PY, Schaefer C, Krauss RM, Iribarren C, Risch N. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet 2018; 50:401-413. [PMID: 29507422 PMCID: PMC5942247 DOI: 10.1038/s41588-018-0064-5] [Citation(s) in RCA: 188] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 01/19/2018] [Indexed: 12/16/2022]
Abstract
A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
| | | | - Tanushree Haldar
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Dilrini K Ranatunga
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Marisa W Medina
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Mark N Kvale
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Pui-Yan Kwok
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Ronald M Krauss
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Neil Risch
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. .,Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA.
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789
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Genetic study links components of the autonomous nervous system to heart-rate profile during exercise. Nat Commun 2018; 9:898. [PMID: 29497042 PMCID: PMC5832790 DOI: 10.1038/s41467-018-03395-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 02/09/2018] [Indexed: 01/01/2023] Open
Abstract
Heart rate (HR) responds to exercise by increasing during exercise and recovering after exercise. As such, HR is an important predictor of mortality that researchers believe is modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences has yet to be explained. Here, we perform a large-scale genome-wide analysis of HR increase and HR recovery in 58,818 UK Biobank individuals. Twenty-five independent SNPs in 23 loci are identified to be associated (p < 8.3 × 10−9) with HR increase or HR recovery. A total of 36 candidate causal genes are prioritized that are enriched for pathways related to neuron biology. No evidence is found of a causal relationship with mortality or cardiovascular diseases. However, a nominal association with parental lifespan requires further study. In conclusion, the findings provide new biological and clinical insight into the mechanistic underpinnings of HR response to exercise. The results also underscore the role of the autonomous nervous system in HR recovery. Response of the heart rate (HR) to exercise is associated with cardiac fitness and risk of cardiac death. Here, in a genome-wide association study, Verweij et al. identify 23 loci for HR increase during exercise or HR recovery, and highlight pleiotropy with blood pressure by polygenic risk score analysis.
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790
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A rare missense variant in NR1H4 associates with lower cholesterol levels. Commun Biol 2018; 1:14. [PMID: 30271901 PMCID: PMC6123719 DOI: 10.1038/s42003-018-0015-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 01/11/2018] [Indexed: 12/12/2022] Open
Abstract
Searching for novel sequence variants associated with cholesterol levels is of particular interest due to the causative role of non-HDL cholesterol levels in cardiovascular disease. Through whole-genome sequencing of 15,220 Icelanders and imputation of the variants identified, we discovered a rare missense variant in NR1H4 (R436H) associating with lower levels of total cholesterol (effect = −0.47 standard deviations or −0.55 mmol L−1, p = 4.21 × 10−10, N = 150,211). Importantly, NR1H4 R436H also associates with lower levels of non-HDL cholesterol and, consistent with this, protects against coronary artery disease. NR1H4 encodes FXR that regulates bile acid homeostasis, however, we do not detect a significant association between R436H and biological markers of liver function. Transcriptional profiling of hepatocytes carrying R436H shows that it is not a loss-of-function variant. Rather, we observe changes in gene expression compatible with effects on lipids. These findings highlight the role of FXR in regulation of cholesterol levels in humans. Aimee Deaton et al. identify a rare missense variant in the bile acid receptor gene NR1H4, which is associated with lower levels of total cholesterol in the Icelandic population. Hepatocytes expressing the missense variant showed altered expression of a small number of genes, with enrichment in lipid-related pathways.
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791
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Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, Iwata N, Ikegawa S, Hirata M, Matsuda K, Kubo M, Okada Y, Kamatani Y. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet 2018; 50:390-400. [PMID: 29403010 DOI: 10.1038/s41588-018-0047-6] [Citation(s) in RCA: 474] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 12/21/2017] [Indexed: 12/18/2022]
Abstract
Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 × 10-8), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity, represented by the genetic links among clinical measurements, complex diseases, and relevant cell types. Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements' relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis.
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Affiliation(s)
- Masahiro Kanai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Nana Matoba
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masashi Ikeda
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Makoto Hirata
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan. .,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan.
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
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792
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Tanskanen T, van den Berg L, Välimäki N, Aavikko M, Ness-Jensen E, Hveem K, Wettergren Y, Bexe Lindskog E, Tõnisson N, Metspalu A, Silander K, Orlando G, Law PJ, Tuupanen S, Gylfe AE, Hänninen UA, Cajuso T, Kondelin J, Sarin AP, Pukkala E, Jousilahti P, Salomaa V, Ripatti S, Palotie A, Järvinen H, Renkonen-Sinisalo L, Lepistö A, Böhm J, Mecklin JP, Al-Tassan NA, Palles C, Martin L, Barclay E, Tenesa A, Farrington S, Timofeeva MN, Meyer BF, Wakil SM, Campbell H, Smith CG, Idziaszczyk S, Maughan TS, Kaplan R, Kerr R, Kerr D, Buchanan DD, Win AK, Hopper J, Jenkins M, Newcomb PA, Gallinger S, Conti D, Schumacher FR, Casey G, Cheadle JP, Dunlop MG, Tomlinson IP, Houlston RS, Palin K, Aaltonen LA. Genome-wide association study and meta-analysis in Northern European populations replicate multiple colorectal cancer risk loci. Int J Cancer 2018; 142:540-546. [PMID: 28960316 PMCID: PMC6383773 DOI: 10.1002/ijc.31076] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 08/04/2017] [Accepted: 09/01/2017] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies have been successful in elucidating the genetic basis of colorectal cancer (CRC), but there remains unexplained variability in genetic risk. To identify new risk variants and to confirm reported associations, we conducted a genome-wide association study in 1,701 CRC cases and 14,082 cancer-free controls from the Finnish population. A total of 9,068,015 genetic variants were imputed and tested, and 30 promising variants were studied in additional 11,647 cases and 12,356 controls of European ancestry. The previously reported association between the single-nucleotide polymorphism (SNP) rs992157 (2q35) and CRC was independently replicated (p = 2.08 × 10-4 ; OR, 1.14; 95% CI, 1.06-1.23), and it was genome-wide significant in combined analysis (p = 1.50 × 10-9 ; OR, 1.12; 95% CI, 1.08-1.16). Variants at 2q35, 6p21.2, 8q23.3, 8q24.21, 10q22.3, 10q24.2, 11q13.4, 11q23.1, 14q22.2, 15q13.3, 18q21.1, 20p12.3 and 20q13.33 were associated with CRC in the Finnish population (false discovery rate < 0.1), but new risk loci were not found. These results replicate the effects of multiple loci on the risk of CRC and identify shared risk alleles between the Finnish population isolate and outbred populations.
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Affiliation(s)
- Tomas Tanskanen
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Linda van den Berg
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Niko Välimäki
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Mervi Aavikko
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Eivind Ness-Jensen
- HUNT Research Centre, Department of Public Health, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kristian Hveem
- HUNT Research Centre, Department of Public Health, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Yvonne Wettergren
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Elinor Bexe Lindskog
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Neeme Tõnisson
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Kaisa Silander
- National Institute for Health and Welfare, Helsinki, Finland
| | - Giulia Orlando
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Philip J. Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Sari Tuupanen
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Alexandra E. Gylfe
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Ulrika A. Hänninen
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Tatiana Cajuso
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Johanna Kondelin
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Eero Pukkala
- Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland
- Faculty of Social Sciences, University of Tampere, Tampere, Finland
| | | | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Heikki Järvinen
- Department of Surgery, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
| | | | - Anna Lepistö
- Department of Surgery, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
| | - Jan Böhm
- Department of Pathology, Central Finland Central Hospital, Jyväskylä, Finland
| | - Jukka-Pekka Mecklin
- Department of Surgery, Jyväskylä Central Hospital, University of Eastern Finland, Jyväskylä, Finland
| | - Nada A. Al-Tassan
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Claire Palles
- Wellcome Trust Centre for Human Genetics and NIHR Comprehensive Biomedical Research Centre, Oxford, UK
| | - Lynn Martin
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Ella Barclay
- Wellcome Trust Centre for Human Genetics and NIHR Comprehensive Biomedical Research Centre, Oxford, UK
| | - Albert Tenesa
- Colon Cancer Genetics Group, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK
- The Roslin Institute, University of Edinburgh, Easter Bush, Roslin, UK
| | - Susan Farrington
- Colon Cancer Genetics Group, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK
| | - Maria N. Timofeeva
- Colon Cancer Genetics Group, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK
| | - Brian F. Meyer
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Salma M. Wakil
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Christopher G. Smith
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Shelley Idziaszczyk
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Tim S. Maughan
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | | | - Rachel Kerr
- Oxford Cancer Centre, Department of Oncology, University of Oxford, Churchill Hospital, Oxford, UK
| | - David Kerr
- Nuffield Department of Clinical Laboratory Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Aung K. Win
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - John Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark Jenkins
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Polly A. Newcomb
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Steve Gallinger
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - David Conti
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fredrick R. Schumacher
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jeremy P. Cheadle
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Malcolm G. Dunlop
- Colon Cancer Genetics Group, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK
| | - Ian P. Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Richard S. Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Kimmo Palin
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Lauri A. Aaltonen
- Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
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793
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Abstract
Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.
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Affiliation(s)
- Russell Thomson
- Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia.
| | - Rebekah McWhirter
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
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794
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Nielsen JB, Fritsche LG, Zhou W, Teslovich TM, Holmen OL, Gustafsson S, Gabrielsen ME, Schmidt EM, Beaumont R, Wolford BN, Lin M, Brummett CM, Preuss MH, Refsgaard L, Bottinger EP, Graham SE, Surakka I, Chu Y, Skogholt AH, Dalen H, Boyle AP, Oral H, Herron TJ, Kitzman J, Jalife J, Svendsen JH, Olesen MS, Njølstad I, Løchen ML, Baras A, Gottesman O, Marcketta A, O'Dushlaine C, Ritchie MD, Wilsgaard T, Loos RJF, Frayling TM, Boehnke M, Ingelsson E, Carey DJ, Dewey FE, Kang HM, Abecasis GR, Hveem K, Willer CJ. Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development. Am J Hum Genet 2018; 102:103-115. [PMID: 29290336 DOI: 10.1016/j.ajhg.2017.12.003] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 12/04/2017] [Indexed: 01/03/2023] Open
Abstract
Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. The pathogenesis of AF remains poorly understood, which contributes to the current lack of highly effective treatments. To understand the genetic variation and biology underlying AF, we undertook a genome-wide association study (GWAS) of 6,337 AF individuals and 61,607 AF-free individuals from Norway, including replication in an additional 30,679 AF individuals and 278,895 AF-free individuals. Through genotyping and dense imputation mapping from whole-genome sequencing, we tested almost nine million genetic variants across the genome and identified seven risk loci, including two novel loci. One novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 × 10-18) comprised intronic and several highly correlated missense variants situated in the I-, A-, and M-bands of titin, which is the largest protein in humans and responsible for the passive elasticity of heart and skeletal muscle. The other novel locus (lead SNV rs56202902; p = 1.54 × 10-11) covered a large, gene-dense chromosome 1 region that has previously been linked to cardiac conduction. Pathway and functional enrichment analyses suggested that many AF-associated genetic variants act through a mechanism of impaired muscle cell differentiation and tissue formation during fetal heart development.
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Affiliation(s)
- Jonas B Nielsen
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lars G Fritsche
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Wei Zhou
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Oddgeir L Holmen
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Cardiology, St. Olav's University Hospital, Trondheim 7030, Norway
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
| | - Maiken E Gabrielsen
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Ellen M Schmidt
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robin Beaumont
- Royal Devon & Exeter NHS Foundation Trust and University of Exeter Barrack Road Exeter, Exeter EX2 5WD, UK
| | - Brooke N Wolford
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maoxuan Lin
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chad M Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lena Refsgaard
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mindich Child Health Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yunhan Chu
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Anne Heidi Skogholt
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Håvard Dalen
- Department of Cardiology, St. Olav's University Hospital, Trondheim 7030, Norway; Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger 7600, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Alan P Boyle
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hakan Oral
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Todd J Herron
- Department of Internal Medicine, Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jacob Kitzman
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - José Jalife
- Department of Internal Medicine, Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI 48109, USA; Fundacion Centro Nacional de Investigaciones Cardiovasculares, Madrid 28029, Spain
| | - Jesper H Svendsen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Morten S Olesen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark; Department of Biomedicine, University of Copenhagen, Copenhagen 2200, Denmark
| | - Inger Njølstad
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø 9019, Norway
| | - Maja-Lisa Løchen
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø 9019, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
| | | | | | | | | | - Tom Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø 9019, Norway
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Timothy M Frayling
- Royal Devon & Exeter NHS Foundation Trust and University of Exeter Barrack Road Exeter, Exeter EX2 5WD, UK
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hyun M Kang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gonçalo R Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Kristian Hveem
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger 7600, Norway; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger 7600, Norway.
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
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795
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Li J, Tang W, Zhang YW, Chen KN, Wang C, Liu Y, Zhan Q, Wang C, Wang SB, Xie SQ, Wang L. Genome-Wide Association Studies for Five Forage Quality-Related Traits in Sorghum ( Sorghum bicolor L.). FRONTIERS IN PLANT SCIENCE 2018; 9:1146. [PMID: 30186292 PMCID: PMC6111974 DOI: 10.3389/fpls.2018.01146] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 07/18/2018] [Indexed: 05/20/2023]
Abstract
Understanding the genetic function of the forage quality-related traits, including crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), hemicellulose (HC), and cellulose (CL) contents, is essential for the identification of forage quality genes and selection of effective molecular markers in sorghum. In this study, we genotyped 245 sorghum accessions by 85,585 single-nucleotide polymorphisms (SNPs) and obtained the phenotypic data from four environments. The SNPs and phenotypic data were applied to multi-locus genome-wide association studies (GWAS) with the mrMLM software. A total of 42 SNPs were identified to be associated with the five forage quality-related traits. Moreover, three and two quantitative trait nucleotides (QTNs) were simultaneously detected among them by three and two multi-locus methods, respectively. One QTN on chromosome 5 was found to be associated simultaneously with CP, NDF, and ADF. Furthermore, 3, 2, 2, 5, and 2 candidate genes were identified to be responsible for CP, NDF, ADF, HC, and CL contents, respectively. These results provided insightful information of the forage quality-related traits and would facilitate the genetic improvement of sorghum forage quality in the future.
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Affiliation(s)
- Jieqin Li
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Weijie Tang
- College of Horticulture, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, China
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing, China
| | - Ya-Wen Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Kai-Ning Chen
- College of Horticulture, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, China
| | - Chenchen Wang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yanlong Liu
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Qiuwen Zhan
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Chunming Wang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing, China
| | - Shi-Bo Wang
- College of Horticulture, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, China
| | - Shang-Qian Xie
- College of Horticulture, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, China
- *Correspondence: Shang-Qian Xie
| | - Lihua Wang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
- Lihua Wang
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796
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Gumpinger AC, Roqueiro D, Grimm DG, Borgwardt KM. Methods and Tools in Genome-wide Association Studies. Methods Mol Biol 2018; 1819:93-136. [PMID: 30421401 DOI: 10.1007/978-1-4939-8618-7_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.
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Affiliation(s)
- Anja C Gumpinger
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Damian Roqueiro
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dominik G Grimm
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Karsten M Borgwardt
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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797
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Abstract
Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.
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Affiliation(s)
- Aurélien Macé
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltán Kutalik
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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798
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Wang H, Aragam B, Xing EP. Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2017; 2017:431-438. [PMID: 29629235 DOI: 10.1109/bibm.2017.8217687] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of individual relationships in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and humans, and discuss the knowledge we discover with our model.
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Affiliation(s)
- Haohan Wang
- Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
| | - Bryon Aragam
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
| | - Eric P Xing
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
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799
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Pilling LC, Kuo CL, Sicinski K, Tamosauskaite J, Kuchel GA, Harries LW, Herd P, Wallace R, Ferrucci L, Melzer D. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging (Albany NY) 2017; 9:2504-2520. [PMID: 29227965 PMCID: PMC5764389 DOI: 10.18632/aging.101334] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/26/2017] [Indexed: 12/22/2022]
Abstract
We undertook a genome-wide association study (GWAS) of parental longevity in European descent UK Biobank participants. For combined mothers' and fathers' attained age, 10 loci were associated (p<5*10-8), including 8 previously identified for traits including survival, Alzheimer's and cardiovascular disease. Of these, 4 were also associated with longest 10% survival (mothers age ≥90 years, fathers ≥87 years), with 2 additional associations including MC2R intronic variants (coding for the adrenocorticotropic hormone receptor). Mother's age at death was associated with 3 additional loci (2 linked to autoimmune conditions), and 8 for fathers only. An attained age genetic risk score associated with parental survival in the US Health and Retirement Study and the Wisconsin Longitudinal Study and with having a centenarian parent (n=1,181) in UK Biobank. The results suggest that human longevity is highly polygenic with prominent roles for loci likely involved in cellular senescence and inflammation, plus lipid metabolism and cardiovascular conditions. There may also be gender specific routes to longevity.
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Affiliation(s)
- Luke C. Pilling
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
| | - Chia-Ling Kuo
- Department of Community Medicine and Health Care, Connecticut Institute for Clinical and Translational Science, Institute for Systems Genomics, University of Connecticut Health Center, CT 06269 USA
| | - Kamil Sicinski
- Center for Demography of Health and Aging, University of Wisconsin, Madison, WI 53706, USA
| | - Jone Tamosauskaite
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
| | - George A. Kuchel
- UConn Center on Aging, University of Connecticut, Farmington, CT 06030, USA
| | - Lorna W. Harries
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Pamela Herd
- La Follette School of Public Affairs and the Department of Sociology, University of Wisconsin, Madison, WI 53706, USA
| | - Robert Wallace
- College of Public Health, University of Iowa, Iowa City, IA 52242, USA
| | | | - David Melzer
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
- UConn Center on Aging, University of Connecticut, Farmington, CT 06030, USA
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800
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Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, Mahajan A, Saleheen D, Emdin C, Alam D, Alves AC, Amouyel P, di Angelantonio E, Arveiler D, Assimes TL, Auer PL, Baber U, Ballantyne CM, Bang LE, Benn M, Bis JC, Boehnke M, Boerwinkle E, Bork-Jensen J, Bottinger EP, Brandslund I, Brown M, Busonero F, Caulfield MJ, Chambers JC, Chasman DI, Chen YE, Chen YDI, Chowdhury R, Christensen C, Chu AY, Connell JM, Cucca F, Cupples LA, Damrauer SM, Davies G, Deary IJ, Dedoussis G, Denny JC, Dominiczak A, Dubé MP, Ebeling T, Eiriksdottir G, Esko T, Farmaki AE, Feitosa MF, Ferrario M, Ferrieres J, Ford I, Fornage M, Franks PW, Frayling TM, Frikke-Schmidt R, Fritsche L, Frossard P, Fuster V, Ganesh SK, Gao W, Garcia ME, Gieger C, Giulianini F, Goodarzi MO, Grallert H, Grarup N, Groop L, Grove ML, Gudnason V, Hansen T, Harris TB, Hayward C, Hirschhorn JN, Holmen OL, Huffman J, Huo Y, Hveem K, Jabeen S, Jackson AU, Jakobsdottir J, Jarvelin MR, Jensen GB, Jørgensen ME, Jukema JW, Justesen JM, Kamstrup PR, Kanoni S, Karpe F, Kee F, Khera AV, Klarin D, Koistinen HA, Kooner JS, Kooperberg C, Kuulasmaa K, Kuusisto J, Laakso M, Lakka T, Langenberg C, Langsted A, Launer LJ, Lauritzen T, Liewald DCM, Lin LA, Linneberg A, Loos RJ, Lu Y, Lu X, Mägi R, Malarstig A, Manichaikul A, Manning AK, Mäntyselkä P, Marouli E, Masca NGD, Maschio A, Meigs JB, Melander O, Metspalu A, Morris AP, Morrison AC, Mulas A, Müller-Nurasyid M, Munroe PB, Neville MJ, Nielsen JB, Nielsen SF, Nordestgaard BG, Ordovas JM, Mehran R, O’Donnell CJ, Orho-Melander M, Molony CM, Muntendam P, Padmanabhan S, Palmer CNA, Pasko D, Patel AP, Pedersen O, Perola M, Peters A, Pisinger C, Pistis G, Polasek O, Poulter N, Psaty BM, Rader DJ, Rasheed A, Rauramaa R, Reilly D, Reiner AP, Renström F, Rich SS, Ridker PM, Rioux JD, Robertson NR, Roden DM, Rotter JI, Rudan I, Salomaa V, Samani NJ, Sanna S, Sattar N, Schmidt EM, Scott RA, Sever P, Sevilla RS, Shaffer CM, Sim X, Sivapalaratnam S, Small KS, Smith AV, Smith BH, Somayajula S, Southam L, Spector TD, Speliotes EK, Starr JM, Stirrups KE, Stitziel N, Strauch K, Stringham HM, Surendran P, Tada H, Tall AR, Tang H, Tardif JC, Taylor KD, Trompet S, Tsao PS, Tuomilehto J, Tybjaerg-Hansen A, van Zuydam NR, Varbo A, Varga TV, Virtamo J, Waldenberger M, Wang N, Wareham NJ, Warren HR, Weeke PE, Weinstock J, Wessel J, Wilson JG, Wilson PWF, Xu M, Yaghootkar H, Young R, Zeggini E, Zhang H, Zheng NS, Zhang W, Zhang Y, Zhou W, Zhou Y, Zoledziewska M, Howson JMM, Danesh J, McCarthy MI, Cowan C, Abecasis G, Deloukas P, Musunuru K, Willer CJ, Kathiresan S. Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet 2017; 49:1758-1766. [PMID: 29083408 PMCID: PMC5709146 DOI: 10.1038/ng.3977] [Citation(s) in RCA: 401] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 09/26/2017] [Indexed: 02/02/2023]
Abstract
We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.
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Affiliation(s)
- Dajiang J. Liu
- Department of Public Health Sciences, Institute of Personalized Medicine, Penn State College of Medicine, Hershey, Pennsylvania, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Haojie Yu
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA
| | - Adam S. Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge, UK
| | - Xiao Wang
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Danish Saleheen
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Connor Emdin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | - Philippe Amouyel
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Risk factors and molecular determinants of aging-related diseases, Lille, France
| | - Emanuele di Angelantonio
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge, UK
| | - Dominique Arveiler
- Department of Epidemiology and Public Health, EA 3430, University of Strasbourg, Strasbourg, France
| | - Themistocles L. Assimes
- VA Palo Alto Health Care System, Palo Alto, California, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Paul L. Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Usman Baber
- Cardiovascular Institute, Mount Sinai Medical Center, Icahn School of Medicine, Mount Sinai, New York, New York, USA
| | | | - Lia E. Bang
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marianne Benn
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Denmark, Denmark
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Michael Boehnke
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Ichan School of Medicine at Mount Sinai, New York, New York, USA
| | - Ivan Brandslund
- Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Morris Brown
- Clinical Pharmacology Unit, University of Cambridge, Addenbrookes Hospital, Cambridge, UK
| | - Fabio Busonero
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London, Queen Mary University of London, Charterhouse Square, London, UK
- The Barts Heart Centre, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Daniel I. Chasman
- Division of Preventive Medicine, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Y. Eugene Chen
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Departments of Pediatrics and Medicine, Los Angeles, California, USA
| | - Rajiv Chowdhury
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Audrey Y. Chu
- Division of Preventive Medicine, Boston, Massachusetts, USA
- NHLBI Framingham Heart Study, Framingham, Massachusetts, USA
| | - John M Connell
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
- Dipartimento di Scienze Biomediche, Universita’ degli Studi di Sassari, Sassari, Italy
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- NHLBI Framingham Heart Study, Framingham, Massachusetts, USA
| | - Scott M. Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Joshua C. Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Anna Dominiczak
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, Quebec, Canada
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Tapani Ebeling
- Department of Medicine, Oulu University Hospital and University of Oulu, Oulu, Finland
| | | | - Tõnu Esko
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Marco Ferrario
- Research Centre in Epidemiology and Preventive Medicine – EPIMED, Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Jean Ferrieres
- Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse, Toulouse, France
| | - Ian Ford
- University of Glasgow, Glasgow, UK
| | - Myriam Fornage
- Institute of Molecular Medicine, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | | | - Valentin Fuster
- Cardiovascular Institute, Mount Sinai Medical Center, Icahn School of Medicine, Mount Sinai, New York, New York, USA
| | - Santhi K. Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Wei Gao
- Department of Cardiology, Peking University Third Hospital, Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Ministry of Health, Beijing, China
| | | | - Christian Gieger
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Mark O. Goodarzi
- Department of Medicine and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Harald Grallert
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Clinical Research Centre, Lund University, Malmö, Sweden
| | - Megan L. Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Vilmundur Gudnason
- The Icelandic Heart Association, Kopavogur, Iceland
- The University of Iceland, Reykjavik, Iceland
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Tamara B. Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Joel N. Hirschhorn
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, USA
| | - Oddgeir L. Holmen
- Department of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
- St Olav Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Jennifer Huffman
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Yong Huo
- Department of Cardiology, Peking University First Hospital, Beijing, China
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Dept of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Anne U Jackson
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Johanna Jakobsdottir
- The Icelandic Heart Association, Kopavogur, Iceland
- The University of Iceland, Reykjavik, Iceland
| | | | - Gorm B Jensen
- The Copenhagen City Heart Study, Frederiksberg Hospital, Denmark
| | - Marit E. Jørgensen
- Steno Diabetes Center, Gentofte, Denmark
- National Institute of Public Health, Southern Denmark University, Denmark
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- The Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
| | - Johanne M. Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pia R. Kamstrup
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Frank Kee
- Director, UKCRC Centre of Excellence for Public Health, Queens University, Belfast, Northern Ireland
| | - Amit V. Khera
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Derek Klarin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Heikki A. Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki; and Department of Medicine, and Abdominal Center: Endocrinology, Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London, UK
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Kari Kuulasmaa
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Timo Lakka
- Department of Physiology, Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Anne Langsted
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Denmark, Denmark
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA
| | - Torsten Lauritzen
- Department of Public Health, Section of General Practice, University of Aarhus, Aarhus, Denmark
| | - David CM Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Li An Lin
- Institute of Molecular Medicine, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Allan Linneberg
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Research Center for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Ichan School of Medicine at Mount Sinai, New York, New York, USA
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, New York, USA
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Ichan School of Medicine at Mount Sinai, New York, New York, USA
| | - Xiangfeng Lu
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Anders Malarstig
- Cardiovascular Genetics and Genomics Group, Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Pharmatherapeutics Clinical Research, Pfizer Worldwide R&D, Sollentuna, Sweden
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Alisa K. Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Pekka Mäntyselkä
- Unit of Primary Health Care, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nicholas GD Masca
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester UK
| | - Andrea Maschio
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - James B. Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Olle Melander
- Department of Clinical Sciences, University Hospital Malmo Clinical Research Center, Lund University, Malmo, Sweden
| | | | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Martina Müller-Nurasyid
- Department of Medicine I, Ludwig-Maximilians-University, Munich, Germany
- DZHK German Centre for Cardiovascular Research, partner site Munich Heart Alliance, Munich, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany
| | - Patricia B. Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London, Queen Mary University of London, Charterhouse Square, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jonas B. Nielsen
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Sune F Nielsen
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Denmark, Denmark
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Denmark, Denmark
| | - Jose M. Ordovas
- Department of Cardiovascular Epidemiology and Population Genetics, National Center for Cardiovascular Investigation, Madrid, Spain
- IMDEA-Alimentacion, Madrid, Spain
- Nutrition and Genomics Laboratory, Jean Mayer-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA
| | - Roxana Mehran
- Cardiovascular Institute, Mount Sinai Medical Center, Icahn School of Medicine, Mount Sinai, New York, New York, USA
| | - Christoper J. O’Donnell
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Marju Orho-Melander
- Department of Clinical Sciences, University Hospital Malmo Clinical Research Center, Lund University, Malmo, Sweden
| | - Cliona M. Molony
- Genetics, Merck Sharp & Dohme Corp., Kenilworth, New Jersey, USA
| | | | - Sandosh Padmanabhan
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Colin NA Palmer
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Aniruddh P. Patel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Markus Perola
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Institute of Molecular Medicine FIMM, University of Helsinki, Finland
| | - Annette Peters
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- DZHK German Centre for Cardiovascular Research, partner site Munich Heart Alliance, Munich, Germany
| | - Charlotta Pisinger
- Research Center for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
| | - Giorgio Pistis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Neil Poulter
- International Centre for Circulatory Health, Imperial College London, UK
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
- Departments of Epidemiology and Health Services, University of Washington, Seattle, Washington, USA
| | - Daniel J. Rader
- Departments of Genetics, Medicine, and Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Dermot Reilly
- Genetics, Merck Sharp & Dohme Corp., Kenilworth, New Jersey, USA
| | - Alex P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Frida Renström
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Boston, Massachusetts, USA
| | | | - Neil R Robertson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Departments of Pediatrics and Medicine, Los Angeles, California, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Veikko Salomaa
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester UK
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Naveed Sattar
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ellen M. Schmidt
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Peter Sever
- International Centre for Circulatory Health, Imperial College London, UK
| | | | - Christian M. Shaffer
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Xueling Sim
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117549, Singapore
| | - Suthesh Sivapalaratnam
- Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, NL
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Albert V. Smith
- The Icelandic Heart Association, Kopavogur, Iceland
- The University of Iceland, Reykjavik, Iceland
| | - Blair H Smith
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Lorraine Southam
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Elizabeth K. Speliotes
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Kathleen E Stirrups
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Nathan Stitziel
- Cardiovascular Division, Departments of Medicine and Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
- The McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Heather M Stringham
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Praveen Surendran
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Hayato Tada
- Division of Cardiovascular Medicine, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Alan R. Tall
- Division of Molecular Medicine, Department of Medicine, Columbia University, New York, New York, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Departments of Pediatrics and Medicine, Los Angeles, California, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Philip S. Tsao
- VA Palo Alto Health Care System, Palo Alto, California, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jaakko Tuomilehto
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Dasman Diabetes Institute, Dasman, Kuwait
- Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
- Saudi Diabetes Research Group, King Abdulaziz University, Fahd Medical Research Center, Jeddah, Saudi Arabia
| | - Anne Tybjaerg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Natalie R van Zuydam
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Anette Varbo
- Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Denmark, Denmark
| | - Tibor V Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Jarmo Virtamo
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Melanie Waldenberger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nan Wang
- Division of Molecular Medicine, Department of Medicine, Columbia University, New York, New York, USA
| | - Nick J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London, Queen Mary University of London, Charterhouse Square, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Peter E. Weeke
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- The Heart Centre, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Joshua Weinstock
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Jennifer Wessel
- Department of Epidemiology, Indiana University Fairbanks School of Public Health, Indianapolis, Indiana, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Peter W. F. Wilson
- Atlanta VA Medical Center, Decatur, Georgia, USA
- Emory Clinical Cardiovascular Research Institute, Atlanta, Georgia, USA
| | - Ming Xu
- Department of Cardiology, Institute of Vascular Medicine, Peking University Third Hospital, Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Beijing, China
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Robin Young
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - He Zhang
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - Yan Zhang
- Department of Cardiology, Peking University First Hospital, Beijing, China
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | | | - Joanna MM Howson
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Chad Cowan
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Goncalo Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Kiran Musunuru
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Cristen J. Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Sekar Kathiresan
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
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