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Qin Y, Yang X, Ning Z. CAUSAL ROLES OF SERUM URIC ACID LEVELS AND GOUT IN SEPSIS: A MENDELIAN RANDOMIZATION STUDY. Shock 2024; 62:44-50. [PMID: 38517245 DOI: 10.1097/shk.0000000000002365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
ABSTRACT Objective: Several epidemiological studies have identified a potential link between serum uric acid (UA), gout, and sepsis. The primary objective of this study is to delve deeper into this connection, investigating the causal effect of UA and gout on sepsis by applying Mendelian randomization (MR). Methods: The causal relationship was analyzed using data from Genome-Wide Association Study (GWAS). Inverse variance weighting (IVW) was used as the main analysis method. Three complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple median method. Horizontal pleiotropy was identified by MR-Egger intercept test. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. The leave-one-out method was used as a sensitivity analysis. Results: The IVW results indicated that there was a positive causal relationship between UA and sepsis (critical care) (odds ratio [OR] = 0.24, 95% confidence interval [CI]: 0.04 to 0.43, P = 0.018, F = 4,291.20). There was no significant association between UA and sepsis (28-day death in critical care) (OR = 0.10, 95% CI = -0.29 to 0.50, P = 0.604). There was no significant association between gout and sepsis (critical care) (OR = 0.85, 95% CI = -4.87 to 6.57, P = 0.771), and sepsis (28-day death in critical care) (OR = -6.30, 95% CI = -17.41 to 4.81, P = 0.267). Horizontal pleiotropy was absent in this study. The results were robust under all sensitivity analyses. Conclusion: The study revealed that elevated UA levels were causally linked with sepsis (critical care). No causal relationship had been found between UA and sepsis (28-day death in critical care), as well as between gout and sepsis.
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Affiliation(s)
| | - Xia Yang
- Department of General Practice, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Zong Ning
- Department of General Practice, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
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Zhao SS, Burgess S. Use of Mendelian randomization to assess the causal status of modifiable exposures for rheumatic diseases. Best Pract Res Clin Rheumatol 2024:101967. [PMID: 38951047 DOI: 10.1016/j.berh.2024.101967] [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: 05/29/2024] [Revised: 06/14/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024]
Abstract
The explosion in Mendelian randomization (MR) publications is hard to ignore and shows no signs of slowing. Clinician readers, who may not be familiar with jargon-ridden methods, are expected to discern the good from the many low-quality studies that make overconfident claims of causality or stretch the plausibility of what MR can investigate. We aim to equip readers with foundational concepts, contextualized using examples in rheumatology, to appraise the many MR papers that are or will appear in their journals. We highlight the importance of assessing whether exposures are under plausibly specific genetic influence, whether the hypothesized causal pathways make biological sense, and whether results stand up to replication and use of control outcomes. Quality of research can vary substantially using MR as with any design, and all methods have inherent limitations. MR studies have provided and can still contribute valuable insights in the context of evidence triangulation.
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Affiliation(s)
- Sizheng Steven Zhao
- Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Science, School of Biological Sciences, Faculty of Biological Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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Gustavson K, Torvik FA, Davey Smith G, Røysamb E, Eilertsen EM. Familial confounding or measurement error? How to interpret findings from sibling and co-twin control studies. Eur J Epidemiol 2024; 39:587-603. [PMID: 38879863 PMCID: PMC11249619 DOI: 10.1007/s10654-024-01132-6] [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: 01/15/2024] [Accepted: 05/07/2024] [Indexed: 07/16/2024]
Abstract
Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30-90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper.
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Affiliation(s)
- Kristin Gustavson
- Department of Psychology, University of Oslo, Oslo, Norway.
- Norwegian Institute of Public Health, Oslo, Norway.
| | - Fartein Ask Torvik
- Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Espen Røysamb
- Norwegian Institute of Public Health, Oslo, Norway
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
| | - Espen M Eilertsen
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
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Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. Mendelian randomization: causal inference leveraging genetic data. Psychol Med 2024; 54:1461-1474. [PMID: 38639006 DOI: 10.1017/s0033291724000321] [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] [Indexed: 04/20/2024]
Abstract
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
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Affiliation(s)
- Lane G Chen
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
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Morris TT, von Hinke S, Pike L, Ingram NR, Davey Smith G, Munafò MR, Davies NM. Implications of the genomic revolution for education research and policy. BRITISH EDUCATIONAL RESEARCH JOURNAL 2024; 50:923-943. [PMID: 38974368 PMCID: PMC11225938 DOI: 10.1002/berj.3784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 03/04/2022] [Indexed: 07/09/2024]
Abstract
Research at the intersection of social science and genomics, 'sociogenomics', is transforming our understanding of the interplay between genomics, individual outcomes and society. It has interesting and maybe unexpected implications for education research and policy. Here we review the growing sociogenomics literature and discuss its implications for educational researchers and policymakers. We cover key concepts and methods in genomic research into educational outcomes, how genomic data can be used to investigate social or environmental effects, the methodological strengths and limitations of genomic data relative to other observational social data, the role of intergenerational transmission and potential policy implications. The increasing availability of genomic data in studies can produce a wealth of new evidence for education research. This may provide opportunities for disentangling the environmental and genomic factors that influence educational outcomes and identifying potential mechanisms for intervention.
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Affiliation(s)
- Tim T. Morris
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- Population Health SciencesBristol Medical SchoolUniversity of BristolOakfield GroveBarley HouseBristolUK
| | - Stephanie von Hinke
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- School of EconomicsUniversity of BristolUK
- Erasmus School of EconomicsErasmus University RotterdamRotterdamThe Netherlands
| | - Lindsey Pike
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- Population Health SciencesBristol Medical SchoolUniversity of BristolOakfield GroveBarley HouseBristolUK
| | | | - George Davey Smith
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- Population Health SciencesBristol Medical SchoolUniversity of BristolOakfield GroveBarley HouseBristolUK
| | - Marcus R. Munafò
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- School of Psychological ScienceUniversity of BristolBristolUK
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUK
- Population Health SciencesBristol Medical SchoolUniversity of BristolOakfield GroveBarley HouseBristolUK
- K.G. Jebsen Center for Genetic EpidemiologyDepartment of Public Health and NursingNorwegian University of Science and TechnologyTrondheimNorway
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Gao Y, Li C, Li J, Li L. Causal relationships of physical activity and leisure sedentary behaviors with COPD: A Mendelian randomization study. Arch Gerontol Geriatr 2024; 121:105364. [PMID: 38430688 DOI: 10.1016/j.archger.2024.105364] [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: 12/15/2023] [Revised: 01/26/2024] [Accepted: 02/04/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Chronic diseases such as chronic obstructive pulmonary disease (COPD) have been linked to low levels of physical activity (PA) and higher frequency of leisure sedentary behavior (LSB). The main causes of COPD include respiratory and peripheral muscle dysfunction, low levels of PA, and LSB which are associated with a higher risk of developing COPD. The attribution relationship between PA or LSB and COPD risk or COPD respiratory insufficiency is unclear. To explore this further, we conducted a Mendelian randomization (MR) study using a genotype-simulated randomized trial group to systematically evaluate the causal relationships of PA/LSB on COPD risk and respiratory insufficiency. METHODS The exposure data were obtained from large-scale genome-wide association studies (GWAS), including the PA dataset (N = 729,373) and LSB dataset (N = 1,109,337). The outcome data were derived from the Finn-Gen COPD dataset (N = 381,392). The causal effects were estimated with IVW1, MR-Egger, and WM2. Sensitivity analysis was conducted using Cochran's Q test, MR-Egger intercept test, MR-PRESSO3, leave-one-out analysis, and funnel plot to estimate the robustness of our findings. RESULTS Genetically predicted leisure television (TV) watching significantly increased the risk of COPD (OR = 2.4895, 95 % CI: 1.85259 to 3.34536; P = 1.44 × 10-9) and COPD respiratory insufficiency (OR = 2.55, 95 % CI: 1.53 to 4.27; P = 3.54 × 10-4). No casual effect of other PA or LSB phenotypes on COPD risk or respiratory insufficiency was observed. CONCLUSION Our study provides evidence that TV watching may increase the risk of COPD and its related respiratory insufficiency. These findings emphasized the importance of promoting regular physical exercise and reducing leisure sedentary behavior to prevent COPD.
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Affiliation(s)
- Yixuan Gao
- School of Sport Science, Beijing Sport University, 48 Xinxi Road, Beijing 100084, China; Laboratory of Sports Stress and Adaptation of General Administration of Sports, 48 Xinxi Road, Beijing 100084, China
| | - Conghui Li
- School of Sport Science, Beijing Sport University, 48 Xinxi Road, Beijing 100084, China
| | - Junping Li
- School of Sport Science, Beijing Sport University, 48 Xinxi Road, Beijing 100084, China; Laboratory of Sports Stress and Adaptation of General Administration of Sports, 48 Xinxi Road, Beijing 100084, China.
| | - Ling Li
- Physical and Military Education, Jingdezhen Ceramic University, Jiangxi Province 333403, China
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Yang J, Zhou J, Liu H, Hao J, Hu S, Zhang P, Wu H, Gao Y, Tang W. Blood lipid levels mediating the effects of sex hormone-binding globulin on coronary heart disease: Mendelian randomization and mediation analysis. Sci Rep 2024; 14:11993. [PMID: 38796576 PMCID: PMC11127952 DOI: 10.1038/s41598-024-62695-8] [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: 02/10/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
Observational studies indicate that serum sex hormone-binding globulin (SHBG) levels are inversely correlated with blood lipid levels and coronary heart disease (CHD) risk. Given that dyslipidemia is an established risk factor for CHD, we aim to employ Mendelian randomization (MR) in conjunction with mediation analysis to confirm the mediating role of blood lipid levels in the association between SHBG and CHD. First, we assessed the causality between serum SHBG levels and five cardiovascular diseases using univariable MR. The results revealed causality between SHBG levels and reduced risk of CHD, myocardial infarction, as well as hypertension. Specifically, the most significant reduction was observed in CHD risk, with an odds ratio of 0.73 (95% CI 0.63-0.86) for each one-standard-deviation increase in SHBG. The summary-level data of serum SHBG levels and CHD are derived from a sex-specific genome-wide association study (GWAS) conducted by UK Biobank (sample size = 368,929) and a large-scale GWAS meta-analysis (60,801 cases and 123,504 controls), respectively. Subsequently, we further investigated the mediating role of blood lipid level in the association between SHBG and CHD. Mediation analysis clarified the mediation proportions for four mediators: high cholesterol (48%), very low-density lipoprotein cholesterol (25.1%), low-density lipoprotein cholesterol (18.5%), and triglycerides (44.3%). Summary-level data for each mediator were sourced from the UK Biobank and publicly available GWAS. The above results confirm negative causality between serum SHBG levels and the risk of CHD, myocardial infarction, and hypertension, with the causal effect on reducing CHD risk largely mediated by the improvement of blood lipid profiles.
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Affiliation(s)
- Juntao Yang
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China
| | - Jiedong Zhou
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China
| | - Hanxuan Liu
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Jinjin Hao
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Songqing Hu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peipei Zhang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Haowei Wu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yefei Gao
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Weiliang Tang
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China.
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He G, Chen J, Hao W, Hu W. Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study. Diabetol Metab Syndr 2024; 16:89. [PMID: 38658966 PMCID: PMC11044463 DOI: 10.1186/s13098-024-01327-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The interaction of dysbiosis of gut microbiota (GM) with diabetic nephropathy (DN) drew our attention and a better understanding of GM on DN might provide potential therapeutic approaches. However, the exact causal effect of GM on DN remains unknown. METHODS We applied two-sample Mendelian Randomization (MR) analysis, including inverse variance weighted (IVW), MR-Egger methods, etc., to screen the significant bacterial taxa based on the GWAS data. Sensitivity analysis was conducted to assess the robustness of MR results. To identify the most critical factor on DN, Mendelian randomization-Bayesian model averaging (MR-BMA) method was utilized. Then, whether the reverse causality existed was verified by reverse MR analysis. Finally, transcriptome MR analysis was performed to investigate the possible mechanism of GM on DN. RESULTS At locus-wide significance levels, the results of IVW suggested that order Bacteroidales (odds ratio (OR) = 1.412, 95% confidence interval (CI): 1.025-1.945, P = 0.035), genus Akkermansia (OR = 1.449, 95% CI: 1.120-1.875, P = 0.005), genus Coprococcus 1 (OR = 1.328, 95% CI: 1.066-1.793, P = 0.015), genus Marvinbryantia (OR = 1.353, 95% CI: 1.037-1.777, P = 0.030) and genus Parasutterella (OR = 1.276, 95% CI: 1.022-1.593, P = 0.032) were risk factors for DN. Reversely, genus Eubacterium ventriosum (OR = 0.756, 95% CI: 0.594-0.963, P = 0.023), genus Ruminococcus gauvreauii (OR = 0.663, 95% CI: 0.506-0.870, P = 0.003) and genus Erysipelotrichaceae (UCG003) (OR = 0.801, 95% CI: 0.644-0.997, P = 0.047) were negatively associated with the risk of DN. Among these taxa, genus Ruminococcus gauvreauii played a crucial role in DN. No significant heterogeneity or pleiotropy in the MR result was found. Mapped genes (FDR < 0.05) related to GM had causal effects on DN, while FCGR2B and VNN2 might be potential therapeutic targets. CONCLUSIONS This work provided new evidence for the causal effect of GM on DN occurrence and potential biomarkers for DN. The significant bacterial taxa in our study provided new insights for the 'gut-kidney' axis, as well as unconventional prevention and treatment strategies for DN.
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Affiliation(s)
- Ganyuan He
- Department of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Provincial Geriatrics Institute, Southern Medical University, Guangzhou, China
| | - Jiayi Chen
- Department of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Provincial Geriatrics Institute, Southern Medical University, Guangzhou, China
| | - Wenke Hao
- Department of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Provincial Geriatrics Institute, Southern Medical University, Guangzhou, China.
| | - Wenxue Hu
- Department of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Provincial Geriatrics Institute, Southern Medical University, Guangzhou, China.
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Sun T, He X, Chen X, Huaqing Y, Zhang H, Zhao M, Du L, Zhao B, Hou J, Li X, Liu Y. Delaying age at first sexual intercourse provides protection against oral cavity cancer: a mendelian randomization study. Front Oncol 2024; 14:1361527. [PMID: 38699645 PMCID: PMC11063229 DOI: 10.3389/fonc.2024.1361527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/01/2024] [Indexed: 05/05/2024] Open
Abstract
Aim To investigate whether age at first sexual intercourse could lead to any changes in the risk of oral cavity cancer. Methods A two-sample mendelian randomization was conducted using genetic variants associated with age at first sexual intercourse in UK biobank as instrumental variables. Summary data of Northern American from a previous genome-wide association study aimed at oral cavity cancer was served as outcome. Three analytical methods: inverse variance-weighted, mendelian randomization Egger, and weighted median were used to perform the analysis, among which inverse variance-weighted was set as the primary method. Robustness of the results was assessed through Cochran Q test, mendelian randomization Egger intercept tests, MR PRESSO, leave one out analysis and funnel plot. Results The primary analysis provided substantial evidence of a positive causal relationship age at first sexual intercourse and the risk of oral cavity cancer (p = 0.0002), while a delayed age at first sexual intercourse would lead to a decreased risk of suffering oral cavity cancer (β = -1.013). The secondary outcomes confirmed the results (all β < 0) and all assessments supported the robustness, too (all p > 0.05). Conclusion The study demonstrates that a delayed sexual debut would provide protection against OCC, thus education on delaying sexual intercourse should be recommended.
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Affiliation(s)
- Ting Sun
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Xin He
- Department of Emergency, Guangyuan Central Hospital, Guangyuan, China
| | - Xing Chen
- Department of Dentistry and Oral Surgery, Guangyuan Central Hospital, Guangyuan, China
- Department of Dentistry and Oral Surgery, North Sichuan Medical College, Nanchong, China
| | - Yang Huaqing
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Haimei Zhang
- Department of Dentistry and Oral Surgery, Guangyuan Central Hospital, Guangyuan, China
| | - Min Zhao
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Li Du
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Bin Zhao
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Junping Hou
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Xudong Li
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
| | - Yu Liu
- Department of Oncology, Guangyuan Central Hospital, Guangyuan, China
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Demange PA, Boomsma DI, van Bergen E, Nivard MG. Evaluating the causal relationship between educational attainment and mental health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.01.26.23285029. [PMID: 36747639 PMCID: PMC9901051 DOI: 10.1101/2023.01.26.23285029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We investigate the causal relationship between educational attainment (EA) and mental health using two research designs. First, we compare the relationship between EA and 18 psychiatric diagnoses within sibship in Dutch national registry data (N=1.7 million), thereby controlling for unmeasured familial factors. Second, we apply two-sample Mendelian Randomization, which uses genetic variants related to EA or psychiatric diagnosis as instrumental variables, to test whether there is a causal relation in either direction. Our results suggest that lower levels of EA causally increase the risk of MDD, ADHD, alcohol dependence, GAD and PTSD diagnoses. We also find evidence of a causal effect of ADHD on EA. For schizophrenia, anorexia nervosa, OCD, and bipolar disorder, results were inconsistent across the different approaches, highlighting the importance of using multiple research designs to understand complex relationships such as between EA and mental health.
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Affiliation(s)
- Perline A Demange
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Mental Health, Amsterdam, the Netherlands
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
- Amsterdam Reproduction & Development Research Institute, Amsterdam, the Netherlands
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Mental Health, Amsterdam, the Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
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Dixon P, Martin RM, Harrison S. Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer. Med Decis Making 2024; 44:283-295. [PMID: 38426435 PMCID: PMC10988994 DOI: 10.1177/0272989x241232607] [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: 07/29/2023] [Accepted: 01/23/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation. METHODS Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer. RESULTS Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication. IMPLICATIONS Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value. HIGHLIGHTS The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.
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Affiliation(s)
- Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M. Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- UK Health Security Agency
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12
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Liu Z, Fan T, Mo X, Kan J, Zhang B. Association between multiple sclerosis and cancer risk: A two-sample Mendelian randomization study. PLoS One 2024; 19:e0298271. [PMID: 38502664 PMCID: PMC10950213 DOI: 10.1371/journal.pone.0298271] [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: 07/10/2023] [Accepted: 01/22/2024] [Indexed: 03/21/2024] Open
Abstract
Multiple Sclerosis (MS) is an immune-related disease and the relationship between MS and cancer has raised attention. Previous studies of the relationship between MS and cancer have reached conflicting conclusions. In this study, the two-sample MR method is used to investigate whether MS has a causal correlation with cancers and offer scientific evidence for cancer prevention. Single nucleotide polymorphisms (SNPs) related to MS were obtained from the genome-wide association study (GWAS) based on International Multiple Sclerosis Genetics Consortium (IMSGC) and SNPs related to 15 types of cancers were obtained from the GWASs based on UK Biobank. Inverse variance weighted (IVW) method was mainly used to assess causal effects. Sensitivity analyses were conducted with Cochran's Q-test, MR Egger intercept, leave-one-out test, and MR Steiger method. IVW analysis showed that MS was only associated with a marginal increased risk of cervical cancer (OR 1.0004, 95% CI 1.0002-1.0007, p = 0.0003). Sensitivity analyses showed that the results of MR analysis were robust and found no heterogeneity, no pleiotropy, and no reverse causation. In conclusion, this study finds no causal relationship between MS and 15 types of cancers except cervical cancer.
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Affiliation(s)
- Zeyu Liu
- VIP Inpatient Department, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Teng Fan
- VIP Inpatient Department, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Xiaoyan Mo
- VIP Inpatient Department, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jun Kan
- VIP Inpatient Department, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Bei Zhang
- VIP Inpatient Department, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
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13
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Xie T, Zhu B, Li HR, Xu JF, Mao Y. Educational attainment, income, and attention deficit hyperactivity disorder: A mediation analysis based on two-step Mendelian randomization. Soc Sci Med 2024; 345:116680. [PMID: 38394947 DOI: 10.1016/j.socscimed.2024.116680] [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: 08/29/2023] [Revised: 11/17/2023] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Previous studies have reported the relationship between educational attainment and attention deficit hyperactivity disorder (ADHD). However, the mechanism of this relationship remains unknown. It is well known that educational attainment correlates with income. Therefore, based on summary data from a genome-wide association study we used two-step Mendelian randomization (MR) to explore the role of income between education and ADHD. The inverse variance weighted (IVW) method was used in our analysis. The IVW results suggested that educational attainment and income were protective factors against ADHD. Educational attainment affects ADHD through income [ADHD: Beta = -0.68, 95% confidence interval (CI) = -0.87, -0.49; female: Beta = -0.87, 95% CI = -1.28, -0.47; male: Beta = -1.01, 95% CI = -1.34, -0.68; childhood: Beta = -0.52, 95% CI = -0.74, -0.30; late-diagnosed: Beta = -0.78, 95% CI = -1.11, -0.47; persistent: Beta = -0.82, 95% CI = -1.33, -0.31]. Income also affected ADHD through educational attainment [female: Beta = -1.08, 95% CI = -1.35, -0.83; male: Beta = -1.16, 95% CI = -1.57, -0.77; persistent: Beta = -1.48, 95% CI = -2.09, -0.94]. In the final analysis, data with heterogeneity were analyzed using IVW random effects results. The mechanism is that income will mediate the relationship between educational attainment and ADHD.
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Affiliation(s)
- Tao Xie
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Bin Zhu
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
| | - Hao-Ran Li
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Jin-Feng Xu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, 610041, China; West China School of Medicine, Sichuan University, Chengdu, 610041, China.
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China.
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14
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Darrous L, Hemani G, Davey Smith G, Kutalik Z. PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment. Nat Commun 2024; 15:1420. [PMID: 38360877 PMCID: PMC10869347 DOI: 10.1038/s41467-024-45655-8] [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: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.
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Affiliation(s)
- Liza Darrous
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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15
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Clayton GL, Borges MC, Lawlor DA. The impact of reproductive factors on the metabolic profile of females from menarche to menopause. Nat Commun 2024; 15:1103. [PMID: 38320991 PMCID: PMC10847109 DOI: 10.1038/s41467-023-44459-6] [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: 06/02/2022] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
We explore the relation between age at menarche, parity and age at natural menopause with 249 metabolic traits in over 65,000 UK Biobank women using multivariable regression, Mendelian randomization and negative control (parity only). Older age of menarche is related to a less atherogenic metabolic profile in multivariable regression and Mendelian randomization, which is largely attenuated when accounting for adult body mass index. In multivariable regression, higher parity relates to more particles and lipids in VLDL, which are not observed in male negative controls. In multivariable regression and Mendelian randomization, older age at natural menopause is related to lower concentrations of inflammation markers, but we observe inconsistent results for LDL-related traits due to chronological age-specific effects. For example, older age at menopause is related to lower LDL-cholesterol in younger women but slightly higher in older women. Our findings support a role of reproductive traits on later life metabolic profile and provide insights into identifying novel markers for the prevention of adverse cardiometabolic outcomes in women.
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Affiliation(s)
- Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
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16
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Wang Z, Davey Smith G, Loos RJF, den Hoed M. Distilling causality between physical activity traits and obesity via Mendelian randomization. COMMUNICATIONS MEDICINE 2023; 3:173. [PMID: 38036650 PMCID: PMC10689836 DOI: 10.1038/s43856-023-00407-5] [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: 04/19/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Whether obesity is a cause or consequence of low physical activity levels and more sedentary time has not yet been fully elucidated. Better instrumental variables and a more thorough consideration of potential confounding variables that may influence the causal inference between physical activity and obesity are needed. METHODS Leveraging results from our recent genome-wide association study for leisure time moderate-to-vigorous intensity (MV) physical activity and screen time, we here disentangle the causal relationships between physical activity, sedentary behavior, education-defined by years of schooling-and body mass index (BMI), using multiple univariable and multivariable Mendelian Randomization (MR) approaches. RESULTS Univariable MR analyses suggest bidirectional causal effects of physical activity and sedentary behavior with BMI. However, multivariable MR analyses that take years of schooling into account suggest that more MV physical activity causes a lower BMI, and a higher BMI causes more screen time, but not vice versa. In addition, more years of schooling causes higher levels of MV physical activity, less screen time, and lower BMI. CONCLUSIONS In conclusion, our results highlight the beneficial effect of education on improved health and suggest that a more physically active lifestyle leads to lower BMI, while sedentary behavior is a consequence of higher BMI.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
| | - Ruth J F Loos
- The 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
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden.
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17
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Andreu-Bernabeu Á, González-Peñas J, Arango C, Díaz-Caneja CM. Socioeconomic status and severe mental disorders: a bidirectional multivariable Mendelian randomisation study. BMJ MENTAL HEALTH 2023; 26:e300821. [PMID: 38007229 PMCID: PMC10680010 DOI: 10.1136/bmjment-2023-300821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/18/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Despite the evidence supporting the relationship between socioeconomic status (SES) and severe mental disorders (SMD), the directionality of the associations between income or education and mental disorders is still poorly understood. OBJECTIVE To investigate the potential bidirectional causal relationships between genetic liability to the two main components of SES (income and educational attainment (EA)) on three SMD: schizophrenia, bipolar disorder (BD) and depression. METHODS We performed a bidirectional, two-sample univariable Mendelian randomisation (UVMR) and multivariable Mendelian randomisation (MVMR) study using SES phenotypes (income, n=397 751 and EA, n=766 345) and SMD (schizophrenia, n=127 906; BD, n=51 710 and depression, n=500 119) genome-wide association studies summary-statistics to dissect the potential direct associations of income and EA with SMD. FINDINGS UVMR showed that genetic liability to higher income was associated with decreased risk of schizophrenia and depression, with a smaller reverse effect of schizophrenia and depression on income. Effects were comparable after adjusting for EA in the MVMR. UMVR showed bidirectional negative associations between genetic liability to EA and depression and positive associations between genetic liability to EA and BD, with no significant effects on schizophrenia. After accounting for income, MVMR showed a bidirectional positive direction between genetic liability to EA and BD and schizophrenia but not with depression. CONCLUSIONS Our results suggest a heterogeneous link pattern between SES and SMD. We found a negative bidirectional association between genetic liability to income and the risk of schizophrenia and depression. On the contrary, we found a positive bidirectional relationship of genetic liability to EA with schizophrenia and BD, which only becomes apparent after adjusting for income in the case of schizophrenia. CLINICAL IMPLICATIONS These findings shed light on the directional mechanisms between social determinants and mental disorders and suggest that income and EA should be studied separately in relation to mental illness.
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Affiliation(s)
- Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Universidad Complutense de Madrid, Madrid, Spain
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Universidad Complutense de Madrid, Madrid, Spain
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18
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Richmond RC, Howe LJ, Heilbron K, Jones S, Liu J, Wang X, Weedon MN, Rutter MK, Lawlor DA, Davey Smith G, Vetter C. Correlations in sleeping patterns and circadian preference between spouses. Commun Biol 2023; 6:1156. [PMID: 37957254 PMCID: PMC10643442 DOI: 10.1038/s42003-023-05521-7] [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: 02/14/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Spouses may affect each other's sleeping behaviour. In 47,420 spouse-pairs from the UK Biobank, we found a weak positive phenotypic correlation between spouses for self-reported sleep duration (r = 0.11; 95% CI = 0.10, 0.12) and a weak inverse correlation for chronotype (diurnal preference) (r = -0.11; -0.12, -0.10), which replicated in up to 127,035 23andMe spouse-pairs. Using accelerometer data on 3454 UK Biobank spouse-pairs, the correlation for derived sleep duration was similar to self-report (r = 0.12; 0.09, 0.15). Timing of diurnal activity was positively correlated (r = 0.24; 0.21, 0.27) in contrast to the inverse correlation for chronotype. In Mendelian randomization analysis, positive effects of sleep duration (mean difference=0.13; 0.04, 0.23 SD per SD) and diurnal activity (0.49; 0.03, 0.94) were observed, as were inverse effects of chronotype (-0.15; -0.26, -0.04) and snoring (-0.15; -0.27, -0.04). Findings support the notion that an individual's sleep may impact that of their partner, promoting opportunities for sleep interventions at the family-level.
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Affiliation(s)
- Rebecca C Richmond
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK.
| | - Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
| | - Karl Heilbron
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Samuel Jones
- Institute for Molecular Medicine FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Junxi Liu
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
- Oxford Population Health, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xin Wang
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, USA
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Martin K Rutter
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
- National Institute of Health Research Biomedical Research Centre, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
- National Institute of Health Research Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Céline Vetter
- Circadian and Sleep Epidemiology Laboratory, Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
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19
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Howe LJ, Rasheed H, Jones PR, Boomsma DI, Evans DM, Giannelis A, Hayward C, Hopper JL, Hughes A, Lahtinen H, Li S, Lind PA, Martin NG, Martikainen P, Medland SE, Morris TT, Nivard MG, Pingault JB, Silventoinen K, Smith JA, Willoughby EA, Wilson JF. Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study. Int J Epidemiol 2023; 52:1579-1591. [PMID: 37295953 PMCID: PMC10555788 DOI: 10.1093/ije/dyad079] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 05/12/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Previous Mendelian randomization (MR) studies using population samples (population MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. MR using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis. METHODS Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72 932 siblings from UK Biobank and the Norwegian HUNT study, and summary-level data from a within-sibship Genome-wide Association Study including >140 000 individuals. RESULTS Both population and within-sibship MR estimates provided evidence that educational attainment decreased BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect. CONCLUSIONS These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independently of potential demographic and family-level confounders.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Paul R Jones
- Department of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Registry, Vrije Universiteit, Amsterdam, Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D)
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hannu Lahtinen
- Population Research Unit, University of Helsinki, Helsinki, Finland
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Pekka Martikainen
- Population Research Unit, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Germany
- Department of Public Health Sciences, Stockholm University, Sweden
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Australia
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Registry, Vrije Universiteit, Amsterdam, Netherlands
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | | | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | | | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, UK
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20
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Johnson EC, Salvatore JE, Lai D, Merikangas AK, Nurnberger JI, Tischfield JA, Xuei X, Kamarajan C, Wetherill L, Rice JP, Kramer JR, Kuperman S, Foroud T, Slesinger PA, Goate AM, Porjesz B, Dick DM, Edenberg HJ, Agrawal A. The collaborative study on the genetics of alcoholism: Genetics. GENES, BRAIN, AND BEHAVIOR 2023; 22:e12856. [PMID: 37387240 PMCID: PMC10550788 DOI: 10.1111/gbb.12856] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/02/2023] [Accepted: 06/17/2023] [Indexed: 07/01/2023]
Abstract
This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA). COGA was designed during the linkage era to identify genes affecting the risk for alcohol use disorder (AUD) and related problems, and was among the first AUD-focused studies to subsequently adopt a genome-wide association (GWAS) approach. COGA's family-based structure, multimodal assessment with gold-standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping continues to provide insights into the etiology of AUD and related disorders. These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons. COGA is one of the few AUD genetics projects that includes a substantial number of participants of African ancestry. The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large-scale GWAS consortia. COGA's wealth of publicly available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits.
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Affiliation(s)
- Emma C. Johnson
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Jessica E. Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical SchoolRutgers UniversityPiscatawayNew JerseyUSA
| | - Dongbing Lai
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Alison K. Merikangas
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John I. Nurnberger
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Xiaoling Xuei
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Chella Kamarajan
- Department of Psychiatry and Behavioral SciencesState University of New York Health Sciences UniversityBrooklynNew YorkUSA
| | - Leah Wetherill
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - John P. Rice
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - John R. Kramer
- Department of Psychiatry, Carver College of MedicineUniversity of IowaIowa CityIowaUSA
| | - Samuel Kuperman
- Department of Psychiatry, Carver College of MedicineUniversity of IowaIowa CityIowaUSA
| | - Tatiana Foroud
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Paul A. Slesinger
- Departments of Neuroscience and Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Alison M. Goate
- Departments of Genetics and Genomic Sciences, Neuroscience, and NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral SciencesState University of New York Health Sciences UniversityBrooklynNew YorkUSA
| | - Danielle M. Dick
- Department of Psychiatry, Robert Wood Johnson Medical SchoolRutgers UniversityPiscatawayNew JerseyUSA
| | - Howard J. Edenberg
- Department of Medical & Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana UniversityIndianapolisIndianaUSA
| | - Arpana Agrawal
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
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21
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Havdahl A, Hughes AM, Sanderson E, Ask H, Cheesman R, Reichborn-Kjennerud T, Andreassen OA, Corfield EC, Hannigan L, Magnus P, Njølstad PR, Stoltenberg C, Torvik FA, Brandlistuen R, Smith GD, Ystrom E, Davies NM. Intergenerational effects of parental educational attainment on parenting and childhood educational outcomes: Evidence from MoBa using within-family Mendelian randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.22.23285699. [PMID: 36865116 PMCID: PMC9980223 DOI: 10.1101/2023.02.22.23285699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The intergenerational transmission of educational attainment from parents to their children is one of the most important and studied relationships in social science. Longitudinal studies have found strong associations between parents' and their children's educational outcomes, which could be due to the effects of parents. Here we provide new evidence about whether parents' educational attainment affects their parenting behaviours and children's early educational outcomes using within-family Mendelian randomization and data from 40,879 genotyped parent-child trios from the Norwegian Mother, Father and Child Cohort (MoBa) study. We found evidence suggesting that parents' educational attainment affects their children's educational outcomes from age 5 to 14. More studies are needed to provide more samples of parent-child trios and assess the potential consequences of selection bias and grandparental effects.
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Affiliation(s)
- Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Amanda M Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Helga Ask
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ted Reichborn-Kjennerud
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elizabeth C. Corfield
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Laurie Hannigan
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Per Magnus
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Pål R. Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Camilla Stoltenberg
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Fartein Ask Torvik
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ragnhild Brandlistuen
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, BS8 2BN, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London W1T 7NF
- Department of Statistical Sciences, University College London, London WC1E 6BT, UK
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22
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Bilghese M, Manansala R, Jaishankar D, Jala J, Benjamin DJ, Kimball M, Auer PL, Livermore MA, Turley P. A General Approach to Adjusting Genetic Studies for Assortative Mating. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555983. [PMID: 37732240 PMCID: PMC10508715 DOI: 10.1101/2023.09.01.555983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM.
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Affiliation(s)
- Marta Bilghese
- Department of Finance, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Manansala
- Center for Health Economics Research & Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Campus Drie Eiken, Antwerp, Belgium
| | | | - Jonathan Jala
- UCLA Anderson School of Management, Los Angeles, CA, USA
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Miles Kimball
- Department of Economics, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee WI, USA
| | | | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
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23
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Zhang X, Sun W, Li N, Jian X, Geng T, Wu L, Wang Y, Wang B, Zheng D. Causality assessment of circulating Vitamin D level on venous thromboembolism: A Mendelian randomization study. Nutr Metab Cardiovasc Dis 2023; 33:1800-1807. [PMID: 37414665 DOI: 10.1016/j.numecd.2023.05.019] [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: 11/08/2022] [Revised: 04/16/2023] [Accepted: 05/08/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND AND AIMS The associations of vitamin D level with venous thromboembolism (VTE) reported in observational studies, whereas these causal associations were uncertain in European population. Therefore, we used Mendelian randomization (MR) method to explore the causal associations between 25-hydroxyvitamin D (25(OH)D) concentrations and the risk of VTE and its subtypes [including deep vein thrombosis (DVT) and pulmonary embolism (PE)]. METHODS AND RESULTS We used three kinds of genetic instruments to proxy the exposure of 25(OH)D, including genetic variants significantly associated with 25(OH)D, expression quantitative trait loci of 25(OH)D target genes, and genetic variants within or nearby 25(OH)D target genes. MR analyses did not provide any evidence for the associations of 25(OH)D levels with VTE and its subtypes (p > 0.05). The summary-data-based MR (SMR) analyses indicated that elevated expression of VDR was associated with decreased risk of VTE (OR = 0.81; 95% CI, 0.65-0.998; p = 0.047) and PE (OR = 0.67; 95% CI, 0.50-0.91; p = 0.011), and expression of AMDHD1 was associated with PE (OR = 0.93; 95% CI, 0.88-0.99; p = 0.027). MR analysis provided a significant causal effect of 25(OH)D level mediated by gene AMDHD1 on PE risk (OR = 0.09; 95% CI, 0.01-0.60; p = 0.012). CONCLUSION Our MR analysis did not support causal association of 25(OH)D level with the risk of VTE and its subtypes. In addition, the expression of VDR and AMDHD1 involved in vitamin D metabolism showed a strong association with VTE or PE and might represent targets for these conditions.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Wen Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Xuening Jian
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Youxin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden.
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24
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 136] [Impact Index Per Article: 136.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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25
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Yu B, Zachrisson HD, Cheesman R, Ystrom E, Nes RB. Boys with overweight status lagged behind girls with overweight status in reading: evidence from mendelian randomization. J Clin Epidemiol 2023; 159:199-205. [PMID: 37156340 DOI: 10.1016/j.jclinepi.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 04/05/2023] [Accepted: 05/01/2023] [Indexed: 05/10/2023]
Abstract
OBJECTIVES We investigated the relationship between childhood weight status and academic achievement across sexes and different school subjects in Norway. STUDY DESIGN AND SETTING We used data from the Norwegian Mother, Father and Child Cohort Study (MoBa), which includes genetic data (N = 13,648, 8-year-old children). We employed within-family mendelian randomization, using a body mass index (BMI) polygenic risk score as an instrument to address unobserved heterogeneity. RESULTS Contrary to most previous findings, we observed that overweight status (including obesity) has more detrimental effects on reading achievement in boys than in girls; the test scores of boys with overweight were about a standard deviation lower than those of normal weight boys, and the negative effects on reading achievement became stronger in the later grade. CONCLUSION Previous obesity prevention studies have mainly targeted girls, based on the assumption that the obesity penalty is greater for girls. Our findings highlight that particular attention to boys with overweight may help reduce the existing gender gap in academic achievement.
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Affiliation(s)
- Baeksan Yu
- Department of Education, Gwangju National University of Education, Gwangju, South Korea.
| | | | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ragnhild Bang Nes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Department of Mental Health and Suicide, Norwegian Institute of Public Health, Oslo, Norway; Department of Philosophy, Classics, and History of Arts and Ideas, University of Oslo, Oslo, Norway
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26
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Chen D, Wang X, Huang T, Jia J. Genetic support of a causal relationship between cannabis use and educational attainment: a two-sample Mendelian randomization study of European ancestry. Addiction 2023; 118:698-710. [PMID: 36465060 DOI: 10.1111/add.16090] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 11/03/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND AND AIMS Excessive cannabis use may lead to lower educational attainment. However, this association may be due to confounders and reverse causality. We tested the potential causal relationship between cannabis use disorder (CUD) or life-time cannabis use (LCU) and educational attainment. DESIGN Bidirectional two-sample Mendelian randomization (MR) study was conducted. Our primary method was inverse-variance weighted (IVW) MR, with a series of sensitivity analyses. Multivariable MR (MVMR) was performed to estimate any direct effect independent of intelligence, smoking initiation or attention deficit hyperactivity disorder (ADHD). SETTING AND PARTICIPANTS European ancestry individuals. The sample sizes of the genome-wide association study ranged from 55 374 to 632 802 participants. MEASUREMENTS Genetic variants of CUD, LCU or educational attainment. FINDINGS Using univariable MR, we found evidence of a potential causal effect of genetic liability to CUD on a lower educational attainment [MR, 95% confidence interval (CI)inverse variance weighted (IVW) = -1.2 month (-1.9 month, -0.5 month); P = 0.0008]. However, we found no evidence of an effect of genetic liability to LCU on educational attainment [MR, 95% CIIVW = 0.5 month (-1.5 month, 2.6 month), P = 0.6032]. Reverse direction analysis suggested that genetic liability to higher educational attainment had a potential causal effect on lower risk of CUD [odds ratio (OR), 95% CIIVW = 0.39 (0.29, 0.52), P = 1.69 × 10-10 ]. We also found evidence of potential causal effect from genetic liability to higher educational attainment to higher risk of LCU [OR, 95% CIIVW = 1.35 (1.11, 1.66), P = 0.0033]. CONCLUSIONS Genetic liability to cannabis use disorder may lead to lower educational attainment. Genetic liability to higher educational attainment may also lead to higher life-time cannabis use risk and lower cannabis use disorder risk. However, the bidirectional effect between cannabis use disorder and educational attainment may be due to shared risk factors (e.g. attention-deficit hyperactivity disorder).
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Affiliation(s)
- Dongze Chen
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xinpei Wang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Department of Global Health, School of Public Health, Peking University, Beijing, China.,Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
| | - Jinzhu Jia
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China.,Center for Statistical Science, Peking University, Beijing, China
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27
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Woolf B, Sallis HM, Munafò MR. Exploring the Lifetime Effect of Children on Wellbeing Using Two-Sample Mendelian Randomisation. Genes (Basel) 2023; 14:716. [PMID: 36980988 PMCID: PMC10048211 DOI: 10.3390/genes14030716] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/17/2023] Open
Abstract
BACKGROUND Observational research implies a negative effect of having children on wellbeing. OBJECTIVES To provide Mendelian randomisation evidence of the effect of having children on parental wellbeing. DESIGN Two-sample Mendelian randomisation. SETTING Non-clinical European ancestry participants. PARTICIPANTS We used the UK Biobank (460,654 male and female European ancestry participants) as a source of genotype-exposure associations, the Social Science Genetics Consortia (SSGAC) (298,420 male and female European ancestry participants), and the Within-Family Consortia (effective sample of 22,656 male and female European ancestry participants) as sources of genotype-outcome associations. INTERVENTIONS The lifetime effect of an increase in the genetic liability to having children. PRIMARY AND SECONDARY OUTCOME MEASURES The primary analysis was an inverse variance weighed analysis of subjective wellbeing measured in the 2016 SSGAC Genome Wide Association Study (GWAS). Secondary outcomes included pleiotropy robust estimators applied in the SSGAC and an analysis using the Within-Family consortia GWAS. RESULTS We did not find strong evidence of a negative (standard deviation) change in wellbeing (β = 0.153 (95% CI: -0.210 to 0.516) per child parented. Secondary outcomes were generally slightly deflated (e.g., -0.049 [95% CI: -0.533 to 0.435] for the Within-Family Consortia and 0.090 [95% CI: -0.167 to 0.347] for weighted median), implying the presence of some residual confounding and pleiotropy. CONCLUSIONS Contrary to the existing literature, our results are not compatible with a measurable negative effect of number of children on the average wellbeing of a parent over their life course. However, we were unable to explore non-linearities, interactions, or time-varying effects.
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Affiliation(s)
- Benjamin Woolf
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 1TN, UK
| | - Hannah M. Sallis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Marcus R. Munafò
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
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28
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Zhang X, Geng T, Li N, Wu L, Wang Y, Zheng D, Guo B, Wang B. Associations of Lipids and Lipid-Lowering Drugs with Risk of Vascular Dementia: A Mendelian Randomization Study. Nutrients 2022; 15:nu15010069. [PMID: 36615727 PMCID: PMC9824558 DOI: 10.3390/nu15010069] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Accumulating observational studies suggested that hypercholesterolemia is associated with vascular dementia (VaD); however, the causality between them remains unclear. Hence, the aim of this study is to infer causal associations of circulating lipid-related traits [including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), apolipoprotein A-I (apoA-I), and apolipoprotein B (apoB)] with VaD jointly using univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR methods. Then, the summary-data-based MR (SMR) and two-sample MR analysis were conducted to investigate the association of lipid-lowering drugs target genes expression (including HMGCR, PCSK9, NPC1L1, and APOB) and LDL-C level mediated by these target genes with VaD. The results of forward MR analyses found that genetically predicted HDL-C, LDL-C, TG, apoA-I, and apoB concentrations were not significantly associated with the risk of VaD (all p > 0.05). Notably, there was suggestive evidence for a causal effect of genetically predicted VaD on HDL-C via reverse MR analysis [odds ratio (OR), 0.997; 95% confidence interval (CI), 0.994−0.999; p = 0.022]. On the contrary, the MR results showed no significant relationship between VaD with LDL-C, TG, apoA-I, and apoB. The results for the SMR method found that there was no evidence of association for expression of HMGCR, PCSK9, NPC1L1, and APOB gene with risk of VaD. Furthermore, the result of MR analysis provided evidence for the decreased LDL-C level mediated by gene HMGCR reduced the risk of VaD (OR, 18.381; 95% CI, 2.092−161.474; p = 0.009). Oppositely, none of the IVW methods indicated any causal effects for the other three genes. Using genetic data, this study provides evidence that the VaD risk may cause a reduction of HDL-C level. Additionally, the finding supports the hypothesis that lowering LDL-C levels using statins may be an effective prevention strategy for VaD risk, which requires clinical trials to confirm this result in the future.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing 100028, China
| | - Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Youxin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Bo Guo
- Department of Hematology, The Second Medical Centre & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
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29
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Borges MC, Haycock P, Zheng J, Hemani G, Howe LJ, Schmidt AF, Staley JR, Lumbers RT, Henry A, Lemaitre RN, Gaunt TR, Holmes MV, Davey Smith G, Hingorani AD, Lawlor DA. The impact of fatty acids biosynthesis on the risk of cardiovascular diseases in Europeans and East Asians: a Mendelian randomization study. Hum Mol Genet 2022; 31:4034-4054. [PMID: 35796550 PMCID: PMC9703943 DOI: 10.1093/hmg/ddac153] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/11/2022] [Accepted: 06/24/2022] [Indexed: 11/14/2022] Open
Abstract
Despite early interest, the evidence linking fatty acids to cardiovascular diseases (CVDs) remains controversial. We used Mendelian randomization to explore the involvement of polyunsaturated (PUFA) and monounsaturated (MUFA) fatty acids biosynthesis in the etiology of several CVD endpoints in up to 1 153 768 European (maximum 123 668 cases) and 212 453 East Asian (maximum 29 319 cases) ancestry individuals. As instruments, we selected single nucleotide polymorphisms mapping to genes with well-known roles in PUFA (i.e. FADS1/2 and ELOVL2) and MUFA (i.e. SCD) biosynthesis. Our findings suggest that higher PUFA biosynthesis rate (proxied by rs174576 near FADS1/2) is related to higher odds of multiple CVDs, particularly ischemic stroke, peripheral artery disease and venous thromboembolism, whereas higher MUFA biosynthesis rate (proxied by rs603424 near SCD) is related to lower odds of coronary artery disease among Europeans. Results were unclear for East Asians as most effect estimates were imprecise. By triangulating multiple approaches (i.e. uni-/multi-variable Mendelian randomization, a phenome-wide scan, genetic colocalization and within-sibling analyses), our results are compatible with higher low-density lipoprotein (LDL) cholesterol (and possibly glucose) being a downstream effect of higher PUFA biosynthesis rate. Our findings indicate that PUFA and MUFA biosynthesis are involved in the etiology of CVDs and suggest LDL cholesterol as a potential mediating trait between PUFA biosynthesis and CVDs risk.
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Affiliation(s)
- Maria-Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Phillip Haycock
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Laurence J Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - A Floriaan Schmidt
- Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London WC1E 6DD, UK
- Department of Cardiology, Division Heart and Lungs, UMC Utrecht, Utrecht 3584 CX, The Netherlands
| | - James R Staley
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Health Data Research UK London, University College London NW1 2DA, UK
- UCL British Heart Foundation Research Accelerator, London NW1 2DA, UK
| | - Albert Henry
- Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London WC1E 6DD, UK
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- UCL British Heart Foundation Research Accelerator, London NW1 2DA, UK
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA WA 98101, USA
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
| | - Aroon D Hingorani
- Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London WC1E 6DD, UK
- Health Data Research UK London, University College London NW1 2DA, UK
- UCL British Heart Foundation Research Accelerator, London NW1 2DA, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PN, UK
- NIHR Bristol Biomedical Research Centre, Bristol BS8 2BN, UK
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Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores. Nat Commun 2022; 13:4801. [PMID: 35999215 PMCID: PMC9399113 DOI: 10.1038/s41467-022-32003-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/12/2022] [Indexed: 12/12/2022] Open
Abstract
Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.
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Walker VM, Zheng J, Gaunt TR, Smith GD. Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond. Annu Rev Biomed Data Sci 2022; 5:1-17. [PMID: 35363507 PMCID: PMC7614231 DOI: 10.1146/annurev-biodatasci-122120-024910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.
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Affiliation(s)
- Venexia M. Walker
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
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Dixon P, Harrison S, Hollingworth W, Davies NM, Davey Smith G. Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization. ECONOMICS AND HUMAN BIOLOGY 2022; 46:101154. [PMID: 35803012 DOI: 10.1016/j.ehb.2022.101154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 06/15/2022] [Accepted: 06/28/2022] [Indexed: 05/27/2023]
Abstract
Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.
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Affiliation(s)
- Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom.
| | - Sean Harrison
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom
| | | | - Neil M Davies
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, University of Bristol, United Kingdom; NIHR Biomedical Research Centre, University of Bristol, United Kingdom
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Havdahl A, Wootton RE, Leppert B, Riglin L, Ask H, Tesli M, Bugge Askeland R, Hannigan LJ, Corfield E, Øyen AS, Andreassen OA, Tilling K, Davey Smith G, Thapar A, Reichborn-Kjennerud T, Stergiakouli E. Associations Between Pregnancy-Related Predisposing Factors for Offspring Neurodevelopmental Conditions and Parental Genetic Liability to Attention-Deficit/Hyperactivity Disorder, Autism, and Schizophrenia: The Norwegian Mother, Father and Child Cohort Study (MoBa). JAMA Psychiatry 2022; 79:799-810. [PMID: 35793100 PMCID: PMC9260642 DOI: 10.1001/jamapsychiatry.2022.1728] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/10/2022] [Indexed: 02/02/2023]
Abstract
Importance Several maternal exposures during pregnancy are considered predisposing factors for offspring neurodevelopmental conditions. However, many of these exposures may be noncausal and biased by maternal genetic liability. Objective To assess whether pregnancy-related predisposing factors for offspring neurodevelopmental conditions are associated with maternal genetic liability for attention-deficit/hyperactivity disorder (ADHD), autism, and schizophrenia and to compare associations for maternal genetic liability with those for paternal genetic liability, which could indicate that paternal exposures are not suitable negative controls for maternal exposures. Design, Setting, and Participants The Norwegian Mother, Father and Child Cohort Study (MoBa) is a population-based pregnancy cohort that recruited parents from June 1999 to December 2008. Polygenic scores (PGS) for ADHD, autism, and schizophrenia were derived in mothers and fathers. The associations between maternal PGS and 37 pregnancy-related measures were estimated, and these results were compared with those from paternal PGS predicting paternal measures during the mother's pregnancy. Analysis took place between March 2021 and March 2022. Exposures PGS for ADHD, autism, and schizophrenia, calculated (using discovery effect size estimates and threshold of P < .05) from the largest available genome-wide association studies. Main Outcomes and Measures Self-reported pregnancy-related measures capturing lifestyle behaviors, metabolism, infectious and autoimmune diseases, other physical health conditions, and medication use. Results Data were available for up to 14 539 mothers (mean [SD] age, 30.00 [4.45] years) and 14 897 fathers (mean [SD] age, 32.46 [5.13] years) of European ancestry. Modest but robust associations were observed between specific pregnancy-related measures and maternal PGS, including ADHD PGS with asthma (odds ratio [OR], 1.15 [95% CI, 1.06-1.25]), smoking (OR, 1.26 [95% CI, 1.19-1.33]), prepregnancy body mass index (β, 0.25 [95% CI, 0.18-0.31]), pregnancy weight gain (β, 0.20 [95% CI, 0.10-0.30]), taking folate (OR, 0.92 [95% CI, 0.88-0.96]), and not taking supplements (OR, 1.09 [95% CI, 1.04-1.14]). Schizophrenia PGS was associated with coffee consumption (OR, 1.09 [95% CI, 1.05-1.12]), smoking (OR, 1.12 [95% CI, 1.06-1.19]), prepregnancy body mass index (β, -0.18 [95% CI, -0.25 to -0.11]), and pregnancy weight gain (β, 0.17 [95% CI, 0.07-0.27]). All 3 PGSs associated with symptoms of depression/anxiety (ADHD: OR, 1.15 [95% CI, 1.09-1.22]; autism: OR, 1.13 [95% CI, 1.06-1.19]; schizophrenia: OR, 1.13 [95% CI, 1.07-1.20]). Associations were largely consistent for maternal and paternal PGS, except ADHD PGS and smoking (fathers: OR, 1.13 [95% CI, 1.09-1.17]). Conclusions and Relevance In this study, genetic liability to neurodevelopmental conditions that is passed from mothers to children was associated with several pregnancy-related factors and may therefore confound associations between these pregnancy-related factors and offspring neurodevelopment that have previously been thought to be causal. It is crucial that future study designs account for genetic confounding to obtain valid causal inferences so that accurate advice can be given to pregnant individuals.
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Affiliation(s)
- Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Robyn E. Wootton
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Beate Leppert
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lucy Riglin
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- Wolfson Centre for Young People’s Mental Health, Cardiff University, Cardiff, United Kingdom
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ragna Bugge Askeland
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Laurie J. Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Elizabeth Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Anne-Siri Øyen
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kate Tilling
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Anita Thapar
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- Wolfson Centre for Young People’s Mental Health, Cardiff University, Cardiff, United Kingdom
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Evie Stergiakouli
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Zhang X, Cong R, Geng T, Zhang J, Liu D, Tian Q, Meng X, Song M, Wu L, Zheng D, Wang W, Wang B, Wang Y. Assessment of the Causal Effects of IgG N-Glycosylation Level on Risk of Dementia: A 2-Sample Mendelian Randomization Study. J Alzheimers Dis 2022; 88:1435-1441. [DOI: 10.3233/jad-220074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer’s disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation. Objective: The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR). Methods: Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers. Results: There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all p > 0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change. Conclusion: Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Ruyi Cong
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing, China
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong
| | - Jinxia Zhang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Di Liu
- Centre for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Qiuyue Tian
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Xiaoni Meng
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Manshu Song
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Lijuan Wu
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Deqiang Zheng
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Wei Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
- Centre for Precision Health, Edith Cowan University, Perth, Australia
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
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Evaluating indirect genetic effects of siblings using singletons. PLoS Genet 2022; 18:e1010247. [PMID: 35797272 PMCID: PMC9262210 DOI: 10.1371/journal.pgen.1010247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 05/10/2022] [Indexed: 12/23/2022] Open
Abstract
Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear. Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PGS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PGS corresponds to a 0.025 year increase in the index individual's years in schooling (95% C.I. 0.013, 0.036). Our results suggest that older siblings may influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.
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Yang Q, Sanderson E, Tilling K, Borges MC, Lawlor DA. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Eur J Epidemiol 2022; 37:683-700. [PMID: 35622304 PMCID: PMC9329407 DOI: 10.1007/s10654-022-00874-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/18/2022] [Indexed: 12/19/2022]
Abstract
With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.
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Affiliation(s)
- Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
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37
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Soler Artigas M, Sánchez-Mora C, Rovira P, Vilar-Ribó L, Ramos-Quiroga JA, Ribasés M. Mendelian randomization analysis for attention deficit/hyperactivity disorder: studying a broad range of exposures and outcomes. Int J Epidemiol 2022; 52:386-402. [PMID: 35690959 PMCID: PMC10114062 DOI: 10.1093/ije/dyac128] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/26/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder caused by a combination of genetic and environmental factors and is often thought as an entry point into a negative life trajectory, including risk for comorbid disorders, poor educational achievement or low income. In the present study, we aimed to clarify the causal relationship between ADHD and a comprehensive range of related traits. METHODS We used genome-wide association study (GWAS) summary statistics for ADHD (n = 53 293) and 124 traits related to anthropometry, cognitive function and intelligence, early life exposures, education and employment, lifestyle and environment, longevity, neurological, and psychiatric and mental health or personality and psychosocial factors available in the MR-Base database (16 067 ≤n ≤766 345). To investigate their causal relationship with ADHD, we used two-sample Mendelian randomization (MR) with a range of sensitivity analyses, and validated MR findings using causal analysis using summary effect estimates (CAUSE), aiming to avoid potential false-positive results. RESULTS Our findings strengthen previous evidence of a causal effect of ADHD liability on smoking and major depression, and are consistent with a causal effect on odds of decreased average total household income [odds ratio (OR) = 0.966, 95% credible interval (CrI) = (0.954, 0.979)] and increased lifetime number of sexual partners [OR = 1.023, 95% CrI = (1.013, 1.033)]. We also found evidence for a causal effect on ADHD for liability of arm predicted mass and weight [OR = 1.452, 95% CrI = (1.307, 1.614) and OR = 1.430, 95% CrI = (1.326, 1.539), respectively] and time spent watching television [OR = 1.862, 95% CrI = (1.545, 2.246)], and evidence for a bidirectional effect for age of first sexual intercourse [beta = -0.058, 95% CrI = (-0.072, -0.044) and OR = 0.413, 95% CrI = (0.372, 0.457), respectively], odds of decreased age completed full-time education [OR = 0.972, 95% CrI = (0.962, 0.981) and OR = 0.435, 95% CrI = (0.356, 0.533), respectively] and years of schooling [beta = -0.036, 95% CrI = (-0.048, -0.024) and OR = 0.458, 95% CrI = (0.411, 0.511), respectively]. CONCLUSIONS Our results may contribute to explain part of the widespread co-occurring traits and comorbid disorders across the lifespan of individuals with ADHD and may open new opportunities for developing preventive strategies for ADHD and for negative ADHD trajectories.
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Affiliation(s)
- María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.,Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Cristina Sánchez-Mora
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.,Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Paula Rovira
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Vicerectorat de Recerca, postdoctoral researcher Margarita Salas, Universitat de Barcelona, Barcelona, Spain.,Departament of Psychiatry, Faculty of Medicine, Universidad de Granada, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.,Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.,Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
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38
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Fang S, Wade KH, Hughes DA, Fitzgibbon S, Yip V, Timpson NJ, Corbin LJ. A multivariant recall-by-genotype study of the metabolomic signature of BMI. Obesity (Silver Spring) 2022; 30:1298-1310. [PMID: 35598895 PMCID: PMC9324973 DOI: 10.1002/oby.23441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE This study estimated the effect of BMI on circulating metabolites in young adults using a recall-by-genotype study design. METHODS A recall-by-genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites. RESULTS After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high-BMI GRS group (β = -0.32, 95% CI: -0.46 to -0.18, Benjamini-Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet-related metabolites, including hippurate, which had lower mean abundance in individuals in the high-BMI GRS group (β = -0.29, 95% CI: -0.44 to -0.15, Benjamini-Hochberg adjusted p = 0.008). CONCLUSIONS Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.
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Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Kaitlin H. Wade
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - David A. Hughes
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Sophie Fitzgibbon
- Bristol Bioresource LaboratoriesPopulation Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Vikki Yip
- Bristol Bioresource LaboratoriesPopulation Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Laura J. Corbin
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
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39
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Abstract
Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.
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Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- VA Puget Sound Health Care System, Seattle, WA, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
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40
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Wade KH, Yarmolinsky J, Giovannucci E, Lewis SJ, Millwood IY, Munafò MR, Meddens F, Burrows K, Bell JA, Davies NM, Mariosa D, Kanerva N, Vincent EE, Smith-Byrne K, Guida F, Gunter MJ, Sanderson E, Dudbridge F, Burgess S, Cornelis MC, Richardson TG, Borges MC, Bowden J, Hemani G, Cho Y, Spiller W, Richmond RC, Carter AR, Langdon R, Lawlor DA, Walters RG, Vimaleswaran KS, Anderson A, Sandu MR, Tilling K, Davey Smith G, Martin RM, Relton CL. Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer. Cancer Causes Control 2022; 33:631-652. [PMID: 35274198 PMCID: PMC9010389 DOI: 10.1007/s10552-022-01562-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 02/10/2022] [Indexed: 02/08/2023]
Abstract
Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.
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Affiliation(s)
- Kaitlin H Wade
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK.
| | - James Yarmolinsky
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Edward Giovannucci
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Sarah J Lewis
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marcus R Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Fleur Meddens
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Kimberley Burrows
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Joshua A Bell
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Neil M Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniela Mariosa
- International Agency for Research On Cancer (IARC), Lyon, France
| | | | - Emma E Vincent
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Florence Guida
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Marc J Gunter
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Eleanor Sanderson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Tom G Richardson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Jack Bowden
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Research Innovation Learning and Development (RILD) Building, University of Exeter Medical School, Exeter, UK
| | - Gibran Hemani
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Yoonsu Cho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Wes Spiller
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Rebecca C Richmond
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Alice R Carter
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Ryan Langdon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Annie Anderson
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland, UK
| | - Meda R Sandu
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Bristol, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Caroline L Relton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
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41
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Isungset MA, Freese J, Andreassen OA, Lyngstad TH. Birth order differences in education originate in postnatal environments. PNAS NEXUS 2022; 1:pgac051. [PMID: 36713322 PMCID: PMC9802280 DOI: 10.1093/pnasnexus/pgac051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023]
Abstract
Siblings share many environments and much of their genetics. Yet, siblings turn out different. Intelligence and education are influenced by birth order, with earlier-born siblings outperforming later-borns. We investigate whether birth order differences in education are caused by biological differences present at birth, that is, genetic differences or in utero differences. Using family data that spans two generations, combining registry, survey, and genotype information, this study is based on the Norwegian Mother, Father, and Child Cohort Study (MoBa). We show that there are no genetic differences by birth order as captured by polygenic scores (PGSs) for educational attainment. Earlier-born have lower birth weight than later-born, indicating worse uterine environments. Educational outcomes are still higher for earlier-born children when we adjust for PGSs and in utero variables, indicating that birth order differences arise postnatally. Finally, we consider potential environmental influences, such as differences according to maternal age, parental educational attainment, and sibling genetic nurture. We show that birth order differences are not biological in origin, but pinning down their specific causes remains elusive.
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Affiliation(s)
- Martin Arstad Isungset
- Department of Sociology and Human Geography, University of Oslo, PO Box 1096, Blindern, 0317 Oslo, Norway
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA 94305, USA
| | - Ole A Andreassen
- Institute of Clinical Medicine, University of Oslo, PO Box 4956, Nydalen, 0424 Oslo, Norway
- NORMENT,Division of Mental Health and Addiction, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Torkild Hovde Lyngstad
- Department of Sociology and Human Geography, University of Oslo, PO Box 1096, Blindern, 0317 Oslo, Norway
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42
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Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, Davies NM. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022; 54:581-592. [PMID: 35534559 PMCID: PMC9110300 DOI: 10.1038/s41588-022-01062-7] [Citation(s) in RCA: 114] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | | | | | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Jared V Balbona
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Christopher R Bauer
- BioMarin Pharmaceutical Inc., Novato, CA, USA
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - Elizabeth Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | | | - Deepika R Dokuru
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sudheer Giddaluru
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Scott D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shona M Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Skøyen, Oslo, Norway
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Pekka Martikainen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Rostock, Germany
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Pål Rasmus Njølstad
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | | | | | - John K Hewitt
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Robert Plomin
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Meike Bartels
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Øyvind Naess
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Cristen J Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
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Howe LJ, Brumpton B, Rasheed H, Åsvold BO, Davey Smith G, Davies NM. Taller height and risk of coronary heart disease and cancer, a within-sibship Mendelian randomization study. eLife 2022; 11:72984. [PMID: 35302490 PMCID: PMC8947759 DOI: 10.7554/elife.72984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 03/09/2022] [Indexed: 11/15/2022] Open
Abstract
Background: Taller people have a lower risk of coronary heart disease but a higher risk of many cancers. Mendelian randomization (MR) studies in unrelated individuals (population MR) have suggested that these relationships are potentially causal. However, population MR studies are sensitive to demography (population stratification, assortative mating) and familial (indirect genetic) effects. Methods: In this study, we performed within-sibship MR analyses using 78,988 siblings, a design robust against demography and indirect genetic effects of parents. For comparison, we also applied population MR and estimated associations with measured height. Results: Within-sibship MR estimated that 1 SD taller height lowers the odds of coronary heart disease by 14% (95% CI: 3–23%) but increases the odds of cancer by 18% (95% CI: 3–34%), highly consistent with population MR and height-disease association estimates. There was some evidence that taller height reduces systolic blood pressure and low-density lipoprotein cholesterol, which may mediate some of the protective effects of taller height on coronary heart disease risk. Conclusions: For the first time, we have demonstrated that the purported effects of height on adulthood disease risk are unlikely to be explained by demographic or familial factors, and so likely reflect an individual-level causal effect. Disentangling the mechanisms via which height affects disease risk may improve the understanding of the etiologies of atherosclerosis and carcinogenesis. Funding: This project was conducted by researchers at the MRC Integrative Epidemiology Unit (MC_UU_00011/1) and also supported by a Norwegian Research Council Grant number 295989.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Ben Brumpton
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørn Olav Åsvold
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Neil Martin Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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Yu Y, Hou L, Shi X, Sun X, Liu X, Yu Y, Yuan Z, Li H, Xue F. Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods. PLoS Genet 2022; 18:e1010107. [PMID: 35298462 PMCID: PMC8963545 DOI: 10.1371/journal.pgen.1010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 03/29/2022] [Accepted: 02/16/2022] [Indexed: 11/18/2022] Open
Abstract
Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods. It is well known that nonrandom selection in one-sample Mendelian Randomization (MR) can result in biased estimates and inflated type I error rates. Actually, two-sample MR analyses are more prone to be affected by nonrandom selection than one-sample MR analyses, because two samples for genome-wide association studies (GWAS) may be selected each under different mechanisms from the source population. Summary-level genetic association statistics in two-sample MR may be derived from different study designs such as case-control, case-only and cohort studies, which further inevitably affect the causal effect estimation of exposure on outcome. In this study, we firstly propose a theorem for causal effect invariance under different selection mechanisms. In the simulation, we design 49 combinations of nonrandom selection mechanisms in sample I and sample II, which are widespread in practical applications. The simulation results reveal that the selection mechanisms in sample II have a larger influence on biases and type I error rates than those in sample I. As an illustrative example, we find the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on the HbA1c levels.
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Affiliation(s)
- Yuanyuan Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Lei Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Xiaoru Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Xinhui Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Yifan Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Hongkai Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- * E-mail: (HL); (FX)
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- * E-mail: (HL); (FX)
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Modeling assortative mating and genetic similarities between partners, siblings, and in-laws. Nat Commun 2022; 13:1108. [PMID: 35233010 PMCID: PMC8888605 DOI: 10.1038/s41467-022-28774-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/10/2022] [Indexed: 12/17/2022] Open
Abstract
Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners (n = 26,681), siblings (n = 2,170), siblings-in-law (n = 3,905), and co-siblings-in-law (n = 1,763) in the Norwegian Mother, Father and Child Cohort Study. Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment (rg = 0.37), height (rg = 0.13), and depression (rg = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (rg = 0.68) and between siblings-in-law (rg = 0.25) and co-siblings-in-law (rg = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.
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Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD. Mendelian randomization. NATURE REVIEWS. METHODS PRIMERS 2022; 2:6. [PMID: 37325194 PMCID: PMC7614635 DOI: 10.1038/s43586-021-00092-5] [Citation(s) in RCA: 405] [Impact Index Per Article: 202.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 06/17/2023]
Abstract
Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.
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Affiliation(s)
- Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Michael V. Holmes
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Marcus R. Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Tom Palmer
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Public Health, City University of New York, New York, USA
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, UK
| | - Qingyuan Zhao
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
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47
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Dardani C, Riglin L, Leppert B, Sanderson E, Rai D, Howe LD, Davey Smith G, Tilling K, Thapar A, Davies NM, Anderson E, Stergiakouli E. Is genetic liability to ADHD and ASD causally linked to educational attainment? Int J Epidemiol 2022; 50:2011-2023. [PMID: 34999873 PMCID: PMC8743131 DOI: 10.1093/ije/dyab107] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 05/09/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The association patterns of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) with educational attainment (EA) are complex; children with ADHD and ASD are at risk of poor academic outcomes, and parental EA has been associated with risk of ADHD/ASD in the offspring. Little is known on the causal links between ADHD, ASD, EA and the potential contribution of cognitive ability. METHODS Using the latest genome-wide association studies (GWAS) summary data on ADHD, ASD and EA, we applied two-sample Mendelian randomization (MR) to assess the effects of genetic liability to ADHD and ASD on EA. Reverse direction analyses were additionally performed. Multivariable MR was performed to estimate any effects independent of cognitive ability. RESULTS Genetic liability to ADHD had a negative effect on EA, independently of cognitive ability (MVMRIVW: -1.7 months of education per doubling of genetic liability to ADHD; 95% CI: -2.8 to -0.7), whereas genetic liability to ASD a positive effect (MVMRIVW: 30 days per doubling of the genetic liability to ASD; 95% CI: 2 to 53). Reverse direction analyses suggested that genetic liability to higher EA had an effect on lower risk of ADHD, independently of cognitive ability (MVMRIVWOR: 0.33 per SD increase; 95% CI: 0.26 to 0.43) and increased risk of ASD (MRIVWOR: 1.51 per SD increase; 95% CI: 1.29 to 1.77), which was partly explained by cognitive ability (MVMRIVWOR per SD increase: 1.24; 95%CI: 0.96 to 1.60). CONCLUSIONS Genetic liability to ADHD and ASD is likely to affect educational attainment, independently of underlying cognitive ability.
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Affiliation(s)
- Christina Dardani
- Centre of Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucy Riglin
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Beate Leppert
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dheeraj Rai
- Centre of Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anita Thapar
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Emma Anderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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48
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Abstract
Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.
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Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol BS1 3NU, United Kingdom
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49
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Jin C, Li R, Deng T, Lin Z, Li H, Yang Y, Su Q, Wang J, Yang Y, Wang J, Chen G, Wang Y. Association between dried fruit intake and pan-cancers incidence risk: A two-sample Mendelian randomization study. Front Nutr 2022; 9:899137. [PMID: 35923199 PMCID: PMC9339715 DOI: 10.3389/fnut.2022.899137] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/27/2022] [Indexed: 12/20/2022] Open
Abstract
Background Observational studies have revealed that dried fruit intake may be associated with cancer incidence; however, confounding factors make the results prone to be disturbed. Therefore, we conducted a two-sample Mendelian randomization (MR) study to explore the causal relationship between dried fruit intake and 11 site-specific cancers. Materials and methods Forty-three single nucleoside polymers (SNPs) with robust genome-wide association study (GWAS) evidence, strongly correlated with dried fruit intake, were used as instrumental variables (IVs) in this study. The summary-level genetic datasets of site-specific cancers were obtained from the Oncoarray oral cavity and oropharyngeal cancer consortium, International Lung Cancer Consortium, Breast Cancer Association Consortium (BCAC), Ovarian Cancer Association Consortium, PanScan1, and GWAS of other scholars. We analyzed the causality between dried fruit intake and 11 site-specific cancers using the inverse-variance-weighted (IVW) and weighted median (WM) methods. For the results of the MR analysis, Cochran's Q test was used to check for heterogeneity, and multiplicative random effects were used to evaluate the heterogeneity further. Gene pleiotropy was tested using MR-Egger regression and MR-PRESSO methods. In addition, the main results of this study were validated by using the summary statistical data from the FinnGen and UK Biobank databases, and adjusted body mass index (BMI), years of education, fresh fruit intake, and vitamin C using multivariable MR analysis to ensure the stability of the research results. Results The evidence from IVW analyses showed that each increase of dried fruit intake by one standard deviation was statistically significantly associated with 82.68% decrease of oral cavity/pharyngeal cancer incidence risk (P = 0.0131), 67.01% decrease of lung cancer incidence risk (P = 0.0011), 77% decrease of squamous cell lung cancer incidence risk (P = 0.0026), 53.07% decrease of breast cancer incidence risk (P = 4.62 × 10-5), 39.72% decrease of ovarian cancer incidence risk (P = 0.0183), 97.26% decrease of pancreatic cancer incidence risk (P = 0.0280), 0.53% decrease of cervical cancer incidence risk (P = 0.0482); however, there was no significant effect on lung adenocarcinoma (P = 0.4343), endometrial cancer (P = 0.8742), thyroid cancer (P = 0.6352), prostate cancer (P = 0.5354), bladder cancer (P = 0.8996), and brain cancer (P = 0.8164). In the validation part of the study results, the causal relationship between dried fruit intake and lung cancer (P = 0.0043), squamous cell lung cancer (P = 0.0136), and breast cancer (P = 0.0192) was determined. After adjusting for the potential impact of confounders, the causal relationship between dried fruit intake and lung cancer (P = 0.0034), squamous cell lung cancer (P = 0.046), and breast cancer (P = 0.0001) remained. The sensitivity analysis showed that our results were stable and reliable. Conclusion The intake of dried fruits may have a protective effect against some site-specific cancers. Therefore, health education and a reasonable adjustment of dietary proportions may help in the primary prevention of cancer.
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Affiliation(s)
- Chen Jin
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Rui Li
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Tuo Deng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zixia Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haoqi Li
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Yan Yang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Qing Su
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Jingxian Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Yi Yang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Juejin Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
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Hazewinkel AD, Richmond RC, Wade KH, Dixon P. Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission. ECONOMICS AND HUMAN BIOLOGY 2022; 44:101088. [PMID: 34894623 PMCID: PMC8784824 DOI: 10.1016/j.ehb.2021.101088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 10/27/2021] [Accepted: 11/21/2021] [Indexed: 05/31/2023]
Abstract
We analyze how measures of adiposity - body mass index (BMI) and waist hip ratio (WHR) - causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.
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Affiliation(s)
- Audinga-Dea Hazewinkel
- Population Health Sciences, Bristol Medical School, University of Bristol, UK; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK.
| | - Rebecca C Richmond
- Population Health Sciences, Bristol Medical School, University of Bristol, UK; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
| | - Kaitlin H Wade
- Population Health Sciences, Bristol Medical School, University of Bristol, UK; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
| | - Padraig Dixon
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK; Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
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