651
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Singh S, Brandenburg JT, Choudhury A, Gómez-Olivé FX, Ramsay M. Systematic Review of Genomic Associations with Blood Pressure and Hypertension in Populations with African-Ancestry. Front Genet 2021; 12:699445. [PMID: 34745203 PMCID: PMC8564494 DOI: 10.3389/fgene.2021.699445] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/10/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Despite hypertension being highly prevalent in individuals with African-ancestry, they are under-represented in large genome-wide association studies. Inclusion of African participants is essential to better understand genetic associations with blood pressure-related traits in Africans. This systematic review critically evaluates existing studies with African-ancestry participants and identifies knowledge gaps. Methods: We followed the PRISMA protocol, HuGE Review handbook to identify literature on original research, in English, on genetic association studies for blood pressure-related traits (systolic and diastolic blood pressure, pulse and mean-arterial pressure, and hypertension) in populations with African-ancestry (January 2007 to April 2020). A narrative synthesis of the evidence was conducted. Results: Twelve studies with African-ancestry participants met the eligibility criteria, within which 10 studies met the additional genetic association data criteria (i.e., reporting only on African-ancestry participants). Across the five blood pressure-related traits, 26 genome-wide significantly associated SNPs were identified, with six SNPs linked to more than one trait, illustrating pleiotropic effects. Among the SNP associations, 12 had not previously been described in non-African studies. Discussion: The limited number of relevant studies highlights the dearth of genomic association studies on participants with African-ancestry, especially those located within Africa. Variations in study methodology, participant inclusion, adjustment for covariates (e.g., antihypertensive medication) and relatively small sample sizes make comparisons challenging, and have resulted in fewer significant associations, compared to large European studies. Regional variation in the prevalence and associated risk factors of hypertension across Africa makes a compelling argument to develop African cohorts to facilitate large genomic studies, using African-centric arrays. Data harmonisation and comparable study designs, such as described in the H3Africa CHAIR initiative, provide a good example toward achieving this goal. Other relevant information: SS and J-TB were funded by the South African National Research Foundation. MR is a South African Research Chair in Genomics and Bioinformatics of African populations hosted by the University of the Witwatersrand, funded by the Department of Science and Innovation, and administered by the NRF. This review was registered at PROSPERO (registration number: CRD42020179221) and OSF (registration DOI: 10.17605/OSF.IO/QT2HA).
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Affiliation(s)
- S Singh
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Human Genetics, School of Pathology, National Health Laboratory Service and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - J-T Brandenburg
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - A Choudhury
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - F X Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - M Ramsay
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Human Genetics, School of Pathology, National Health Laboratory Service and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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652
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Chan II, Kwok MK, Schooling CM. The total and direct effects of systolic and diastolic blood pressure on cardiovascular disease and longevity using Mendelian randomisation. Sci Rep 2021; 11:21799. [PMID: 34750372 PMCID: PMC8575942 DOI: 10.1038/s41598-021-00895-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 09/29/2021] [Indexed: 01/26/2023] Open
Abstract
The 2017 American College of Cardiology/American Heart Association (ACC/AHA) blood pressure (BP) guidelines lowered the hypertension threshold to ≥ 130/80 mmHg, but the role of diastolic BP remains contested. This two-sample mendelian randomisation study used replicated genetic variants predicting systolic and diastolic BP applied to the UK Biobank and large genetic consortia, including of cardiovascular diseases and parental lifespan, to obtain total and direct effects. Systolic and diastolic BP had positive total effects on CVD (odds ratio (OR) per standard deviation 2.15, 95% confidence interval (CI) 1.95, 2.37 and OR 1.91, 95% CI 1.73, 2.11, respectively). Direct effects were similar for systolic BP (OR 1.83, 95% CI 1.48, 2.25) but completely attenuated for diastolic BP (1.18, 95% CI 0.97, 1.44), although diastolic BP was associated with coronary artery disease (OR 1.24, 95% CI 1.03, 1.50). Systolic and diastolic BP had similarly negative total (- 0.20 parental attained age z-score, 95% CI - 0.22, - 0.17 and - 0.17, 95% CI - 0.20, - 0.15, respectively) and direct negative effects on longevity. Our findings suggest systolic BP has larger direct effects than diastolic BP on CVD, but both have negative effects (total and direct) on longevity, supporting the 2017 ACC/AHA guidelines lowering both BP targets.
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Affiliation(s)
- Io Ieong Chan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong SAR, China
| | - Man Ki Kwok
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong SAR, China.
- Graduate School of Public Health and Health Policy, City University of New York, New York, USA.
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653
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Sato N, Fudono A, Imai C, Takimoto H, Tarui I, Aoyama T, Yago S, Okamitsu M, Mizutani S, Miyasaka N. Placenta mediates the effect of maternal hypertension polygenic score on offspring birth weight: a study of birth cohort with fetal growth velocity data. BMC Med 2021; 19:260. [PMID: 34732167 PMCID: PMC8567693 DOI: 10.1186/s12916-021-02131-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low birth weight (LBW) and fetal growth restriction are associated with the development of cardio-metabolic diseases later in life. A recent Mendelian randomization study concluded that the susceptibility of LBW infants to develop hypertension during adulthood is due to the inheritance of hypertension genes from the mother and not to an unfavorable intrauterine environment. Therein, a negative linear association has been assumed between genetically estimated maternal blood pressure (BP) and birth weight, while the observed relationship between maternal BP and birth weight is substantially different from that assumption. As many hypertension genes are likely involved in vasculature development and function, we hypothesized that BP-increasing genetic variants could affect birth weight by reducing the growth of the placenta, a highly vascular organ, without overtly elevating the maternal BP. METHODS Using a birth cohort in the Japanese population possessing time-series fetal growth velocity data as a target and a GWAS summary statistics of BioBank Japan as a base data, we performed polygenic score (PGS) analyses for systolic BP (SBP), diastolic BP, mean arterial pressure, and pulse pressure. A causal mediation analysis was performed to assess the meditation effect of placental weight on birth weight reduced by maternal BP-increasing PGS. Maternal genetic risk score constituted of only "vasculature-related" BP single nucleotide polymorphisms (SNPs) was constructed to examine the involvement of vascular genes in the mediation effect of placental weight. We identified gestational week in which maternal SBP-increasing PGS significantly decreased fetal growth velocity. RESULTS We observed that maternal SBP-increasing PGS was negatively associated with offspring birth weight. A causal mediation analysis revealed that a large proportion of the total maternal PGS effect on birth weight was mediated by placental weight. The placental mediation effect was remarkable when genetic risk score was constituted of "vasculature-related" BP SNPs. The inverse association between maternal SBP PGS and fetal growth velocity only became apparent in late gestation. CONCLUSIONS Our study suggests that maternal hypertension genes are strongly associated with placental growth and that fetal growth inhibition is induced through the intrauterine environment established by the placenta.
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Affiliation(s)
- Noriko Sato
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. .,Institute of Advanced Biomedical Engineering and Science, The Public Health Research Foundation, Tokyo, Japan.
| | - Ayako Fudono
- Comprehensive Reproductive Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Chihiro Imai
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Hidemi Takimoto
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Iori Tarui
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Tomoko Aoyama
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Satoshi Yago
- Child and Family Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Motoko Okamitsu
- Child and Family Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Shuki Mizutani
- Institute of Advanced Biomedical Engineering and Science, The Public Health Research Foundation, Tokyo, Japan
| | - Naoyuki Miyasaka
- Comprehensive Reproductive Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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654
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Veeneman RR, Vermeulen JM, Abdellaoui A, Sanderson E, Wootton RE, Tadros R, Bezzina CR, Denys D, Munafò MR, Verweij KJH, Treur JL. Exploring the Relationship Between Schizophrenia and Cardiovascular Disease: A Genetic Correlation and Multivariable Mendelian Randomization Study. Schizophr Bull 2021; 48:463-473. [PMID: 34730178 PMCID: PMC8886584 DOI: 10.1093/schbul/sbab132] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Individuals with schizophrenia have a reduced life-expectancy compared to the general population, largely due to an increased risk of cardiovascular disease (CVD). Clinical and epidemiological studies have been unable to unravel the nature of this relationship. We obtained summary-data of genome-wide-association studies of schizophrenia (N = 130 644), heart failure (N = 977 323), coronary artery disease (N = 332 477), systolic and diastolic blood pressure (N = 757 601), heart rate variability (N = 46 952), QT interval (N = 103 331), early repolarization and dilated cardiomyopathy ECG patterns (N = 63 700). We computed genetic correlations and conducted bi-directional Mendelian randomization (MR) to assess causality. With multivariable MR, we investigated whether causal effects were mediated by smoking, body mass index, physical activity, lipid levels, or type 2 diabetes. Genetic correlations between schizophrenia and CVD were close to zero (-0.02-0.04). There was evidence that liability to schizophrenia causally increases heart failure risk. This effect remained consistent with multivariable MR. There was also evidence that liability to schizophrenia increases early repolarization pattern, largely mediated by BMI and lipids. Finally, there was evidence that liability to schizophrenia increases heart rate variability, a direction of effect contrasting clinical studies. There was weak evidence that higher systolic blood pressure increases schizophrenia risk. Our finding that liability to schizophrenia increases heart failure is consistent with the notion that schizophrenia involves a systemic dysregulation of the body with detrimental effects on the heart. To decrease cardiovascular mortality among individuals with schizophrenia, priority should lie with optimal treatment in early stages of psychosis.
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Affiliation(s)
- Rada R Veeneman
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jentien M Vermeulen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Eleanor Sanderson
- Integrative Epidemiology Unit, University of Bristol, Bristol, UK,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robyn E Wootton
- Integrative Epidemiology Unit, University of Bristol, Bristol, UK,Nic Waals institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Rafik Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada,Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Connie R Bezzina
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marcus R Munafò
- Integrative Epidemiology Unit, University of Bristol, Bristol, UK,Tobacco and Alcohol Research Group, School of Psychological Science, University of Bristol, Bristol, UK
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands,To whom correspondence should be addressed; Meibergdreef 5, 1105 AZ, Amsterdam, The Netherlands; tel: +31(0)20-8913600, e-mail:
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655
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Yuan S, Bruzelius M, Damrauer SM, Larsson SC. Cardiometabolic, Lifestyle, and Nutritional Factors in Relation to Varicose Veins: A Mendelian Randomization Study. J Am Heart Assoc 2021; 10:e022286. [PMID: 34666504 PMCID: PMC8751841 DOI: 10.1161/jaha.121.022286] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background We conducted a 2-sample Mendelian randomization study to assess the associations of cardiometabolic, lifestyle, and nutritional factors with varicose veins. Methods and Results Independent single-nucleotide polymorphisms associated with height (positive control), body mass index, type 2 diabetes, diastolic and systolic blood pressure, smoking, alcohol and coffee consumption, 7 circulating vitamins (A, B6, B9, B12, C, 25-hydroxyvitamin D, and E), and 5 circulating minerals (calcium, iron, magnesium, selenium, and zinc) at the genome-wide significance level were used as instrumental variables. Summary-level data for the genetic associations with varicose veins were obtained from the UK Biobank (8763 cases and 352 431 noncases) and the FinnGen consortium (13 928 cases and 153 951 noncases). Genetically predicted higher height, body mass index, smoking, and circulating iron levels were associated with an increased risk of varicose veins. The odds ratios (ORs) per 1-SD increase in the exposure were 1.34 (95% CI, 1.25-1.43) for height, 1.39 (95% CI, 1.27-1.52) for body mass index, 1.12 (95% CI, 1.04-1.22) for the prevalence of smoking initiation, and 1.24 (95% CI, 1.16-1.33) for iron. Higher genetically predicted systolic blood pressure and circulating calcium and zinc levels were associated with a reduced risk of varicose veins, whereas the association for systolic blood pressure did not persist after adjustment for genetically predicted height. The OR was 0.75 (95% CI, 0.62-0.92) per 1-SD increase in calcium levels and 0.97 (95% CI, 0.95-0.98) for zinc. Conclusions This study identified several modifiable risk factors for varicose veins.
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Affiliation(s)
- Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden
| | - Maria Bruzelius
- Coagulation Unit Department of Hematology Karolinska University Hospital Stockholm Sweden.,Department of Medicine Solna Karolinska Institutet Stockholm Sweden
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center Philadelphia PA.,Department of Surgery University of Pennsylvania Perelman School of Medicine Philadelphia PA
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden.,Unit of Medical Epidemiology Department of Surgical Sciences Uppsala University Uppsala Sweden
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656
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Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet 2021; 17:e1009922. [PMID: 34793444 PMCID: PMC8639093 DOI: 10.1371/journal.pgen.1009922] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 12/02/2021] [Accepted: 11/02/2021] [Indexed: 11/19/2022] Open
Abstract
With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
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657
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Wang H, Noordam R, Cade BE, Schwander K, Winkler TW, Lee J, Sung YJ, Bentley AR, Manning AK, Aschard H, Kilpeläinen TO, Ilkov M, Brown MR, Horimoto AR, Richard M, Bartz TM, Vojinovic D, Lim E, Nierenberg JL, Liu Y, Chitrala K, Rankinen T, Musani SK, Franceschini N, Rauramaa R, Alver M, Zee PC, Harris SE, van der Most PJ, Nolte IM, Munroe PB, Palmer ND, Kühnel B, Weiss S, Wen W, Hall KA, Lyytikäinen LP, O'Connell J, Eiriksdottir G, Launer LJ, de Vries PS, Arking DE, Chen H, Boerwinkle E, Krieger JE, Schreiner PJ, Sidney S, Shikany JM, Rice K, Chen YDI, Gharib SA, Bis JC, Luik AI, Ikram MA, Uitterlinden AG, Amin N, Xu H, Levy D, He J, Lohman KK, Zonderman AB, Rice TK, Sims M, Wilson G, Sofer T, Rich SS, Palmas W, Yao J, Guo X, Rotter JI, Biermasz NR, Mook-Kanamori DO, Martin LW, Barac A, Wallace RB, Gottlieb DJ, Komulainen P, Heikkinen S, Mägi R, Milani L, Metspalu A, Starr JM, Milaneschi Y, Waken RJ, Gao C, Waldenberger M, Peters A, Strauch K, Meitinger T, Roenneberg T, Völker U, Dörr M, Shu XO, Mukherjee S, Hillman DR, Kähönen M, Wagenknecht LE, Gieger C, Grabe HJ, Zheng W, et alWang H, Noordam R, Cade BE, Schwander K, Winkler TW, Lee J, Sung YJ, Bentley AR, Manning AK, Aschard H, Kilpeläinen TO, Ilkov M, Brown MR, Horimoto AR, Richard M, Bartz TM, Vojinovic D, Lim E, Nierenberg JL, Liu Y, Chitrala K, Rankinen T, Musani SK, Franceschini N, Rauramaa R, Alver M, Zee PC, Harris SE, van der Most PJ, Nolte IM, Munroe PB, Palmer ND, Kühnel B, Weiss S, Wen W, Hall KA, Lyytikäinen LP, O'Connell J, Eiriksdottir G, Launer LJ, de Vries PS, Arking DE, Chen H, Boerwinkle E, Krieger JE, Schreiner PJ, Sidney S, Shikany JM, Rice K, Chen YDI, Gharib SA, Bis JC, Luik AI, Ikram MA, Uitterlinden AG, Amin N, Xu H, Levy D, He J, Lohman KK, Zonderman AB, Rice TK, Sims M, Wilson G, Sofer T, Rich SS, Palmas W, Yao J, Guo X, Rotter JI, Biermasz NR, Mook-Kanamori DO, Martin LW, Barac A, Wallace RB, Gottlieb DJ, Komulainen P, Heikkinen S, Mägi R, Milani L, Metspalu A, Starr JM, Milaneschi Y, Waken RJ, Gao C, Waldenberger M, Peters A, Strauch K, Meitinger T, Roenneberg T, Völker U, Dörr M, Shu XO, Mukherjee S, Hillman DR, Kähönen M, Wagenknecht LE, Gieger C, Grabe HJ, Zheng W, Palmer LJ, Lehtimäki T, Gudnason V, Morrison AC, Pereira AC, Fornage M, Psaty BM, van Duijn CM, Liu CT, Kelly TN, Evans MK, Bouchard C, Fox ER, Kooperberg C, Zhu X, Lakka TA, Esko T, North KE, Deary IJ, Snieder H, Penninx BWJH, Gauderman WJ, Rao DC, Redline S, van Heemst D. Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure. Mol Psychiatry 2021; 26:6293-6304. [PMID: 33859359 PMCID: PMC8517040 DOI: 10.1038/s41380-021-01087-0] [Show More Authors] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 03/18/2021] [Accepted: 03/29/2021] [Indexed: 02/02/2023]
Abstract
Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint < 5 × 10-8), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (Pint < 5 × 10-8). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (Pint = 2 × 10-6). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (Pint < 10-3). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.
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Affiliation(s)
- Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alisa K Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Michael R Brown
- 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, TX, USA
| | - Andrea R Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil
| | - Melissa Richard
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elise Lim
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jovia L Nierenberg
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Yongmei Liu
- Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute Duke University School of Medicine, Durham, NC, USA
| | - Kumaraswamynaidu Chitrala
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Solomon K Musani
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Maris Alver
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
| | - Phyllis C Zee
- Division of Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL, USA
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, UK
| | | | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- German Center for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kelly A Hall
- School of Public Health, The University of Adelaide, Adelaide, SA, Australia
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jeff O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Paul S de Vries
- 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, TX, USA
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Han Chen
- 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, TX, USA
- Center for Precision Health, School of Public Health & School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 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, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jose E Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil
| | - Pamela J Schreiner
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | | | - James M Shikany
- Division of Preventive Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, UW Medicine Sleep Center, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Daniel Levy
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Kurt K Lohman
- Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute Duke University School of Medicine, Durham, NC, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Treva K Rice
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Mario Sims
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Gregory Wilson
- JHS Graduate Training and Education Center, Jackson State University, Jackson, MS, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Walter Palmas
- Division of General Medicine, Department of Medicine, Columbia University, New York, NY, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Nienke R Biermasz
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Lisa W Martin
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ana Barac
- MedStar Heart and Vascular Institute, Washington, DC, USA
| | - Robert B Wallace
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA
| | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Sami Heikkinen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John M Starr
- Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, HJ, The Netherlands
| | - R J Waken
- Division of Cardiology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- 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
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Till Roenneberg
- Institute and Polyclinic for Occupational-, Social- and Environmental Medicine, LMU Munich, Munich, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- German Center for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- German Center for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Sutapa Mukherjee
- Sleep Health Service, Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, SA, Australia
- Adelaide Institute for Sleep Health, Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - David R Hillman
- Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Hans J Grabe
- Department Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lyle J Palmer
- School of Public Health, The University of Adelaide, Adelaide, SA, Australia
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - 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, TX, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Myriam Fornage
- 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, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, Departments of Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Ervin R Fox
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Timo A Lakka
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Ian J Deary
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, HJ, The Netherlands
| | - W James Gauderman
- Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands.
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658
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Portilla-Fernández E, Hwang SJ, Wilson R, Maddock J, Hill WD, Teumer A, Mishra PP, Brody JA, Joehanes R, Ligthart S, Ghanbari M, Kavousi M, Roks AJM, Danser AHJ, Levy D, Peters A, Ghasemi S, Schminke U, Dörr M, Grabe HJ, Lehtimäki T, Kähönen M, Hurme MA, Bartz TM, Sotoodehnia N, Bis JC, Thiery J, Koenig W, Ong KK, Bell JT, Meisinger C, Wardlaw JM, Starr JM, Seissler J, Then C, Rathmann W, Ikram MA, Psaty BM, Raitakari OT, Völzke H, Deary IJ, Wong A, Waldenberger M, O'Donnell CJ, Dehghan A. Meta-analysis of epigenome-wide association studies of carotid intima-media thickness. Eur J Epidemiol 2021; 36:1143-1155. [PMID: 34091768 PMCID: PMC8629903 DOI: 10.1007/s10654-021-00759-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 04/26/2021] [Indexed: 12/21/2022]
Abstract
Common carotid intima-media thickness (cIMT) is an index of subclinical atherosclerosis that is associated with ischemic stroke and coronary artery disease (CAD). We undertook a cross-sectional epigenome-wide association study (EWAS) of measures of cIMT in 6400 individuals. Mendelian randomization analysis was applied to investigate the potential causal role of DNA methylation in the link between atherosclerotic cardiovascular risk factors and cIMT or clinical cardiovascular disease. The CpG site cg05575921 was associated with cIMT (beta = -0.0264, p value = 3.5 × 10-8) in the discovery panel and was replicated in replication panel (beta = -0.07, p value = 0.005). This CpG is located at chr5:81649347 in the intron 3 of the aryl hydrocarbon receptor repressor gene (AHRR). Our results indicate that DNA methylation at cg05575921 might be in the pathway between smoking, cIMT and stroke. Moreover, in a region-based analysis, 34 differentially methylated regions (DMRs) were identified of which a DMR upstream of ALOX12 showed the strongest association with cIMT (p value = 1.4 × 10-13). In conclusion, our study suggests that DNA methylation may play a role in the link between cardiovascular risk factors, cIMT and clinical cardiovascular disease.
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Affiliation(s)
- Eliana Portilla-Fernández
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jane Maddock
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alexander Teumer
- Intitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Griefswald, Greifswald, Germany
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Anton J M Roks
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - A H Jan Danser
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Sahar Ghasemi
- Intitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Griefswald, Greifswald, Germany
| | - Ulf Schminke
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Research), Partner Site Griefswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mikko A Hurme
- Department of Microbiology and Immunology, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital, Leipzig, Leipzig, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Ken K Ong
- MRC Epidemiology Unit and Department of Paediatrics, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Christine Meisinger
- Independent Research Group, Clinical Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jochen Seissler
- Diabetes Zentrum, Medizinische Klinik und Poliklinik IV - Campus Innenstadt, Klinikum Der Ludwig-Maximilians-Universität München, Munich, Germany
- Clinical Cooperation Group Diabetes, Ludwig-Maximilians-Universität München and Helmholtz Zentrum München, Munich, Germany
| | - Cornelia Then
- Diabetes Zentrum, Medizinische Klinik und Poliklinik IV - Campus Innenstadt, Klinikum Der Ludwig-Maximilians-Universität München, Munich, Germany
- Clinical Cooperation Group Diabetes, Ludwig-Maximilians-Universität München and Helmholtz Zentrum München, Munich, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research, Neuherberg, Germany
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- 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
| | - Henry Völzke
- Intitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Griefswald, Greifswald, Germany
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Christopher J O'Donnell
- Cardiology Section and Center for Population Genomics, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, Room 157, Norfolk Place, St Mary's Campus, London, UK.
- UK Dementia Research Institute at Imperial College London, London, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
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659
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Niu M, Wang Y, Zhang L, Tu R, Liu X, Hou J, Huo W, Mao Z, Wang C, Bie R. Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China. Hypertens Res 2021; 44:1483-1491. [PMID: 34480134 DOI: 10.1038/s41440-021-00738-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/31/2021] [Accepted: 08/04/2021] [Indexed: 12/17/2022]
Abstract
Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to determine whether genetic information can improve the prediction of incident hypertension using machine learning approaches in a prospective study. The study recruited 4592 subjects without hypertension at baseline from a cohort study conducted in rural China. A polygenic risk score (PGGRS) was calculated using 13 SNPs. According to a ratio of 7:3, subjects were randomly allocated to the train and test datasets. Models with and without the PGGRS were established using the train dataset with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) methods. The discrimination and reclassification of models were estimated using the test dataset. The PGGRS showed a significant association with the risk of incident hypertension (HR (95% CI), 1.046 (1.004, 1.090), P = 0.031) irrespective of baseline blood pressure. Models that did not include the PGGRS achieved AUCs (95% CI) of 0.785 (0.763, 0.807), 0.790 (0.768, 0.811), 0.838 (0.817, 0.857), and 0.854 (0.835, 0.873) for the Cox, ANN, RF, and GBM methods, respectively. The addition of the PGGRS led to the improvement of the AUC by 0.001, 0.008, 0.023, and 0.017; IDI by 1.39%, 2.86%, 4.73%, and 4.68%; and NRI by 25.05%, 13.01%, 44.87%, and 22.94%, respectively. Incident hypertension risk was better predicted by the traditional+PGGRS model, especially when machine learning approaches were used, suggesting that genetic information may have the potential to identify new hypertension cases using machine learning methods in resource-limited areas. CLINICAL TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375 .
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Affiliation(s)
- Miaomiao Niu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yikang Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Liying Zhang
- School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Runqi Tu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Xiaotian Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Jian Hou
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Wenqian Huo
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Zhenxing Mao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
| | - Ronghai Bie
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
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660
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Schaid DJ, Dikilitas O, Sinnwell JP, Kullo IJ. Penalized mediation models for multivariate data. Genet Epidemiol 2021; 46:32-50. [PMID: 34664742 DOI: 10.1002/gepi.22433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/04/2021] [Accepted: 10/04/2021] [Indexed: 11/11/2022]
Abstract
Statistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high-dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single-nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.
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Affiliation(s)
- Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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661
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Ding J, Tu Z, Chen H, Liu Z. Identifying modifiable risk factors of lung cancer: Indications from Mendelian randomization. PLoS One 2021; 16:e0258498. [PMID: 34662362 PMCID: PMC8523078 DOI: 10.1371/journal.pone.0258498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/28/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Lung cancer is the major cause of mortality in tumor patients. While its incidence rate has recently declined, it is still far from satisfactory and its potential modifiable risk factors should be explored. METHODS We performed a two-sample Mendelian randomization (MR) study to investigate the causal relationship between potentially modifiable risk factors (namely smoking behavior, alcohol intake, anthropometric traits, blood pressure, lipidemic traits, glycemic traits, and fasting insulin) and lung cancer. Besides, a bi-directional MR analysis was carried out to disentangle the complex relationship between different risk factors. Inverse-variance weighted (IVW) was utilized to combine the estimation for each SNP. Cochrane's Q value was used to evaluate heterogeneity and two methods, including MR-Egger intercept and MR-PRESSO, were adopted to detect horizontal pleiotropy. RESULTS Three kinds of smoking behavior were all causally associated with lung cancer. Overall, smokers were more likely to suffer from lung cancer compared with non-smokers (OR = 2.58 [1.95, 3.40], p-value = 2.07 x 10-11), and quitting smoking could reduce the risk (OR = 4.29[2.60, 7.07], p-value = 1.23 x 10-8). Furthermore, we found a dose-response relationship between the number of cigarettes and lung cancer (OR = 6.10 [5.35, 6.96], p-value = 4.43x10-161). Lower HDL cholesterol could marginally increase the risk of lung cancer, but become insignificant after Bonferroni correction (OR = 0.82 [0.68, 1.00], p-value = 0.045). In addition, we noted no direct causal relationship between other risk factors and lung cancer. Neither heterogeneity nor pleiotropy was observed in this study. However, when treating the smoking behavior as the outcome, we found the increased BMI could elevate the number of cigarettes per day (beta = 0.139[0.104, 0.175], p-value = 1.99x10-14) and a similar effect was observed for the waist circumference and hip circumference. Additionally, the elevation of SBP could also marginally increase the number of cigarettes per day (beta = 0.001 [0.0002, 0.002], p-value = 0.018). CONCLUSION Smoking behavior might be the most direct and effective modifiable way to reduce the risk of lung cancer. Meanwhile, smoking behavior can be affected by other risk factors, especially obesity.
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Affiliation(s)
- Jie Ding
- Cancer Center, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Zhenxing Tu
- Department of Hand Surgery, The Second Hospital of Tangshan, Tangshan, Hebei Province, China
| | - Hongquan Chen
- Department of Bone Surgery, Affiliated Hospital of North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Zhiguang Liu
- Department of Pulmonary and Critical Care Medicine, Affiliated Changzhou Second People’s Hospital affiliated to Nanjing Medical University, Changzhou, Jiangsu Province, China
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662
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Qiao J, Zhang M, Wang T, Huang S, Zeng P. Evaluating Causal Relationship Between Metabolites and Six Cardiovascular Diseases Based on GWAS Summary Statistics. Front Genet 2021; 12:746677. [PMID: 34721534 PMCID: PMC8554206 DOI: 10.3389/fgene.2021.746677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/29/2021] [Indexed: 01/23/2023] Open
Abstract
Cardiovascular diseases (CVDs) remain the main cause of morbidity and mortality worldwide. The pathological mechanism and underlying biological processes of these diseases with metabolites remain unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of metabolites on these diseases by making full use of the latest GWAS summary statistics for 486 metabolites and six major CVDs. Extensive sensitivity analyses were implemented to validate our MR results. We also conducted linkage disequilibrium score regression (LDSC) and colocalization analysis to investigate whether MR findings were driven by genetic similarity or hybridization between LD and disease-associated gene loci. We identified a total of 310 suggestive associations across all metabolites and CVDs, and finally obtained four significant associations, including bradykinin, des-arg(9) (odds ratio [OR] = 1.160, 95% confidence intervals [CIs]: 1.080-1.246, false discovery rate [FDR] = 0.022) on ischemic stroke, N-acetylglycine (OR = 0.946, 95%CIs: 0.920-0.973, FDR = 0.023), X-09026 (OR = 0.845, 95%CIs: 0.779-0.916, FDR = 0.021) and X-14473 (OR = 0.938, 95%CIs = 0.907-0.971, FDR = 0.040) on hypertension. Sensitivity analyses showed that these causal associations were robust, the LDSC and colocalization analyses demonstrated that the identified associations were unlikely confused by LD. Moreover, we identified 15 important metabolic pathways might be involved in the pathogenesis of CVDs. Overall, our work identifies several metabolites that have a causal relationship with CVDs, and improves our understanding of the pathogenesis and treatment strategies for these diseases.
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Affiliation(s)
- Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Meng Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, China
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663
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Gkatzionis A, Burgess S, Conti DV, Newcombe PJ. Bayesian variable selection with a pleiotropic loss function in Mendelian randomization. Stat Med 2021; 40:5025-5045. [PMID: 34155684 PMCID: PMC8446304 DOI: 10.1002/sim.9109] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 04/17/2021] [Accepted: 06/07/2021] [Indexed: 01/04/2023]
Abstract
Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David V. Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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664
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Georges A, Yang ML, Berrandou TE, Bakker MK, Dikilitas O, Kiando SR, Ma L, Satterfield BA, Sengupta S, Yu M, Deleuze JF, Dupré D, Hunker KL, Kyryachenko S, Liu L, Sayoud-Sadeg I, Amar L, Brummett CM, Coleman DM, d’Escamard V, de Leeuw P, Fendrikova-Mahlay N, Kadian-Dodov D, Li JZ, Lorthioir A, Pappaccogli M, Prejbisz A, Smigielski W, Stanley JC, Zawistowski M, Zhou X, Zöllner S, FEIRI investigators de LeeuwPeter1213, International Stroke Genetics Consortium (ISGC) Intracranial Aneurysm Working Group, MEGASTROKE, Amouyel P, De Buyzere ML, Debette S, Dobrowolski P, Drygas W, Gornik HL, Olin JW, Piwonski J, Rietzschel ER, Ruigrok YM, Vikkula M, Warchol Celinska E, Januszewicz A, Kullo IJ, Azizi M, ARCADIA Investigators, Jeunemaitre X, Persu A, Kovacic JC, Ganesh SK, Bouatia-Naji N. Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases. Nat Commun 2021; 12:6031. [PMID: 34654805 PMCID: PMC8521585 DOI: 10.1038/s41467-021-26174-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 09/17/2021] [Indexed: 12/23/2022] Open
Abstract
Fibromuscular dysplasia (FMD) is an arteriopathy associated with hypertension, stroke and myocardial infarction, affecting mostly women. We report results from the first genome-wide association meta-analysis of six studies including 1556 FMD cases and 7100 controls. We find an estimate of SNP-based heritability compatible with FMD having a polygenic basis, and report four robustly associated loci (PHACTR1, LRP1, ATP2B1, and LIMA1). Transcriptome-wide association analysis in arteries identifies one additional locus (SLC24A3). We characterize open chromatin in arterial primary cells and find that FMD associated variants are located in arterial-specific regulatory elements. Target genes are broadly involved in mechanisms related to actin cytoskeleton and intracellular calcium homeostasis, central to vascular contraction. We find significant genetic overlap between FMD and more common cardiovascular diseases and traits including blood pressure, migraine, intracranial aneurysm, and coronary artery disease.
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Affiliation(s)
- Adrien Georges
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Min-Lee Yang
- grid.214458.e0000000086837370Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Takiy-Eddine Berrandou
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Mark K. Bakker
- grid.5477.10000000120346234Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Ozan Dikilitas
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902 USA
| | - Soto Romuald Kiando
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Lijiang Ma
- grid.59734.3c0000 0001 0670 2351Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Benjamin A. Satterfield
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902 USA
| | - Sebanti Sengupta
- grid.214458.e0000000086837370Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - Mengyao Yu
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Jean-François Deleuze
- grid.418135.a0000 0004 0641 3404Centre National de Recherche en Génomique Humaine, Institut de Génomique, CEA and Fondation Jean Dausset-CEPH, Evry, France
| | - Delia Dupré
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Kristina L. Hunker
- grid.214458.e0000000086837370Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Sergiy Kyryachenko
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Lu Liu
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Ines Sayoud-Sadeg
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
| | - Laurence Amar
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France ,grid.414093.b0000 0001 2183 5849Hypertension Unit, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015 Paris, France
| | - Chad M. Brummett
- grid.214458.e0000000086837370Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI USA
| | - Dawn M. Coleman
- grid.214458.e0000000086837370Vascular Surgery Section, Department of Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109 USA
| | - Valentina d’Escamard
- grid.59734.3c0000 0001 0670 2351Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Peter de Leeuw
- grid.412966.e0000 0004 0480 1382Department of Internal Medicine, Division of General Internal Medicine, Section Vascular Medicine, Maastricht University Medical Centre, Maastricht University, Maastricht, the Netherlands ,grid.5012.60000 0001 0481 6099CARIM School for Cardiovascular Diseases, Maastricht University Medical Centre, Maastricht University, Maastricht, the Netherlands
| | - Natalia Fendrikova-Mahlay
- grid.239578.20000 0001 0675 4725Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Daniella Kadian-Dodov
- grid.59734.3c0000 0001 0670 2351Zena and Michael A. Wiener Cardiovascular Institute and Marie-Josée and Henry R, Kravis Center for Cardiovascular Health Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Jun Z. Li
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Aurélien Lorthioir
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France ,grid.414093.b0000 0001 2183 5849Hypertension Unit, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015 Paris, France
| | - Marco Pappaccogli
- grid.7942.80000 0001 2294 713XDivision of Cardiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, 1200 Brussels, Belgium ,grid.7605.40000 0001 2336 6580Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Aleksander Prejbisz
- grid.418887.aDepartment of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Witold Smigielski
- grid.10789.370000 0000 9730 2769Department of Demography, University of Lodz, Lodz, Poland
| | - James C. Stanley
- grid.214458.e0000000086837370Vascular Surgery Section, Department of Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109 USA
| | - Matthew Zawistowski
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI USA
| | - Xiang Zhou
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI USA
| | - Sebastian Zöllner
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI USA
| | | | | | | | - Philippe Amouyel
- grid.503422.20000 0001 2242 6780Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Labex DISTALZ - Risk factors and molecular determinants of aging-related disease, F-59000 Lille, France
| | - Marc L. De Buyzere
- grid.5342.00000 0001 2069 7798Department of Cardiovascular Diseases, Ghent University and Ghent University Hospital, Ghent, Belgium
| | - Stéphanie Debette
- grid.42399.350000 0004 0593 7118Department of Neurology, Bordeaux University Hospital, Inserm U1219, Bordeaux, France
| | - Piotr Dobrowolski
- grid.418887.aDepartment of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Wojciech Drygas
- grid.418887.aDepartment of Epidemiology, Cardiovascular Disease Prevention, and Health Promotion, National Institute of Cardiology, Warsaw, Poland
| | - Heather L. Gornik
- grid.239578.20000 0001 0675 4725Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Jeffrey W. Olin
- grid.59734.3c0000 0001 0670 2351Zena and Michael A. Wiener Cardiovascular Institute and Marie-Josée and Henry R, Kravis Center for Cardiovascular Health Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Jerzy Piwonski
- grid.418887.aDepartment of Epidemiology, Cardiovascular Disease Prevention, and Health Promotion, National Institute of Cardiology, Warsaw, Poland
| | - Ernst R. Rietzschel
- grid.5342.00000 0001 2069 7798Department of Cardiovascular Diseases, Ghent University and Ghent University Hospital, Ghent, Belgium
| | - Ynte M. Ruigrok
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902 USA
| | - Miikka Vikkula
- grid.7942.80000 0001 2294 713XHuman Molecular Genetics, de Duve Institute, Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Ewa Warchol Celinska
- grid.418887.aDepartment of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Andrzej Januszewicz
- grid.418887.aDepartment of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Iftikhar J. Kullo
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902 USA ,grid.66875.3a0000 0004 0459 167XGonda Vascular Center, Mayo Clinic, Rochester, MN 55902 USA
| | - Michel Azizi
- grid.414093.b0000 0001 2183 5849Hypertension Unit, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015 Paris, France ,grid.512950.aUniversité de Paris, Inserm, Centre d’Investigation Clinique 1418, F-75006 Paris, France
| | | | - Xavier Jeunemaitre
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France ,grid.414093.b0000 0001 2183 5849Department of Genetics, Assistance-Publiques-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015 Paris, France
| | - Alexandre Persu
- grid.7942.80000 0001 2294 713XDivision of Cardiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, 1200 Brussels, Belgium ,grid.7942.80000 0001 2294 713XPole of Cardiovascular Research, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Jason C. Kovacic
- grid.59734.3c0000 0001 0670 2351Zena and Michael A. Wiener Cardiovascular Institute and Marie-Josée and Henry R, Kravis Center for Cardiovascular Health Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.1057.30000 0000 9472 3971Victor Chang Cardiac Research Institute, Darlinghurst, NSW Australia ,grid.1005.40000 0004 4902 0432St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW Australia
| | - Santhi K. Ganesh
- grid.214458.e0000000086837370Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Nabila Bouatia-Naji
- grid.508487.60000 0004 7885 7602PARCC, INSERM, Université de Paris, F-750015 Paris, France
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665
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Meeks KAC, Bentley AR, Gouveia MH, Chen G, Zhou J, Lei L, Adeyemo AA, Doumatey AP, Rotimi CN. Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits. Genome Med 2021; 13:156. [PMID: 34620218 PMCID: PMC8499470 DOI: 10.1186/s13073-021-00971-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/16/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans. METHODS We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans (n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses. RESULTS GWAS identified 39 significant (P value < 5 × 10-8) loci across all 13 traits. Notably, 14 loci were African-ancestry specific. In this first GWAS for adipsin and ghrelin, we detected 13 and 4 genome-wide significant loci respectively. Stratified analyses by sex, BMI, and T2D showed a strong effect of these variables on detected loci. Eight novel loci were successfully replicated: adipsin (3), GIP (1), GLP-1 (1), and insulin (3). Annotation of these loci revealed promising links between these adipocytokines and cardiometabolic outcomes as illustrated by rs201751833 for adipsin and blood pressure and locus rs759790 for insulin level and T2D in lean individuals. CONCLUSIONS Our study identified genetic variants underlying variation in multiple adipocytokines, including the first loci for adipsin and ghrelin. We identified population differences in variants associated with adipocytokines and highlight the importance of stratification for discovery of loci. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. Overall, our work contributes to the understanding of the biology linking adipocytokines to cardiometabolic traits.
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Affiliation(s)
- Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Lin Lei
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
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666
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Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 2021; 22:658-671. [PMID: 34302145 DOI: 10.1038/s41576-021-00387-z] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia. .,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia.
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667
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Habibe JJ, Clemente-Olivo MP, de Vries CJ. How (Epi)Genetic Regulation of the LIM-Domain Protein FHL2 Impacts Multifactorial Disease. Cells 2021; 10:2611. [PMID: 34685595 PMCID: PMC8534169 DOI: 10.3390/cells10102611] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 01/13/2023] Open
Abstract
Susceptibility to complex pathological conditions such as obesity, type 2 diabetes and cardiovascular disease is highly variable among individuals and arises from specific changes in gene expression in combination with external factors. The regulation of gene expression is determined by genetic variation (SNPs) and epigenetic marks that are influenced by environmental factors. Aging is a major risk factor for many multifactorial diseases and is increasingly associated with changes in DNA methylation, leading to differences in gene expression. Four and a half LIM domains 2 (FHL2) is a key regulator of intracellular signal transduction pathways and the FHL2 gene is consistently found as one of the top hyper-methylated genes upon aging. Remarkably, FHL2 expression increases with methylation. This was demonstrated in relevant metabolic tissues: white adipose tissue, pancreatic β-cells, and skeletal muscle. In this review, we provide an overview of the current knowledge on regulation of FHL2 by genetic variation and epigenetic DNA modification, and the potential consequences for age-related complex multifactorial diseases.
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Affiliation(s)
- Jayron J. Habibe
- Department of Medical Biochemistry, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, and Amsterdam Gastroenterology, Endocrinology and Metabolism, 1105 AZ Amsterdam, The Netherlands; (J.J.H.); (M.P.C.-O.)
- Department of Physiology, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, 1081 HV Amsterdam, The Netherlands
| | - Maria P. Clemente-Olivo
- Department of Medical Biochemistry, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, and Amsterdam Gastroenterology, Endocrinology and Metabolism, 1105 AZ Amsterdam, The Netherlands; (J.J.H.); (M.P.C.-O.)
| | - Carlie J. de Vries
- Department of Medical Biochemistry, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, and Amsterdam Gastroenterology, Endocrinology and Metabolism, 1105 AZ Amsterdam, The Netherlands; (J.J.H.); (M.P.C.-O.)
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668
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Neale ZE, Kuo SIC, Dick DM. A systematic review of gene-by-intervention studies of alcohol and other substance use. Dev Psychopathol 2021; 33:1410-1427. [PMID: 32602428 PMCID: PMC7772257 DOI: 10.1017/s0954579420000590] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.
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Affiliation(s)
- Zoe E. Neale
- Department of Psychology, Virginia Commonwealth University
| | | | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University
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669
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Takahashi Y, Yamazaki K, Kamatani Y, Kubo M, Matsuda K, Asai S. A genome-wide association study identifies a novel candidate locus at the DLGAP1 gene with susceptibility to resistant hypertension in the Japanese population. Sci Rep 2021; 11:19497. [PMID: 34593835 PMCID: PMC8484335 DOI: 10.1038/s41598-021-98144-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 09/03/2021] [Indexed: 01/11/2023] Open
Abstract
Numerous genetic variants associated with hypertension and blood pressure are known, but there is a paucity of evidence from genetic studies of resistant hypertension, especially in Asian populations. To identify novel genetic loci associated with resistant hypertension in the Japanese population, we conducted a genome-wide association study with 2705 resistant hypertension cases and 21,296 mild hypertension controls, all from BioBank Japan. We identified one novel susceptibility candidate locus, rs1442386 on chromosome 18p11.3 (DLGAP1), achieving genome-wide significance (odds ratio (95% CI) = 0.85 (0.81–0.90), P = 3.75 × 10−8) and 18 loci showing suggestive association, including rs62525059 of 8q24.3 (CYP11B2) and rs3774427 of 3p21.1 (CACNA1D). We further detected biological processes associated with resistant hypertension, including chemical synaptic transmission, regulation of transmembrane transport, neuron development and neurological system processes, highlighting the importance of the nervous system. This study provides insights into the etiology of resistant hypertension in the Japanese population.
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Affiliation(s)
- Yasuo Takahashi
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.
| | - Keiko Yamazaki
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.,Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Satoshi Asai
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan. .,Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.
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670
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Del Pinto R, Landi L, Grassi G, Sforza NM, Cairo F, Citterio F, Paolantoni G, D'Aiuto F, Ferri C, Monaco A, Pietropaoli D. Hypertension and Periodontitis: A Joint Report by the Italian Society of Hypertension (SIIA) and the Italian Society of Periodontology and Implantology (SIdP). High Blood Press Cardiovasc Prev 2021; 28:427-438. [PMID: 34562228 PMCID: PMC8484186 DOI: 10.1007/s40292-021-00466-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 02/07/2023] Open
Abstract
An accumulating body of evidence supports an independent association between high blood pressure (BP) and periodontitis, possibly mediated by low-grade inflammation. This joint report by the Italian Society of Hypertension (SIIA) and the Italian Society of Periodontology and Implantology (SIdP) working group on Hypertension and Periodontitis (Hy-Per Group) provides a review of the evidence on this topic encompassing epidemiology, biological plausibility, relevance, magnitude, and treatment management. Consensus recommendations are provided for health professionals on how to manage BP in individuals showing signs of poor oral health. In summary, (1) large epidemiological studies highlight that individuals with periodontal diseases have increased risk for high/uncontrolled BP independent of confounders; (2) mechanistically, low-grade inflammation might have a causal role in the association; (3) BP profile and control might benefit from periodontal treatment in pre-hypertensive and hypertensive individuals; (4) oral health status should be evaluated as a potential risk factor for high/uncontrolled BP, and effective oral care should be included as an adjunct lifestyle measure during hypertension management. Further research is needed to optimize BP management in individuals with poor oral health.
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Affiliation(s)
- Rita Del Pinto
- Unit of Internal Medicine and Nephrology, Department of Life, Health and Environmental Sciences, Center for Hypertension and Cardiovascular Prevention, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
| | - Luca Landi
- Private Practice, Via della Balduina, 114, 00136, Rome, Italy.
| | - Guido Grassi
- Department of Medicine and Surgery, Clinica Medica, University of Milano-Bicocca, Milan, Italy
| | | | - Francesco Cairo
- Research Unit in Periodontology and Periodontal Medicine, Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy
| | - Filippo Citterio
- Department of Surgical Sciences, C.I.R. Dental School, University of Turin, Turin, Italy
| | | | - Francesco D'Aiuto
- Periodontology Unit, UCL Eastman Dental Institute and Hospital, University College London, London, UK
| | - Claudio Ferri
- Unit of Internal Medicine and Nephrology, Department of Life, Health and Environmental Sciences, Center for Hypertension and Cardiovascular Prevention, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy.
| | - Annalisa Monaco
- Unit of Oral Diseases, Department of Life, Health and Environmental Sciences, Prevention and Translational Research, Dental Clinic, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
| | - Davide Pietropaoli
- Unit of Oral Diseases, Department of Life, Health and Environmental Sciences, Prevention and Translational Research, Dental Clinic, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
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671
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Schmidt AF, Hunt NB, Gordillo-Marañón M, Charoen P, Drenos F, Kivimaki M, Lawlor DA, Giambartolomei C, Papacosta O, Chaturvedi N, Bis JC, O'Donnell CJ, Wannamethee G, Wong A, Price JF, Hughes AD, Gaunt TR, Franceschini N, Mook-Kanamori DO, Zwierzyna M, Sofat R, Hingorani AD, Finan C. Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease. Nat Commun 2021; 12:5640. [PMID: 34561430 PMCID: PMC8463530 DOI: 10.1038/s41467-021-25703-3] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 08/23/2021] [Indexed: 02/08/2023] Open
Abstract
Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer's disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration.
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Affiliation(s)
- Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
- UCL British Heart Foundation Research Accelerator, London, UK.
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Nicholas B Hunt
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Maria Gordillo-Marañón
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Pimphen Charoen
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Bangkok, Thailand
| | - Fotios Drenos
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Life Sciences, College of Health, Medicine, and Life Sciences, Brunel University London, Uxbridge, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | | | - Olia Papacosta
- Primary Care and Population Health, University College London, London, UK
| | - Nishi Chaturvedi
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Christopher J O'Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
| | - Goya Wannamethee
- Primary Care and Population Health, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Alun D Hughes
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Bristol Biomedical Research Centre, University Hospitals Bristol National Health Service Foundation Trust and University of Bristol, Bristol, UK
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Magdalena Zwierzyna
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Health Data Research UK, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
- Health Data Research UK, London, UK
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672
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Metabolomics Signature of Plasma Renin Activity and Linkage with Blood Pressure Response to Beta Blockers and Thiazide Diuretics in Hypertensive European American Patients. Metabolites 2021; 11:metabo11090645. [PMID: 34564461 PMCID: PMC8466669 DOI: 10.3390/metabo11090645] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 01/13/2023] Open
Abstract
Plasma renin activity (PRA) is a predictive biomarker of blood pressure (BP) response to antihypertensives in European–American hypertensive patients. We aimed to identify the metabolic signatures of baseline PRA and the linkages with BP response to β-blockers and thiazides. Using data from the Pharmacogenomic Evaluation of Antihypertensive Responses-2 (PEAR-2) trial, multivariable linear regression adjusting for age, sex and baseline systolic-BP (SBP) was performed on European–American individuals treated with metoprolol (n = 198) and chlorthalidone (n = 181), to test associations between 856 metabolites and baseline PRA. Metabolites with a false discovery rate (FDR) < 0.05 or p < 0.01 were tested for replication in 463 European–American individuals treated with atenolol or hydrochlorothiazide. Replicated metabolites were then tested for validation based on the directionality of association with BP response. Sixty-three metabolites were associated with baseline PRA, of which nine, including six lipids, were replicated. Of those replicated, two metabolites associated with higher baseline PRA were validated: caprate was associated with greater metoprolol SBP response (β = −1.7 ± 0.6, p = 0.006) and sphingosine-1-phosphate was associated with reduced hydrochlorothiazide SBP response (β = 7.6 ± 2.8, p = 0.007). These metabolites are clustered with metabolites involved in sphingolipid, phospholipid, and purine metabolic pathways. The identified metabolic signatures provide insights into the mechanisms underlying BP response.
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673
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You R, Chen L, Xu L, Zhang D, Li H, Shi X, Zheng Y, Chen L. High Level of Uromodulin Increases the Risk of Hypertension: A Mendelian Randomization Study. Front Cardiovasc Med 2021; 8:736001. [PMID: 34540925 PMCID: PMC8440862 DOI: 10.3389/fcvm.2021.736001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/09/2021] [Indexed: 12/21/2022] Open
Abstract
Background: The association of uromodulin and hypertension has been observed in clinical studies, but not proven by a causal relationship. We conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between uromodulin and blood pressure. Methods: We selected single nucleotide polymorphisms (SNPs) related to urinary uromodulin (uUMOD) and serum uromodulin (sUMOD) from a large Genome-Wide Association Studies (GWAS) meta-analysis study and research in PubMed. Six datasets based on the UK Biobank and the International Consortium for Blood Pressure (ICBP) served as outcomes with a large sample of hypertension (n = 46,188), systolic blood pressure (SBP, n = 1,194,020), and diastolic blood pressure (DBP, n = 1,194,020). The inverse variance weighted (IVW) method was performed in uUMOD MR analysis, while methods of IVW, MR-Egger, Weighted median, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) were utilized on sUMOD MR analysis. Results: MR analysis of IVM showed the odds ratio (OR) of the uUMOD to hypertension (“ukb-b-14057” and “ukb-b-14177”) is 1.04 (95% Confidence Interval (CI), 1.03-1.04, P < 0.001); the effect sizes of the uUMOD to SBP are 1.10 (Standard error (SE) = 0.25, P = 8.92E-06) and 0.03 (SE = 0.01, P = 2.70E-04) in “ieu-b-38” and “ukb-b-20175”, respectively. The β coefficient of the uUMOD to DBP is 0.88 (SE = 0.19, P = 4.38E-06) in “ieu-b-39” and 0.05 (SE = 0.01, P = 2.13E-10) in “ukb-b-7992”. As for the sUMOD, the OR of hypertension (“ukb-b-14057” and “ukb-b-14177”) is 1.01 (95% CI 1.01–1.02, all P < 0.001). The β coefficient of the SBP is 0.37 (SE = 0.07, P = 1.26E-07) in “ieu-b-38” and 0.01 (SE = 0.003, P = 1.04E-04) in “ukb-b-20175”. The sUMOD is causally associated with elevated DBP (“ieu-b-39”: β = 0.313, SE = 0.050, P = 3.43E-10; “ukb-b-7992”: β = 0.018, SE = 0.003, P = 8.41E-09). Conclusion: Our results indicated that high urinary and serum uromodulin levels are potentially detrimental in elevating blood pressure, and serve as a causal risk factor for hypertension.
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Affiliation(s)
- Ruilian You
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Lanlan Chen
- First Clinical Medical College of Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Lubin Xu
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Dingding Zhang
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haitao Li
- China-Japan Friendship Hospital, Jilin University, Changchun, China
| | - Xiaoxiao Shi
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yali Zheng
- Department of Nephrology, Affiliated Ningxia People's Hospital of Ningxia Medical University, Yinchuan, China
| | - Limeng Chen
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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674
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Goulet D, O'Loughlin J, Sylvestre MP. Association of Genetic Variants With Body-Mass Index and Blood Pressure in Adolescents: A Replication Study. Front Genet 2021; 12:690335. [PMID: 34539733 PMCID: PMC8440872 DOI: 10.3389/fgene.2021.690335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 08/05/2021] [Indexed: 11/13/2022] Open
Abstract
The strong correlation between adiposity and blood pressure (BP) might be explained in part by shared genetic risk factors. A recent study identified three nucleotide variants [rs16933812 (PAX5), rs7638110 (MRPS22), and rs9930333 (FTO)] associated with both body mass index (BMI) and systolic blood pressure (SBP) in adolescents age 12-18years. We attempted to replicate these findings in a sample of adolescents of similar age. A total of 713 adolescents were genotyped and had anthropometric indicators and blood pressure measured at age 13, 15, 17, and 24years. Using linear mixed models, we assessed associations of these variants with BMI and SBP. In our data, rs9930333 (FTO) was associated with body mass index, but not systolic blood pressure. Neither rs16933812 (PAX5) nor rs7638110 (MRPS22) were associated with body mass index or systolic blood pressure. Although, differences in phenotypic definitions and in genetic architecture across populations may explain some of the discrepancy across studies, nucleotide variant selection in the initial study may have led to false-positive results that could not be replicated.
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Affiliation(s)
- Danick Goulet
- École de santé publique, Université de Montréal, Montréal, QC, Canada
| | - Jennifer O'Loughlin
- École de santé publique, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Marie-Pierre Sylvestre
- École de santé publique, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
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675
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Jäger S, Cabral M, Kopp JF, Hoffmann P, Ng E, Whitfield JB, Morris AP, Lind L, Schwerdtle T, Schulze MB. Blood copper and risk of cardiometabolic diseases-A Mendelian randomization study. Hum Mol Genet 2021; 31:783-791. [PMID: 34523676 PMCID: PMC8895748 DOI: 10.1093/hmg/ddab275] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/27/2021] [Accepted: 09/11/2021] [Indexed: 12/20/2022] Open
Abstract
Observational evidence links higher blood levels of copper with higher risk of cardiovascular diseases. However, whether those associations reflect causal links or can be attributed to confounding is still not fully clear. We investigated causal effects of copper on the risk of cardiometabolic endpoints (stroke, coronary artery disease [CAD] and type 2 diabetes) and cardiometabolic risk factors in two-sample Mendelian randomization (MR) studies. The selection of genetic instruments for blood copper levels relied on meta-analysis of genome-wide association studies in three independent studies (European Prospective Investigation into Cancer and Nutrition-Potsdam study, Prospective investigation of the Vasculature in Uppsala Seniors study, Queensland Institute of Medical Research studies). For the selected instruments, outcome associations were drawn from large public genetic consortia on the respective disease endpoints (MEGASTROKE, Cardiogram, DIAGRAM) and cardiometabolic risk factors. MR results indicate an inverse association for genetically higher copper levels with risk of CAD (odds ratio [95% confidence interval] = 0.92 [0.86–0.99], P = 0.022) and systolic blood pressure (beta [standard error (SE)] = −0.238 [0.121]; P = 0.049). Multivariable MR incorporating copper and systolic blood pressure into one model suggested systolic blood pressure as mediating factor between copper and CAD risk. In contrast to previous observational evidence establishing higher blood copper levels as risk-increasing factor for cardiometabolic diseases, this study suggests that higher levels of genetically predicted copper might play a protective role for the development of CAD and systolic blood pressure.
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Affiliation(s)
- Susanne Jäger
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,TraceAge-DFG Research Unit on Interactions of Essential Trace Elements in Healthy and Diseased Elderly, Potsdam-Berlin-Jena, Germany
| | - Maria Cabral
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,TraceAge-DFG Research Unit on Interactions of Essential Trace Elements in Healthy and Diseased Elderly, Potsdam-Berlin-Jena, Germany
| | - Johannes F Kopp
- TraceAge-DFG Research Unit on Interactions of Essential Trace Elements in Healthy and Diseased Elderly, Potsdam-Berlin-Jena, Germany.,Department of Food Chemistry, Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Per Hoffmann
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Human Genetics, Division of Genomics, Life & Brain Research Centre, University Hospital of Bonn, Bonn, Germany
| | - Esther Ng
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Tanja Schwerdtle
- TraceAge-DFG Research Unit on Interactions of Essential Trace Elements in Healthy and Diseased Elderly, Potsdam-Berlin-Jena, Germany.,Department of Food Chemistry, Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany.,German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Str. 8-10, 10589, Berlin, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,TraceAge-DFG Research Unit on Interactions of Essential Trace Elements in Healthy and Diseased Elderly, Potsdam-Berlin-Jena, Germany.,Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
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676
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Higgins H, Mason AM, Larsson SC, Gill D, Langenberg C, Burgess S. Estimating the Population Benefits of Blood Pressure Lowering: A Wide-Angled Mendelian Randomization Study in UK Biobank. J Am Heart Assoc 2021; 10:e021098. [PMID: 34459231 PMCID: PMC8649307 DOI: 10.1161/jaha.121.021098] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/13/2021] [Indexed: 11/19/2022]
Abstract
Background The causal relevance of elevated blood pressure for several cardiovascular diseases (CVDs) is uncertain, as is the population impact of blood pressure lowering. This study systematically assesses evidence of causality for various CVDs in a 2-sample Mendelian randomization framework, and estimates the potential reduction in the prevalence of these diseases attributable to long-term population shifts in the distribution of systolic blood pressure (SBP). Methods and Results We investigated associations of genetically predicted SBP as predicted by 256 genetic variants with 21 CVDs in UK Biobank, a population-based cohort of UK residents. The sample consisted of 376 703 participants of European ancestry, aged 40 to 69 years at recruitment. Genetically predicted SBP was positively associated with 14 of the outcomes (P<0.002), including dilated cardiomyopathy, endocarditis, peripheral vascular disease, and rheumatic heart disease. Using genetic variation to estimate the long-term impact of blood pressure lowering on disease in a middle-aged to early late-aged UK-based population, population reductions in SBP were predicted to result in an overall 16.9% (95% CI, 12.2%-21.3%) decrease in morbidity for a 5-mm Hg decrease from a population mean of 137.7 mm Hg, 30.8% (95% CI, 22.8%-38.0%) decrease for a 10-mm Hg decrease, and 56.2% (95% CI, 43.7%-65.9%) decrease for a 22.7-mm Hg decrease in SBP (22.7 mm Hg represents a shift from the current mean SBP to 115 mm Hg). Conclusions Risk of many CVDs is influenced by long-term differences in SBP. The burden of a broad range of CVDs could be substantially reduced by long-term population-wide reductions in the distribution of blood pressure.
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Affiliation(s)
- Hannah Higgins
- Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Amy M. Mason
- Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Susanna C. Larsson
- Department of Surgical SciencesUppsala UniversityUppsalaSweden
- Unit of Cardiovascular and Nutritional EpidemiologyInstitute of Environmental MedicineKarolinska InstitutetStockholmSweden
| | - Dipender Gill
- Department of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUnited Kingdom
- Department of GeneticsNovo Nordisk Research Centre OxfordOxfordUnited Kingdom
- Clinical Pharmacology and Therapeutics SectionInstitute of Medical and Biomedical Education and Institute for Infection and ImmunitySt George's, University of LondonLondonUnited Kingdom
- Clinical Pharmacology GroupPharmacy and Medicines DirectorateSt George's University Hospitals National Health Service Foundation TrustLondonUnited Kingdom
| | - Claudia Langenberg
- Medical Research Council Epidemiology UnitUniversity of CambridgeUnited Kingdom
- Computational MedicineBerlin Institute of HealthCharité UniversitätsmedizinBerlinGermany
| | - Stephen Burgess
- Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Medical Research Council Biostatistics UnitUniversity of CambridgeCambridgeUnited Kingdom
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677
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Niu M, Zhang L, Wang Y, Tu R, Liu X, Wang C, Bie R. Lifestyle Score and Genetic Factors With Hypertension and Blood Pressure Among Adults in Rural China. Front Public Health 2021; 9:687174. [PMID: 34485217 PMCID: PMC8416040 DOI: 10.3389/fpubh.2021.687174] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/21/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Although high genetic risk and unhealthful lifestyles are associated with a high risk of hypertension, but the combined relationship between lifestyle score and genetic factors on blood pressure remains limited, especially in resource-constrained areas. Aim: To explore the separate and joint effects between genetic and lifestyle factors on blood pressure and hypertension in rural areas. Methods: In 4,592 adults from rural China with a 3-year of follow-up, a genetic risk score (GRS) was established using 13 single nucleotide polymorphisms (SNPs) and the lifestyle score was calculated including factors diet, body mass index (BMI), smoking status, drinking status, and physical activity. The associations of genetic and lifestyle factors with blood pressure and hypertension were determined with generalized linear and logistic regression models, respectively. Results: The high-risk GRS was found to be associated with evaluated blood pressure and hypertension and the healthful lifestyle with diastolic blood pressure (DBP) level. Individuals with unhealthful lifestyles in the high GRS risk group had an odds ratio (OR) (95% CI) of 1.904 (1.006, 3.603) for hypertension than those with a healthful lifestyle in the low GRS risk group. Besides, the relative risk (RR), attributable risk (AR), and population attributable risk (PAR) for unhealthful lifestyle are 1.39, 5.87, 0.04%, respectively, and the prevented fraction for the population (PFP) for healthful lifestyle is 9.47%. Conclusion: These results propose a joint effect between genetic and lifestyle factors on blood pressure and hypertension. The findings provide support for adherence to a healthful lifestyle in hypertension precision prevention. Clinical Trial Registration: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.
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Affiliation(s)
- Miaomiao Niu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Liying Zhang
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yikang Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Runqi Tu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Xiaotian Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Ronghai Bie
- Department of Preventive Medicine, Henan University of Chinese Medicine, Zhengzhou, China
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678
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Sun J, Ding W, Liu X, Zhao M, Xi B. Serum metabolites of hypertension among Chinese adolescents aged 12-17 years. J Hum Hypertens 2021; 36:925-932. [PMID: 34480101 DOI: 10.1038/s41371-021-00602-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 08/17/2021] [Accepted: 08/26/2021] [Indexed: 11/09/2022]
Abstract
The regulatory mechanisms of hypertension in youth are incompletely understood. We aimed to identify potential serum metabolic alterations associated with hypertension in adolescents. A 1:1 age- and sex-matched case-control study including 30 hypertensive adolescents aged 12-17 years and 30 normotensive adolescents for the training set and 14 hypertensive adolescents and 14 normotensive adolescents for the test set was performed, which came from one cross-sectional study in Ningxia, China. Hypertension was defined based on blood pressure (BP) values measured on three different occasions according to the BP reference of Chinese children and adolescents. Untargeted ultra-high-performance liquid tandem chromatography quadrupole time of flight mass spectrometry was used to identify differential metabolites between hypertensive and normotensive adolescents. A total of 77 metabolites in positive mode and 101 in negative mode were identified (VIP > 1.0 and P < 0.05). After adjustment for the false discovery rate, 4 differential metabolites in positive mode and 10 in negative mode were found (Q value < 0.05). The logistic regression model adjusted for body mass index and lipid profile selected four significant metabolites (4-hydroxybutanoic acid, L-serine, acetone, and pterostilbene). The main metabolic pathways of amino acid metabolism, pantothenate and CoA biosynthesis, glyoxylate and dicarboxylate metabolism, fructose and mannose metabolism, and linoleic acid metabolism may contribute to the development of hypertension in Chinese adolescents. Based on the receiver operating characteristic plot, 4-hydroxybutanoic acid, L-serine, acetone, and pterostilbene may preliminarily help distinguish hypertension from normal BP in adolescents, with AUC values of 0.857 in the training set and 0.934 in the test set. The identified metabolites and pathways may foster a better understanding of hypertension pathogenesis in Chinese adolescents.
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Affiliation(s)
- Jiahong Sun
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Wenqing Ding
- Department of Children and Adolescents Health Care, School of Public Health, Ningxia Medical University, Ningxia, China
| | - Xue Liu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Min Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Bo Xi
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
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679
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Thayer TE, Huang S, Farber-Eger E, Beckman JA, Brittain EL, Mosley JD, Wells QS. Using genetics to detangle the relationships between red cell distribution width and cardiovascular diseases: a unique role for body mass index. Open Heart 2021; 8:e001713. [PMID: 34521746 PMCID: PMC8442102 DOI: 10.1136/openhrt-2021-001713] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 08/27/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Red cell distribution width (RDW) is an enigmatic biomarker associated with the presence and severity of multiple cardiovascular diseases (CVDs). It is unclear whether elevated RDW contributes to, results from, or is pleiotropically related to CVDs. We used contemporary genetic techniques to probe for evidence of aetiological associations between RDW, CVDs, and CVD risk factors. METHODS Using an electronic health record (EHR)-based cohort, we built and deployed a genetic risk score (GRS) for RDW to test for shared genetic architecture between RDW and the cardiovascular phenome. We also created GRSs for common CVDs (coronary artery disease, heart failure, atrial fibrillation, peripheral arterial disease, venous thromboembolism) and CVD risk factors (body mass index (BMI), low-density lipoprotein, high-density lipoprotein, systolic blood pressure, diastolic blood pressure, serum triglycerides, estimated glomerular filtration rate, diabetes mellitus) to test each for association with RDW. Significant GRS associations were further interrogated by two-sample Mendelian randomisation (MR). In a separate EHR-based cohort, RDW values from 1-year pre-gastric bypass surgery and 1-2 years post-gastric bypass surgery were compared. RESULTS In a cohort of 17 937 subjects, there were no significant associations between the RDW GRS and CVDs. Of the CVDs and CVD risk factors, only genetically predicted BMI was associated with RDW. In subsequent analyses, BMI was associated with RDW by multiple MR methods. In subjects undergoing bariatric surgery, RDW decreased postsurgery and followed a linear relationship with BMI change. CONCLUSIONS RDW is unlikely to be aetiologically upstream or downstream of CVDs or CVD risk factors except for BMI. Genetic and clinical association analyses support an aetiological relationship between BMI and RDW.
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Affiliation(s)
- Timothy E Thayer
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shi Huang
- Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric Farber-Eger
- VICTR, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Joshua A Beckman
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Evan L Brittain
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Quinn S Wells
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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680
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Curtis D. Analysis of 200,000 Exome-Sequenced UK Biobank Subjects Implicates Genes Involved in Increased and Decreased Risk of Hypertension. Pulse (Basel) 2021; 9:17-29. [PMID: 34722352 PMCID: PMC8527905 DOI: 10.1159/000517419] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/10/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Previous analyses have identified common variants along with some specific genes and rare variants which are associated with risk of hypertension, but much remains to be discovered. METHODS AND RESULTS Exome-sequenced UK Biobank participants were phenotyped based on having a diagnosis of hypertension or taking anti-hypertensive medication to produce a sample of 66,123 cases and 134,504 controls. Variants with minor allele frequency (MAF) <0.01 were subjected to a gene-wise weighted burden analysis, with higher weights assigned to variants which are rarer and/or predicted to have more severe effects. Of 20,384 genes analysed, 2 genes were exome-wide significant, DNMT3A and FES. Also strongly implicated were GUCY1A1 and GUCY1B1, which code for the subunits of soluble guanylate cyclase. There was further support for the previously reported effects of variants in NPR1 and protective effects of variants in DBH. An inframe deletion in CACNA1D with MAF = 0.005, rs72556363, is associated with modestly increased risk of hypertension. Other biologically plausible genes highlighted consist of CSK, AGTR1, ZYX, and PREP. All variants implicated were rare, and cumulatively they are not predicted to make a large contribution to the population risk of hypertension. CONCLUSIONS This approach confirms and clarifies previously reported findings and also offers novel insights into biological processes influencing hypertension risk, potentially facilitating the development of improved therapeutic interventions. This research has been conducted using the UK Biobank Resource.
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Affiliation(s)
- David Curtis
- UCL Genetics Institute, University College London, London, United Kingdom
- Centre for Psychiatry, Queen Mary University of London, London, United Kingdom
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681
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Tremblay J, Haloui M, Attaoua R, Tahir R, Hishmih C, Harvey F, Marois-Blanchet FC, Long C, Simon P, Santucci L, Hizel C, Chalmers J, Marre M, Harrap S, Cífková R, Krajčoviechová A, Matthews DR, Williams B, Poulter N, Zoungas S, Colagiuri S, Mancia G, Grobbee DE, Rodgers A, Liu L, Agbessi M, Bruat V, Favé MJ, Harwood MP, Awadalla P, Woodward M, Hussin JG, Hamet P. Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control. Diabetologia 2021; 64:2012-2025. [PMID: 34226943 PMCID: PMC8382653 DOI: 10.1007/s00125-021-05491-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10-21 and p = 9.6 × 10-31, respectively) and a 4.4-fold (p = 6.8 × 10-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.
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Affiliation(s)
- Johanne Tremblay
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada.
| | - Mounsif Haloui
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Redha Attaoua
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Ramzan Tahir
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Camil Hishmih
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - François Harvey
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | | | - Carole Long
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Paul Simon
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Lara Santucci
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - Candan Hizel
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada
| | - John Chalmers
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Michel Marre
- Clinique Ambroise Paré, Neuilly-sur-Seine, and Centre de Recherches des Cordeliers, Paris, France
| | - Stephen Harrap
- Department of Physiology, University of Melbourne, Melbourne, VIC, Australia
| | - Renata Cífková
- Center for Cardiovascular Prevention, First Faculty of Medicine, Charles University in Prague and Thomayer Hospital, Prague, Czech Republic
| | - Alena Krajčoviechová
- Center for Cardiovascular Prevention, First Faculty of Medicine, Charles University in Prague and Thomayer Hospital, Prague, Czech Republic
| | - David R Matthews
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Bryan Williams
- University College London, Institute of Cardiovascular Science, London, UK
| | - Neil Poulter
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Sophia Zoungas
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | | | - Giuseppe Mancia
- Istituto Auxologico Italiano, University of Milano, Bicocca, Italy
| | - Diederick E Grobbee
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Anthony Rodgers
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Liusheng Liu
- Beijing Hypertension League Institute, Beijing, China
| | | | - Vanessa Bruat
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | | | | | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics and Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Mark Woodward
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia.
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK.
- The George Institute for Global Health, School of Public Health, Imperial College London, London, UK.
| | - Julie G Hussin
- Montreal Heart Institute, Research Center, Montréal, Québec, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Pavel Hamet
- Department of Medicine, University of Montréal, CRCHUM, Québec, Canada.
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682
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Awany D, Allali I, Chimusa ER. Dissecting genome-wide studies for microbiome-related metabolic diseases. Hum Mol Genet 2021; 29:R73-R80. [PMID: 32478833 DOI: 10.1093/hmg/ddaa105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 05/14/2020] [Accepted: 05/29/2020] [Indexed: 12/14/2022] Open
Abstract
Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome and the environment. However, our knowledge of the genetic and microbiome components involved and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual's ancestry in these studies, we leverage 4932 whole-genome sequence samples from 18 worldwide ethnic groups to examine genetic diversity in currently reported variants associated with MetD. The analyses show marked differences in gene-specific proportion of pathogenic single-nucleotide polymorphisms (SNPs) and gene-specific SNPs MAFs across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome and the environment is needed.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Imane Allali
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, and Genomic Center of Human Pathologies, Faculty of Medicine and Pharmacy, Mohammed V University, Agdal Rabat, B.P, 8007 N.U, Morocco
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, Cape Town, South Africa.,Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
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683
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Boumitri M, Rai NK, Drawz PE. Genetic Risk Scores and Blood Pressure - The Heart is What Matters. KIDNEY360 2021; 2:1209-1211. [PMID: 35369658 PMCID: PMC8676381 DOI: 10.34067/kid.0003272021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 06/14/2023]
Affiliation(s)
- Mirna Boumitri
- Division of Nephrology and Hypertension, University of Minnesota, Minneapolis, Minnesota
| | - Nayanjot K. Rai
- Division of Nephrology and Hypertension, University of Minnesota, Minneapolis, Minnesota
| | - Paul E. Drawz
- Division of Nephrology and Hypertension, University of Minnesota, Minneapolis, Minnesota
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684
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Lin M, Caberto C, Wan P, Li Y, Lum-Jones A, Tiirikainen M, Pooler L, Nakamura B, Sheng X, Porcel J, Lim U, Setiawan VW, Le Marchand L, Wilkens LR, Haiman CA, Cheng I, Chiang CWK. Population-specific reference panels are crucial for genetic analyses: an example of the CREBRF locus in Native Hawaiians. Hum Mol Genet 2021; 29:2275-2284. [PMID: 32491157 DOI: 10.1093/hmg/ddaa083] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 01/10/2023] Open
Abstract
Statistical imputation applied to genome-wide array data is the most cost-effective approach to complete the catalog of genetic variation in a study population. However, imputed genotypes in underrepresented populations incur greater inaccuracies due to ascertainment bias and a lack of representation among reference individuals, further contributing to the obstacles to study these populations. Here we examined the consequences due to the lack of representation by genotyping in a large number of self-reported Native Hawaiians (N = 3693) a functionally important, Polynesian-specific variant in the CREBRF gene, rs373863828. We found the derived allele was significantly associated with several adiposity traits with large effects (e.g. ~ 1.28 kg/m2 per allele in body mass index as the most significant; P = 7.5 × 10-5), consistent with the original findings in Samoans. Due to the current absence of Polynesian representation in publicly accessible reference sequences, rs373863828 or its proxies could not be tested through imputation using these existing resources. Moreover, the association signals at the entire CREBRF locus could not be captured by alternative approaches, such as admixture mapping. In contrast, highly accurate imputation can be achieved even if a small number (<200) of internally constructed Polynesian reference individuals were available; this would increase sample size and improve the statistical evidence of associations. Taken together, our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci such as CREBRF. Yet, they could be easily detected and prioritized with improved representation of diverse populations in sequencing studies.
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Affiliation(s)
- Meng Lin
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christian Caberto
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Peggy Wan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA
| | - Annette Lum-Jones
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Maarit Tiirikainen
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Loreall Pooler
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Brooke Nakamura
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Xin Sheng
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jacqueline Porcel
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Unhee Lim
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Veronica Wendy Setiawan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.,Quantitative Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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685
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Loh NY, Noordam R, Christodoulides C. Telomere length and metabolic syndrome traits: A Mendelian randomisation study. Aging Cell 2021; 20:e13445. [PMID: 34312982 PMCID: PMC8373272 DOI: 10.1111/acel.13445] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/05/2021] [Accepted: 07/09/2021] [Indexed: 12/13/2022] Open
Abstract
Observational studies have revealed associations between short leucocyte telomere length (LTL), a TL marker in somatic tissues and multiple Metabolic Syndrome (MetS) traits. Animal studies have supported these findings by showing that increased telomere attrition leads to adipose tissue dysfunction and insulin resistance. We investigated the associations between genetically instrumented LTL and MetS traits using Mendelian Randomisation (MR). Fifty-two independent variants identified at FDR<0.05 from a genome-wide association study (GWAS) including 78,592 Europeans and collectively accounting for 2.93% of LTL variance were selected as genetic instruments for LTL. Summary-level data for MetS traits and for the MetS as a binary phenotype were obtained from the largest publicly available GWAS and two-sample MR analyses were used to estimate the associations of LTL with these traits. The combined effect of the genetic instruments was modelled using inverse variance weighted regression and sensitivity analyses with MR-Egger, weighted-median and MR-PRESSO were performed to test for and correct horizonal pleiotropy. Genetically instrumented longer LTL was associated with higher waist-to-hip ratio adjusted for body mass index (β = 0.045 SD, SE = 0.018, p = 0.01), raised systolic (β = 1.529 mmHg, SE = 0.332, p = 4x10-6 ) and diastolic (β = 0.633 mmHg, SE = 0.222, p = 0.004) blood pressure, and increased MetS risk (OR = 1.133, 95% CI 1.057-1.215). Consistent results were obtained in sensitivity analyses, which provided no evidence of unbalanced horizontal pleiotropy. Telomere shortening might not be a major driver of cellular senescence and dysfunction in human adipose tissue. Future experimental studies should examine the mechanistic bases for the links between longer LTL and increased upper-body fat distribution and raised blood pressure.
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Affiliation(s)
- Nellie Y. Loh
- Oxford Centre for Diabetes, Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK
| | - Raymond Noordam
- Department of Internal Medicine Section of Gerontology and Geriatrics Leiden University Medical Center Leiden The Netherlands
| | - Constantinos Christodoulides
- Oxford Centre for Diabetes, Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK
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686
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Abraham G, Rutten-Jacobs L, Inouye M. Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases. Stroke 2021; 52:2983-2991. [PMID: 34399584 PMCID: PMC7611731 DOI: 10.1161/strokeaha.120.032619] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.
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Affiliation(s)
- Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia
| | - Loes Rutten-Jacobs
- Personalized Health Care Data Science, Real World Data, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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687
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Genetic Variation in the ASTN2 Locus in Cardiovascular, Metabolic and Psychiatric Traits: Evidence for Pleiotropy Rather Than Shared Biology. Genes (Basel) 2021; 12:genes12081194. [PMID: 34440368 PMCID: PMC8391428 DOI: 10.3390/genes12081194] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 11/24/2022] Open
Abstract
Background: The link between cardiometabolic and psychiatric illness has long been attributed to human behaviour, however recent research highlights shared biological mechanisms. The ASTN2 locus has been previously implicated in psychiatric and cardiometabolic traits, therefore this study aimed to systematically investigate the genetic architecture of ASTN2 in relation to a wide range of relevant traits. Methods: Baseline questionnaire, assessment and genetic data of 402111 unrelated white British ancestry individuals from the UK Biobank was analysed. Genetic association analyses were conducted using PLINK 1.07, assuming an additive genetic model and adjusting for age, sex, genotyping chip, and population structure. Conditional analyses and linkage disequilibrium assessment were used to determine whether cardiometabolic and psychiatric signals were independent. Results: Associations between genetic variants in the ASTN2 locus and blood pressure, total and central obesity, neuroticism, anhedonia and mood instability were identified. All analyses support the independence of the cardiometabolic traits from the psychiatric traits. In silico analyses provide support for the central obesity signal acting through ASTN2, however most of the other signals are likely acting through other genes in the locus. Conclusions: Our systematic analysis demonstrates that ASTN2 has pleiotropic effects on cardiometabolic and psychiatric traits, rather than contributing to shared pathology.
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688
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Rødevand L, Bahrami S, Frei O, Chu Y, Shadrin A, O'Connell KS, Smeland OB, Elvsåshagen T, Hindley GFL, Djurovic S, Dale AM, Lagerberg TV, Steen NE, Andreassen OA. Extensive bidirectional genetic overlap between bipolar disorder and cardiovascular disease phenotypes. Transl Psychiatry 2021; 11:407. [PMID: 34301917 PMCID: PMC8302675 DOI: 10.1038/s41398-021-01527-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 12/13/2022] Open
Abstract
Patients with bipolar disorder (BIP) have a high risk of cardiovascular disease (CVD), despite considerable individual variation. The mechanisms underlying comorbid CVD in BIP remain largely unknown. We investigated polygenic overlap between BIP and CVD phenotypes, including CVD risk factors and coronary artery disease (CAD). We analyzed large genome-wide association studies of BIP (n = 51,710) and CVD phenotypes (n = 159,208-795,640), using bivariate causal mixture model (MiXeR), which estimates the total amount of shared genetic variants, and conjunctional false discovery rate (FDR), which identifies specific overlapping loci. MiXeR revealed polygenic overlap between BIP and body mass index (BMI) (82%), diastolic and systolic blood pressure (20-22%) and CAD (11%) despite insignificant genetic correlations. Using conjunctional FDR < 0.05, we identified 129 shared loci between BIP and CVD phenotypes, mainly BMI (n = 69), systolic (n = 53), and diastolic (n = 53) blood pressure, of which 22 are novel BIP loci. There was a pattern of mixed effect directions of the shared loci between BIP and CVD phenotypes. Functional analyses indicated that the shared loci are linked to brain-expressed genes and involved in neurodevelopment, lipid metabolism, chromatin assembly/disassembly and intracellular processes. Altogether, the study revealed extensive polygenic overlap between BIP and comorbid CVD, implicating shared molecular genetic mechanisms. The mixed effect directions of the shared loci suggest variation in genetic susceptibility to CVD across BIP subgroups, which may underlie the heterogeneity of CVD comorbidity in BIP patients. The findings suggest more focus on targeted lifestyle interventions and personalized pharmacological treatment to reduce CVD comorbidity in BIP.
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Affiliation(s)
- Linn Rødevand
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Shahram Bahrami
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Yunhan Chu
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torbjørn Elvsåshagen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Trine V Lagerberg
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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689
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Brouwers S, Sudano I, Kokubo Y, Sulaica EM. Arterial hypertension. Lancet 2021; 398:249-261. [PMID: 34019821 DOI: 10.1016/s0140-6736(21)00221-x] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/05/2021] [Indexed: 02/07/2023]
Abstract
Arterial hypertension is the most important contributor to the global burden of disease; however, disease control remains poor. Although the diagnosis of hypertension is still based on office blood pressure, confirmation with out-of-office blood pressure measurements (ie, ambulatory or home monitoring) is strongly recommended. The definition of hypertension differs throughout various guidelines, but the indications for antihypertensive therapy are relatively similar. Lifestyle adaptation is absolutely key in non-pharmacological treatment. Pharmacologically, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, calcium channel blockers, and diuretics are the first-line agents, with advice for the use of single-pill combination therapy by most guidelines. As a fourth-line agent, spironolactone should be considered. The rapidly evolving field of device-based therapy, especially renal denervation, will further broaden therapeutic options. Despite being a largely controllable condition, the actual rates of awareness, treatment, and control of hypertension are disappointingly low. Further improvements throughout the process of patient screening, diagnosis, treatment, and follow-up need to be urgently addressed.
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Affiliation(s)
- Sofie Brouwers
- Department of Cardiology, Cardiovascular Center Aalst, OLV Hospital Aalst, Aalst, Belgium; Department of Experimental Pharmacology, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium.
| | - Isabella Sudano
- University Heart Center, Cardiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Yoshihiro Kokubo
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Elisabeth M Sulaica
- Pharmacy Practice and Translational Research, University of Houston College of Pharmacy, Houston, TX, USA
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690
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Muiño E, Cárcel-Márquez J, Carrera C, Llucià-Carol L, Gallego-Fabrega C, Cullell N, Lledós M, Castillo J, Sobrino T, Campos F, Rodríguez-Castro E, Millán M, Muñoz-Narbona L, Bustamante A, López-Cancio E, Ribó M, Álvarez-Sabín J, Jiménez-Conde J, Roquer J, Giralt-Steinhauer E, Soriano-Tárraga C, Vives-Bauza C, Navarro RD, Tur S, Obach V, Arenillas JF, Segura T, Serrano-Heras G, Martí-Fàbregas J, Delgado-Mederos R, Camps-Renom P, Prats-Sánchez L, Guisado D, Guasch M, Marin R, Martínez-Domeño A, Freijo-Guerrero MDM, Moniche F, Cabezas JA, Castellanos M, Krupinsky J, Strbian D, Tatlisumak T, Thijs V, Lemmens R, Slowik A, Pera J, Heitsch L, Ibañez L, Cruchaga C, Dhar R, Lee JM, Montaner J, Fernández-Cadenas I, on behalf of International Stroke Genetic Consortium, the Spanish Stroke Genetic Consortium. RP11-362K2.2:RP11-767I20.1 Genetic Variation Is Associated with Post-Reperfusion Therapy Parenchymal Hematoma. A GWAS Meta-Analysis. J Clin Med 2021; 10:jcm10143137. [PMID: 34300314 PMCID: PMC8305811 DOI: 10.3390/jcm10143137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/05/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Stroke is one of the most common causes of death and disability. Reperfusion therapies are the only treatment available during the acute phase of stroke. Due to recent clinical trials, these therapies may increase their frequency of use by extending the time-window administration, which may lead to an increase in complications such as hemorrhagic transformation, with parenchymal hematoma (PH) being the more severe subtype, associated with higher mortality and disability rates. Our aim was to find genetic risk factors associated with PH, as that could provide molecular targets/pathways for their prevention/treatment and study its genetic correlations to find traits sharing genetic background. We performed a GWAS and meta-analysis, following standard quality controls and association analysis (fastGWAS), adjusting age, NIHSS, and principal components. FUMA was used to annotate, prioritize, visualize, and interpret the meta-analysis results. The total number of patients in the meta-analysis was 2034 (216 cases and 1818 controls). We found rs79770152 having a genome-wide significant association (beta 0.09, p-value 3.90 × 10−8) located in the RP11-362K2.2:RP11-767I20.1 gene and a suggestive variant (rs13297983: beta 0.07, p-value 6.10 × 10−8) located in PCSK5 associated with PH occurrence. The genetic correlation showed a shared genetic background of PH with Alzheimer’s disease and white matter hyperintensities. In addition, genes containing the ten most significant associations have been related to aggregated amyloid-β, tau protein, white matter microstructure, inflammation, and matrix metalloproteinases.
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Affiliation(s)
- Elena Muiño
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
| | - Jara Cárcel-Márquez
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
| | - Caty Carrera
- Neurovascular Research Laboratory, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, 08025 Barcelona, Spain;
| | - Laia Llucià-Carol
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
| | - Cristina Gallego-Fabrega
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Natalia Cullell
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
- Stroke Pharmacogenomics and Genetics, Fundació MútuaTerrassa per la Docència i la Recerca, 08221 Terrassa, Spain
| | - Miquel Lledós
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
| | - José Castillo
- Clinical Neurosciences Research Laboratories, Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain; (J.C.); (T.S.); (F.C.)
| | - Tomás Sobrino
- Clinical Neurosciences Research Laboratories, Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain; (J.C.); (T.S.); (F.C.)
| | - Francisco Campos
- Clinical Neurosciences Research Laboratories, Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain; (J.C.); (T.S.); (F.C.)
| | - Emilio Rodríguez-Castro
- Department of Neurology, Hospital Clínico Universitario de Santiago, 15706 Santiago de Compostela, Spain;
| | - Mònica Millán
- Department of Neuroscience, Hospital Germans Trias i Pujol, 08025 Badalona, Spain; (M.M.); (L.M.-N.); (A.B.)
| | - Lucía Muñoz-Narbona
- Department of Neuroscience, Hospital Germans Trias i Pujol, 08025 Badalona, Spain; (M.M.); (L.M.-N.); (A.B.)
| | - Alejandro Bustamante
- Department of Neuroscience, Hospital Germans Trias i Pujol, 08025 Badalona, Spain; (M.M.); (L.M.-N.); (A.B.)
| | - Elena López-Cancio
- Stroke Unit, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
| | - Marc Ribó
- Stroke Unit, Hospital Universitario Valle de Hebrón, 08025 Barcelona, Spain;
| | - José Álvarez-Sabín
- Department of Neurology, Hospital Universitario Valle de Hebrón, Universidad Autónoma de Barcelona, 08025 Barcelona, Spain;
| | - Jordi Jiménez-Conde
- Department of Neurology, Neurovascular Research Group, Instituto de Investigaciones Médicas Hospital del Mar-Hospital del Mar, 08025 Barcelona, Spain; (J.J.-C.); (J.R.); (E.G.-S.); (C.S.-T.)
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group, Instituto de Investigaciones Médicas Hospital del Mar-Hospital del Mar, 08025 Barcelona, Spain; (J.J.-C.); (J.R.); (E.G.-S.); (C.S.-T.)
| | - Eva Giralt-Steinhauer
- Department of Neurology, Neurovascular Research Group, Instituto de Investigaciones Médicas Hospital del Mar-Hospital del Mar, 08025 Barcelona, Spain; (J.J.-C.); (J.R.); (E.G.-S.); (C.S.-T.)
| | - Carolina Soriano-Tárraga
- Department of Neurology, Neurovascular Research Group, Instituto de Investigaciones Médicas Hospital del Mar-Hospital del Mar, 08025 Barcelona, Spain; (J.J.-C.); (J.R.); (E.G.-S.); (C.S.-T.)
| | - Cristófol Vives-Bauza
- Neurobiology Laboratory, Instituto de Investigación Sanitaria de Palma, 07120 Mallorca, Spain;
| | - Rosa Díaz Navarro
- Department of Neurology, Hospital Universitari Son Espases, 07120 Mallorca, Spain; (R.D.N.); (S.T.)
| | - Silvia Tur
- Department of Neurology, Hospital Universitari Son Espases, 07120 Mallorca, Spain; (R.D.N.); (S.T.)
| | - Victor Obach
- Department of Neurology, Hospital Clínic i Provincial de Barcelona, 08025 Barcelona, Spain;
| | - Juan F. Arenillas
- Department of Neurology, Hospital Clínico Universitario, University of Valladolid, 47003 Valladolid, Spain;
| | - Tomás Segura
- Department of Neurology, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain;
| | - Gemma Serrano-Heras
- Experimental Research Unit, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain;
| | - Joan Martí-Fàbregas
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Raquel Delgado-Mederos
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Pol Camps-Renom
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Luis Prats-Sánchez
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Daniel Guisado
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Marina Guasch
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Rebeca Marin
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | - Alejandro Martínez-Domeño
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain; (J.M.-F.); (R.D.-M.); (P.C.-R.); (L.P.-S.); (D.G.); (M.G.); (R.M.); (A.M.-D.)
| | | | - Francisco Moniche
- Department of Neurology, Virgen del Rocío, Instituto de Biomedicina de Sevilla, 41013 Seville, Spain; (F.M.); (J.A.C.); (J.M.)
| | - Juan Antonio Cabezas
- Department of Neurology, Virgen del Rocío, Instituto de Biomedicina de Sevilla, 41013 Seville, Spain; (F.M.); (J.A.C.); (J.M.)
| | - Mar Castellanos
- Department of Neurology, Complejo Hospitalario Universitario A Coruña, 15006 A Coruña, Spain;
| | - Jerzy Krupinsky
- School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK;
- Neurology Unit, Hospital Universitari Mútua Terrassa, 08221 Terrassa, Spain
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital, FI-00029 Helsinki, Finland;
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neurosciences and Physiology, Sahlgrenska Academy at University of Gothenburg, 41345 Gothenburg, Sweden;
- Department of Neurology, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg VIC 3072, Australia;
- Department of Neurology, Austin Health, Heidelberg VIC 3072, Australia
| | - Robin Lemmens
- Department of Neurology, University Hospitals Leuven, Campus Gasthuisberg, 3000 Leuven, Belgium;
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, 31-007 Kraków, Poland; (A.S.); (J.P.)
| | - Joanna Pera
- Department of Neurology, Jagiellonian University Medical College, 31-007 Kraków, Poland; (A.S.); (J.P.)
| | - Laura Heitsch
- Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO 63110-1010, USA;
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110-1010, USA; (R.D.); (J.-M.L.)
| | - Laura Ibañez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110-1010, USA; (L.I.); (C.C.)
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110-1010, USA; (L.I.); (C.C.)
| | - Rajat Dhar
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110-1010, USA; (R.D.); (J.-M.L.)
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110-1010, USA; (R.D.); (J.-M.L.)
| | - Joan Montaner
- Department of Neurology, Virgen del Rocío, Instituto de Biomedicina de Sevilla, 41013 Seville, Spain; (F.M.); (J.A.C.); (J.M.)
| | - Israel Fernández-Cadenas
- Stroke Pharmacogenomics and Genetics Group, Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (E.M.); (J.C.-M.); (L.L.-C.); (C.G.-F.); (N.C.); (M.L.)
- Correspondence:
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691
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De Lillo A, D'Antona S, Pathak GA, Wendt FR, De Angelis F, Fuciarelli M, Polimanti R. Cross-ancestry genome-wide association studies identified heterogeneous loci associated with differences of allele frequency and regulome tagging between participants of European descent and other ancestry groups from the UK Biobank. Hum Mol Genet 2021; 30:1457-1467. [PMID: 33890984 PMCID: PMC8283210 DOI: 10.1093/hmg/ddab114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 04/14/2021] [Accepted: 01/09/2021] [Indexed: 01/28/2023] Open
Abstract
To investigate cross-ancestry genetics of complex traits, we conducted a phenome-wide analysis of loci with heterogeneous effects across African, Admixed-American, Central/South Asian, East Asian, European and Middle Eastern participants of the UK Biobank (N = 441 331). Testing 843 phenotypes, we identified 82 independent genomic regions mapping variants showing genome-wide significant (GWS) associations (P < 5 × 10-8) in the trans-ancestry meta-analysis and GWS heterogeneity among the ancestry-specific effects. These included (i) loci with GWS association in one ancestry and concordant but heterogeneous effects among the other ancestries and (ii) loci with a GWS association in one ancestry group and an experiment-wide significant discordant effect (P < 6.1 × 10-4) in at least another ancestry. Since the trans-ancestry GWS associations were mostly driven by the European ancestry sample size, we investigated the differences of the allele frequency (ΔAF) and linkage disequilibrium regulome tagging (ΔLD) between European populations and the other ancestries. Within loci with concordant effects, the degree of heterogeneity was associated with European-Middle Eastern ΔAF (P = 9.04 × 10-6) and ΔLD of European populations with respect to African, Admixed-American and Central/South Asian groups (P = 8.21 × 10-4, P = 7.17 × 10-4 and P = 2.16 × 10-3, respectively). Within loci with discordant effects, ΔAF and ΔLD of European populations with respect to African and Central/South Asian ancestries were associated with the degree of heterogeneity (ΔAF: P = 7.69 × 10-3 and P = 5.31 × 10-3, ΔLD: P = 0.016 and P = 2.65 × 10-4, respectively). Considering the traits associated with cross-ancestry heterogeneous loci, we observed enrichments for blood biomarkers (P = 5.7 × 10-35) and physical appearance (P = 1.38 × 10-4). This suggests that these specific phenotypic classes may present considerable cross-ancestry heterogeneity owing to large allele frequency and LD variation among worldwide populations.
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Affiliation(s)
- Antonella De Lillo
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Salvatore D'Antona
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Maria Fuciarelli
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
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692
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Tesi N, van der Lee S, Hulsman M, Holstege H, Reinders MJT. snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets. Nucleic Acids Res 2021; 49:W603-W612. [PMID: 34048563 PMCID: PMC8262737 DOI: 10.1093/nar/gkab410] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/19/2021] [Accepted: 05/01/2021] [Indexed: 02/06/2023] Open
Abstract
Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer.
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Affiliation(s)
- Niccolo Tesi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Sven van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marc Hulsman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
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693
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Heilbron K, Mozaffari SV, Vacic V, Yue P, Wang W, Shi J, Jubb AM, Pitts SJ, Wang X. Advancing drug discovery using the power of the human genome. J Pathol 2021; 254:418-429. [PMID: 33748968 PMCID: PMC8251523 DOI: 10.1002/path.5664] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/31/2022]
Abstract
Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating the discovery of therapies, present examples of how human genetics evidence supports successful drug targets, and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct-to-consumer platforms in the era of fast-paced big data biotechnology, and how diverse genetic and health data can benefit society. © 2021 23andMe, Inc. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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694
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Xue H, Shen X, Pan W. Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. Am J Hum Genet 2021; 108:1251-1269. [PMID: 34214446 PMCID: PMC8322939 DOI: 10.1016/j.ajhg.2021.05.014] [Citation(s) in RCA: 208] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/25/2021] [Indexed: 12/23/2022] Open
Abstract
With the increasing availability of large-scale GWAS summary data on various complex traits and diseases, there have been tremendous interests in applications of Mendelian randomization (MR) to investigate causal relationships between pairs of traits using SNPs as instrumental variables (IVs) based on observational data. In spite of the potential significance of such applications, the validity of their causal conclusions critically depends on some strong modeling assumptions required by MR, which may be violated due to the widespread (horizontal) pleiotropy. Although many MR methods have been proposed recently to relax the assumptions by mainly dealing with uncorrelated pleiotropy, only a few can handle correlated pleiotropy, in which some SNPs/IVs may be associated with hidden confounders, such as some heritable factors shared by both traits. Here we propose a simple and effective approach based on constrained maximum likelihood and model averaging, called cML-MA, applicable to GWAS summary data. To deal with more challenging situations with many invalid IVs with only weak pleiotropic effects, we modify and improve it with data perturbation. Extensive simulations demonstrated that the proposed methods could control the type I error rate better while achieving higher power than other competitors. Applications to 48 risk factor-disease pairs based on large-scale GWAS summary data of 3 cardio-metabolic diseases (coronary artery disease, stroke, and type 2 diabetes), asthma, and 12 risk factors confirmed its superior performance.
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Affiliation(s)
- Haoran Xue
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
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695
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Gill D, Zuber V, Dawson J, Pearson-Stuttard J, Carter AR, Sanderson E, Karhunen V, Levin MG, Wootton RE, Klarin D, Tsao PS, Tsilidis KK, Damrauer SM, Burgess S, Elliott P. Risk factors mediating the effect of body mass index and waist-to-hip ratio on cardiovascular outcomes: Mendelian randomization analysis. Int J Obes (Lond) 2021; 45:1428-1438. [PMID: 34002035 PMCID: PMC8236409 DOI: 10.1038/s41366-021-00807-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 02/23/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Higher body mass index (BMI) and waist-to-hip ratio (WHR) increase the risk of cardiovascular disease, but the extent to which this is mediated by blood pressure, diabetes, lipid traits, and smoking is not fully understood. METHODS Using consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits, and smoking mediated an effect of BMI and WHR on the risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke. RESULTS The odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% -23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI -20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome. CONCLUSIONS Measures to reduce obesity will lower the risk of cardiovascular disease primarily by impacting downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK.
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK.
- Novo Nordisk Research Centre Oxford, Oxford, UK.
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Jesse Dawson
- University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, UK
| | - Jonathan Pearson-Stuttard
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Alice R Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Michael G Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Robyn E Wootton
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Derek Klarin
- Malcom Randall VA Medical Center, Gainesville, FL, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, Fl, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Livermore, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, London, UK
- Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London, UK
- Health Data Research UK-London, London, UK
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696
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Mordi IR, Lumbers RT, Palmer CNA, Pearson ER, Sattar N, Holmes MV, Lang CC. Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study. Diabetes Care 2021; 44:1699-1705. [PMID: 34088700 PMCID: PMC8323186 DOI: 10.2337/dc20-2518] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/17/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF). RESEARCH DESIGN AND METHODS Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators. RESULTS Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11-1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00-1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01-2.19; P = 0.042), although again with evidence of pleiotropy. CONCLUSIONS These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered.
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Affiliation(s)
- Ify R Mordi
- Division of Molecular and Clinical Medicine, University of Dundee, Dundee, U.K.
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, U.K
- Health Data Research UK London, University College London, U.K
- UCL British Heart Foundation Research Accelerator, London, U.K
| | - Colin N A Palmer
- Division of Population Health and Genomics, University of Dundee, Dundee, U.K
| | - Ewan R Pearson
- Division of Population Health and Genomics, University of Dundee, Dundee, U.K
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, U.K
- Clinical Trial Service and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
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697
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Kässens JC, Wienbrandt L, Ellinghaus D. BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data. Gigascience 2021; 10:giab047. [PMID: 34184051 PMCID: PMC8239664 DOI: 10.1093/gigascience/giab047] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/02/2021] [Accepted: 06/09/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples. RESULTS Here we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. CONCLUSIONS Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.
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Affiliation(s)
- Jan Christian Kässens
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
- Haematology Lab Kiel, Klinik für Innere Medizin II, University Hospital Schleswig-Holstein, Langer Segen 8-10, 24105 Kiel, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
- Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
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698
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Kelley GA, Kelley KS, Stauffer BL. Isometric exercise and inter-individual response differences on resting systolic and diastolic blood pressure in adults: a meta-analysis of randomized controlled trials. Blood Press 2021; 30:310-321. [PMID: 34176377 DOI: 10.1080/08037051.2021.1940837] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
PURPOSE Isometric exercise (IE) has been shown to reduce resting systolic blood pressure (SBP) and diastolic blood pressure (DBP) in adults. However, no one to date has determined whether true inter-individual response differences (IIRD) versus random variability exist with respect to IE and resting SBP and DBP in adults ≥18 years of age. The purpose of the current study was to address this gap. METHODS AND MATERIALS Using the meta-analytic approach, randomised controlled trials from a recent meta-analysis that examined the effects of IE on resting SBP and DBP were included. Change outcome standard deviations for SBP and DBP from IE and control groups were used to calculate true IIRD from each study. The inverse variance heterogeneity (IVhet) model was used to pool results. RESULTS Pooled changes for true IIRD in SBP (16 studies, 411 participants) were 3.3 mmHg (95% confidence interval, -3.1 to 5.6 mmHg) while tau (τ) was 4.2. For DBP, true IIRD (16 studies, 411 participants) were 2.3 mmHg (95% confidence interval, -0.7 to 3.3 mmHg) while tau (τ) was 2.2. The 95% prediction interval for true IIRD in a future study was -5.8 to 7.4 mmHg for SBP and -2.7 to 4.2 mmHg for DBP. The percent chance, i.e. probability, of a clinically meaningful difference of 2 mmHg was 68% for SBP and 75% for DBP, both of which were only considered as 'possibly clinically important'. CONCLUSION While IE reduces resting SBP and DBP in adults, the results of the current study suggest that random variability versus true IIRD account for any potential differences as a result of IE on changes in resting SBP and DBP in adults. Thus, a search for potential moderators and mediators, including potential genetic interactions associated with IE, may not be warranted.
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Affiliation(s)
- George A Kelley
- Department of Epidemiology and Biostatistics, School of Public Health, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV, USA
| | - Kristi S Kelley
- Department of Epidemiology and Biostatistics, School of Public Health, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV, USA
| | - Brian L Stauffer
- Department of Medicine, Division of Cardiology, Denver Health Medical Center, University of Colorado at Denver, Aurora, CO, USA
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699
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Sun X, Ho JE, Gao H, Evangelou E, Yao C, Huan T, Hwang SJ, Courchesne P, Larson MG, Levy D, Ma J, Liu C. Associations of Alcohol Consumption with Cardiovascular Disease-Related Proteomic Biomarkers: The Framingham Heart Study. J Nutr 2021; 151:2574-2582. [PMID: 34159370 PMCID: PMC8417922 DOI: 10.1093/jn/nxab186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 05/17/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Alcohol consumption and cardiovascular disease (CVD) have a complex relation. OBJECTIVES We examined the associations between alcohol consumption, fasting plasma proteins, and CVD risk. METHODS We performed cross-sectional association analyses of alcohol consumption with 71 CVD-related plasma proteins, and also performed prospective association analyses of alcohol consumption and protein concentrations with 3 CVD risk factors (obesity, hypertension, and diabetes) in 6745 Framingham Heart Study (FHS) participants (mean age 49 y; 53% women). RESULTS A unit increase in log10 transformed alcohol consumption (g/d) was associated with an increased risk of hypertension (HR = 1.14; 95% CI: 1.04, 1.26; P = 0.007), and decreased risks of obesity (HR = 0.80; 95% CI: 0.71, 0.91; P = 4.6 × 10-4) and diabetes (HR: 0.68; 95% CI: 0.58, 0.80; P = 5.1 × 10-6) in a median of 13-y (interquartile = 7, 14) of follow-up. We identified 43 alcohol-associated proteins in a discovery sample (n = 4348, false discovery rate <0.05) and 20 of them were significant (P <0.05/43) in an independent validation sample (n = 2397). Eighteen of the 20 proteins were inversely associated with alcohol consumption. Four of the 20 proteins demonstrated 3-way associations, as expected, with alcohol consumption and CVD risk factors. For example, a greater concentration of APOA1 was associated with higher alcohol consumption (P = 1.2 × 10-65), and it was also associated with a lower risk of diabetes (P = 8.5 × 10-6). However, several others showed unexpected 3-way associations. CONCLUSIONS We identified 20 alcohol-associated proteins in 6745 FHS samples. These alcohol-associated proteins demonstrated complex relations with the 3 CVD risk factors. Future studies with integration of more proteomic markers and larger sample size are warranted to unravel the complex relation between alcohol consumption and CVD risk.
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Affiliation(s)
- Xianbang Sun
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Jennifer E Ho
- Division of Cardiology, Department of Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - He Gao
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Chen Yao
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA,Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Tianxiao Huan
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA,Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA,Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Paul Courchesne
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA,Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Martin G Larson
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA,Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA
| | - Daniel Levy
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA,Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
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Larsson SC, Gill D. Association of Serum Magnesium Levels With Risk of Intracranial Aneurysm: A Mendelian Randomization Study. Neurology 2021; 97:e341-e344. [PMID: 34158381 PMCID: PMC8362358 DOI: 10.1212/wnl.0000000000012244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/19/2021] [Indexed: 11/30/2022] Open
Abstract
Objective Magnesium has been implicated in regulating blood pressure and vascular endothelial cell function, but its role in the pathophysiology of intracranial aneurysm is not known. Here we performed a Mendelian randomization analysis to investigate the association between serum magnesium concentration and risk of intracranial aneurysm. Methods Five single-nucleotide polymorphisms strongly associated with serum magnesium concentrations in a genome-wide association study in 23,829 individuals of European ancestry were used as genetic instruments. Genetic association estimates for intracranial aneurysm were obtained from a genome-wide association study in 79,429 individuals (7,495 cases and 71,934 controls). The inverse variance weighted method was used in the primary analyses to obtain the causal estimates. Results Higher genetically predicted serum magnesium concentrations were associated with lower risk of intracranial aneurysm. The odds ratios per 0.1 mmol/L increment in genetically predicted serum magnesium concentrations were 0.66 (95% confidence interval [CI] 0.49–0.91) for intracranial aneurysm (unruptured and ruptured combined), 0.57 (95% CI 0.30–1.06) for unruptured intracranial aneurysm, and 0.67 (95% CI 0.48–0.92) for aneurysmal subarachnoid hemorrhage. Conclusion This study provides evidence to support that increased serum magnesium concentrations reduce the risk of intracranial aneurysm and associated hemorrhage.
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Affiliation(s)
- Susanna C Larsson
- From the Unit of Cardiovascular and Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Unit of Medical Epidemiology (S.C.L.), Department of Surgical Sciences, Uppsala University, Sweden; Department of Epidemiology and Biostatistics (D.G.), School of Public Health, St Mary's Hospital, Imperial College London; Clinical Pharmacology and Therapeutics Section (D.G.), Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London; Clinical Pharmacology Group (D.G.), Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London; and Novo Nordisk Research Centre Oxford (D.G.), UK.
| | - Dipender Gill
- From the Unit of Cardiovascular and Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Unit of Medical Epidemiology (S.C.L.), Department of Surgical Sciences, Uppsala University, Sweden; Department of Epidemiology and Biostatistics (D.G.), School of Public Health, St Mary's Hospital, Imperial College London; Clinical Pharmacology and Therapeutics Section (D.G.), Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London; Clinical Pharmacology Group (D.G.), Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London; and Novo Nordisk Research Centre Oxford (D.G.), UK
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