1
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Haworth S, Timpson NJ, Divaris K. Mendelian randomization studies of periodontitis: Understanding benefits and natural limitations in an applied context. J Clin Periodontol 2024. [PMID: 39013836 DOI: 10.1111/jcpe.14029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 05/31/2024] [Indexed: 07/18/2024]
Abstract
Mendelian randomization (MR) is a flexible analytical tool that has been widely applied to strengthen causal inference in observational epidemiology and is now gaining attention in many areas including periodontal research. The interpretation of results drawn from MR is based on a series of assumptions, which can be unrealistic or difficult to meet faithfully in some settings. However, we argue that with care, this does not necessarily prevent valuable deployment of the approach. We argue that clarity of presentation as well as careful assessment of specific analytical conditions is a fundamental part of all MR analyses. To that end, awareness of its limitations should also guide the design of MR investigations and the presentation of results rather than rule out its use altogether. Notably, considerations similar to those known to be important in conventional epidemiological settings apply to MR. While MR studies are valuable in their contrast to other study limitations, the application of this technique must be carefully cross-examined. Specific considerations include possible confounders, recruitment strategy and phenotypic measurement and differential analysis properties across studies. In the case of periodontal research, current MR applications are limited by the available evidence base for genetic contributions to periodontitis; however, this sets a specific scene for the strategic use of MR and shines light on a need for greater research emphasis on the genetics of the condition and intermediaries. This article provides a perspective on the uses and inherent limitations of MR studies and the importance of adhering to basic epidemiological principles when designing them.
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Affiliation(s)
- Simon Haworth
- Bristol Dental School, University of Bristol, Bristol, UK
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Kimon Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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2
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Kim B, Kim DS, Shin JG, Leem S, Cho M, Kim H, Gu KN, Seo JY, You SW, Martin AR, Park SG, Kim Y, Jeong C, Kang NG, Won HH. Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color. Nat Commun 2024; 15:4874. [PMID: 38849341 PMCID: PMC11161515 DOI: 10.1038/s41467-024-49031-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
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Affiliation(s)
- Beomsu Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Joong-Gon Shin
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Sangseob Leem
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Minyoung Cho
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hanji Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Ki-Nam Gu
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Jung Yeon Seo
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Seung Won You
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02141, USA
| | - Sun Gyoo Park
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Yunkwan Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Nae Gyu Kang
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea.
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea.
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3
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Liao Y, Yu H, Zhang Y, Lu Z, Sun Y, Guo L, Guo J, Kang Z, Feng X, Sun Y, Wang G, Su Z, Lu T, Yang Y, Li W, Lv L, Yan H, Zhang D, Yue W. Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation. Mol Psychiatry 2024; 29:1857-1868. [PMID: 38336841 DOI: 10.1038/s41380-024-02447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.
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Affiliation(s)
- Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong, 272067, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
| | - Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Liangkun Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Jing Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Zhewei Kang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Xiaoyang Feng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yutao Sun
- No.5 Hospital, Tangshan, Hebei, 063000, China
| | - Guishan Wang
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Zhonghua Su
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yongfeng Yang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Wenqiang Li
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Luxian Lv
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
- Chinese Institute for Brain Research, Beijing, 102206, China
- Institute for Brain Research and Rehabilitation (IBRR), Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
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4
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Momin MM, Zhou X, Hyppönen E, Benyamin B, Lee SH. Cross-ancestry genetic architecture and prediction for cholesterol traits. Hum Genet 2024; 143:635-648. [PMID: 38536467 DOI: 10.1007/s00439-024-02660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/13/2024] [Indexed: 05/18/2024]
Abstract
While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.
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Affiliation(s)
- Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
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5
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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6
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Chen TT, Kim J, Lam M, Chuang YF, Chiu YL, Lin SC, Jung SH, Kim B, Kim S, Cho C, Shim I, Park S, Ahn Y, Okbay A, Jang H, Kim HJ, Seo SW, Park WY, Ge T, Huang H, Feng YCA, Lin YF, Myung W, Chen CY, Won HH. Shared genetic architectures of educational attainment in East Asian and European populations. Nat Hum Behav 2024; 8:562-575. [PMID: 38182883 PMCID: PMC10963262 DOI: 10.1038/s41562-023-01781-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.
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Affiliation(s)
- Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Jaeyoung Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Yi-Fang Chuang
- Institute of Public Health and International Health Program, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Ling Chiu
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan
- Department of Medical Research, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Soyeon Kim
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Sanghyeon Park
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Yeeun Ahn
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yen-Chen Anne Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei City, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan.
- Department of Public Health and Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea.
| | | | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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7
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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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8
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Meng X, Navoly G, Giannakopoulou O, Levey DF, Koller D, Pathak GA, Koen N, Lin K, Adams MJ, Rentería ME, Feng Y, Gaziano JM, Stein DJ, Zar HJ, Campbell ML, van Heel DA, Trivedi B, Finer S, McQuillin A, Bass N, Chundru VK, Martin HC, Huang QQ, Valkovskaya M, Chu CY, Kanjira S, Kuo PH, Chen HC, Tsai SJ, Liu YL, Kendler KS, Peterson RE, Cai N, Fang Y, Sen S, Scott LJ, Burmeister M, Loos RJF, Preuss MH, Actkins KV, Davis LK, Uddin M, Wani AH, Wildman DE, Aiello AE, Ursano RJ, Kessler RC, Kanai M, Okada Y, Sakaue S, Rabinowitz JA, Maher BS, Uhl G, Eaton W, Cruz-Fuentes CS, Martinez-Levy GA, Campos AI, Millwood IY, Chen Z, Li L, Wassertheil-Smoller S, Jiang Y, Tian C, Martin NG, Mitchell BL, Byrne EM, Awasthi S, Coleman JRI, Ripke S, Sofer T, Walters RG, McIntosh AM, Polimanti R, Dunn EC, Stein MB, Gelernter J, Lewis CM, Kuchenbaecker K. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. Nat Genet 2024; 56:222-233. [PMID: 38177345 PMCID: PMC10864182 DOI: 10.1038/s41588-023-01596-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/26/2023] [Indexed: 01/06/2024]
Abstract
Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.
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Affiliation(s)
| | | | | | - Daniel F Levey
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Gita A Pathak
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Nastassja Koen
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - J Michael Gaziano
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Heather J Zar
- SAMRC Unit on Child and Adolescent Health, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Megan L Campbell
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Bhavi Trivedi
- Blizard Institute, Queen Mary University of London, London, UK
| | - Sarah Finer
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Nick Bass
- Division of Psychiatry, UCL, London, UK
| | | | | | | | | | | | - Susan Kanjira
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Po-Hsiu Kuo
- Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsi-Chung Chen
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
- Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science and Division of Psychiatry, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County, Taiwan
| | | | - Roseann E Peterson
- Department of Psychiatry, VCU, Richmond, VA, USA
- Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany
- Computational Health Centre, Helmholtz Munich, Neuherberg, Germany
- Department of Medicine, Technical University of Munich, Munich, Germany
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Margit Burmeister
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ky'Era V Actkins
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Monica Uddin
- College of Public Health, University of South Florida, Tampa, FL, USA
| | - Agaz H Wani
- College of Public Health, University of South Florida, Tampa, FL, USA
| | - Derek E Wildman
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Allison E Aiello
- Robert N. Butler Columbia Aging Center, Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Masahiro Kanai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - George Uhl
- Neurology and Pharmacology, University of Maryland, Maryland VA Healthcare System, Baltimore, MD, USA
| | - William Eaton
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Carlos S Cruz-Fuentes
- Departamento de Genética, Instituto Nacional de Psiquiatría 'Ramón de la Fuente Muñíz', Mexico City, Mexico
| | - Gabriela A Martinez-Levy
- Departamento de Genética, Instituto Nacional de Psiquiatría 'Ramón de la Fuente Muñíz', Mexico City, Mexico
| | - Adrian I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | | | - Yunxuan Jiang
- Department of Biostatistics, Emory University, Atlanta, GA, USA
- 23andMe, Inc., Mountain View, CA, USA
| | - Chao Tian
- 23andMe, Inc., Mountain View, CA, USA
| | - Nicholas G Martin
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Swapnil Awasthi
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
| | - Jonathan R I Coleman
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute for Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Renato Polimanti
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Erin C Dunn
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Murray B Stein
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Cathryn M Lewis
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
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9
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Silva S, Fatumo S, Nitsch D. Mendelian randomization studies on coronary artery disease: a systematic review and meta-analysis. Syst Rev 2024; 13:29. [PMID: 38225600 PMCID: PMC10790478 DOI: 10.1186/s13643-023-02442-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. We aimed to summarize what is currently known with regard to causal modifiable risk factors associated with CAD in populations of diverse ancestries through conducting a systematic review and meta-analysis of Mendelian randomization (MR) studies on CAD. METHODS The databases Embase, Medline, Cochrane Library and Web of Science were searched on the 19th and 20th of December 2022 for MR studies with CAD as a primary outcome; keywords of the search strategy included "coronary artery disease" and "mendelian randomization". Studies were included if they were published in the English language, included only human participants, employed Mendelian randomization as the primary methodology and studied CAD as the outcome of interest. The exclusion criteria resulted in the removal of studies that did not align with the predefined inclusion criteria, as well as studies which were systematic reviews themselves, and used the same exposure and outcome source as another study. An ancestry-specific meta-analysis was subsequently conducted on studies which investigated either body mass index, lipid traits, blood pressure or type 2 diabetes as an exposure variable. Assessment of publication bias and sensitivity analyses was conducted for risk of bias assessment in the included studies. RESULTS A total of 1781 studies were identified through the database searches after de-duplication was performed, with 47 studies included in the quantitative synthesis after eligibility screening. Approximately 80% of all included study participants for MR studies on CAD were of European descent irrespective of the exposure of interest, while no study included individuals of African ancestry. We found no evidence of differences in terms of direction of causation between ancestry groups; however, the strength of the respective relationships between each exposure and CAD were different, with this finding most evident when blood pressure was the exposure of interest. CONCLUSIONS Findings from this review suggest that patterns regarding the causational relationship between modifiable risk factors and CAD do not differ in terms of direction when compared across diverse ancestry populations. Differences in the observed strengths of the respective relationships however are indicative of the value of increasing representation in non-European populations, as novel genetic pathways or functional SNPs relating to CAD may be uncovered through a more global analysis. SYSTEMATIC REVIEW REGISTRATION The protocol for this systematic review was registered to the International Prospective Register of Systematic Reviews (PROSPERO) and is publicly available online (CRD42021272726).
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Affiliation(s)
- Sarah Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda.
| | - Segun Fatumo
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda.
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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10
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Dokuru DR, Horwitz TB, Freis SM, Stallings MC, Ehringer MA. South Asia: The Missing Diverse in Diversity. Behav Genet 2024; 54:51-62. [PMID: 37917228 PMCID: PMC11129896 DOI: 10.1007/s10519-023-10161-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/26/2023] [Indexed: 11/04/2023]
Abstract
South Asia, making up around 25% of the world's population, encompasses a wide range of individuals with tremendous genetic and environmental diversity. This region, which spans eight countries, is home to over 4500 anthropologically defined groups that speak numerous languages and have an array of religious beliefs and cultures, making it one of the most diverse places in the world. Much of the region's rich genetic diversity and structure is the result of a complex combination of population history, migration patterns, and endogamous practices. Despite the overwhelming size and diversity, South Asians have often been underrepresented in genetic research, making up less than 2% of the participants in genetic studies. This has led to a lack of population specific understanding of genetic disease risks. We aim to raise awareness about underlying genetic diversity in this ancestry group, call attention to the lack of representation of the group, and to highlight strategies for future studies in South Asians.
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Affiliation(s)
- Deepika R Dokuru
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30 St, Boulder, CO, 80303, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
| | - Tanya B Horwitz
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30 St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Samantha M Freis
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30 St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30 St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Marissa A Ehringer
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30 St, Boulder, CO, 80303, USA
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
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11
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Xu J, Liu D, Hassan A, Genovese G, Cote AC, Fennessy B, Cheng E, Charney AW, Knowles JA, Ayub M, Peterson RE, Bigdeli TB, Huckins LM. Evaluation of imputation performance of multiple reference panels in a Pakistani population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.22.23300448. [PMID: 38234809 PMCID: PMC10793543 DOI: 10.1101/2023.12.22.23300448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Genotype imputation is crucial for GWAS, but reference panels and existing benchmarking studies prioritize European individuals. Consequently, it is unclear which publicly available reference panel should be used for Pakistani individuals, and whether ancestry composition or sample size of the panel matters more for imputation accuracy. Our study compared different reference panels to impute genotype data in 1814 Pakistani individuals, finding the best performance balancing accuracy and coverage with meta-imputation with TOPMed and the expanded 1000 Genomes (ex1KG) reference. Imputation accuracy of ex1KG outperformed TOPMed despite its 30-fold smaller sample size, supporting efforts to create future panels with diverse populations.
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Affiliation(s)
- Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dongjing Liu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arsalan Hassan
- University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan
- Institute of Omics and Health Research, Lahore, Pakistan
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alanna C. Cote
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Esther Cheng
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - James A. Knowles
- The Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA
| | | | - Roseann E. Peterson
- Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA
| | - Tim B. Bigdeli
- Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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12
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Silva S, Nitsch D, Fatumo S. Genome-wide association studies on coronary artery disease: A systematic review and implications for populations of different ancestries. PLoS One 2023; 18:e0294341. [PMID: 38019802 PMCID: PMC10686512 DOI: 10.1371/journal.pone.0294341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/28/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Cardiovascular diseases are some of the leading causes of death worldwide, with coronary artery disease leading as one of the primary causes of mortality in both the developing and developed worlds. Despite its prevalence, there is a disproportionately small number of studies conducted in populations of non-European ancestry, with the limited sample sizes of such studies further restricting the power and generalizability of respective findings. This research aimed at understanding the differences in the genetic architecture of coronary artery disease (CAD) in populations of diverse ancestries in order to contribute towards the understanding of the pathophysiology of coronary artery disease. METHODS We performed a systematic review on the 6th of October, 2022 summarizing genome-wide association studies on coronary artery disease, while employing the GWAS Catalog as an independent database to support the search. We developed a framework to assess the methodological quality of each study. We extracted and grouped associated single nucleotide polymorphisms and genes according to ancestry groups of participants. RESULTS We identified 3100 studies, of which, 36 relevant studies were included in this research. Three of the studies that were included were not listed in the GWAS Catalog, highlighting the value of conducting an independent search alongside established databases in order to ensure the full research landscape has been captured. 743,919 CAD case participants from 25 different countries were analysed, with 61% of the studies identified in this research conducted in populations of European ancestry. No studies investigated populations of Africans living in continental Africa or admixed American ancestry groups besides African-Americans, while limited sample sizes were included of population groups besides Europeans and East Asians. This observed disproportionate population representation highlights the gaps in the literature, which limits our ability to understand coronary artery disease as a global disease. 71 genetic loci were identified to be associated with coronary artery disease in more than one article, with ancestry-specific genetic loci identified in each respective population group which were not detected in studies of other ancestries. CONCLUSIONS Although the replication and validation of these variants are still warranted, these finding are indicative of the value of including diverse ancestry populations in GWAS reference panels, as a more comprehensive understanding of the genetic architecture and pathophysiology of CAD can be achieved.
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Affiliation(s)
- Sarah Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Segun Fatumo
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
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13
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Law JH, Sultan N, Finer S, Fudge N. Advancing the communication of genetic risk for cardiometabolic diseases: a critical interpretive synthesis. BMC Med 2023; 21:432. [PMID: 37953248 PMCID: PMC10641935 DOI: 10.1186/s12916-023-03150-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Genetics play an important role in risk for cardiometabolic diseases-including type 2 diabetes, cardiovascular disease and obesity. Existing research has explored the clinical utility of genetic risk tools such as polygenic risk scores-and whether interventions communicating genetic risk information using these tools can impact on individuals' cognitive appraisals of disease risk and/or preventative health behaviours. Previous systematic reviews suggest mixed results. To expand current understanding and address knowledge gaps, we undertook an interpretive, reflexive method of evidence synthesis-questioning the theoretical basis behind current interventions that communicate genetic risk information and exploring how the effects of genetic risk tools can be fully harnessed for cardiometabolic diseases. METHODS We obtained 189 records from a combination of database, website and grey literature searches-supplemented with reference chaining and expert subject knowledge within the review team. Using pre-defined critical interpretive synthesis methods, quantitative and qualitative evidence was synthesised and critiqued alongside theoretical understanding from surrounding fields of behavioural and social sciences. FINDINGS Existing interventions communicating genetic risk information focus predominantly on the "self", targeting individual-level cognitive appraisals, such as perceived risk and perceived behavioural control. This approach risks neglecting the role of contextual factors and upstream determinants that can reinforce individuals' interpretations of risk. It also assumes target populations to embody an "ascetic subject of compliance"-the idea of a patient who strives to comply diligently with professional medical advice, logically and rationally adopting any recommended lifestyle changes. We developed a synthesising argument-"beyond the ascetic subject of compliance"-grounded in three major limitations of this perspective: (1) difficulty applying existing theories/models to diverse populations, (2) the role of familial variables and (3) the need for a life course perspective. CONCLUSIONS Interventions communicating genetic risk information should account for wider influences that can affect individuals' responses to risk at different levels-including through interactions with their family systems, socio-cultural environments and wider health provision. PROTOCOL REGISTRATION PROSPERO CRD42021289269.
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Affiliation(s)
- Jing Hui Law
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Najia Sultan
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sarah Finer
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Health NHS Trust, London, UK
| | - Nina Fudge
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
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14
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Bocher O, Gilly A, Park YC, Zeggini E, Morris AP. Bridging the diversity gap: Analytical and study design considerations for improving the accuracy of trans-ancestry genetic prediction. HGG ADVANCES 2023; 4:100214. [PMID: 37448981 PMCID: PMC10336686 DOI: 10.1016/j.xhgg.2023.100214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Genetic prediction of common complex disease risk is an essential component of precision medicine. Currently, genome-wide association studies (GWASs) are mostly composed of European-ancestry samples and resulting polygenic scores (PGSs) have been shown to poorly transfer to other ancestries partly due to heterogeneity of allelic effects between populations. Fixed-effects (FETA) and random-effects (RETA) trans-ancestry meta-analyses do not model such ancestry-related heterogeneity, while ancestry-specific (AS) scores may suffer from low power due to low sample sizes. In contrast, trans-ancestry meta-regression (TAMR) builds ancestry-aware PGS that account for more complex trans-ancestry architectures. Here, we examine the predictive performance of these four PGSs under multiple genetic architectures and ancestry configurations. We show that the predictive performance of FETA and RETA is strongly affected by cross-ancestry genetic heterogeneity, while AS PGS performance decreases in under-represented target populations. TAMR PGS is also impacted by heterogeneity but maintains good prediction performance in most situations, especially in ancestry-diverse scenarios. In simulations of human complex traits, TAMR scores currently explain 25% more phenotypic variance than AS in triglyceride levels and 33% more phenotypic variance than FETA in type 2 diabetes in most non-European populations. Importantly, a high proportion of non-European-ancestry individuals is needed to reach prediction levels that are comparable in those populations to the one observed in European-ancestry studies. Our results highlight the need to rebalance the ancestral composition of GWAS to enable accurate prediction in non-European-ancestry groups, and demonstrate the relevance of meta-regression approaches for compensating some of the current population biases in GWAS.
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Affiliation(s)
| | | | | | - Eleftheria Zeggini
- ITG, Helmholtz Zentrum München, Munich, Germany
- Technical University of Munich, Munich, Germany
- Klinikum Rechts der Isar, Munich, Germany
| | - Andrew P. Morris
- ITG, Helmholtz Zentrum München, Munich, Germany
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
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15
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Hassanin E, Maj C, Klinkhammer H, Krawitz P, May P, Bobbili DR. Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history. BMC Med Genomics 2023; 16:164. [PMID: 37438803 PMCID: PMC10339617 DOI: 10.1186/s12920-023-01598-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND & AIMS We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population. METHODS To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual's genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population. RESULTS The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71-2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53-0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51-0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition. CONCLUSION Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further.
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Affiliation(s)
- Emadeldin Hassanin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
- Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg
| | - Dheeraj Reddy Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg.
- Wellytics Technologies Pvt Ltd, Hyderabad, India.
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16
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Patel AP, Wang M, Ruan Y, Koyama S, Clarke SL, Yang X, Tcheandjieu C, Agrawal S, Fahed AC, Ellinor PT, Tsao PS, Sun YV, Cho K, Wilson PWF, Assimes TL, van Heel DA, Butterworth AS, Aragam KG, Natarajan P, Khera AV. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med 2023; 29:1793-1803. [PMID: 37414900 PMCID: PMC10353935 DOI: 10.1038/s41591-023-02429-x] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
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Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
| | - Yunfeng Ruan
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satoshi Koyama
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | - Shoa L Clarke
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Xiong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | | | - Saaket Agrawal
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Akl C Fahed
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Philip S Tsao
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Yan V Sun
- Veteran Affairs Atlanta Healthcare System, Decatur, GA, USA
| | - Kelly Cho
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | | | - Themistocles L Assimes
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Krishna G Aragam
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Boston, MA, USA.
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17
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Kononov S, Azarova I, Klyosova E, Bykanova M, Churnosov M, Solodilova M, Polonikov A. Lipid-Associated GWAS Loci Predict Antiatherogenic Effects of Rosuvastatin in Patients with Coronary Artery Disease. Genes (Basel) 2023; 14:1259. [PMID: 37372439 DOI: 10.3390/genes14061259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
We have shown that lipid-associated loci discovered by genome-wide association studies (GWAS) have pleiotropic effects on lipid metabolism, carotid intima-media thickness (CIMT), and CAD risk. Here, we investigated the impact of lipid-associated GWAS loci on the efficacy of rosuvastatin therapy in terms of changes in plasma lipid levels and CIMT. The study comprised 116 CAD patients with hypercholesterolemia. CIMT, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) were measured at baseline and after 6 and 12 months of follow-up, respectively. Genotyping of fifteen lipid-associated GWAS loci was performed by the MassArray-4 System. Linear regression analysis adjusted for sex, age, body mass index, and rosuvastatin dose was used to estimate the phenotypic effects of polymorphisms, and p-values were calculated through adaptive permutation tests by the PLINK software, v1.9. Over one-year rosuvastatin therapy, a decrease in CIMT was linked to rs1689800, rs4846914, rs12328675, rs55730499, rs9987289, rs11220463, rs16942887, and rs881844 polymorphisms (Pperm < 0.05). TC change was associated with rs55730499, rs11220463, and rs6065906; LDL-C change was linked to the rs55730499, rs1689800, and rs16942887 polymorphisms; and TG change was linked to polymorphisms rs838880 and rs1883025 (Pperm < 0.05). In conclusion, polymorphisms rs1689800, rs55730499, rs11220463, and rs16942887 were found to be predictive markers for multiple antiatherogenic effects of rosuvastatin in CAD patients.
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Affiliation(s)
- Stanislav Kononov
- Department of Internal Medicine No. 2, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
| | - Iuliia Azarova
- Department of Biological Chemistry, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia
| | - Elena Klyosova
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
| | - Marina Bykanova
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State University, 85 Pobedy Street, 308015 Belgorod, Russia
| | - Maria Solodilova
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia
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18
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Jacobs BM, Schalk L, Dunne A, Scalfari A, Nandoskar A, Gran B, Mein CA, Sellers C, Spilker C, Rog D, Visentin E, Bezzina EL, Uzochukwu E, Tallantyre E, Wozniak E, Sacre E, Hassan-Smith G, Ford HL, Harris J, Bradley J, Breedon J, Brooke J, Kreft KL, Tuite Dalton K, George K, Papachatzaki M, O'Malley M, Peter M, Mattoscio M, Rhule N, Evangelou N, Vinod N, Quinn O, Shamji R, Kaimal R, Boulton R, Tanveer R, Middleton R, Murray R, Bellfield R, Hoque S, Patel S, Raj S, Gumus S, Mitchell S, Sawcer S, Arun T, Pogreban T, Brown TL, Begum T, Antoine V, Rashid W, Noyce AJ, Silber E, Morris H, Giovannoni G, Dobson R. ADAMS project: a genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis based in the UK. BMJ Open 2023; 13:e071656. [PMID: 37197821 PMCID: PMC10193065 DOI: 10.1136/bmjopen-2023-071656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/14/2023] [Indexed: 05/19/2023] Open
Abstract
PURPOSE Genetic studies of multiple sclerosis (MS) susceptibility and severity have focused on populations of European ancestry. Studying MS genetics in other ancestral groups is necessary to determine the generalisability of these findings. The genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis (ADAMS) project aims to gather genetic and phenotypic data on a large cohort of ancestrally-diverse individuals with MS living in the UK. PARTICIPANTS Adults with self-reported MS from diverse ancestral backgrounds. Recruitment is via clinical sites, online (https://app.mantal.co.uk/adams) or the UK MS Register. We are collecting demographic and phenotypic data using a baseline questionnaire and subsequent healthcare record linkage. We are collecting DNA from participants using saliva kits (Oragene-600) and genotyping using the Illumina Global Screening Array V.3. FINDINGS TO DATE As of 3 January 2023, we have recruited 682 participants (n=446 online, n=55 via sites, n=181 via the UK MS Register). Of this initial cohort, 71.2% of participants are female, with a median age of 44.9 years at recruitment. Over 60% of the cohort are non-white British, with 23.5% identifying as Asian or Asian British, 16.2% as Black, African, Caribbean or Black British and 20.9% identifying as having mixed or other backgrounds. The median age at first symptom is 28 years, and median age at diagnosis is 32 years. 76.8% have relapsing-remitting MS, and 13.5% have secondary progressive MS. FUTURE PLANS Recruitment will continue over the next 10 years. Genotyping and genetic data quality control are ongoing. Within the next 3 years, we aim to perform initial genetic analyses of susceptibility and severity with a view to replicating the findings from European-ancestry studies. In the long term, genetic data will be combined with other datasets to further cross-ancestry genetic discoveries.
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Affiliation(s)
- Benjamin M Jacobs
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Luisa Schalk
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Angie Dunne
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College London, London, UK
| | | | - Bruno Gran
- Department of Neurology, Nottingham University Hospitals NHS Trust, Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | - Charles A Mein
- Barts and the London Genome Centre, Queen Mary University of London, London, UK
| | - Charlotte Sellers
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Cord Spilker
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - David Rog
- Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Trust, Manchester, UK
| | - Elisa Visentin
- Research and Innovation, Queen's Hospital, BHRUT, London, UK
| | | | - Emeka Uzochukwu
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Emma Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Clinical Neurology, University Hospital of Wales, Cardiff, UK
| | - Eva Wozniak
- Barts and the London Genome Centre, Queen Mary University of London, London, UK
| | - Eve Sacre
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | | | - Helen L Ford
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Jade Harris
- Northern Care Alliance NHS Trust, Manchester, UK
| | | | - Joshua Breedon
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Karim L Kreft
- Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Katila George
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Martin O'Malley
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Michelle Peter
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Miriam Mattoscio
- Department of neuroscience, Queen's Hospital, BHRUT NHS Trust, Romford, UK
| | - Neisha Rhule
- Queen Elizabeth Hospital (Lewisham and Greenwich NHS Trust), London, UK
| | - Nikos Evangelou
- Department of Neurology, Nottingham University Hospitals NHS Trust; Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | | | - Outi Quinn
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - Ramya Shamji
- Research and Innovation, Queen's Hospital, BHRUT, London, UK
| | - Rashmi Kaimal
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Rebecca Boulton
- Department of Neurology, Nottingham University Hospitals NHS Trust; Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | - Riffat Tanveer
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Rod Middleton
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Roxanne Murray
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Ruth Bellfield
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - Sadid Hoque
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Shakeelah Patel
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Sonia Raj
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Stephanie Gumus
- Mid and South Essex NHS Foundation Trust, Southend-on-Sea, UK
| | | | - Stephen Sawcer
- University of Cambridge, Department of Clinical Neuroscience, Addenbrookes Hospital, Hills Road, Cambridge, UK
| | - Tarunya Arun
- University Hospitals of Coventry and Warwickshire, Coventry, UK
| | | | - Terri-Louise Brown
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Thamanna Begum
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Waqar Rashid
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Eli Silber
- Kings College Hospital and Lewisham and Greenwich NHS Trusts, London, UK
| | - Huw Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
| | - Gavin Giovannoni
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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19
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Sofer T, Kurniansyah N, Murray M, Ho YL, Abner E, Esko T, Huffman JE, Cho K, Wilson PWF, Gottlieb DJ. Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex. EBioMedicine 2023; 90:104536. [PMID: 36989840 PMCID: PMC10065974 DOI: 10.1016/j.ebiom.2023.104536] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) for obstructive sleep apnoea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence. METHODS We performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analysed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totalling 916,696 individuals. FINDINGS MVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value < 0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, previously unreported, genome-wide significant regions. INTERPRETATION Sex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared aetiology and causal pathways. FUNDING Described in acknowledgements.
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Affiliation(s)
- Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael Murray
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto Veterans Institute for Research, Palo Alto, CA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
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Shoham N, Dunca D, Cooper C, Hayes JF, McQuillin A, Bass N, Lewis G, Kuchenbaecker K. Investigating the association between schizophrenia and distance visual acuity: Mendelian randomisation study. BJPsych Open 2023; 9:e33. [PMID: 36746515 PMCID: PMC9970182 DOI: 10.1192/bjo.2023.6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/28/2022] [Accepted: 01/07/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Increased rates of visual impairment are observed in people with schizophrenia. AIMS We assessed whether genetically predicted poor distance acuity is causally associated with schizophrenia, and whether genetically predicted schizophrenia is causally associated with poorer visual acuity. METHOD We used bidirectional, two-sample Mendelian randomisation to assess the effect of poor distance acuity on schizophrenia risk, poorer visual acuity on schizophrenia risk and schizophrenia on visual acuity, in European and East Asian ancestry samples ranging from approximately 14 000 to 500 000 participants. Genetic instrumental variables were obtained from the largest available summary statistics: for schizophrenia, from the Psychiatric Genomics Consortium; for visual acuity, from the UK Biobank; and for poor distance acuity, from a meta-analysis of case-control samples. We used the inverse variance-weighted method and sensitivity analyses to test validity of results. RESULTS We found little evidence that poor distance acuity was causally associated with schizophrenia (odds ratio 1.00, 95% CI 0.91-1.10). Genetically predicted schizophrenia was associated with poorer visual acuity (mean difference in logMAR score: 0.024, 95% CI 0.014-0.033) in European ancestry samples, with a similar but less precise effect that in smaller East Asian ancestry samples (mean difference: 0.186, 95% CI -0.008 to 0.379). CONCLUSIONS Genetic evidence supports schizophrenia being a causal risk factor for poorer visual acuity, but not the converse. This highlights the importance of visual care for people with psychosis and refutes previous hypotheses that visual impairment is a potential target for prevention of schizophrenia.
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Affiliation(s)
- Natalie Shoham
- Division of Psychiatry, University College London, UK; and Islington Early Intervention Service, Camden and Islington NHS Foundation Trust, St Pancras Hospital, London, UK
| | - Diana Dunca
- UCL Genetics Institute, University College London, UK
| | - Claudia Cooper
- Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, UK; and Tower Hamlets Memory Service, East London NHS Foundation Trust, London, UK
| | - Joseph F. Hayes
- Division of Psychiatry, University College London, UK; and Camden and Islington NHS Foundation Trust, St Pancras Hospital, London, UK
| | | | - Nick Bass
- Division of Psychiatry, University College London, UK; and Tower Hamlets Memory Service, East London NHS Foundation Trust, London, UK
| | - Gemma Lewis
- Division of Psychiatry, University College London, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, UK; and UCL Genetics Institute, University College London, UK
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O'Sullivan JW, Ashley EA, Elliott PM. Polygenic risk scores for the prediction of cardiometabolic disease. Eur Heart J 2023; 44:89-99. [PMID: 36478054 DOI: 10.1093/eurheartj/ehac648] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/28/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022] Open
Abstract
Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.
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Affiliation(s)
- Jack W O'Sullivan
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Euan A Ashley
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Perry M Elliott
- UCL Institute of Cardiovascular Science, Gower Street, London WC1E 6BT, UK
- St. Bartholomew's Hospital, W Smithfield, London EC1A 7BE, UK
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Jacobs BM, Peter M, Giovannoni G, Noyce AJ, Morris HR, Dobson R. Towards a global view of multiple sclerosis genetics. Nat Rev Neurol 2022; 18:613-623. [PMID: 36075979 DOI: 10.1038/s41582-022-00704-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2022] [Indexed: 11/09/2022]
Abstract
Multiple sclerosis (MS) is a neuroimmunological disorder of the CNS with a strong heritable component. The genetic architecture of MS susceptibility is well understood in populations of European ancestry. However, the extent to which this architecture explains MS susceptibility in populations of non-European ancestry remains unclear. In this Perspective article, we outline the scientific arguments for studying MS genetics in ancestrally diverse populations. We argue that this approach is likely to yield insights that could benefit individuals with MS from all ancestral groups. We explore the logistical and theoretical challenges that have held back this field to date and conclude that, despite these challenges, inclusion of participants of non-European ancestry in MS genetics studies will ultimately be of value to all patients with MS worldwide.
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Affiliation(s)
- Benjamin Meir Jacobs
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University London, London, UK. .,Department of Neurology, Royal London Hospital, London, UK.
| | - Michelle Peter
- NHS North Thames Genomic Laboratory Hub, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Gavin Giovannoni
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University London, London, UK.,Department of Neurology, Royal London Hospital, London, UK.,Blizard Institute, Queen Mary University London, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University London, London, UK.,Department of Neurology, Royal London Hospital, London, UK.,Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Huw R Morris
- Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University London, London, UK.,Department of Neurology, Royal London Hospital, London, UK
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Jukarainen S, Kiiskinen T, Kuitunen S, Havulinna AS, Karjalainen J, Cordioli M, Rämö JT, Mars N, Samocha KE, Ollila HM, Pirinen M, Ganna A. Genetic risk factors have a substantial impact on healthy life years. Nat Med 2022; 28:1893-1901. [PMID: 36097220 PMCID: PMC9499866 DOI: 10.1038/s41591-022-01957-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; 'lost healthy life years'). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.
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Affiliation(s)
- Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara Kuitunen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Mattia Cordioli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Joel T Rämö
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Nina Mars
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Kaitlin E Samocha
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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