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Wang Y, Chen H, Peloso GM, DeStefano AL, Dupuis J. Exploiting family history in aggregation unit-based genetic association tests. Eur J Hum Genet 2022; 30:1355-1362. [PMID: 34690355 PMCID: PMC9712547 DOI: 10.1038/s41431-021-00980-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022] Open
Abstract
The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.
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Affiliation(s)
- Yanbing Wang
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, 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, 77030, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Gina M Peloso
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Anita L DeStefano
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA.
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Staplin N, Herrington WG, Murgia F, Ibrahim M, Bull KR, Judge PK, Ng SYA, Turner M, Zhu D, Emberson J, Landray MJ, Baigent C, Haynes R, Hopewell JC. Determining the Relationship Between Blood Pressure, Kidney Function, and Chronic Kidney Disease: Insights From Genetic Epidemiology. Hypertension 2022; 79:2671-2681. [PMID: 36082669 PMCID: PMC9640248 DOI: 10.1161/hypertensionaha.122.19354] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND It is well established that decreased kidney function can increase blood pressure (BP), but it is unproven whether moderately elevated BP causes chronic kidney disease (CKD) or glomerular hyperfiltration. METHODS 311 119 White British UK Biobank participants were included in logistic regression analyses to estimate the odds of CKD (defined as long-term kidney replacement therapy, estimated glomerular filtration rate [eGFR]< 60mL/min/1.73m2, or urinary albumin:creatinine ratio ≥3 mg/mmol) associated with higher genetically predicted BP using genetic risk scores comprising 219 systolic and 223 diastolic BP loci. Analyses estimating associations with clinical categories of eGFR and urinary albumin:creatinine ratio were also conducted, with an eGFR ≥120 mL (min·1.73m2) considered evidence of glomerular hyperfiltration. RESULTS 21 623 participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. 1828 participants had an eGFR ≥120 mL/min/1.73m2. Each genetically predicted 10 mmHg higher systolic BP and 5 mmHg higher diastolic BP were associated with a 37% (95% CI, 1.29-1.45) and 19% (1.14-1.25) higher odds of CKD, respectively. Associations were evident for both the reduced eGFR and albuminuria components of the CKD outcome. The odds of hyperfiltration (versus an eGFR ≥60 and <90 mL/min/1.73m2 were 49% higher (95% CI, 1.21-1.84) for each genetically predicted 10 mmHg higher systolic BP. Associations with CKD and hyperfiltration were similar irrespective of preexisting diabetes, vascular disease, or different levels of adiposity. CONCLUSIONS In this general population, genetic epidemiological evidence supports a causal role of life-long differences in BP for decreased kidney function, glomerular hyperfiltration, and albuminuria. Physiological autoregulation may not afford complete renal protection against the moderate BP elevations.
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Affiliation(s)
- Natalie Staplin
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - William G Herrington
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Health Data Research UK (W.G.H., M.J.L.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Federico Murgia
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Maysson Ibrahim
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Katherine R Bull
- Nuffield Department of Medicine (K.R.B.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Parminder K Judge
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Sarah Y A Ng
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Michael Turner
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Doreen Zhu
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Jonathan Emberson
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Martin J Landray
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom.,Health Data Research UK (W.G.H., M.J.L.), University of Oxford, Oxford, United Kingdom.,National Institute for Health Research Oxford Biomedical Research Centre (M.J.L.), University of Oxford, Oxford, United Kingdom
| | - Colin Baigent
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Richard Haynes
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Jemma C Hopewell
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
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Hay R, Cullen B, Graham N, Lyall DM, Aman A, Pell JP, Ward J, Smith DJ, Strawbridge RJ. Genetic analysis of the PCSK9 locus in psychological, psychiatric, metabolic and cardiovascular traits in UK Biobank. Eur J Hum Genet 2022; 30:1380-1390. [PMID: 35501368 PMCID: PMC9712543 DOI: 10.1038/s41431-022-01107-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/11/2022] [Accepted: 04/12/2022] [Indexed: 11/09/2022] Open
Abstract
The association between severe mental illness (SMI) and cardiovascular and metabolic disease (CMD) is poorly understood. PCSK9 is expressed in systems critical to both SMI and CMD and influences lipid homeostasis and brain function. We systematically investigated relationships between genetic variation within the PCSK9 locus and risk for both CMD and SMI. UK Biobank recruited ~500,000 volunteers and assessed a wide range of SMI and CMD phenotypes. We used genetic data from white British ancestry individuals of UK Biobank. Genetic association analyses were conducted in PLINK, with statistical significance defined by the number of independent SNPs. Conditional analyses and linkage disequilibrium assessed the independence of SNPs and the presence of multiple signals. Two genetic risk scores of lipid-lowering alleles were calculated and used as proxies for putative lipid-lowering effects of PCSK9. PCSK9 variants were associated with central adiposity, venous thrombosis embolism, systolic blood pressure, mood instability, and neuroticism (all p < 1.16 × 10-4). No secondary signals were identified. Conditional analyses and high linkage disequilibrium (r2 = 0.98) indicated that mood instability and central obesity may share a genetic signal. Genetic risk scores suggested that the lipid-lowering effects of PCSK9 may be causal for greater mood instability and higher neuroticism. This is the first study to implicate the PCSK9 locus in mood-disorder symptoms and related traits, as well as the shared pathology of SMI and CMD. PCSK9 effects on mood may occur via lipid-lowering mechanisms. Further work is needed to understand whether repurposing PCSK9-targeting therapies might improve SMI symptoms and prevent CMD.
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Affiliation(s)
- Rachel Hay
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Breda Cullen
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Nicholas Graham
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Donald M Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Alisha Aman
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jill P Pell
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.
- Health Data Research UK, Glasgow, UK.
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden.
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54
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Abramova MY, Ponomarenko IV, Churnosov MI. The Polymorphic Locus rs167479 of the RGL3 Gene Is Associated with the Risk of Severe Preeclampsia. RUSS J GENET+ 2022. [DOI: 10.1134/s102279542212002x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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55
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Hoffmann TJ, Lu M, Oni-Orisan A, Lee C, Risch N, Iribarren C. A large genome-wide association study of QT interval length utilizing electronic health records. Genetics 2022; 222:iyac157. [PMID: 36271874 PMCID: PMC9713425 DOI: 10.1093/genetics/iyac157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/22/2022] [Indexed: 12/13/2022] Open
Abstract
QT interval length is an important risk factor for adverse cardiovascular outcomes; however, the genetic architecture of QT interval remains incompletely understood. We conducted a genome-wide association study of 76,995 ancestrally diverse Kaiser Permanente Northern California members enrolled in the Genetic Epidemiology Research on Adult Health and Aging cohort using 448,517 longitudinal QT interval measurements, uncovering 9 novel variants, most replicating in 40,537 individuals in the UK Biobank and Population Architecture using Genomics and Epidemiology studies. A meta-analysis of all 3 cohorts (n = 117,532) uncovered an additional 19 novel variants. Conditional analysis identified 15 additional variants, 3 of which were novel. Little, if any, difference was seen when adjusting for putative QT interval lengthening medications genome-wide. Using multiple measurements in Genetic Epidemiology Research on Adult Health and Aging increased variance explained by 163%, and we show that the ≈6 measurements in Genetic Epidemiology Research on Adult Health and Aging was equivalent to a 2.4× increase in sample size of a design with a single measurement. The array heritability was estimated at ≈17%, approximately half of our estimate of 36% from family correlations. Heritability enrichment was estimated highest and most significant in cardiovascular tissue (enrichment 7.2, 95% CI = 5.7-8.7, P = 2.1e-10), and many of the novel variants included expression quantitative trait loci in heart and other relevant tissues. Comparing our results to other cardiac function traits, it appears that QT interval has a multifactorial genetic etiology.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Meng Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Akinyemi Oni-Orisan
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA 94143, USA
| | - Catherine Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Neil Risch
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
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56
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Churnosov M, Abramova M, Reshetnikov E, Lyashenko IV, Efremova O, Churnosova M, Ponomarenko I. Polymorphisms of hypertension susceptibility genes as a risk factors of preeclampsia in the Caucasian population of central Russia. Placenta 2022; 129:51-61. [PMID: 36219912 DOI: 10.1016/j.placenta.2022.09.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/18/2022] [Accepted: 09/14/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The study was designed to assess the effects of hypertension (HT) susceptibility genes polymorphisms in the development of preeclampsia (PE) in Caucasians from Central Russia. METHODS PE patients (n = 452) and women control group (n = 498) were genotyped for 10 polymorphisms of HT/blood pressure (BP) susceptibility genes (according to the previously published GWAS in Caucasian populations) including AC026703.1 (rs1173771), HFE (rs1799945), BAG6 (rs805303), PLCE1 (rs932764), OBFC1 (rs4387287), ARHGAP42 (rs633185), CERS5 (rs7302981), ATP2B1 (rs2681472), TBX2 (rs8068318) and RGL3 (rs167479). A logistic regression method was applied to search for associations between SNPs and PE. The relationship between SNP-SNP interactions and PE risk was analyzed by performing MB-MDR. RESULTS The rs1799945 gene in HFE significantly independently increased the risk of developing PE (OR = 2.24) and rs805303 in BAG6 was associated with a reduced risk in the occurrence of PE (OR = 0.55-0.78). Among the 10 SNPs examined, nine SNPs were associated with PEs within the 10 most significant SNP-SNP interaction models. Loci rs7302981 CERS5, rs805303 BAG6 and rs932764 PLCE1 contributed to the largest number of epistatic models (50% or more). DISCUSSION The present study is the first to report an association between polymorphisms of HT/BP susceptibility genes important for GWAS and the risk of PE in Caucasians from Central Russia. Our pathway-based functional annotation of the PE risk variants highlights the potential regulatory function (epigenetic/eQTL/sQTL/non-synonymous) that nine genetic risk markers and their 115 highly correlated variants exert on 155 genes. The study shows that these genes may function cooperatively in key signaling pathways in PE biology.
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Affiliation(s)
- Mikhail Churnosov
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia.
| | - Maria Abramova
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Evgeny Reshetnikov
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Igor V Lyashenko
- Belgorod State National Research University, Department of English Philology and Cross-cultural Communication, Belgorod, Russia
| | - Olesya Efremova
- Kharkiv National Medical University, Department of Medical Genetics, Kharkov, Ukraine; Grishchenko Clinic of Reproductive Medicine, Kharkov, Ukraine
| | - Maria Churnosova
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Irina Ponomarenko
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- Praveen Surendran
- 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, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- 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, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- 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, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- 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, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- 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, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Ma J, Chen X. Advances in pathogenesis and treatment of essential hypertension. Front Cardiovasc Med 2022; 9:1003852. [PMID: 36312252 PMCID: PMC9616110 DOI: 10.3389/fcvm.2022.1003852] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/26/2022] [Indexed: 11/23/2022] Open
Abstract
Hypertension is a significant risk factor for cardiovascular and cerebrovascular diseases and the leading cause of premature death worldwide. However, the pathogenesis of the hypertension, especially essential hypertension, is complex and requires in-depth studies. Recently, new findings about essential hypertension have emerged, and these may provide important theoretical bases and therapeutic tools to break through the existing bottleneck of essential hypertension. In this review, we demonstrated important advances in the different pathogenesis areas of essential hypertension, and highlighted new treatments proposed in these areas, hoping to provide insight for the prevention and treatment of the essential hypertension.
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59
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Portilla-Fernandez E, Klarin D, Hwang SJ, Biggs ML, Bis JC, Weiss S, Rospleszcz S, Natarajan P, Hoffmann U, Rogers IS, Truong QA, Völker U, Dörr M, Bülow R, Criqui MH, Allison M, Ganesh SK, Yao J, Waldenberger M, Bamberg F, Rice KM, Essers J, Kapteijn DMC, van der Laan SW, de Knegt RJ, Ghanbari M, Felix JF, Ikram MA, Kavousi M, Uitterlinden AG, Roks AJM, Danser AHJ, Tsao PS, Damrauer SM, Guo X, Rotter JI, Psaty BM, Kathiresan S, Völzke H, Peters A, Johnson C, Strauch K, Meitinger T, O’Donnell CJ, Dehghan A, VA Million Veteran Program. Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study. Hum Mol Genet 2022; 31:3566-3579. [PMID: 35234888 PMCID: PMC9558840 DOI: 10.1093/hmg/ddac051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 11/13/2022] Open
Abstract
Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.
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Affiliation(s)
- Eliana Portilla-Fernandez
- 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
| | - Derek Klarin
- 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, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, NHLBI/NIH, Bethesda MD, USA
- National Heart Lung and Blood Institute's Intramural Research Program's Framingham Heart Study, Framingham, MA, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Stefan Weiss
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Susanne Rospleszcz
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Pradeep Natarajan
- 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, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ian S Rogers
- Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Quynh A Truong
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Michael H Criqui
- Department of Family Medicine, University of California, San Diego, CA, USA
| | - Matthew Allison
- Department of Family Medicine, University of California, San Diego, CA, USA
| | - Santhi K Ganesh
- Department of Internal Medicine and Human Genetics, University of Michigan, Ann Arbor, MI, 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
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jeroen Essers
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiation Oncology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniek M C Kapteijn
- Laboratory of Experimental Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sander W van der Laan
- Laboratory of Clinical Chemistry & Hematology, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Rob J de Knegt
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Janine F Felix
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Internal Medicine, 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
| | - Philip S Tsao
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, 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
| | - 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
| | - Sekar Kathiresan
- 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, Cambridge, MA, USA
| | - Henry Völzke
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Craig Johnson
- Collaborative Health Studies Coordinating Center, Department of Biostatistics in the School of Public Health, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Meitinger
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Human Genetics, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
| | - Christopher J O’Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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Genomic basis of insularity and ecological divergence in barn owls (Tyto alba) of the Canary Islands. Heredity (Edinb) 2022; 129:281-294. [PMID: 36175501 PMCID: PMC9613907 DOI: 10.1038/s41437-022-00562-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 11/14/2022] Open
Abstract
Islands, and the particular organisms that populate them, have long fascinated biologists. Due to their isolation, islands offer unique opportunities to study the effect of neutral and adaptive mechanisms in determining genomic and phenotypical divergence. In the Canary Islands, an archipelago rich in endemics, the barn owl (Tyto alba), present in all the islands, is thought to have diverged into a subspecies (T. a. gracilirostris) on the eastern ones, Fuerteventura and Lanzarote. Taking advantage of 40 whole-genomes and modern population genomics tools, we provide the first look at the origin and genetic makeup of barn owls of this archipelago. We show that the Canaries hold diverse, long-standing and monophyletic populations with a neat distinction of gene pools from the different islands. Using a new method, less sensitive to structure than classical FST, to detect regions involved in local adaptation to insular environments, we identified a haplotype-like region likely under selection in all Canaries individuals and genes in this region suggest morphological adaptations to insularity. In the eastern islands, where the subspecies is present, genomic traces of selection pinpoint signs of adapted body proportions and blood pressure, consistent with the smaller size of this population living in a hot arid climate. In turn, genomic regions under selection in the western barn owls from Tenerife showed an enrichment in genes linked to hypoxia, a potential response to inhabiting a small island with a marked altitudinal gradient. Our results illustrate the interplay of neutral and adaptive forces in shaping divergence and early onset speciation.
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Le Floch E, Cosentino T, Larsen CK, Beuschlein F, Reincke M, Amar L, Rossi GP, De Sousa K, Baron S, Chantalat S, Saintpierre B, Lenzini L, Frouin A, Giscos-Douriez I, Ferey M, Abdellatif AB, Meatchi T, Empana JP, Jouven X, Gieger C, Waldenberger M, Peters A, Cusi D, Salvi E, Meneton P, Touvier M, Deschasaux M, Druesne-Pecollo N, Boulkroun S, Fernandes-Rosa FL, Deleuze JF, Jeunemaitre X, Zennaro MC. Identification of risk loci for primary aldosteronism in genome-wide association studies. Nat Commun 2022; 13:5198. [PMID: 36057693 PMCID: PMC9440917 DOI: 10.1038/s41467-022-32896-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/23/2022] [Indexed: 11/23/2022] Open
Abstract
Primary aldosteronism affects up to 10% of hypertensive patients and is responsible for treatment resistance and increased cardiovascular risk. Here we perform a genome-wide association study in a discovery cohort of 562 cases and 950 controls and identify three main loci on chromosomes 1, 13 and X; associations on chromosome 1 and 13 are replicated in a second cohort and confirmed by a meta-analysis involving 1162 cases and 3296 controls. The association on chromosome 13 is specific to men and stronger in bilateral adrenal hyperplasia than aldosterone producing adenoma. Candidate genes located within the two loci, CASZ1 and RXFP2, are expressed in human and mouse adrenals in different cell clusters. Their overexpression in adrenocortical cells suppresses mineralocorticoid output under basal and stimulated conditions, without affecting cortisol biosynthesis. Our study identifies the first risk loci for primary aldosteronism and highlights new mechanisms for the development of aldosterone excess.
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Affiliation(s)
- Edith Le Floch
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | | | - Casper K Larsen
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | - Felix Beuschlein
- Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, 80336, Munich, Germany
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich (USZ) und Universität Zürich (UZH), Zürich, Switzerland
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, 80336, Munich, Germany
| | - Laurence Amar
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France
| | - Gian-Paolo Rossi
- DMCS 'G. Patrassi' University of Padova Medical School, University Hospital, 35126, Padova, Italy
| | - Kelly De Sousa
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | - Stéphanie Baron
- Université Paris Cité, F-75006, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie, Paris, France
| | - Sophie Chantalat
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Benjamin Saintpierre
- Université Paris Cité, Institut Cochin, Genom'IC platform, INSERM, CNRS, 75014, Paris, France
| | - Livia Lenzini
- DMCS 'G. Patrassi' University of Padova Medical School, University Hospital, 35126, Padova, Italy
| | - Arthur Frouin
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | | | - Matthis Ferey
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | | | - Tchao Meatchi
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service d'Anatomie Pathologique, Paris, France
| | | | - Xavier Jouven
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Cardiologie, Paris, France
| | - Christian Gieger
- 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
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - 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
- German Research Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Daniele Cusi
- Institute of Biomedical Technologies National Research Council of Italy, Milan, Italy
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Pierre Meneton
- UMR_1142, INSERM, Sorbonne Université, Université Paris 13, Paris, France
| | - Mathilde Touvier
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | - Mélanie Deschasaux
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | - Nathalie Druesne-Pecollo
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | | | | | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Xavier Jeunemaitre
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France
| | - Maria-Christina Zennaro
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France.
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France.
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Life-Course Associations between Blood Pressure-Related Polygenic Risk Scores and Hypertension in the Bogalusa Heart Study. Genes (Basel) 2022; 13:genes13081473. [PMID: 36011384 PMCID: PMC9408577 DOI: 10.3390/genes13081473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/30/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic information may help to identify individuals at increased risk for hypertension in early life, prior to the manifestation of elevated blood pressure (BP) values. We examined 369 Black and 832 White Bogalusa Heart Study (BHS) participants recruited in childhood and followed for approximately 37 years. The multi-ancestry genome-wide polygenic risk scores (PRSs) for systolic BP (SBP), diastolic BP (DBP), and hypertension were tested for an association with incident hypertension and stage 2 hypertension using Cox proportional hazards models. Race-stratified analyses were adjusted for baseline age, age2, sex, body mass index, genetic principal components, and BP. In Black participants, each standard deviation increase in SBP and DBP PRS conferred a 38% (p = 0.009) and 22% (p = 0.02) increased risk of hypertension and a 74% (p < 0.001) and 50% (p < 0.001) increased risk of stage 2 hypertension, respectively, while no association was observed with the hypertension PRSs. In Whites, each standard deviation increase in SBP, DBP, and hypertension PRS conferred a 24% (p < 0.05), 29% (p = 0.01), and 25% (p < 0.001) increased risk of hypertension, and a 27% (p = 0.08), 29% (0.01), and 42% (p < 0.001) increased risk of stage 2 hypertension, respectively. The addition of BP PRSs to the covariable-only models generally improved the C-statistics (p < 0.05). Multi-ancestry BP PRSs demonstrate the utility of genomic information in the early life prediction of hypertension.
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Kelly TN, Sun X, He KY, Brown MR, Taliun SAG, Hellwege JN, Irvin MR, Mi X, Brody JA, Franceschini N, Guo X, Hwang SJ, de Vries PS, Gao Y, Moscati A, Nadkarni GN, Yanek LR, Elfassy T, Smith JA, Chung RH, Beitelshees AL, Patki A, Aslibekyan S, Blobner BM, Peralta JM, Assimes TL, Palmas WR, Liu C, Bress AP, Huang Z, Becker LC, Hwa CM, O'Connell JR, Carlson JC, Warren HR, Das S, Giri A, Martin LW, Craig Johnson W, Fox ER, Bottinger EP, Razavi AC, Vaidya D, Chuang LM, Chang YPC, Naseri T, Jain D, Kang HM, Hung AM, Srinivasasainagendra V, Snively BM, Gu D, Montasser ME, Reupena MS, Heavner BD, LeFaive J, Hixson JE, Rice KM, Wang FF, Nielsen JB, Huang J, Khan AT, Zhou W, Nierenberg JL, Laurie CC, Armstrong ND, Shi M, Pan Y, Stilp AM, Emery L, Wong Q, Hawley NL, Minster RL, Curran JE, Munroe PB, Weeks DE, North KE, Tracy RP, Kenny EE, Shimbo D, Chakravarti A, Rich SS, Reiner AP, Blangero J, Redline S, Mitchell BD, Rao DC, Ida Chen YD, Kardia SLR, Kaplan RC, Mathias RA, He J, Psaty BM, Fornage M, Loos RJF, Correa A, Boerwinkle E, Rotter JI, Kooperberg C, Edwards TL, et alKelly TN, Sun X, He KY, Brown MR, Taliun SAG, Hellwege JN, Irvin MR, Mi X, Brody JA, Franceschini N, Guo X, Hwang SJ, de Vries PS, Gao Y, Moscati A, Nadkarni GN, Yanek LR, Elfassy T, Smith JA, Chung RH, Beitelshees AL, Patki A, Aslibekyan S, Blobner BM, Peralta JM, Assimes TL, Palmas WR, Liu C, Bress AP, Huang Z, Becker LC, Hwa CM, O'Connell JR, Carlson JC, Warren HR, Das S, Giri A, Martin LW, Craig Johnson W, Fox ER, Bottinger EP, Razavi AC, Vaidya D, Chuang LM, Chang YPC, Naseri T, Jain D, Kang HM, Hung AM, Srinivasasainagendra V, Snively BM, Gu D, Montasser ME, Reupena MS, Heavner BD, LeFaive J, Hixson JE, Rice KM, Wang FF, Nielsen JB, Huang J, Khan AT, Zhou W, Nierenberg JL, Laurie CC, Armstrong ND, Shi M, Pan Y, Stilp AM, Emery L, Wong Q, Hawley NL, Minster RL, Curran JE, Munroe PB, Weeks DE, North KE, Tracy RP, Kenny EE, Shimbo D, Chakravarti A, Rich SS, Reiner AP, Blangero J, Redline S, Mitchell BD, Rao DC, Ida Chen YD, Kardia SLR, Kaplan RC, Mathias RA, He J, Psaty BM, Fornage M, Loos RJF, Correa A, Boerwinkle E, Rotter JI, Kooperberg C, Edwards TL, Abecasis GR, Zhu X, Levy D, Arnett DK, Morrison AC. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension. Hypertension 2022; 79:1656-1667. [PMID: 35652341 PMCID: PMC9593435 DOI: 10.1161/hypertensionaha.122.19324] [Show More Authors] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/12/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure. METHODS We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants. RESULTS Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P<5×10-8). Among them, a rare intergenic variant at novel locus, LOC100506274, was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; P=4.99×10-8) but not stage-2 analysis (P=0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; P=4.18×10-7) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; P=7.28×10-23). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (P<1×10-6 and P<1×10-4, respectively). DISCUSSION We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
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Affiliation(s)
- Tanika N Kelly
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
- Translational Sciences Institute (T.N.K., J.H.), Tulane University, New Orleans, LA
| | - Xiao Sun
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Karen Y He
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH (K.Y.H., X.Z.)
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health (M.R.B., P.D.d.V., J.E.H., E.B., A.C.M.), The University of Texas Health Science Center at Houston' Houston' TX
| | - Sarah A Gagliano Taliun
- Department of Biostatistics (S.A.G.T., S.D., H.M.K., J.L., G.R.A.), University of Michigan, Ann Arbor' MI
| | - Jacklyn N Hellwege
- Division of Genetic Medicine, Department of Medicine (J.N.H.), Vanderbilt University Medical Center, Nashville, TN
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville' TN (J.N.H., A.G., A.M.H., T.L.E.)
| | - Marguerite R Irvin
- Department of Epidemiology (M.R.I., S.A., N.D.A.), University of Alabama at Birmingham' AL
| | - Xuenan Mi
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.E.N.), University of Washington, Seattle' WA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill (N.F.)
| | - 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 (X.G., Y.-D.I.C., J.I.R., D.L.)
| | - Shih-Jen Hwang
- National Heart, Lung and Blood Institute, Population Sciences Branch, National Institutes of Health, Framingham, MA (S.-J.H.)
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health (M.R.B., P.D.d.V., J.E.H., E.B., A.C.M.), The University of Texas Health Science Center at Houston' Houston' TX
| | - Yan Gao
- Department of Physiology and Biophysics (Y.G., E.E.K., R.J.F.L.), University of Mississippi Medical Center, Jackson' MS
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine (A.M., G.N.N.), The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine (A.M., G.N.N.), The Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Medicine (G.N.N.), The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Lisa R Yanek
- Division of General Internal Medicine, Department of Medicine (L.R.Y., D.V.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Tali Elfassy
- Division of Epidemiology, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami' FL (T.E.)
| | - Jennifer A Smith
- Department of Epidemiology (J.A.S., S.L.R.K.), University of Michigan, Ann Arbor' MI
| | - Ren-Hua Chung
- Institute of Population Sciences, National Health Research Institutes, Taiwan (R.-H.C.)
| | - Amber L Beitelshees
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore' MD (A.L.B., J.R.O., Y.-P.C.C., M.E.M., B.D.M.)
| | - Amit Patki
- Department of Biostatistics (A.P., V.S.), University of Alabama at Birmingham' AL
| | - Stella Aslibekyan
- Department of Epidemiology (M.R.I., S.A., N.D.A.), University of Alabama at Birmingham' AL
| | - Brandon M Blobner
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle' WA
- Department of Human Genetics (B.M.B., R.L.M., D.E.W.), University of Pittsburgh, PA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville' TX (J.M.P., J.E.C., J.B.)
| | - Themistocles L Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford' CA (T.L.A.)
- Division of Cardiology Medicine, Palo Alto VA HealthCare System, Palo Alto' CA (T.L.A.)
| | - Walter R Palmas
- Division of General Medicine, Department of Medicine, Columbia University, New York, NY (W.R.P.)
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston' MA (C.L.)
| | - Adam P Bress
- Division of Health System Innovation and Research, Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City' UT (A.P.B.)
| | - Zhijie Huang
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Lewis C Becker
- Division of Cardiology, Department of Medicine (L.C.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Chii-Min Hwa
- Taichung Veterans General Hospital, Taichung, Taiwan (C.-M.H.)
| | - Jeffrey R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore' MD (A.L.B., J.R.O., Y.-P.C.C., M.E.M., B.D.M.)
| | - Jenna C Carlson
- Department of Biostatistics, Graduate School of Public Health (J.C.C.), University of Pittsburgh, PA
| | - Helen R Warren
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom
- National Institute for Health Research Barts Cardiovascular Biomedical Research Centre (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom
| | - Sayantan Das
- Department of Biostatistics (S.A.G.T., S.D., H.M.K., J.L., G.R.A.), University of Michigan, Ann Arbor' MI
| | - Ayush Giri
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville' TN (J.N.H., A.G., A.M.H., T.L.E.)
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University, Nashville, TN (A.G.)
| | - Lisa W Martin
- Division of Cardiology, Department of Medicine, George Washington University, Washington, DC (L.W.M.)
| | - W Craig Johnson
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Ervin R Fox
- Division of Cardiology, Department of Medicine (E.R.F.), University of Mississippi Medical Center, Jackson' MS
| | - Erwin P Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai (E.P.B.), The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander C Razavi
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Dhananjay Vaidya
- Division of General Internal Medicine, Department of Medicine (L.R.Y., D.V.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Lee-Ming Chuang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei' Taiwan (L.-M.C.)
| | - Yen-Pei C Chang
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore' MD (A.L.B., J.R.O., Y.-P.C.C., M.E.M., B.D.M.)
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia' Samoa (T.N.)
| | - Deepti Jain
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Hyun Min Kang
- Department of Biostatistics (S.A.G.T., S.D., H.M.K., J.L., G.R.A.), University of Michigan, Ann Arbor' MI
| | - Adriana M Hung
- Division of Nephrology and Hypertension, Department of Medicine (A.M.H.), Vanderbilt University Medical Center, Nashville, TN
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville' TN (J.N.H., A.G., A.M.H., T.L.E.)
| | | | - Beverly M Snively
- Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC (B.M.S.)
| | - Dongfeng Gu
- Department of Epidemiology and Key Laboratory of Cardiovascular Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G., J.H.)
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore' MD (A.L.B., J.R.O., Y.-P.C.C., M.E.M., B.D.M.)
| | | | - Benjamin D Heavner
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Jonathon LeFaive
- Department of Biostatistics (S.A.G.T., S.D., H.M.K., J.L., G.R.A.), University of Michigan, Ann Arbor' MI
| | - James E Hixson
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health (M.R.B., P.D.d.V., J.E.H., E.B., A.C.M.), The University of Texas Health Science Center at Houston' Houston' TX
| | - Kenneth M Rice
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Fei Fei Wang
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Jonas B Nielsen
- Department of Internal Medicine: Cardiology (J.B.N.), University of Michigan, Ann Arbor' MI
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark (J.B.N.)
| | - Jianfeng Huang
- Translational Sciences Institute (T.N.K., J.H.), Tulane University, New Orleans, LA
- Department of Epidemiology and Key Laboratory of Cardiovascular Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G., J.H.)
| | - Alyna T Khan
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics (W.Z.), University of Michigan, Ann Arbor' MI
| | - Jovia L Nierenberg
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Cathy C Laurie
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Nicole D Armstrong
- Department of Epidemiology (M.R.I., S.A., N.D.A.), University of Alabama at Birmingham' AL
| | - Mengyao Shi
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Yang Pan
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Adrienne M Stilp
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Leslie Emery
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Quenna Wong
- Department of Biostatistics, School of Public Health (W.C.J., D.J., B.D.H., K.M.R., F.F.E., A.T.K., C.C.L., A.M.S., L.E., Q.W.), University of Washington, Seattle' WA
| | - Nicola L Hawley
- Department of Chronic Disease Epidemiology, Yale University, New Haven, CT (N.L.H.)
| | - Ryan L Minster
- Department of Human Genetics (B.M.B., R.L.M., D.E.W.), University of Pittsburgh, PA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville' TX (J.M.P., J.E.C., J.B.)
| | - Patricia B Munroe
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom
- National Institute for Health Research Barts Cardiovascular Biomedical Research Centre (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom
| | - Daniel E Weeks
- Department of Human Genetics (B.M.B., R.L.M., D.E.W.), University of Pittsburgh, PA
- Department of Biostatistics (D.E.W.), University of Pittsburgh, PA
| | - Kari E North
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.E.N.), University of Washington, Seattle' WA
| | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington' VT (R.P.T.)
| | - Eimear E Kenny
- Department of Physiology and Biophysics (Y.G., E.E.K., R.J.F.L.), University of Mississippi Medical Center, Jackson' MS
- Department of Genetics and Genomics (E.E.K.), The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Daichi Shimbo
- Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York, NY (D.S.)
| | - Aravinda Chakravarti
- Department of Medicine (A.C.), University of Mississippi Medical Center, Jackson' MS
| | - Stephen S Rich
- Center for Public Health, University of Virginia, Charlottesville' VA (S.S.R.)
| | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (A.P.R., C.K.)
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville' TX (J.M.P., J.E.C., J.B.)
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA (S.R.)
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore' MD (A.L.B., J.R.O., Y.-P.C.C., M.E.M., B.D.M.)
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore' MD (B.D.M.)
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R.)
| | - 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 (X.G., Y.-D.I.C., J.I.R., D.L.)
| | - Sharon L R Kardia
- Department of Epidemiology (J.A.S., S.L.R.K.), University of Michigan, Ann Arbor' MI
| | - Robert C Kaplan
- Division of Social Medicine, Albert Einstein College of Medicine, Bronx, NY (R.C.K.)
| | - Rasika A Mathias
- Division of Allergy & Clinical Immunology, Department of Medicine (R.A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jiang He
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
| | - Bruce M Psaty
- Department of Epidemiology (T.N.K., X.S., X.M., Z.H., A.C.R., J.L.N., M.S., Y.P., J.H.), Tulane University, New Orleans, LA
- Kaiser Permanente Washington Health Research Institute, Seattle' WA (B.M.P.)
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine (M.F.), The University of Texas Health Science Center at Houston' Houston' TX
- Human Genetics Center (M.F.), The University of Texas Health Science Center at Houston' Houston' TX
| | - Ruth J F Loos
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance' CA (X.G., Y.-D.I.C., J.I.R., D.L.)
- The Mindich Child Health and Development Institute (R.J.F.L.), The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Adolfo Correa
- Center for Human Genetics and Genomics, New York University Grossman School of Medicine, New York, NY (A.C.)
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health (M.R.B., P.D.d.V., J.E.H., E.B., A.C.M.), The University of Texas Health Science Center at Houston' Houston' TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX (E.B.)
| | - 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 (X.G., Y.-D.I.C., J.I.R., D.L.)
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (A.P.R., C.K.)
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine (T.L.E.), Vanderbilt University Medical Center, Nashville, TN
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville' TN (J.N.H., A.G., A.M.H., T.L.E.)
| | - Gonçalo R Abecasis
- Department of Biostatistics (S.A.G.T., S.D., H.M.K., J.L., G.R.A.), University of Michigan, Ann Arbor' MI
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH (K.Y.H., X.Z.)
| | - Daniel Levy
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance' CA (X.G., Y.-D.I.C., J.I.R., D.L.)
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY (D.K.A.)
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health (M.R.B., P.D.d.V., J.E.H., E.B., A.C.M.), The University of Texas Health Science Center at Houston' Houston' TX
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Yan D, Sun Y, Zhou X, Si W, Liu J, Li M, Wu M. Regulatory effect of gut microbes on blood pressure. Animal Model Exp Med 2022; 5:513-531. [PMID: 35880388 PMCID: PMC9773315 DOI: 10.1002/ame2.12233] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/25/2022] [Indexed: 12/30/2022] Open
Abstract
Hypertension is an important global public health issue because of its high morbidity as well as the increased risk of other diseases. Recent studies have indicated that the development of hypertension is related to the dysbiosis of the gut microbiota in both animals and humans. In this review, we outline the interaction between gut microbiota and hypertension, including gut microbial changes in hypertension, the effect of microbial dysbiosis on blood pressure (BP), indicators of gut microbial dysbiosis in hypertension, and the microbial genera that affect BP at the taxonomic level. For example, increases in Lactobacillus, Roseburia, Coprococcus, Akkermansia, and Bifidobacterium are associated with reduced BP, while increases in Streptococcus, Blautia, and Prevotella are associated with elevated BP. Furthermore, we describe the potential mechanisms involved in the regulation between gut microbiota and hypertension. Finally, we summarize the commonly used treatments of hypertension that are based on gut microbes, including fecal microbiota transfer, probiotics and prebiotics, antibiotics, and dietary supplements. This review aims to find novel potential genera for improving hypertension and give a direction for future studies on gut microbiota in hypertension.
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Affiliation(s)
- Dong Yan
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina
| | - Ye Sun
- Institute of Medical Laboratory Animal Science, Chinese Academy of Medical Sciences & Comparative Medical CenterPeking Union Medical CollegeBeijingChina
| | - Xiaoyue Zhou
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina
| | - Wenhao Si
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina,Department of Dermatologythe First Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Jieyu Liu
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina
| | - Min Li
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina
| | - Minna Wu
- Xinxiang Key Laboratory of Pathogenic Biology, Department of Pathogenic Biology, School of Basic Medical SciencesXinxiang Medical UniversityXinxiangChina
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65
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Shi G. Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits. Sci Rep 2022; 12:12649. [PMID: 35879408 PMCID: PMC9314370 DOI: 10.1038/s41598-022-16908-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/09/2022] Open
Abstract
Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene-environment and gene-gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well.
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Affiliation(s)
- Gang Shi
- School of Telecommunications Engineering, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
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66
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Alpha globin gene copy number and hypertension risk among Black Americans. PLoS One 2022; 17:e0271031. [PMID: 35834496 PMCID: PMC9282593 DOI: 10.1371/journal.pone.0271031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/22/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Alpha globin is expressed in the endothelial cells of human resistance arteries where it binds to endothelial nitric oxide synthase and limits release of the vasodilator nitric oxide. Genomic deletion of the alpha globin gene (HBA) is common among Black Americans and could lead to increased endothelial nitric oxide signaling and reduced risk of hypertension. METHODS Community-dwelling US adults aged 45 years or older were enrolled and examined from 2003 to 2007, followed by telephone every 6 months, and reexamined from 2013 to 2016. At both visits, trained personnel performed standardized, in-home blood pressure measurements and pill bottle review. Prevalent hypertension was defined as systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg or anti-hypertensive medication use. Droplet digital PCR was used to determine HBA copy number. The associations of HBA copy number with prevalent hypertension, resistant hypertension, and incident hypertension were estimated using multivariable regression. RESULTS Among 9,684 Black participants, 7,439 (77%) had hypertension at baseline and 1,044 of those had treatment-resistant hypertension. 1,000 participants were not hypertensive at baseline and participated in a follow up visit; 517 (52%) developed hypertension over median 9.2 years follow-up. Increased HBA copy number was not associated with prevalent hypertension (PR = 1.00; 95%CI 0.98,1.02), resistant hypertension (PR = 0.95; 95%CI 0.86,1.05), or incident hypertension (RR = 0.96; 95%CI 0.86,1.07). CONCLUSIONS There were no associations between increased HBA copy number and risk of hypertension. These findings suggest that variation in alpha globin gene copy number does not modify the risk of hypertension among Black American adults.
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Kirchler M, Konigorski S, Norden M, Meltendorf C, Kloft M, Schurmann C, Lippert C. transferGWAS: GWAS of images using deep transfer learning. Bioinformatics 2022; 38:3621-3628. [PMID: 35640976 DOI: 10.1093/bioinformatics/btac369] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 05/05/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. RESULTS We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. AVAILABILITY AND IMPLEMENTATION Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthias Kirchler
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Stefan Konigorski
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthias Norden
- Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Anesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Christian Meltendorf
- Department of Electrical Engineering - Mechatronics - Optometry, Beuth University of Applied Sciences Berlin, 13353 Berlin, Germany
| | - Marius Kloft
- Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Claudia Schurmann
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
| | - Christoph Lippert
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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68
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Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation. Int J Mol Sci 2022; 23:ijms23147557. [PMID: 35886906 PMCID: PMC9323755 DOI: 10.3390/ijms23147557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022] Open
Abstract
Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.
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69
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Khan A, Turchin MC, Patki A, Srinivasasainagendra V, Shang N, Nadukuru R, Jones AC, Malolepsza E, Dikilitas O, Kullo IJ, Schaid DJ, Karlson E, Ge T, Meigs JB, Smoller JW, Lange C, Crosslin DR, Jarvik GP, Bhatraju PK, Hellwege JN, Chandler P, Torvik LR, Fedotov A, Liu C, Kachulis C, Lennon N, Abul-Husn NS, Cho JH, Ionita-Laza I, Gharavi AG, Chung WK, Hripcsak G, Weng C, Nadkarni G, Irvin MR, Tiwari HK, Kenny EE, Limdi NA, Kiryluk K. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat Med 2022; 28:1412-1420. [PMID: 35710995 PMCID: PMC9329233 DOI: 10.1038/s41591-022-01869-1] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/11/2022] [Indexed: 01/03/2023]
Abstract
Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Michael C Turchin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Rajiv Nadukuru
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alana C Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ozan Dikilitas
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tian Ge
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David R Crosslin
- Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jacklyn N Hellwege
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paulette Chandler
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Laura Rasmussen Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Noura S Abul-Husn
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Khor BH, Komnenov D, Rossi NF. Impact of Dietary Fructose and High Salt Diet: Are Preclinical Studies Relevant to Asian Societies? Nutrients 2022; 14:2515. [PMID: 35745245 PMCID: PMC9227020 DOI: 10.3390/nu14122515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/08/2022] [Accepted: 06/11/2022] [Indexed: 02/01/2023] Open
Abstract
Fructose consumption, especially in food additives and sugar-sweetened beverages, has gained increasing attention due to its potential association with obesity and metabolic syndrome. The relationship between fructose and a high-salt diet, leading to hypertension and other deleterious cardiovascular parameters, has also become more evident, especially in preclinical studies. However, these studies have been modeled primarily on Western diets. The purpose of this review is to evaluate the dietary habits of individuals from China, Japan, and Korea, in light of the existing preclinical studies, to assess the potential relevance of existing data to East Asian societies. This review is not intended to be exhaustive, but rather to highlight the similarities and differences that should be considered in future preclinical, clinical, and epidemiologic studies regarding the impact of dietary fructose and salt on blood pressure and cardiovascular health worldwide.
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Affiliation(s)
- Ban Hock Khor
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia;
| | - Dragana Komnenov
- Department of Internal Medicine, Wayne State University, Detroit, MI 48201, USA;
| | - Noreen F. Rossi
- Department of Internal Medicine, Wayne State University, Detroit, MI 48201, USA;
- Department of Physiology, Wayne State University, Detroit, MI 48201, USA
- Division of Research, John D. Dingell VA Medical Center, Detroit, MI 38201, USA
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71
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Suleiman M, Kounosu A, Murcott B, Dayi M, Pawluk R, Yoshida A, Viney M, Kikuchi T, Hunt VL. piRNA-like small RNAs target transposable elements in a Clade IV parasitic nematode. Sci Rep 2022; 12:10156. [PMID: 35710810 PMCID: PMC9203780 DOI: 10.1038/s41598-022-14247-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/03/2022] [Indexed: 12/02/2022] Open
Abstract
The small RNA (sRNA) pathways identified in the model organism Caenorhabditis elegans are not widely conserved across nematodes. For example, the PIWI pathway and PIWI-interacting RNAs (piRNAs) are involved in regulating and silencing transposable elements (TE) in most animals but have been lost in nematodes outside of the C. elegans group (Clade V), and little is known about how nematodes regulate TEs in the absence of the PIWI pathway. Here, we investigated the role of sRNAs in the Clade IV parasitic nematode Strongyloides ratti by comparing two genetically identical adult stages (the parasitic female and free-living female). We identified putative small-interfering RNAs, microRNAs and tRNA-derived sRNA fragments that are differentially expressed between the two adult stages. Two classes of sRNAs were predicted to regulate TE activity including (i) a parasite-associated class of 21-22 nt long sRNAs with a 5' uridine (21-22Us) and a 5' monophosphate, and (ii) 27 nt long sRNAs with a 5' guanine/adenine (27GAs) and a 5' modification. The 21-22Us show striking resemblance to the 21U PIWI-interacting RNAs found in C. elegans, including an AT rich upstream sequence, overlapping loci and physical clustering in the genome. Overall, we have shown that an alternative class of sRNAs compensate for the loss of piRNAs and regulate TE activity in nematodes outside of Clade V.
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Affiliation(s)
- Mona Suleiman
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK
| | - Asuka Kounosu
- Parasitology, Department of Infectious Dieses, Faculty of Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan
| | - Ben Murcott
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK
| | - Mehmet Dayi
- Parasitology, Department of Infectious Dieses, Faculty of Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan
- Forestry Vocational School, Duzce University, 81620, Duzce, Turkey
| | - Rebecca Pawluk
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK
| | - Akemi Yoshida
- Laboratory of Genomics, Frontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan
| | - Mark Viney
- Department of Evolution, Ecology and Behaviour, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Taisei Kikuchi
- Parasitology, Department of Infectious Dieses, Faculty of Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan.
| | - Vicky L Hunt
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK.
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Yeung MW, Wang S, van de Vegte YJ, Borisov O, van Setten J, Snieder H, Verweij N, Said MA, van der Harst P. Twenty-Five Novel Loci for Carotid Intima-Media Thickness: A Genome-Wide Association Study in >45 000 Individuals and Meta-Analysis of >100 000 Individuals. Arterioscler Thromb Vasc Biol 2022; 42:484-501. [PMID: 34852643 PMCID: PMC8939707 DOI: 10.1161/atvbaha.121.317007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 11/22/2021] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Carotid artery intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. Twenty susceptibility loci for cIMT were previously identified and the identification of additional susceptibility loci furthers our knowledge on the genetic architecture underlying atherosclerosis. APPROACH AND RESULTS We performed 3 genome-wide association studies in 45 185 participants from the UK Biobank study who underwent cIMT measurements and had data on minimum, mean, and maximum thickness. We replicated 15 known loci and identified 20 novel loci associated with cIMT at P<5×10-8. Seven novel loci (ZNF385D, ADAMTS9, EDNRA, HAND2, MYOCD, ITCH/EDEM2/MMP24, and MRTFA) were identified in all 3 phenotypes. An additional new locus (LOXL1) was identified in the meta-analysis of the 3 phenotypes. Sex interaction analysis revealed sex differences in 7 loci including a novel locus (SYNE3) in males. Meta-analysis of UK Biobank data with a previous meta-analysis led to identification of three novel loci (APOB, FIP1L1, and LOXL4). Transcriptome-wide association analyses implicated additional genes ARHGAP42, NDRG4, and KANK2. Gene set analysis showed an enrichment in extracellular organization and the PDGF (platelet-derived growth factor) signaling pathway. We found positive genetic correlations of cIMT with coronary artery disease rg=0.21 (P=1.4×10-7), peripheral artery disease rg=0.45 (P=5.3×10-5), and systolic blood pressure rg=0.30 (P=4.0×10-18). A negative genetic correlation between average of maximum cIMT and high-density lipoprotein was found rg=-0.12 (P=7.0×10-4). CONCLUSIONS Genome-wide association meta-analyses in >100 000 individuals identified 25 novel loci associated with cIMT providing insights into genes and tissue-specific regulatory mechanisms of proatherosclerotic processes. We found evidence for shared biological mechanisms with cardiovascular diseases.
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Affiliation(s)
- Ming Wai Yeung
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Siqi Wang
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
- Division of Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, the Netherlands (M.W.Y., J.v.S., P.v.d.H.)
| | - Yordi J. van de Vegte
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Oleg Borisov
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Germany (O.B.)
| | - Jessica van Setten
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Niek Verweij
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - M. Abdullah Said
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
- Division of Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, the Netherlands (M.W.Y., J.v.S., P.v.d.H.)
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Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Fernandes Silva L, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, Boehnke M. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun 2022; 13:1644. [PMID: 35347128 PMCID: PMC8960770 DOI: 10.1038/s41467-022-29143-5] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/23/2022] [Indexed: 01/13/2023] Open
Abstract
Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Lap Sum Chan
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Peter VandeHaar
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, 39100, Italy
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Ketian Yu
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, 90024, USA
| | - Daiwei Zhang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Emily C Hector
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Erica Young
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Liron Ganel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Indraniel Das
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
| | - Haley Abel
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
- Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio, 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63108, USA
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, 63110, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT, 06510, USA
| | | | - Jian Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
- Broad Institute of MIT & Harvard, Cambridge, MA, 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, 90024, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, 02139, USA.
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland.
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
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74
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Zhang H, Mo X, Wang A, Peng H, Guo D, Zhong C, Zhu Z, Xu T, Zhang Y. Association of DNA Methylation in Blood Pressure-Related Genes With Ischemic Stroke Risk and Prognosis. Front Cardiovasc Med 2022; 9:796245. [PMID: 35345488 PMCID: PMC8957103 DOI: 10.3389/fcvm.2022.796245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 01/31/2022] [Indexed: 12/16/2022] Open
Abstract
BackgroundA genome-wide association study identified 12 genetic loci influencing blood pressure and implicated a role of DNA methylation. However, the relationship between methylation and ischemic stroke has not yet been clarified. We conducted a large-sample sequencing study to identify blood leukocyte DNA methylations as novel biomarkers for ischemic stroke risk and prognosis based on previously identified genetic loci.MethodsMethylation levels of 17 genes were measured by sequencing in 271 ischemic stroke cases and 323 controls, and the significant associations were validated in another independent sample of 852 cases and 925 controls. The associations between methylation levels and ischemic stroke risk and prognosis were evaluated.ResultsMethylation of AMH, C17orf82, HDAC9, IGFBP3, LRRC10B, PDE3A, PRDM6, SYT7 and TBX2 was significantly associated with ischemic stroke. Compared to participants without any hypomethylated targets, the odds ratio (OR) (95% confidence interval, CI) for those with 9 hypomethylated genes was 1.41 (1.33–1.51) for ischemic stroke. Adding methylation levels of the 9 genes to the basic model of traditional risk factors significantly improved the risk stratification for ischemic stroke. Associations between AMH, HDAC9, IGFBP3, PDE3A and PRDM6 gene methylation and modified Rankin Scale scores were significant after adjustment for covariates. Lower methylation levels of AMH, C17orf82, PRDM6 and TBX2 were significantly associated with increased 3-month mortality. Compared to patients without any hypomethylated targets, the OR (95% CI) for those with 4 hypomethylated targets was 1.12 (1.08–1.15) for 3-month mortality (P = 2.28 × 10−10).ConclusionThe present study identified blood leukocyte DNA methylations as potential factors affecting ischemic stroke risk and prognosis among Han Chinese individuals.
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Affiliation(s)
- Huan Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Xingbo Mo
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Aili Wang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Hao Peng
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Daoxia Guo
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Chongke Zhong
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Zhengbao Zhu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Tan Xu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Yonghong Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
- *Correspondence: Yonghong Zhang
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75
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Zhu X, Zhu L, Wang H, Cooper RS, Chakravarti A. Genome-wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores. Genet Epidemiol 2022; 46:105-121. [PMID: 34989438 PMCID: PMC8863647 DOI: 10.1002/gepi.22440] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/07/2021] [Indexed: 01/21/2023]
Abstract
Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)-International Consortium for Blood Pressure (ICBP) (UKB-ICBP) BP genome-wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11-3.26 fold) heritability for BP traits and increases (1.09- and 2.01-fold) Nagelkerke's R2 for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta-analysis of the UKB-ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Luke Zhu
- Department of Medicine, Center for Human Genetics & GenomicsNew York University Langone HealthNew YorkNew YorkUSA
| | - Heming Wang
- Division of Sleep and Circadian DisordersBrigham and Women's HospitalBostonMassachusettsUSA
| | - Richard S. Cooper
- Department of Public Health Sciences, Stritch School of MedicineLoyola University ChicagoMaywoodIllinoisUSA
| | - Aravinda Chakravarti
- Department of Medicine, Center for Human Genetics & GenomicsNew York University Langone HealthNew YorkNew YorkUSA
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76
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Wang T, Qiao J, Zhang S, Wei Y, Zeng P. Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models. Brief Bioinform 2022; 23:6535679. [PMID: 35212359 DOI: 10.1093/bib/bbac038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/22/2022] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Jiahao Qiao
- Department of Biostatistics at Xuzhou Medical University, China
| | - Shuo Zhang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Yongyue Wei
- Department of Biostatistics at Nanjing Medical University, China
| | - Ping Zeng
- Department of Biostatistics, Center for Medical Statistics and Data Analysis and Key Laboratory of Human Genetics and Environmental Medicine at Xuzhou Medical University, China
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77
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 3167] [Impact Index Per Article: 1055.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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78
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Meloche M, Leclair G, Jutras M, Oussaïd E, Gaulin MJ, Mongrain I, Busseuil D, Tardif JC, Dubé MP, de Denus S. Leveraging large observational studies to discover genetic determinants of drug concentrations: A proof-of-concept study. Clin Transl Sci 2022; 15:1063-1073. [PMID: 35122397 PMCID: PMC9010273 DOI: 10.1111/cts.13230] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 12/16/2022] Open
Abstract
Large, observational genetic studies are commonly used to identify genetic factors associated with diseases and disease‐related traits. Such cohorts have not been commonly used to identify genetic predictors of drug dosing or concentrations, perhaps because of the heterogeneity in drug dosing and formulation, and the random timing of blood sampling. We hypothesized that large sample sizes relative to traditional pharmacokinetic studies would compensate for this variability and enable the identification of pharmacogenetic predictors of drug concentrations. We performed a cross‐sectional, proof‐of‐concept association study to replicate the well‐established association between metoprolol concentrations and CYP2D6 genotype‐inferred metabolizer phenotypes in participants from the Montreal Heart Institute Hospital Cohort undergoing metoprolol therapy. Plasma concentrations of metoprolol and α‐hydroxymetoprolol (α‐OH‐metoprolol) were measured in samples collected randomly regarding the previous metoprolol dose. A total of 999 individuals were included. The metoprolol daily dose ranged from 6.25 to 400 mg (mean 84.3 ± 57.1 mg). CYP2D6‐inferred phenotype was significantly associated with both metoprolol and α‐OH‐metoprolol in unadjusted and adjusted models (all p < 10−14). Models for metoprolol daily dose showed consistent results. Our study suggests that randomly drawn blood samples from biobanks can serve as a new approach to discover genetic associations related to drug concentrations and dosing, with potentially broader implications for genomewide association studies on the pharmacogenomics of drug metabolism.
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Affiliation(s)
- Maxime Meloche
- Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada.,Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
| | - Grégoire Leclair
- Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - Martin Jutras
- Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - Essaïd Oussaïd
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
| | - Marie-Josée Gaulin
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
| | - Ian Mongrain
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
| | - David Busseuil
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Simon de Denus
- Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada.,Montreal Heart Institute, Montreal, Quebec, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada
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79
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First genome-wide association study investigating blood pressure and renal traits in domestic cats. Sci Rep 2022; 12:1899. [PMID: 35115544 PMCID: PMC8813908 DOI: 10.1038/s41598-022-05494-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 12/28/2021] [Indexed: 11/08/2022] Open
Abstract
Hypertension (HTN) and chronic kidney disease (CKD) are common in ageing cats. In humans, blood pressure (BP) and renal function are complex heritable traits. We performed the first feline genome-wide association study (GWAS) of quantitative traits systolic BP and creatinine and binary outcomes HTN and CKD, testing 1022 domestic cats with a discovery, replication and meta-analysis design. No variants reached experimental significance level in the discovery stage for any phenotype. Follow up of the top 9 variants for creatinine and 5 for systolic BP, one SNP reached experimental-wide significance for association with creatinine in the combined meta-analysis (chrD1.10258177; P = 1.34 × 10–6). Exploratory genetic risk score (GRS) analyses were performed. Within the discovery sample, GRS of top SNPs from the BP and creatinine GWAS show strong association with HTN and CKD but did not validate in independent replication samples. A GRS including SNPs corresponding to human CKD genes was not significant in an independent subset of cats. Gene-set enrichment and pathway-based analysis (GSEA) was performed for both quantitative phenotypes, with 30 enriched pathways with creatinine. Our results support the utility of GWASs and GSEA for genetic discovery of complex traits in cats, with the caveat of our findings requiring validation.
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80
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Hao K, Ermel R, Sukhavasi K, Cheng H, Ma L, Li L, Amadori L, Koplev S, Franzén O, d’Escamard V, Chandel N, Wolhuter K, Bryce NS, Venkata VRM, Miller CL, Ruusalepp A, Schunkert H, Björkegren JL, Kovacic JC. Integrative Prioritization of Causal Genes for Coronary Artery Disease. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003365. [PMID: 34961328 PMCID: PMC8847335 DOI: 10.1161/circgen.121.003365] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.
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Affiliation(s)
- Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Sema4, Stamford, CT
| | - Raili Ermel
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia
| | - Katyayani Sukhavasi
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ling Li
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany,Center for Doctoral Studies in Informatics and its Applications, Dept of Informatics, Technische Universität München, Munich, Germany,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Current affiliation: New York University Cardiovascular Research Center, Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, New York University Langone Health, New York, NY
| | - Simon Koplev
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Oscar Franzén
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Valentina d’Escamard
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nirupama Chandel
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kathryn Wolhuter
- Victor Chang Cardiac Research Institute, Darlinghurst, Australia,University of New South Wales, Faculty of Medicine and Health, Sydney, Australia
| | - Nicole S. Bryce
- Victor Chang Cardiac Research Institute, Darlinghurst, Australia,University of New South Wales, Faculty of Medicine and Health, Sydney, Australia
| | | | - Clint L. Miller
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA
| | - Arno Ruusalepp
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Tartu, Estonia,Department of Cardiology, Institute of Clinical Medicine, Tartu University
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany,Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Munich, Germany
| | - Johan L.M. Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Jason C. Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY,Victor Chang Cardiac Research Institute, Darlinghurst, Australia,St Vincent's Clinical School, University of New South Wales, Sydney, Australia
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81
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Canoy D, Harvey NC, Prieto-Alhambra D, Cooper C, Meyer HE, Åsvold BO, Nazarzadeh M, Rahimi K. Elevated blood pressure, antihypertensive medications and bone health in the population: revisiting old hypotheses and exploring future research directions. Osteoporos Int 2022; 33:315-326. [PMID: 34642814 PMCID: PMC8813726 DOI: 10.1007/s00198-021-06190-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/01/2021] [Indexed: 11/17/2022]
Abstract
Blood pressure and bone metabolism appear to share commonalities in their physiologic regulation. Specific antihypertensive drug classes may also influence bone mineral density. However, current evidence from existing observational studies and randomised trials is insufficient to establish causal associations for blood pressure and use of blood pressure-lowering drugs with bone health outcomes, particularly with the risks of osteoporosis and fractures. The availability and access to relevant large-scale biomedical data sources as well as developments in study designs and analytical approaches provide opportunities to examine the nature of the association between blood pressure and bone health more reliably and in greater detail than has ever been possible. It is unlikely that a single source of data or study design can provide a definitive answer. However, with appropriate considerations of the strengths and limitations of the different data sources and analytical techniques, we should be able to advance our understanding of the role of raised blood pressure and its drug treatment on the risks of low bone mineral density and fractures. As elevated blood pressure is highly prevalent and blood pressure-lowering drugs are widely prescribed, even small effects of these exposures on bone health outcomes could be important at a population level.
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Affiliation(s)
- D Canoy
- Deep Medicine, Nuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House 1F, George St., Oxford, OX1 2BQ, UK.
- NIHR Oxford Biomedical Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - N C Harvey
- MRC Life Course Epidemiology Unit, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - D Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - C Cooper
- NIHR Oxford Biomedical Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- MRC Life Course Epidemiology Unit, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - H E Meyer
- Department of Community Medicine and Global Health, Faculty of Medicine, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - B O Åsvold
- Department of Endocrinology, Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - M Nazarzadeh
- Deep Medicine, Nuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House 1F, George St., Oxford, OX1 2BQ, UK
| | - K Rahimi
- Deep Medicine, Nuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House 1F, George St., Oxford, OX1 2BQ, UK
- NIHR Oxford Biomedical Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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82
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Zhang H, Wang A, Xu T, Mo X, Zhang Y. Promoter DNA Methylation in GWAS-Identified Genes as Potential Functional Elements for Blood Pressure: An Observational and Mendelian Randomization Study. Front Genet 2022; 12:791146. [PMID: 35087571 PMCID: PMC8787193 DOI: 10.3389/fgene.2021.791146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/21/2021] [Indexed: 01/03/2023] Open
Abstract
Genome-wide association studies have identified numerous genetic loci for blood pressure (BP). However, the relationships of functional elements inside these loci with BP are not fully understood. This study represented an effort to determine if promoter DNA methylations inside BP-associated loci were associated with BP.We conducted a cross-sectional study investigating the association between promoter DNA methylations of 10 candidate genes and BP in 1,241 Chinese individuals. Twenty-one genomic fragments in the CpG Islands were sequenced. The associations of methylation levels with BP and hypertension were assessed in regression models. Mendelian randomization (MR) analysis was then applied to find supporting evidence for the identified associations.A total of 413 DNA methylation sites were examined in an observational study. Methylation levels of 24 sites in PRDM6, IGFBP3, SYT7, PDE3A, TBX2 and C17orf82 were significantly associated with BP. Methylation levels of PRDM6 and SYT7 were significantly associated with hypertension. Methylation levels of five sites (including cg06713098) in IGFBP3 were significantly associated with DBP. MR analysis found associations between the methylation levels of six CpG sites (cg06713098, cg14228300, cg23193639, cg21268650, cg10677697 and cg04812164) around the IGFBP3 promoter and DBP. Methylation levels of cg14228300 and cg04812164 were associated with SBP. By further applying several MR methods we showed that the associations may not be due to pleiotropy. Association between IGFBP3 mRNA levels in blood cells and BP was also found in MR analysis. This study identified promoter methylation as potential functional element for BP. The identified methylations may be involved in the regulatory pathway linking genetic variants to BP.
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Affiliation(s)
- Huan Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.,Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Aili Wang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.,Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Tan Xu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.,Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Xingbo Mo
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.,Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China.,Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Yonghong Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.,Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
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83
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Wang G, Bhatta L, Moen GH, Hwang LD, Kemp JP, Bond TA, Åsvold BO, Brumpton B, Evans DM, Warrington NM. Investigating a Potential Causal Relationship Between Maternal Blood Pressure During Pregnancy and Future Offspring Cardiometabolic Health. Hypertension 2022; 79:170-177. [PMID: 34784738 PMCID: PMC8654122 DOI: 10.1161/hypertensionaha.121.17701] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/20/2021] [Indexed: 12/20/2022]
Abstract
Observational epidemiological studies have reported that higher maternal blood pressure (BP) during pregnancy is associated with increased future risk of offspring cardiometabolic disease. However, it is unclear whether this association represents a causal relationship through intrauterine mechanisms. We used a Mendelian randomization (MR) framework to examine the relationship between unweighted maternal genetic scores for systolic BP and diastolic BP and a range of cardiometabolic risk factors in the offspring of up to 29 708 genotyped mother-offspring pairs from the UKB study (UK Biobank) and the HUNT study (Trøndelag Health). We conducted similar analyses in up to 21 423 father-offspring pairs from the same cohorts. We confirmed that the BP-associated genetic variants from the general population sample also had similar effects on maternal BP during pregnancy in independent cohorts. We did not detect any association between maternal (or paternal) unweighted genetic scores and cardiometabolic offspring outcomes in the meta-analysis of UKB and HUNT after adjusting for offspring genotypes at the same loci. We find little evidence to support the notion that maternal BP is a major causal risk factor for adverse offspring cardiometabolic outcomes in later life.
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Affiliation(s)
- Geng Wang
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
| | - Gunn-Helen Moen
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway (G.-H.M.)
- Population Health Sciences, Bristol Medical School (G.-H.M., T.A.B.), University of Bristol, United Kingdom
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
| | - John P. Kemp
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Tom A. Bond
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Population Health Sciences, Bristol Medical School (G.-H.M., T.A.B.), University of Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Bjørn Olav Åsvold
- Department of Endocrinology, Clinic of Medicine (B.O.A.), St Olavs Hospital, Trondheim University Hospital, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway (B.O.A., B.B.)
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
- Clinic of Medicine (B.B.), St Olavs Hospital, Trondheim University Hospital, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway (B.O.A., B.B.)
| | - David M. Evans
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Nicole M. Warrington
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
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84
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Leptidis S, Papakonstantinou E, Diakou KI, Pierouli K, Mitsis T, Dragoumani K, Bacopoulou F, Sanoudou D, Chrousos GP, Vlachakis D. Epitranscriptomics of cardiovascular diseases (Review). Int J Mol Med 2022; 49:9. [PMID: 34791505 PMCID: PMC8651226 DOI: 10.3892/ijmm.2021.5064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/20/2021] [Indexed: 11/09/2022] Open
Abstract
RNA modifications have recently become the focus of attention due to their extensive regulatory effects in a vast array of cellular networks and signaling pathways. Just as epigenetics is responsible for the imprinting of environmental conditions on a genetic level, epitranscriptomics follows the same principle at the RNA level, but in a more dynamic and sensitive manner. Nevertheless, its impact in the field of cardiovascular disease (CVD) remains largely unexplored. CVD and its associated pathologies remain the leading cause of death in Western populations due to the limited regenerative capacity of the heart. As such, maintenance of cardiac homeostasis is paramount for its physiological function and its capacity to respond to environmental stimuli. In this context, epitranscriptomic modifications offer a novel and promising therapeutic avenue, based on the fine‑tuning of regulatory cascades, necessary for cardiac function. This review aimed to provide an overview of the most recent findings of key epitranscriptomic modifications in both coding and non‑coding RNAs. Additionally, the methods used for their detection and important associations with genetic variations in the context of CVD were summarized. Current knowledge on cardiac epitranscriptomics, albeit limited still, indicates that the impact of epitranscriptomic editing in the heart, in both physiological and pathological conditions, holds untapped potential for the development of novel targeted therapeutic approaches in a dynamic manner.
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Affiliation(s)
- Stefanos Leptidis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Kalliopi Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Thanasis Mitsis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Flora Bacopoulou
- Laboratory of Molecular Endocrinology, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- First Department of Pediatrics, Center for Adolescent Medicine and UNESCO Chair on Adolescent Health Care, Medical School, Aghia Sophia Children's Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Despina Sanoudou
- Fourth Department of Internal Medicine, Clinical Genomics and Pharmacogenomics Unit, Medical School, 'Attikon' Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Molecular Biology Division, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - George P. Chrousos
- Laboratory of Molecular Endocrinology, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- First Department of Pediatrics, Center for Adolescent Medicine and UNESCO Chair on Adolescent Health Care, Medical School, Aghia Sophia Children's Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- Laboratory of Molecular Endocrinology, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- First Department of Pediatrics, Center for Adolescent Medicine and UNESCO Chair on Adolescent Health Care, Medical School, Aghia Sophia Children's Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
- School of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London WC2R 2LS, UK
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85
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Chan II, Kwok MK, Schooling CM. Blood pressure and risk of cancer: a Mendelian randomization study. BMC Cancer 2021; 21:1338. [PMID: 34915881 PMCID: PMC8675492 DOI: 10.1186/s12885-021-09067-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/26/2021] [Indexed: 12/19/2022] Open
Abstract
Background Previous large observational cohort studies showed higher blood pressure (BP) positively associated with cancer. We used Mendelian randomization (MR) to obtain less confounded estimates of BP on total and site-specific cancers. Methods We applied replicated genetic instruments for systolic and diastolic BP to summary genetic associations with total cancer (37387 cases, 367856 non-cases) from the UK Biobank, and 17 site-specific cancers (663–17881 cases) from a meta-analysis of the UK Biobank and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging. We used inverse-variance weighting with multiplicative random effects as the main analysis, and sensitivity analyses including the weighted median, MR-Egger and multivariable MR adjusted for body mass index and for smoking. For validation, we included breast (Breast Cancer Association Consortium: 133384 cases, 113789 non-cases), prostate (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome Consortium: 79194 cases, 61112 non-cases) and lung (International Lung and Cancer Consortium: 10246 cases, 38295 non-cases) cancer from large consortia. We used asthma as a negative control outcome. Results Systolic and diastolic BP were unrelated to total cancer (OR 0.98 per standard deviation higher [95% confidence interval (CI) 0.89, 1.07] and OR 1.00 [95% CI 0.92, 1.08]) and to site-specific cancers after accounting for multiple testing, with consistent findings from consortia. BP was nominally associated with melanoma and possibly kidney cancer, and as expected, not associated with asthma. Sensitivity analyses using other MR methods gave similar results. Conclusions In contrast to previous observational evidence, BP does not appear to be a risk factor for cancer, although an effect on melanoma and kidney cancer cannot be excluded. Other targets for cancer prevention might be more relevant. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-09067-x.
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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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86
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Haas ME, Pirruccello JP, Friedman SN, Wang M, Emdin CA, Ajmera VH, Simon TG, Homburger JR, Guo X, Budoff M, Corey KE, Zhou AY, Philippakis A, Ellinor PT, Loomba R, Batra P, Khera AV. Machine learning enables new insights into genetic contributions to liver fat accumulation. CELL GENOMICS 2021; 1:100066. [PMID: 34957434 PMCID: PMC8699145 DOI: 10.1016/j.xgen.2021.100066] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 07/13/2021] [Accepted: 09/09/2021] [Indexed: 12/14/2022]
Abstract
Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat (correlation coefficients, 0.97-0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the MTARC1, ADH1B, TRIB1, GPAM, and MAST3 genes (p < 3 × 10-8). A polygenic score integrating these eight genetic variants was strongly associated with future risk of chronic liver disease (hazard ratio > 1.32 per SD score, p < 9 × 10-17). Rare inactivating variants in the APOB or MTTP genes were identified in 0.8% of individuals with steatosis and conferred more than 6-fold risk (p < 2 × 10-5), highlighting a molecular subtype of hepatic steatosis characterized by defective secretion of apolipoprotein B-containing lipoproteins. We demonstrate that our imaging-based machine-learning model accurately estimates liver fat and may be useful in epidemiological and genetic studies of hepatic steatosis.
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Affiliation(s)
- Mary E. Haas
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Molecular Biology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James P. Pirruccello
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Samuel N. Friedman
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Connor A. Emdin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Veeral H. Ajmera
- NAFLD Research Center, Department of Medicine, University of California San Diego, La Jolla, CA 92103, USA
| | - Tracey G. Simon
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- Liver Center, Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Xiuqing Guo
- The Lundquist Institute for Biomedical Innovation and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Matthew Budoff
- The Lundquist Institute for Biomedical Innovation and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Kathleen E. Corey
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- Liver Center, Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Anthony Philippakis
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrick T. Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rohit Loomba
- NAFLD Research Center, Department of Medicine, University of California San Diego, La Jolla, CA 92103, USA
| | - Puneet Batra
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amit V. Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Machine Learning for Health, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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87
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Abstract
Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.
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Kaur H, Crawford DC, Liang J, Benchek P, COGENT BP Consortium, Zhu X, Kallianpur AR, Bush WS. Replication of European hypertension associations in a case-control study of 9,534 African Americans. PLoS One 2021; 16:e0259962. [PMID: 34793544 PMCID: PMC8601554 DOI: 10.1371/journal.pone.0259962] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 10/29/2021] [Indexed: 12/01/2022] Open
Abstract
OBJECTIVE Hypertension is more prevalent in African Americans (AA) than other ethnic groups. Genome-wide association studies (GWAS) have identified loci associated with hypertension and other cardio-metabolic traits like type 2 diabetes, coronary artery disease, and body mass index (BMI), however the AA population is underrepresented in these studies. In this study, we examined a large AA cohort for the generalizability of 14 Metabochip array SNPs with previously reported European hypertension associations. METHODS To evaluate associations, we analyzed genotype data of 14 SNPs for their associations with a diagnosis of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in a case-control study of an AA population (N = 9,534). We also performed an age-stratified analysis (>30, 30≥59 and ≥60 years) following the hypertension definition described by the 8th Joint National Committee (JNC). Associations were adjusted for BMI, age, age2, sex, clinical confounders, and genetic ancestry using multivariable regression models to estimate odds ratios (ORs) and beta-coefficients. Analyses stratified by sex were also conducted. Meta-analyses (including both BioVU and COGENT-BP cohorts) were performed using a random-effects model. RESULTS We found rs880315 to be associated with systolic hypertension (SBP≥140 mmHg) in the entire cohort (OR = 1.14, p = 0.003) and within women only (OR = 1.16, p = 0.012). Variant rs17080093 associated with lower SBP and DBP (β = -2.99, p = 0.0352 and - β = 1.69, p = 0.0184) among younger individuals, particularly in younger women (β = -3.92, p = 0.0025 and β = -1.87, p = 0.0241 for SBP and DBP respectively). SNP rs1530440 associated with higher SBP and DBP measurements (younger individuals β = 4.1, p = 0.039 and β = 2.5, p = 0.043 for SBP and DBP; (younger women β = 4.5, p = 0.025 and β = 2.9, p = 0.028 for SBP and DBP), and hypertension risk in older women (OR = 1.4, p = 0.050). rs16948048 increases hypertension risk in younger individuals (OR = 1.31, p = 0.011). Among mid-age women rs880315 associated with higher risk of hypertension (OR = 1.20, p = 0.027). rs1361831 associated with DBP (β = -1.96, p = 0.02) among individuals older than 60 years. rs3096277 increases hypertension risk among older individuals (OR = 1.26 p = 0.0015), however, this variant also reduces SBP among younger women (β = -2.63, p = 0.0102). CONCLUSION These findings suggest that European-descent and AA populations share genetic loci that contribute to blood pressure traits and hypertension. However, the OR and beta-coefficient estimates differ, and some are age-dependent. Additional genetic studies of hypertension in AA are warranted to identify new loci associated with hypertension and blood pressure traits in this population.
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Affiliation(s)
- Harpreet Kaur
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Jingjing Liang
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Penelope Benchek
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | | | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Asha R. Kallianpur
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States of America
| | - William S. Bush
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
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89
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Howe LJ, Battram T, Morris TT, Hartwig FP, Hemani G, Davies NM, Smith GD. Assortative mating and within-spouse pair comparisons. PLoS Genet 2021; 17:e1009883. [PMID: 34735433 PMCID: PMC8594845 DOI: 10.1371/journal.pgen.1009883] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 11/16/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.
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Affiliation(s)
- Laurence J. Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Thomas Battram
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tim T. Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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90
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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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91
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Wu Y, Xu H, Tu X, Gao Z. The Role of Short-Chain Fatty Acids of Gut Microbiota Origin in Hypertension. Front Microbiol 2021; 12:730809. [PMID: 34650536 PMCID: PMC8506212 DOI: 10.3389/fmicb.2021.730809] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Hypertension is a significant risk factor for cardiovascular and cerebrovascular diseases, and its development involves multiple mechanisms. Gut microbiota has been reported to be closely linked to hypertension. Short-chain fatty acids (SCFAs)-the metabolites of gut microbiota-participate in hypertension development through various pathways, including specific receptors, immune system, autonomic nervous system, metabolic regulation and gene transcription. This article reviews the possible mechanisms of SCFAs in regulating blood pressure and the prospects of SCFAs as a target to prevent and treat hypertension.
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Affiliation(s)
- Yeshun Wu
- Department of Cardiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Hongqing Xu
- Department of Cardiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Xiaoming Tu
- Department of Cardiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Zhenyan Gao
- Department of Cardiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
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92
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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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Abstract
Chronic cardiovascular diseases are associated with inflammatory responses within the blood vessels and end organs. The origin of this inflammation has not been certain, and neither is its relationship to disease clear. There is a need to determine whether this association is causal or coincidental to the processes leading to cardiovascular disease. These processes are themselves complex: many cardiovascular diseases arise in conjunction with the presence of sustained elevation of blood pressure. Inflammatory processes have been linked to hypertension, and causality has been suggested. Evidence of causality poses the difficult challenge of linking the integrated and multifaceted biology of blood pressure regulation with vascular function and complex elements of immune system function. These include both, innate and adaptive immunity, as well as interactions between the host immune system and the omnipresent microorganisms that are encountered in the environment and that colonize and exist in commensal relationship with the host. Progress has been made in this task and has drawn on experimental approaches in animals, much of which have focused on hypertension occurring with prolonged infusion of angiotensin II. These laboratory studies are complemented by studies that seek to inform disease mechanism by examining the genomic basis of heritable disease susceptibility in human populations. In this realm too, evidence has emerged that implicates genetic variation affecting immunity in disease pathogenesis. In this article, we survey the genetic and genomic evidence linking high blood pressure and its end-organ injuries to immune system function and examine evidence that genomic factors can influence disease risk. © 2021 American Physiological Society. Compr Physiol 11:1-22, 2021.
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Affiliation(s)
- Isha S Dhande
- Center for Human Genetics, Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter A Doris
- Center for Human Genetics, Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA
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94
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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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95
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Westerlund AM, Hawe JS, Heinig M, Schunkert H. Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence. Int J Mol Sci 2021; 22:10291. [PMID: 34638627 PMCID: PMC8508897 DOI: 10.3390/ijms221910291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
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Affiliation(s)
- Annie M. Westerlund
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
| | - Johann S. Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
| | - Matthias Heinig
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
- Department of Informatics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Biedersteiner Strasse 29, 80802 Munich, Germany
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96
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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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97
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Lankester J, Zanetti D, Ingelsson E, Assimes TL. Alcohol use and cardiometabolic risk in the UK Biobank: A Mendelian randomization study. PLoS One 2021; 16:e0255801. [PMID: 34379647 PMCID: PMC8357114 DOI: 10.1371/journal.pone.0255801] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 07/25/2021] [Indexed: 12/13/2022] Open
Abstract
Observational studies suggest alcohol use promotes the development of some adverse cardiometabolic traits but protects against others including outcomes related to coronary artery disease. We used Mendelian randomization (MR) to explore causal relationships between the degree of alcohol consumption and several cardiometabolic traits in the UK Biobank. Using the well-established ADH1B Arg47His variant (rs1229984) and up to 24 additional SNPs recently found to be associated with alcohol consumption in an independent dataset as instruments, we conducted two-stage least squares and inverse weighted variance MR analyses, both as one-sample analyses in the UK Biobank and as two-sample analyses in external consortia. In the UK Biobank inverse variance weighted analyses, we found that one additional drink of alcohol per day was positively associated with systolic blood pressure (beta = 2.65 mmHg [1.40, 3.89]), hemorrhagic stroke (OR = 2.25 [1.41, 3.60]), and atrial fibrillation (OR = 1.26 [1.07, 1.48]), which were replicated in multivariable analyses. Alcohol was also associated with all cardiovascular disease and all-cause death. A positive association with myocardial infarction did not replicate in multivariable analysis, with suggestive mediation through blood pressure; similarly, a positive association between alcohol use with type 2 diabetes was mitigated by BMI in multivariable analysis. Findings were generally null in replication with two-sample analyses. Alcohol was not protective for any disease outcome with any analysis method, dataset, or strata. Stratifications by sex and smoking in the UK Biobank revealed higher point estimates of risk for several outcomes for men and mixed results for smoking strata, but no statistically significant heterogeneity. Our results are consistent with an overall harmful and/or null effect of alcohol on cardiometabolic health at all levels of use and suggest that even moderate alcohol use should not be promoted as a part of a healthy diet and lifestyle.
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Affiliation(s)
- Joanna Lankester
- Department of Medicine (Division of Cardiovascular Medicine), Stanford University School of Medicine, Stanford, CA, United States of America
| | - Daniela Zanetti
- Department of Medicine (Division of Cardiovascular Medicine), Stanford University School of Medicine, Stanford, CA, United States of America
| | - Erik Ingelsson
- Department of Medicine (Division of Cardiovascular Medicine), Stanford University School of Medicine, Stanford, CA, United States of America
- GlaxoSmithKline, Inc., Brentford, England, United Kingdom
| | - Themistocles L. Assimes
- Department of Medicine (Division of Cardiovascular Medicine), Stanford University School of Medicine, Stanford, CA, United States of America
- VA Palo Alto Health Care System, Palo Alto, CA, United States of America
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98
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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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99
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Lewis CR, Bonham KS, McCann SH, Volpe AR, D’Sa V, Naymik M, De Both MD, Huentelman MJ, Lemery-Chalfant K, Highlander SK, Deoni SCL, Klepac-Ceraj V. Family SES Is Associated with the Gut Microbiome in Infants and Children. Microorganisms 2021; 9:1608. [PMID: 34442687 PMCID: PMC8398307 DOI: 10.3390/microorganisms9081608] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/12/2021] [Accepted: 07/21/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND While early life exposures such as mode of birth, breastfeeding, and antibiotic use are established regulators of microbiome composition in early childhood, recent research suggests that the social environment may also exert influence. Two recent studies in adults demonstrated associations between socioeconomic factors and microbiome composition. This study expands on this prior work by examining the association between family socioeconomic status (SES) and host genetics with microbiome composition in infants and children. METHODS Family SES was used to predict a latent variable representing six genera abundances generated from whole-genome shotgun sequencing. A polygenic score derived from a microbiome genome-wide association study was included to control for potential genetic associations. Associations between family SES and microbiome diversity were assessed. RESULTS Anaerostipes, Bacteroides, Eubacterium, Faecalibacterium, and Lachnospiraceae spp. significantly loaded onto a latent factor, which was significantly predicted by SES (p < 0.05) but not the polygenic score (p > 0.05). Our results indicate that SES did not predict alpha diversity but did predict beta diversity (p < 0.001). CONCLUSIONS Our results demonstrate that modifiable environmental factors influence gut microbiome composition at an early age. These results are important as our understanding of gut microbiome influences on health continue to expand.
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Affiliation(s)
- Candace R. Lewis
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA; (M.N.); (M.D.D.B.); (M.J.H.)
| | - Kevin S. Bonham
- Department of Biological Sciences, Wellesley College, Wellesley, MA 02481, USA; (K.S.B.); (S.H.M.)
| | - Shelley Hoeft McCann
- Department of Biological Sciences, Wellesley College, Wellesley, MA 02481, USA; (K.S.B.); (S.H.M.)
| | - Alexandra R. Volpe
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI 02903, USA; (A.R.V.); (V.D.); (S.C.L.D.)
| | - Viren D’Sa
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI 02903, USA; (A.R.V.); (V.D.); (S.C.L.D.)
- Department of Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
| | - Marcus Naymik
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA; (M.N.); (M.D.D.B.); (M.J.H.)
| | - Matt D. De Both
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA; (M.N.); (M.D.D.B.); (M.J.H.)
| | - Matthew J. Huentelman
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA; (M.N.); (M.D.D.B.); (M.J.H.)
| | | | - Sarah K. Highlander
- Pathogen and Microbiome Division, Translational Genomics Research Institute North (TGen), Flagstaff, AZ 86005, USA;
| | - Sean C. L. Deoni
- Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI 02903, USA; (A.R.V.); (V.D.); (S.C.L.D.)
- Department of Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
- MNCH D&T, Bill and Melinda Gates Foundation, Seattle, WA 98109, USA
| | - Vanja Klepac-Ceraj
- Department of Biological Sciences, Wellesley College, Wellesley, MA 02481, USA; (K.S.B.); (S.H.M.)
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Hyppönen E, Zhou A. Cardiovascular symptoms affect the patterns of habitual coffee consumption. Am J Clin Nutr 2021; 114:214-219. [PMID: 33711095 DOI: 10.1093/ajcn/nqab014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/11/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Excessive coffee consumption can lead to unpleasant sensations such as tachycardia and heart palpitations. OBJECTIVES Our aim was to investigate if cardiovascular symptoms can lead to alterations in habitual patterns of coffee consumption. METHODS We used information from up to 390,435 European ancestry participants in the UK Biobank, aged 39-73 y. Habitual coffee consumption was self-reported, and systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate were measured at baseline. Cardiovascular symptoms at baseline were based on hospital diagnoses, primary care records, and/or self-report. Mendelian randomization (MR) was used to examine genetic evidence for a causal association between SBP, DBP, and heart rate with habitual coffee consumption. RESULTS Participants with essential hypertension, angina, or heart arrhythmia were all more likely to drink less caffeinated coffee and to be non-habitual or decaffeinated coffee drinkers compared with those who did not report related symptoms (P ≤ 3.5 × 10-8 for all comparisons). Higher SBP and DBP were associated with lower caffeinated coffee consumption at baseline, with consistent genetic evidence to support a causal explanation across all methods [MR-Egger regression (MREggr) β: -0.21 cups/d (95% CI: -0.34, -0.07) per 10 mm Hg higher SBP and -0.33 (-0.61, -0.07) per 10 mm Hg higher DBP)]. In genetic analyses, higher resting heart rate was associated with a greater odds of being a decaffeinated coffee drinker (MREggr OR: 1.71; 95% CI: 1.31, 2.21) per 10 beats/min). CONCLUSIONS We provide causal genetic evidence for cardiovascular system-driven influences on habitual coffee intakes, suggesting that people tend to naturally regulate their coffee consumption based on blood pressure levels and heart rate. These findings suggest that observational studies of habitual coffee intakes are prone to influences by reverse causation, and caution is required when inferred health benefits result from comparisons with coffee abstainers or decaffeinated coffee drinkers.
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Affiliation(s)
- Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia.,Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia.,South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Ang Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia.,Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia.,South Australian Health and Medical Research Institute, Adelaide, Australia
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