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Fu C, Yu F, Liu X, Li B, Li X, Zhang G. The causal relationship between sarcopenia-related traits and ECG indices - A mendelian randomization study. Arch Gerontol Geriatr 2024; 125:105520. [PMID: 38878672 DOI: 10.1016/j.archger.2024.105520] [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: 04/12/2024] [Revised: 05/26/2024] [Accepted: 06/02/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND Sarcopenia is a common geriatric condition closely associated with cardiovascular diseases and other health issues. This study aims to investigate the causal relationship between sarcopenia-related traits and electrocardiogram(ECG) indices. METHODS We conducted a comprehensive analysis utilizing summary data from genome-wide association studies (GWAS) associated with sarcopenia-related traits, including hand grip strength, lean body mass, and walking pace. ECG indices included PR interval, PP interval, ST duration, QRS duration and T wave duration. The primary analytical method employed was the inverse variance-weighted method (IVW). RESULTS According to our study findings, we identified a significant association between sarcopenia-related traits and ECG indices. Specifically, we observed a positive correlation between increased muscle mass and certain ECG indices. For instance, increased limb muscle mass (including left arm, right arm, left leg, and right leg) was associated with prolonged PR interval and QRS duration. This suggests that enhancing muscle mass may impact the timing of cardiac electrical activity. Additionally, increased whole-body fat-free mass showed similar associations with cardiac electrical activity. CONCLUSION Sarcopenia-related traits have a unidirectional causal relationship with ECG indices, indicating that sarcopenia affects cardiac electrical activity.
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Affiliation(s)
- Chunli Fu
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, China; Jinan Clinical Research Center for Geriatric Medicine 202132001, Jinan, China
| | - Fei Yu
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, China; Jinan Clinical Research Center for Geriatric Medicine 202132001, Jinan, China
| | - Xiangju Liu
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, China; Jinan Clinical Research Center for Geriatric Medicine 202132001, Jinan, China
| | - Baoying Li
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, China; Health Management Center (East Area), Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoli Li
- Department of Pharmacy, Qilu Hospital of Shandong University, Jinan, China
| | - Guangyu Zhang
- Department of Radiation Oncology, Qilu Hospital of Shandong University, Jinan, China.
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Zhang X, Lu Q, Luo Y, Wang L, Tian Y, Luo X. The causal relationship between major depression disorder and thyroid diseases: A Mendelian randomization study and mediation analysis. J Affect Disord 2024; 359:287-299. [PMID: 38788859 DOI: 10.1016/j.jad.2024.05.097] [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: 11/02/2023] [Revised: 04/23/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Studies have been conducted on the relationship between depression and thyroid diseases and function, its causal relationship remains unclear. METHODS Using summary statistics of genome-wide association studies of European and East Asian ancestry, we conducted 2-sample bidirectional Mendelian randomization to estimate the association between MDD and thyroid function (European: normal range TSH, T4, T3, fT4, TPOAb levels and TPOAb-positives; East Asian: T4) and thyroid diseases (hypothyroidism, hyperthyroidism, and Hashimoto's thyroiditis), and used Mediation analysis to evaluate potential mediators (alcohol intake, antidepressant) of the association and calculate the mediated proportions. RESULTS It was observed a significant causal association between MDD on hypothyroidism (P = 8.94 × 10-5), hyperthyroidism (P = 8.68 × 10-3), and hashimoto's thyroiditis (P = 3.97 × 10-5) among European ancestry, which was mediated by Alcohol intake (alcohol intake versus 10 years previously for hypothyroidism (P = 0.026), hashimoto's thyroiditis (P = 0.042), and alcohol intake frequency for hypothyroidism (P = 0.015)) and antidepressant (for hypothyroidism (P = 0.008), hashimoto's thyroiditis (P = 0.010)), but not among East Asian ancestry (PMDD-hypothyroidism = 0.016, but β direction was different; PMDD-hyperthyroidism = 0.438; PMDD-hashimoto's thyroiditis = 0.496). There was no evidence for bidirectional causal association between thyroid function mentioned above and MDD among both ancestry (all P > 0.05). CONCLUSION We importantly observed a significant causal association between MDD on risk of hypothyroidism, hyperthyroidism, and hashimoto's thyroiditis among European ancestry, and Alcohol intake and antidepressant as mediators for prevention of hypothyroidism, hashimoto's thyroiditis attributable to MDD.
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Affiliation(s)
- Xu Zhang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China.
| | - Qiao Lu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Yiping Luo
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Luyao Wang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Yuan Tian
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Xuemei Luo
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
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Wei W, Qi X, Cheng B, He D, Qin X, Zhang N, Zhao Y, Chu X, Shi S, Cai Q, Yang X, Cheng S, Meng P, Hui J, Pan C, Zhao B, Liu L, Wen Y, Liu H, Jia Y, Zhang F. An atlas of causal association between micronutrients and osteoarthritis. Prev Med 2024; 185:108063. [PMID: 38997009 DOI: 10.1016/j.ypmed.2024.108063] [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: 02/18/2024] [Revised: 07/03/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024]
Abstract
OBJECTIVE This study examines the causal relationships between serum micronutrients and site-specific osteoarthritis (OA) using Mendelian Randomization (MR). METHODS This study performed a two-sample MR analysis to explore causal links between 21 micronutrients and 11 OA outcomes. These outcomes encompass overall OA, seven site-specific manifestations, and three joint replacement subtypes. Sensitivity analyses using MR methods, such as the weighted median, MR-Egger, and MR-PRESSO, assessed potential horizontal pleiotropy and heterogeneity. Genome-wide association summary statistical data were utilized for both exposure and outcome data, including up to 826,690 participants with 177,517 OA cases. All data was sourced from Genome-wide association studies datasets from 2009 to 2023. RESULTS In the analysis of associations between 21 micronutrients and 11 OA outcomes, 15 showed Bonferroni-corrected significance (P < 0.000216), without significant heterogeneity or horizontal pleiotropy. Key findings include strong links between gamma-tocopherol and spine OA (OR = 1.70), and folate with hand OA in finger joints (OR = 1.15). For joint replacements, calcium showed a notable association with a reduced likelihood of total knee replacement (TKR) (OR = 0.52) and total joint replacement (TJR) (OR = 0.56). Serum iron was significantly associated with an increased risk of total hip replacement (THR) (OR = 1.23), while folate indicated a protective effect (OR = 0.95). Various sex-specific associations were also uncovered. CONCLUSION These findings underscore the critical role of micronutrients in osteoarthritis, providing valuable insights for preventive care and potential enhancement of treatment outcomes.
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Affiliation(s)
- Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xin Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China; Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Jingni Hui
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Boyue Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
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Davis CN, Toikumo S, Hatoum AS, Khan Y, Pham BK, Pakala SR, Feuer KL, Gelernter J, Sanchez-Roige S, Kember RL, Kranzler HR. Multivariate, Multi-omic Analysis in 799,429 Individuals Identifies 134 Loci Associated with Somatoform Traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.29.24310991. [PMID: 39132487 PMCID: PMC11312645 DOI: 10.1101/2024.07.29.24310991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Somatoform traits, which manifest as persistent physical symptoms without a clear medical cause, are prevalent and pose challenges to clinical practice. Understanding the genetic basis of these disorders could improve diagnostic and therapeutic approaches. With publicly available summary statistics, we conducted a multivariate genome-wide association study (GWAS) and multi-omic analysis of four somatoform traits-fatigue, irritable bowel syndrome, pain intensity, and health satisfaction-in 799,429 individuals genetically similar to Europeans. Using genomic structural equation modeling, GWAS identified 134 loci significantly associated with a somatoform common factor, including 44 loci not significant in the input GWAS and 8 novel loci for somatoform traits. Gene-property analyses highlighted an enrichment of genes involved in synaptic transmission and enriched gene expression in 12 brain tissues. Six genes, including members of the CD300 family, had putatively causal effects mediated by protein abundance. There was substantial polygenic overlap (76-83%) between the somatoform and externalizing, internalizing, and general psychopathology factors. Somatoform polygenic scores were associated most strongly with obesity, Type 2 diabetes, tobacco use disorder, and mood/anxiety disorders in independent biobanks. Drug repurposing analyses suggested potential therapeutic targets, including MEK inhibitors. Mendelian randomization indicated potentially protective effects of gut microbiota, including Ruminococcus bromii . These biological insights provide promising avenues for treatment development.
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5
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Gan Q, Song E, Zhang L, Zhou Y, Wang L, Shan Z, Liang J, Fan S, Pan S, Cao K, Xiao Z. The role of hypertension in the relationship between leisure screen time, physical activity and migraine: a 2-sample Mendelian randomization study. J Headache Pain 2024; 25:122. [PMID: 39048956 PMCID: PMC11267787 DOI: 10.1186/s10194-024-01820-4] [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: 06/05/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND The relationship between lifestyle and migraine is complex, as it remains uncertain which specific lifestyle factors play the most prominent role in the development of migraine, or which modifiable metabolic traits serve as mediators in establishing causality. METHODS Independent genetic variants strongly associated with 20 lifestyle factors were selected as instrumental variables from corresponding genome-wide association studies (GWASs). Summary-level data for migraine were obtained from the FinnGen consortium (18,477 cases and 287,837 controls) as a discovery set and the GWAS meta-analysis data (26,052 cases and 487,214 controls) as a replication set. Estimates derived from the two datasets were combined using fixed-effects meta-analysis. Two-step univariable MR (UVMR) and multivariable Mendelian randomization (MVMR) analyses were conducted to evaluate 19 potential mediators of association and determine the proportions of these mediators. RESULTS The combined effect of inverse variance weighted revealed that a one standard deviation (SD) increase in genetically predicted Leisure screen time (LST) was associated with a 27.7% increase (95% CI: 1.14-1.44) in migraine risk, while Moderate or/and vigorous physical activity (MVPA) was associated with a 26.9% decrease (95% CI: 0.61-0.87) in migraine risk. The results of the mediation analysis indicated that out of the 19 modifiable metabolic risk factors examined, hypertension explains 24.81% of the relationship between LST and the risk of experiencing migraine. Furthermore, hypertension and diastolic blood pressure (DBP) partially weaken the association between MVPA and migraines, mediating 4.86% and 4.66% respectively. CONCLUSION Our research findings indicated that both LST and MVPA in lifestyle have independent causal effects on migraine. Additionally, we have identified that hypertension and DBP play a mediating role in the causal pathway between these two factors and migraine.
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Affiliation(s)
- Quan Gan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Enfeng Song
- Department of Traditional Chinese Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Lily Zhang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Yanjie Zhou
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Lintao Wang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Zhengming Shan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Jingjing Liang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Shanghua Fan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Songqing Pan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Kegang Cao
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
| | - Zheman Xiao
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China.
- Department of Encephalopathy in Traditional Chinese Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China.
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6
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Vue Z, Murphy A, Le H, Neikirk K, Garza-Lopez E, Marshall AG, Mungai M, Jenkins B, Vang L, Beasley HK, Ezedimma M, Manus S, Whiteside A, Forni MF, Harris C, Crabtree A, Albritton CF, Jamison S, Demirci M, Prasad P, Oliver A, Actkins KV, Shao J, Zaganjor E, Scudese E, Rodriguez B, Koh A, Rabago I, Moore JE, Nguyen D, Aftab M, Kirk B, Li Y, Wandira N, Ahmad T, Saleem M, Kadam A, Katti P, Koh HJ, Evans C, Koo YD, Wang E, Smith Q, Tomar D, Williams CR, Sweetwyne MT, Quintana AM, Phillips MA, Hubert D, Kirabo A, Dash C, Jadiya P, Kinder A, Ajijola OA, Miller-Fleming TW, McReynolds MR, Hinton A. MICOS Complex Loss Governs Age-Associated Murine Mitochondrial Architecture and Metabolism in the Liver, While Sam50 Dictates Diet Changes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.20.599846. [PMID: 38979162 PMCID: PMC11230271 DOI: 10.1101/2024.06.20.599846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The liver, the largest internal organ and a metabolic hub, undergoes significant declines due to aging, affecting mitochondrial function and increasing the risk of systemic liver diseases. How the mitochondrial three-dimensional (3D) structure changes in the liver across aging, and the biological mechanisms regulating such changes confers remain unclear. In this study, we employed Serial Block Face-Scanning Electron Microscopy (SBF-SEM) to achieve high-resolution 3D reconstructions of murine liver mitochondria to observe diverse phenotypes and structural alterations that occur with age, marked by a reduction in size and complexity. We also show concomitant metabolomic and lipidomic changes in aged samples. Aged human samples reflected altered disease risk. To find potential regulators of this change, we examined the Mitochondrial Contact Site and Cristae Organizing System (MICOS) complex, which plays a crucial role in maintaining mitochondrial architecture. We observe that the MICOS complex is lost during aging, but not Sam50. Sam50 is a component of the sorting and assembly machinery (SAM) complex that acts in tandem with the MICOS complex to modulate cristae morphology. In murine models subjected to a high-fat diet, there is a marked depletion of the mitochondrial protein SAM50. This reduction in Sam50 expression may heighten the susceptibility to liver disease, as our human biobank studies corroborate that Sam50 plays a genetically regulated role in the predisposition to multiple liver diseases. We further show that changes in mitochondrial calcium dysregulation and oxidative stress accompany the disruption of the MICOS complex. Together, we establish that a decrease in mitochondrial complexity and dysregulated metabolism occur with murine liver aging. While these changes are partially be regulated by age-related loss of the MICOS complex, the confluence of a murine high-fat diet can also cause loss of Sam50, which contributes to liver diseases. In summary, our study reveals potential regulators that affect age-related changes in mitochondrial structure and metabolism, which can be targeted in future therapeutic techniques.
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Affiliation(s)
- Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Alexandria Murphy
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life Sciences, Pennsylvania State University, State College, PA 16801
| | - Han Le
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Edgar Garza-Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Andrea G. Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Margaret Mungai
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Brenita Jenkins
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life Sciences, Pennsylvania State University, State College, PA 16801
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Heather K. Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Mariaassumpta Ezedimma
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Sasha Manus
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Aaron Whiteside
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Maria Fernanda Forni
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520
| | - Chanel Harris
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Biomedical Sciences, School of Graduate Studies, Meharry Medical College, Nashville, TN 37208-3501, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Claude F. Albritton
- Department of Biomedical Sciences, School of Graduate Studies, Meharry Medical College, Nashville, TN 37208-3501, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sydney Jamison
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mert Demirci
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Praveena Prasad
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life Sciences, Pennsylvania State University, State College, PA 16801
| | - Ashton Oliver
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Ky’Era V. Actkins
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Jianqiang Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, IA, 52242, USA
| | - Elma Zaganjor
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Estevão Scudese
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Benjamin Rodriguez
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Alice Koh
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Izabella Rabago
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Johnathan E. Moore
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Desiree Nguyen
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Muhammad Aftab
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Benjamin Kirk
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Yahang Li
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Nelson Wandira
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Taseer Ahmad
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pharmacology, College of Pharmacy, University of Sargodha, Sargodha, Punjab,40100, Pakistan
| | - Mohammad Saleem
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Ashlesha Kadam
- Department of Internal Medicine, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157 USA
| | - Prasanna Katti
- National Heart, Lung and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
- Department of Biology, Indian Institute of Science Education and Research (IISER) Tirupati, AP, 517619, India
| | - Ho-Jin Koh
- Department of Biological Sciences, Tennessee State University, Nashville, TN 37209, USA
| | - Chantell Evans
- Department of Cell Biology, Duke University School of Medicine, Durham, NC, 27708, USA
| | - Young Do Koo
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
- Fraternal Order of Eagles Diabetes Research Center, Iowa City, Iowa, USA1
| | - Eric Wang
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, 92697, USA
| | - Quinton Smith
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, 92697, USA
| | - Dhanendra Tomar
- Department of Pharmacology, College of Pharmacy, University of Sargodha, Sargodha, Punjab,40100, Pakistan
| | - Clintoria R. Williams
- Department of Neuroscience, Cell Biology and Physiology, Wright State University, Dayton, OH 45435 USA
| | - Mariya T. Sweetwyne
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Anita M. Quintana
- Department of Biological Sciences, Border Biomedical Research Center, The University of Texas at El Paso, El Paso, Texas, USA
| | - Mark A. Phillips
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - David Hubert
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - Annet Kirabo
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, 37232, USA
- Vanderbilt Institute for Global Health, Nashville, TN, 37232, USA
| | - Chandravanu Dash
- Department of Microbiology, Immunology and Physiology, Meharry Medical College, Nashville, TN, United States
| | - Pooja Jadiya
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest University School of Medicine, Winston-Salem, NC
| | - André Kinder
- Artur Sá Earp Neto University Center – UNIFASE-FMP, Petrópolis Medical School, Brazil
| | - Olujimi A. Ajijola
- UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, CA, USA
| | - Tyne W. Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Melanie R. McReynolds
- Department of Biochemistry and Molecular Biology, The Huck Institute of the Life Sciences, Pennsylvania State University, State College, PA 16801
| | - Antentor Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
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7
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Hui J, Zhang N, Kang M, Gou Y, Liu C, Zhou R, Liu Y, Wang B, Shi P, Cheng S, Yang X, Pan C, Zhang F. Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study. Nutrients 2024; 16:2042. [PMID: 38999789 PMCID: PMC11243241 DOI: 10.3390/nu16132042] [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: 05/13/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
PURPOSE Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. METHODS Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. RESULTS We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. CONCLUSION In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.
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Affiliation(s)
- Jingni Hui
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Meijuan Kang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Yifan Gou
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chen Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Ruixue Zhou
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Ye Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Bingyi Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Panxing Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
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8
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Johnson EC, Austin-Zimmerman I, Thorpe HHA, Levey DF, Baranger DAA, Colbert SMC, Demontis D, Khokhar JY, Davis LK, Edenberg HJ, Di Forti M, Sanchez-Roige S, Gelernter J, Agrawal A. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. Neuropsychopharmacology 2024:10.1038/s41386-024-01886-3. [PMID: 38906991 DOI: 10.1038/s41386-024-01886-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 06/23/2024]
Abstract
Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz; European ancestry N = 161,405; African ancestry N = 15,846), cannabis use disorder (CanUD; European ancestry N = 886,025; African ancestry N = 120,208), and ever-regular tobacco smoking (Smk; European ancestry N = 805,431; African ancestry N = 24,278) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17-0.62). Genetic instrumental variable analyses suggested the presence of shared heritable factors, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for these shared genetic factors. We identified 327 pleiotropic loci with 439 lead SNPs in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both shared genetic factors and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
| | - Isabelle Austin-Zimmerman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - David A A Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Sarah M C Colbert
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine and Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marta Di Forti
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
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9
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Xie Y, Zhang J, Ni S, Li J. Association of circulating minerals and vitamins with pregnancy complications: a Mendelian randomization study. Front Nutr 2024; 11:1334974. [PMID: 38957867 PMCID: PMC11217313 DOI: 10.3389/fnut.2024.1334974] [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: 11/08/2023] [Accepted: 06/10/2024] [Indexed: 07/04/2024] Open
Abstract
Background Though considerable studies suggesting connections between micronutrients and pregnancy complications, current evidence remains inconsistent and lacks causative confirmation. Our study aimed to explore the causal links between them with a two-sample Mendelian randomization (MR) analysis. Methods Genome-wide association studies (GWAS) data for circulating micronutrients were sourced from GWAS Catalog consortium and PubMed, while data for pregnancy outcomes, including gestational diabetes mellitus (GDM), gestational hypertension (GH), spontaneous abortion (SA), preterm birth (PTB), and stillbirth (SB), were retrieved from the UK Biobank and FinnGen consortia. Causal effects were appraised using inverse variance weighted (IVW), weighted median (WM), and MR-Egger, followed by sensitivity analyses and meta-analysis for validation. Results Genetically predicted higher vitamin E (OR = 0.993, 95% CI 0.987-0.998; p = 0.005) levels were inversely associated with SA risk. Consistent results were obtained in meta-analysis (OR = 0.99, 95% CI 0.99-1.00; p = 0.005). Besides, a potential positive causality between genetic predisposition to vitamin B12 and SB was identified in both IVW (OR = 0.974, 95% CI 0.953-0.996; p = 0.018) and WM analysis (OR = 0.965, 95% CI 0.939-0.993; p = 0.013). However, no causal relationships were observed between other analyzed circulating micronutrients and pregnancy complications. Conclusion This study offers compelling evidence of causal associations between circulating levels of vitamins E, B12 and the risk of SA and SB, respectively. These findings are pivotal for pregnancy complications screening and prevention, potentially guiding clinical practice and public health policies toward targeted nutritional interventions.
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Affiliation(s)
- Yuan Xie
- Department of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Zhang
- Central Laboratory for Research, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shuang Ni
- Department of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ji Li
- Department of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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10
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Zhang W, Liu E, Que H. Association of circulating vitamin levels with thyroid diseases: a Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1360851. [PMID: 38919472 PMCID: PMC11196410 DOI: 10.3389/fendo.2024.1360851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 05/10/2024] [Indexed: 06/27/2024] Open
Abstract
Background Previous observational studies have shown conflicting results of vitamins supplementation for thyroid diseases. The causal relationships between vitamins and thyroid diseases are unclear. Therefore, we conducted a two-sample bidirectional Mendelian randomization (MR) study to explore association of circulating vitamin levels with thyroid diseases. Methods We performed a bidirectional MR analysis using genome-wide association study (GWAS) data. Genetic tool variables for circulating vitamin levels include vitamins A, B9, B12, C, D, and E, Genetic tool variables of thyroid diseases include autoimmune hyperthyroidism, autoimmune hypothyroidism, thyroid nodules (TNs), and Thyroid cancer (TC). Inverse-variance weighted multiplicative random effects (IVW-RE) was mainly used for MR Analysis, weighted median (WM) and MR Egger were used as supplementary methods to evaluate the relationships between circulating vitamin levels and thyroid diseases. Sensitivity and pluripotency were evaluated by Cochran's Q test, MR-PRESSO, Radial MR, MR-Egger regression and leave-one-out analysis. Results Positive MR evidence suggested that circulating vitamin C level is a protective factor in autoimmune hypothyroidism (ORIVW-RE=0.69, 95%CI: 0.58-0.83, p = 1.05E-04). Reverse MR Evidence showed that genetic susceptibility to autoimmune hyperthyroidism is associated with reduced level of circulating vitamin A(ORIVW-RE = 0.97, 95% CI: 0.95-1.00, p = 4.38E-02), genetic susceptibility of TNs was associated with an increased level of circulating vitamin D (ORIVW-RE = 1.02, 95% CI: 1.00-1.03, p = 6.86E-03). No causal and reverse causal relationship was detected between other circulating vitamin levels and thyroid diseases. Conclusion Our findings provide genetic evidence supporting a bi-directional causal relationship between circulating vitamin levels and thyroid diseases. These findings provide information for the clinical application of vitamins prevention and treatment of thyroid diseases.
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Affiliation(s)
- Wenke Zhang
- Department of Traditional Chinese Surgery, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Longhua Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Erhao Liu
- Department of Traditional Chinese Surgery, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Longhua Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Huafa Que
- Department of Traditional Chinese Surgery, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
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11
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik VK, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. Nat Hum Behav 2024; 8:1177-1193. [PMID: 38632388 PMCID: PMC11199106 DOI: 10.1038/s41562-024-01851-6] [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: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Hyunjoon Lee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - 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
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Alexander S Hatoum
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emma C Johnson
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | | | - Nancy J Cox
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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12
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Zhang SH, Feng Y, Zhong MM, Xie JH, Xu W. Association between oxidative stress and chronic orofacial pain and potential druggable targets: Evidence from a Mendelian randomization study. J Oral Rehabil 2024; 51:970-981. [PMID: 38414129 DOI: 10.1111/joor.13663] [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: 07/16/2023] [Revised: 01/22/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Oxidative stress indicators affect chronic orofacial pain (COFP), but how to reduce these effects is uncertain. OBJECTIVES 11 oxidative stress biomarkers were collected as exposures, while four forms of COFP were chosen as outcomes for Mendelian randomization (MR) study. METHODS The effect estimates between oxidative stress and COFP were calculated using inverse variance-weighted MR (IVW-MR). Then, functional mapping and annotation (FUMA) was utilized in order to carry out SNP-based functional enrichment analyses. In addition, the IVW-MR method was applied to combine effect estimates when using genetic variants associated with oxidative stress biomarkers as an instrument for exploring potential druggable targets. RESULTS The results indicated that oxidative stress biomarkers (causal OR of uric acid (UA), 0.998 for myofascial pain, 95% CI 0.996-1.000, p < .05; and OR of glutathione transferase (GST), 1.002 for dentoalveolar pain, 95% CI 1.000-1.003, p < .05) were significantly linked with the probability of COFP. Functional analysis also demonstrated that UA and myofascial pain genes were prominent in nitrogen and uracil metabolism, while GST and dentoalveolar pain genes were enriched in glutathione metabolism. Also, the study provided evidence that solute carrier family 2 member 9 (SLC2A9) and glutathione S-transferase alpha 2 (GSTA2) cause discomfort in the myofascial pain (OR = 1.003, 95% CI 1.000-1.006; p < .05) and dentoalveolar region (OR = 1.001, 95% CI 1.000-1.002; p < .05), respectively. CONCLUSIONS In conclusion, this MR study indicates that genetically predicted myofascial pain was significantly associated with decreased UA and dentoalveolar pain was significantly associated with increased GST level. SLC2A9 inhibitor and GSTA2 inhibitor were novel chronic orofacial pain therapies and biomarkers, but clinical trials are called to examine if these oxidative biomarkers have the protective effect against orofacial pain, and further research are needed to explore the underlying mechanisms.
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Affiliation(s)
- Shao-Hui Zhang
- Department of Stomatology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yao Feng
- Department of Stomatology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Meng-Mei Zhong
- Department of Stomatology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jia-Hao Xie
- Institute of Artificial Intelligence & Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, China
| | - Wei Xu
- Department of Stomatology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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13
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Rich AL, Lin P, Gamazon ER, Zinkel SS. The broad impact of cell death genes on the human disease phenome. Cell Death Dis 2024; 15:251. [PMID: 38589365 PMCID: PMC11002008 DOI: 10.1038/s41419-024-06632-7] [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: 09/13/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
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Affiliation(s)
- Abigail L Rich
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sandra S Zinkel
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
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14
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Kresge HA, Blostein F, Goleva S, Albiñana C, Revez JA, Wray NR, Vilhjálmsson BJ, Zhu Z, McGrath JJ, Davis LK. Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration. Twin Res Hum Genet 2024; 27:69-79. [PMID: 38644690 PMCID: PMC11138239 DOI: 10.1017/thg.2024.19] [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] [Indexed: 04/23/2024]
Abstract
While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the GC gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS25OHD, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS25OHD and PGSDBP scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS25OHD was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the GC gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.
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Affiliation(s)
- Hailey A. Kresge
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Freida Blostein
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Slavina Goleva
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clara Albiñana
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Joana A. Revez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R. Wray
- Department of Psychiatry, University of Oxford, Oxford, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus C, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Zhihong Zhu
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
| | - John J. McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Neurology, Pharmacology and Special Education, Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
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15
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Qiu Y, Li C, Huang Y, Wu C, Li F, Zhang X, Xia D. Exploring the causal associations of micronutrients on urate levels and the risk of gout: A Mendelian randomization study. Clin Nutr 2024; 43:1001-1012. [PMID: 38484526 DOI: 10.1016/j.clnu.2024.03.003] [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: 08/08/2023] [Revised: 02/21/2024] [Accepted: 03/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND & AIMS Growing evidence has indicated a potential association between micronutrient levels, urate levels, and the risk of gout. However, the causal association underlying these associations still remains uncertain. Previous observational studies and randomized controlled trials investigating the association between micronutrients, urate levels, and the risk of gout have been limited in their scope and depth. The aim of this study was to utilize Mendelian randomization (MR) to investigate the causal associations between genetically predicted micronutrient levels, urate levels, and the risk of gout. METHODS In this study, we conducted a comprehensive examination of 10 specific micronutrients (vitamin B6, vitamin B12, vitamin C, vitamin D, folate, calcium, iron, copper, zinc, and selenium) as potential exposures. Two-sample MR analyses were performed to explore their causal associations with urate levels and the risk of gout. In these analyses, gout data were collected from the Global Biobank Meta-Analysis Initiative (N = 1,069,839, N cases = 30,549) and urate levels data from CKDGen Consortium (N = 288,649) by utilizing publicly available summary statistics from independent cohorts of European ancestry. We performed inverse-variance weighted MR analyses as main analyses, along with a range of sensitivity analyses, such as MR-Egger, weighted median, simple mode, weighted mode, Steiger filtering, MR-PRESSO, and Radial MR analysis, to ensure the robustness of our findings. RESULTS The results of our study indicate that there were negative associations between serum vitamin B12 and urate levels, as well as serum folate and the risk of gout. Specifically, we found a negative association between vitamin B12 levels and urate levels, with a β coefficient of -0.324 (95% CI -0.0581 to -0.0066, P = 0.0137) per one standard deviation (SD) increase. Similarly, a negative association was observed between folate levels and gout risk, with an odds ratio of 0.8044 (95% CI 0.6637 to 0.9750, P = 0.0265) per one SD increase. On the other hand, we identified positive associations between serum calcium levels and both urate levels and the risk of gout. Specifically, there was a positive association between serum calcium levels and urate levels (β coefficient: 0.0994, 95% CI 0.0519 to 0.1468, P = 4.11E-05) per one SD increase. Furthermore, a positive association was found between serum calcium levels and the risk of gout, with an odds ratio of 1.1479 (95% CI 1.0460 to 1.2598, P = 0.0036) per one SD increase. These findings were robust in extensive sensitivity analyses. By employing MR-PRESSO and Radial MR to eliminate outliers, the observed associations have been reinforced. No clear associations were found between the other micronutrients and the urate levels, as well as the risk of gout. CONCLUSION Our findings provided evidence that there were negative associations between serum vitamin B12 and urate levels, as well as serum folate and the risk of gout, while positive associations existed between the serum calcium levels and urate levels, as well as the risk of gout.
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Affiliation(s)
- Yu Qiu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cantao Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yan Huang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Chenxi Wu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Fenfen Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaoxi Zhang
- Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Daozong Xia
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
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16
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Liu P, Lv M, Rong Y, Yu S, Wu R. No genetic causal association between iron status and pulmonary artery hypertension: Insights from a two-sample Mendelian randomization. Pulm Circ 2024; 14:e12370. [PMID: 38774814 PMCID: PMC11108639 DOI: 10.1002/pul2.12370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/24/2024] Open
Abstract
To explore the genetic causal association between pulmonary artery hypertension (PAH) and iron status through Mendelian randomization (MR), we conducted MR analysis using publicly available genome-wide association study (GWAS) summary data. Five indicators related to iron status (serum iron, ferritin, total iron binding capacity (TIBC), soluble transferrin receptor (sTfR), and transferrin saturation) served as exposures, while PAH was the outcome. The genetic causal association between these iron status indicators and PAH was assessed using the inverse variance weighted (IVW) method. Cochran's Q statistic was employed to evaluate heterogeneity. We assessed pleiotropy using MR-Egger regression and MR-Presso test. Additionally, we validated our results using the Weighted median, Simple mode, and Weighted mode methods. Based on the IVW method, we found no causal association between iron status (serum iron, ferritin, TIBC, sTfR, and transferrin saturation) and PAH (p β > 0.05). The Weighted median, Simple mode, and Weighted mode methods showed no potential genetic causal association (p β > 0.05 in the three analyses). Additionally, no heterogeneity or horizontal pleiotropy was detected in any of the analyses. Our results show that there are no genetic causal association between iron status and PAH.
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Affiliation(s)
- Peng‐Cheng Liu
- Department of Rheumatology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Meng‐Na Lv
- The First Clinical Medical College of Nanchang UniversityNanchangChina
| | - Yan‐Yan Rong
- Department of Hematology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Shu‐Jiao Yu
- Department of Rheumatology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangChina
| | - Rui Wu
- Department of Rheumatology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangChina
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17
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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18
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Chen B, Wang L, Pu S, Guo L, Chai N, Sun X, Tang X, Ren Y, He J, Hao N. Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses. Sci Rep 2024; 14:6435. [PMID: 38499600 PMCID: PMC10948885 DOI: 10.1038/s41598-024-57100-3] [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: 01/24/2024] [Accepted: 03/14/2024] [Indexed: 03/20/2024] Open
Abstract
Hyperparathyroidism (HPT) manifests as a complex condition with a substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying disease progression remain lacking. Identifying putative genetic drivers and exploring novel drug targets that can impede HPT progression remain critical unmet needs. A Mendelian randomization (MR) analysis was performed to uncover putative therapeutic targets implicated in hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving as genetic instrumental variables were obtained from the eQTLGen Consortium and Genotype-Tissue Expression (GTEx) portal. Hyperparathyroidism summary statistics for single nucleotide polymorphism (SNP) associations were sourced from the FinnGen study (5590 cases; 361,988 controls). Colocalization analysis was performed to determine the probability of shared causal variants underlying SNP-hyperparathyroidism and SNP-eQTL links. Five drug targets (CMKLR1, FSTL1, IGSF11, PIK3C3 and SLC40A1) showed significant causation with hyperparathyroidism in both eQTLGen and GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and solute carrier family 40 member 1 (SLC40A1) showed strong evidence of colocalization with HPT. Multivariable MR and Phenome-Wide Association Study analyses indicated these two targets were not associated with other traits. Additionally, drug prediction analysis implies the potential of these two targets for future clinical applications. This study identifies PIK3C3 and SLC40A1 as potential genetically proxied druggable genes and promising therapeutic targets for hyperparathyroidism. Targeting PIK3C3 and SLC40A1 may offer effective novel pharmacotherapies for impeding hyperparathyroidism progression and reducing disease risk. These findings provide preliminary genetic insight into underlying drivers amenable to therapeutic manipulation, though further investigation is imperative to validate translational potential from preclinical models through clinical applications.
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Affiliation(s)
- Bohong Chen
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Lihui Wang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Shengyu Pu
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Li Guo
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Na Chai
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Xinyue Sun
- Department of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Xiaojiang Tang
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Yu Ren
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Jianjun He
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China.
| | - Na Hao
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China.
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19
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Johnson EC, Austin-Zimmerman I, Thorpe HH, Levey DF, Baranger DA, Colbert SM, Demontis D, Khokhar JY, Davis LK, Edenberg HJ, Forti MD, Sanchez-Roige S, Gelernter J, Agrawal A. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301430. [PMID: 38293235 PMCID: PMC10827265 DOI: 10.1101/2024.01.17.24301430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
| | - Isabelle Austin-Zimmerman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hayley Ha Thorpe
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - David Aa Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO USA
| | - Sarah Mc Colbert
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine and Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marta Di Forti
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
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20
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Pathak GA, Singh K, Choi KW, Fang Y, Kouakou MR, Lee YH, Zhou X, Fritsche LG, Wendt FR, Davis LK, Polimanti R. Genetic Liability to Posttraumatic Stress Disorder Symptoms and Its Association With Cardiometabolic and Respiratory Outcomes. JAMA Psychiatry 2024; 81:34-44. [PMID: 37910111 PMCID: PMC10620678 DOI: 10.1001/jamapsychiatry.2023.4127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/28/2023] [Indexed: 11/03/2023]
Abstract
Importance Posttraumatic stress disorder (PTSD) has been reported to be a risk factor for several physical and somatic symptoms. However, the genetics of PTSD and its potential association with medical outcomes remain unclear. Objective To examine disease categories and laboratory tests from electronic health records (EHRs) that are associated with PTSD polygenic scores. Design, Setting, and Participants This genetic association study was conducted from July 15, 2021, to January 24, 2023, using EHR data from participants across 4 biobanks. The polygenic scores of PTSD symptom severity (PGS-PTSD) were tested with all available phecodes in Vanderbilt University Medical Center's biobank (BioVU), Mass General Brigham (MGB), Michigan Genomics Initiative (MGI), and UK Biobank (UKBB). The significant medical outcomes were tested for overrepresented disease categories and subsequently tested for genetic correlation and 2-sample mendelian randomization (MR) to determine genetically informed associations. Multivariable MR was conducted to assess whether PTSD associations with health outcomes were independent of the genetic effect of body mass index and tobacco smoking. Exposures Polygenic score of PTSD symptom severity. Main Outcomes and Measures A total of 1680 phecodes (ie, International Classification of Diseases, Ninth Revision- and Tenth Revision-based phenotypic definitions of health outcomes) across 4 biobanks and 490 laboratory tests across 2 biobanks (BioVU and MGB). Results In this study including a total of 496 317 individuals (mean [SD] age, 56.8 [8.0] years; 263 048 female [53%]) across the 4 EHR sites, meta-analyzing associations of PGS-PTSD with 1680 phecodes from 496 317 individuals showed significant associations to be overrepresented from mental health disorders (fold enrichment = 3.15; P = 5.81 × 10-6), circulatory system (fold enrichment = 3.32; P = 6.39 × 10-12), digestive (fold enrichment = 2.42; P = 2.16 × 10-7), and respiratory outcomes (fold enrichment = 2.51; P = 8.28 × 10-5). The laboratory measures scan with PGS-PTSD in BioVU and MGB biobanks revealed top associations in metabolic and immune domains. MR identified genetic liability to PTSD symptom severity as an associated risk factor for 12 health outcomes, including alcoholism (β = 0.023; P = 1.49 × 10-4), tachycardia (β = 0.045; P = 8.30 × 10-5), cardiac dysrhythmias (β = 0.016, P = 3.09 × 10-5), and acute pancreatitis (β = 0.049, P = 4.48 × 10-4). Several of these associations were robust to genetic effects of body mass index and smoking. We observed a bidirectional association between PTSD symptoms and nonspecific chest pain and C-reactive protein. Conclusions and Relevance Results of this study suggest the broad health repercussions associated with the genetic liability to PTSD across 4 biobanks. The circulatory and respiratory systems association was observed to be overrepresented in all 4 biobanks.
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Affiliation(s)
- Gita A. Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Veteran Affairs Connecticut Healthcare Center, West Haven
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Karmel W. Choi
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor
| | - Manuela R. Kouakou
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Veteran Affairs Connecticut Healthcare Center, West Haven
| | - Younga Heather Lee
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor
| | - Lars G. Fritsche
- Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor
- Rogel Cancer Center, University of Michigan Medicine, Ann Arbor
- Center for Statistical Genetics, School of Public Health, University of Michigan Medicine, Ann Arbor
| | - Frank R. Wendt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Veteran Affairs Connecticut Healthcare Center, West Haven
- Department of Anthropology, University of Toronto, Mississauga, Ontario, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Veteran Affairs Connecticut Healthcare Center, West Haven
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21
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Sealock JM, Chen G, Davis LK. Anti-Inflammatory Action of Antidepressants: Investigating the Longitudinal Effect of Antidepressants on White Blood Cell Count. Complex Psychiatry 2023; 9:1-10. [PMID: 36743422 PMCID: PMC9892923 DOI: 10.1159/000528605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Antidepressants have documented anti-inflammatory effects on pro-inflammatory biomarkers. However, the long-term effects of antidepressants on inflammatory markers and the effects of different antidepressant classes on pro-inflammatory biomarkers are largely unexplored. Here, we evaluate the short- and long-term effects of all antidepressant classes on a clinical immune marker, white blood cell count (WBC). Methods Using a retrospective study design, we extracted WBC count and prescription medications from electronic health records at Vanderbilt University Medical Center. We created a longitudinal model to evaluate the short- and long-term effects of these medications on WBC count. We validated our longitudinal model using two known anti-inflammatory medications, biologic immunosuppressants, and chemotherapy, and one medication class without known immunomodulatory properties, contraceptives. We used the longitudinal model to determine the effects of antidepressant use on WBC count stratified by drug class. Results Biologic immunosuppressant and chemotherapy use was associated with decreased WBC count, but contraceptive use did not associate with changes in WBC count, validating our longitudinal modeling approach. All antidepressant classes were associated with decreased WBC count in the long-term cohorts. SSRI and atypical use also associated with decreased WBC count in the short-term cohort. Conclusions Using electronic health record data, we show all antidepressant classes exhibit anti-inflammatory effects on a clinical immune marker, WBC count. Additionally, our results indicate that in some cases the anti-inflammatory effects of antidepressants persist over at least a 1-year time frame. Our work contributes to the immunomodulatory knowledge of antidepressants and motivates future studies investigating alternative therapeutic routes for antidepressants.
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Affiliation(s)
- Julia M. Sealock
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Guanhua Chen
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA,Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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22
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An U, Pazokitoroudi A, Alvarez M, Huang L, Bacanu S, Schork AJ, Kendler K, Pajukanta P, Flint J, Zaitlen N, Cai N, Dahl A, Sankararaman S. Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries. Nat Genet 2023; 55:2269-2276. [PMID: 37985819 PMCID: PMC10703681 DOI: 10.1038/s41588-023-01558-w] [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: 08/01/2022] [Accepted: 10/04/2023] [Indexed: 11/22/2023]
Abstract
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or 'fill-in' missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.
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Affiliation(s)
- Ulzee An
- Computer Science Department, UCLA, Los Angeles, CA, USA.
| | | | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Lianyun Huang
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Silviu Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Kenneth Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Noah Zaitlen
- Neurology Department, UCLA, Los Angeles, CA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Sriram Sankararaman
- Computer Science Department, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, UCLA, Los Angeles, CA, USA.
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23
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Sun Q, Rowland B, Wang W, Miller-Fleming TW, Cox N, Graff M, Faucon A, Shuey MM, Blue EE, Auer P, Li Y, Sankaran VG, Reiner AP, Raffield LM. Genetic examination of hematological parameters in SARS-CoV-2 infection and COVID-19. Blood Cells Mol Dis 2023; 103:102782. [PMID: 37558590 PMCID: PMC10507673 DOI: 10.1016/j.bcmd.2023.102782] [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: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023]
Abstract
People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 related outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Wanjiang Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Nancy Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Misa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Annika Faucon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Megan M Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elizabeth E Blue
- Department of Medicine, Division of Medical Genetics, University of Washington, Brotman Baty Institute for Precision Medicine, Seattle, WA, United States
| | - Paul Auer
- Division of Biostatistics, Institute for Health and Equity, Cancer Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
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24
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Fu L, Cheng H, Gao L, Zhao X, Mi J. Genetically proxied vitamin B12 and homocysteine in relation to life course adiposity and body composition. Diabetes Metab Syndr 2023; 17:102883. [PMID: 37922594 DOI: 10.1016/j.dsx.2023.102883] [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: 06/20/2023] [Revised: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVE Observational studies explore the association between vitamin B12 and obesity. However, causality is not reflected by such observations. We performed a bi-directional Mendelian randomization (MR) study to elucidate the causal relationship of vitamin B12 and homocysteine (Hcy) with life course adiposity and body composition. METHODS Two-sample MR analysis was conducted. Independent genetic variants associated with vitamin B12 and Hcy from large-scale genome-wide association studies (GWASs) were utilized as genetic instruments, and their causal effects on five life course adiposity phenotypes (birth weight, body mass index (BMI), childhood BMI, waist circumference, waist-to-hip ratio) and three body compositions (body fat mass, body fat-free mass, body fat percentage) were estimated from UK Biobank, other consortia, and large-scale GWASs. The inverse variance weighting (IVW, main analysis), bi-directional MR, and other six sensitivity MR analyses were performed. RESULTS Genetically proxied higher vitamin B12 concentrations were robustly associated with reduced BMI (Beta = -0.01, 95% confidence interval (CI) -0.016 to -0.004, P = 7.60E-04), body fat mass (Beta = -0.012, 95%CI -0.018 to -0.007, P = 1.69E-05), and body fat percentage (Beta = -0.005, 95%CI -0.009 to -0.002, P = 4.12E-03) per SD unit by IVW and other sensitivity analyses. Stratification analysis showed that these results remained significant in females and at different body sites (all P < 0.05 after Bonferroni correction). Bi-directional analyses showed no reverse causation. CONCLUSIONS This study provides strong evidence for the causal effect of vitamin B12 on adiposity. This gives novel clues for intervening obesity in public health and nutrition.
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Affiliation(s)
- Liwan Fu
- Center for Non-Communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Hong Cheng
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Liwang Gao
- Center for Non-Communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xiaoyuan Zhao
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Jie Mi
- Center for Non-Communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China; Key Laboratory of Major Diseases in Children, Ministry of Education, China.
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25
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Du Q, Zheng Z, Wang Y, Yang L, Zhou Z. Genetically predicted thyroid function and risk of colorectal cancer: a bidirectional Mendelian randomization study. J Cancer Res Clin Oncol 2023; 149:14015-14024. [PMID: 37543542 DOI: 10.1007/s00432-023-05233-9] [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: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Observational studies have reported an association between thyroid function and colorectal cancer (CRC), with conflicting results. Elucidating the causal relationship between thyroid function and CRC facilitates the development of new preventive strategies to reduce CRC incidence. METHOD We applied a two-sample Mendelian randomization (MR) method to evaluate the causal relationship between five thyroid-related indexes, including hyperthyroidism, hypothyroidism, thyroid stimulating hormone (TSH), free thyroxine (FT4) and basal metabolic rate (BMR), and CRC. Genome-wide association study statistics for thyroid-related phenotypes were obtained from the ThyroidOmics consortium, and summary statistics for genetic associations with CRC were obtained from the FinnGen consortium. We set a series of criteria to screen single nucleotide polymorphisms (SNPs) as instrumental variables and then performed bidirectional MR analysis, stratified analysis and extensive sensitivity analysis. Multiplicative random-effects inverse variance weighted was the primary analysis method, supplemented by weighted median and MR-Egger. RESULT We identified 12 SNPs for hyperthyroidism, 10 SNPs for hypothyroidism, 41 SNPs for TSH, 18 SNPs for FT4, and 556 SNPs for BMR. Genetically predicted hyperthyroidism, hypothyroidism, TSH, and FT4 were not associated with CRC risk (all P > 0.05). Sensitivity analysis revealed no heterogeneity or pleiotropy. Genetically predicted BMR was significantly associated with increased CRC risk after removing outlier (OR = 1.30, P = 0.0029). Stratified analysis showed that BMR was significantly associated with colon cancer (OR = 1.33, P = 0.0074) but not rectal cancer. In the reverse analysis, there was no evidence of an effect of CRC on thyroid function (all P > 0.05). CONCLUSION Our bidirectional MR analysis provides new insights into the relationship between thyroid function and CRC. CRC prevention may benefit from enhanced screening of high BMR populations.
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Affiliation(s)
- Qiang Du
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Zhaoyang Zheng
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Yong Wang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Lie Yang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, China.
- State Key Laboratory of Biotherapy and Cancer Center, Institute of Digestive Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Zongguang Zhou
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, China
- State Key Laboratory of Biotherapy and Cancer Center, Institute of Digestive Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
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26
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Miao C, Xiao L, Xu X, Huang S, Liu J, Chen K. Circulating vitamin levels mediate the causal relationship between gut microbiota and cholecystitis: a two-step bidirectional Mendelian randomization study. Front Nutr 2023; 10:1268893. [PMID: 37823088 PMCID: PMC10562588 DOI: 10.3389/fnut.2023.1268893] [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: 07/28/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
Background The relationship between gut microbiota and the occurrence of cholecystitis remains unclear. Existing research lacks a clear understanding of how circulating vitamin levels modulate this relationship. Therefore, our study aims to investigate whether circulating vitamin levels mediate the causal relationship between gut microbiota and cholecystitis using a two-step bidirectional Mendelian randomization approach. Methods In this study, we initially employed Linkage Disequilibrium Score Regression (LDSC) analysis to assess the genetic correlation of five circulating vitamin level genome-wide association study (GWAS) summary datasets, thereby avoiding potential sample overlap. Subsequently, we conducted a two-step analysis to investigate the causal effects between gut microbiota and cholecystitis. In the second step, we explored the causal relationship between circulating vitamin levels and cholecystitis and identified the mediating role of vitamin D. The primary method used for causal analysis was the inverse variance-weighted approach. We performed additional sensitivity analyses to ensure result robustness, including the cML-MA method and reverse Mendelian randomization (MR) analysis. Results An increment of one standard deviation in RuminococcaceaeUCG003 was associated with a 25% increased risk of cholecystitis (OR = 1.25, 95%CI = 1.01-1.54, p = 0.04), along with a 3% decrease in 25-hydroxyvitamin D levels (OR = 0.97, 95%CI = 0.944-0.998, p = 0.04). However, following the rigorous Bonferroni correction, every one standard deviation decrease in circulating vitamin D levels was associated with a 33% increased risk of cholecystitis (OR = 0.67, 95%CI = 0.49-0.90, p = 0.008, Padjust = 0.04). Thus, the potential link between gut microbiota and cholecystitis risk might be mediated by circulating vitamin D levels (proportion mediated = 5.5%). Sensitivity analyses provided no evidence of pleiotropy. Conclusion Our study results suggest that an elevated abundance of specific gut microbiota is associated with an increased susceptibility to cholecystitis, with the causal relationship being mediated by circulating vitamin D levels. Further large-scale randomized controlled trials are necessary to validate the causal effects of gut microbiota on cholecystitis risk. This study provides novel insights into cholecystitis prevention through the regulation of gut microbiota.
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Affiliation(s)
- Changhong Miao
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Lu Xiao
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Xinyi Xu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Shuoxuan Huang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Jiajin Liu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Kuang Chen
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
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27
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik V, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.27.23287713. [PMID: 37034728 PMCID: PMC10081388 DOI: 10.1101/2023.03.27.23287713] [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: 04/29/2023]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - 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
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Vanessa Pazdernik
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Yale University School of Public Health, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
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Lu Z, Zhang Y, Sun Y, Liao Y, Kang Z, Feng X, Yan H, Wang L, Lu T, Zhang D, Yue W. Therapeutic outcomes wide association scan of different antipsychotics in patients with schizophrenia: Randomized clinical trials and multi-ancestry validation. Psychiatry Clin Neurosci 2023; 77:486-496. [PMID: 37210704 DOI: 10.1111/pcn.13567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023]
Abstract
AIM This study identified discrepant therapeutic outcomes of antipsychotics. METHODS A total of 5191 patients with schizophrenia were enrolled, 3030 as discovery cohort, 1395 as validation cohort, and 766 as multi-ancestry validation cohort. Therapeutic Outcomes Wide Association Scan was conducted. Types of antipsychotics (one antipsychotic vs other antipsychotics) were dependent variables, therapeutic outcomes including efficacy and safety were independent variables. RESULTS In discovery cohort, olanzapine related to higher risk of weight gain (AIWG, OR: 2.21-2.86), liver dysfunction (OR: 1.75-2.33), sedation (OR: 1.76-2.86), increased lipid level (OR: 2.04-2.12), and lower risk of extrapyramidal syndrome (EPS, OR: 0.14-0.46); risperidone related to higher risk of hyperprolactinemia (OR: 12.45-20.53); quetiapine related to higher risk of sedation (OR = 1.73), palpitation (OR = 2.87), increased lipid level (OR = 1.69), lower risk of hyperprolactinemia (OR: 0.09-0.11), and EPS (OR: 0.15-0.44); aripiprazole related to lower risk of hyperprolactinemia (OR: 0.09-0.14), AIWG (OR = 0.44), sedation (OR: 0.33-0.47), and QTc prolongation (β = -2.17); ziprasidone related to higher risk of increased QT interval (β range: 3.11-3.22), nausea (OR: 3.22-3.91), lower risk of AIWG (OR: 0.27-0.46), liver dysfunction (OR: 0.41-0.38), and increased lipid level (OR: 0.41-0.55); haloperidol related to higher risk of EPS (OR: 2.64-6.29), hyperprolactinemia (OR: 5.45-9.44), and increased salivation (OR: 3.50-3.68). Perphenazine related to higher risk of EPS (OR: 1.89-2.54). Higher risk of liver dysfunction in olanzapine and lower risk of hyperprolactinemia in aripiprazole were confirmed in validation cohort, and higher risk of AIWG in olanzapine and hyperprolactinemia in risperidone were confirmed in multi-ancestry validation cohort. CONCLUSION Future precision medicine should focus on personalized side-effects.
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Affiliation(s)
- Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Zhewei Kang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Xiaoyang Feng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Lifang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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Wu Y, Goleva SB, Breidenbach LB, Kim M, MacGregor S, Gandal MJ, Davis LK, Wray NR. 150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility. CELL GENOMICS 2023; 3:100326. [PMID: 37492107 PMCID: PMC10363821 DOI: 10.1016/j.xgen.2023.100326] [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: 10/05/2022] [Revised: 03/11/2023] [Accepted: 04/20/2023] [Indexed: 07/27/2023]
Abstract
We conducted a genome-wide association study (GWAS) analysis of diverticular disease (DivD) of intestine within 724,372 individuals and identified 150 independent genome-wide significant DNA variants. Integration of the GWAS results with human gut single-cell RNA sequencing data implicated gut myocyte, mesothelial and stromal cells, and enteric neurons and glia in DivD development. Ninety-five genes were prioritized based on multiple lines of evidence, including SLC9A3, a drug target gene of tenapanor used for the treatment of the constipation subtype of irritable bowel syndrome. A DivD polygenic score (PGS) enables effective risk prediction (area under the curve [AUC], 0.688; 95% confidence interval [CI], 0.645-0.732) and the top 20% PGS was associated with ∼3.6-fold increased DivD risk relative to the remaining population. Our statistical and bioinformatic analyses suggest that the mechanism of DivD is through colon structure, gut motility, gastrointestinal mucus, and ionic homeostasis. Our analyses reinforce the link between gastrointestinal disorders and the enteric nervous system through genetics.
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Affiliation(s)
- Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Slavina B. Goleva
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lindsay B. Breidenbach
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioural Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, 511-A Light Hall, 2215 Garland Avenue, Nashville, TN 37232, USA
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
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30
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Lee JH, Kim J, Kim JO, Kwon YJ. Association of non-high-density lipoprotein cholesterol trajectories with the development of non-alcoholic fatty liver disease: an epidemiological and genome-wide association study. J Transl Med 2023; 21:435. [PMID: 37403158 DOI: 10.1186/s12967-023-04291-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) shares common risk factors with cardiovascular diseases. Effects of longitudinal trends in non-high-density lipoprotein (non-HDL) cholesterol on NAFLD development are not understood. This study aimed to assess the relationship between non-HDL cholesterol trajectories and the incidence of NAFLD and to identify genetic differences contributing to NAFLD development between non-HDL cholesterol trajectory groups. METHODS We analyzed data from 2203 adults (aged 40-69 years) who participated in the Korean Genome and Epidemiology Study. During the 6-year exposure periods, participants were classified into an increasing non-HDL cholesterol trajectory group (n = 934) or a stable group (n = 1269). NAFLD was defined using a NAFLD-liver fat score > -0.640. Multiple Cox proportional hazard regression analysis estimated the hazard ratio (HR) and the 95% confidence interval (CI) for the incidence of NAFLD in the increasing group compared with the stable group. RESULTS A genome-wide association study identified significant single-nucleotide polymorphisms (SNPs) associated with NAFLD. During the median 7.8-year of event accrual period, 666 (30.2%) newly developed NAFLD cases were collected. Compared with the stable non-HDL group, the adjusted HR (95% CI) for the incidence of NAFLD in the increasing non-HDL cholesterol group was 1.46 (1.25-1.71). Although there were no significant SNPs, the polygenic risk score was highest in the increasing group, followed by the stable and control groups. CONCLUSION Our study indicates that lifestyle or environmental factors have a greater effect size than genetic factors in NAFLD progression risk. Lifestyle modification could be an effective prevention strategy for NAFLD for people with elevated non-HDL cholesterol.
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Affiliation(s)
- Jun-Hyuk Lee
- Department of Family Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, 01830, Republic of Korea
- Department of Medicine, Hanyang University Graduate School of Medicine, Seoul, 04763, Republic of Korea
| | - Jiyeon Kim
- Institute of Genetic Epidemiology, basgenbio Inc., 64, Keunumul-Ro, Mapo-Gu, Seoul, 04166, Republic of Korea
| | - Jung Oh Kim
- Institute of Genetic Epidemiology, basgenbio Inc., 64, Keunumul-Ro, Mapo-Gu, Seoul, 04166, Republic of Korea.
| | - Yu-Jin Kwon
- Department of Family Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea.
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31
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Niarchou M, Miller-Fleming T, Malow BA, Davis LK. The physical and psychiatric health conditions related to autism genetic scores, across genetic ancestries, sexes and age-groups in electronic health records. J Neurodev Disord 2023; 15:18. [PMID: 37328826 PMCID: PMC10273739 DOI: 10.1186/s11689-023-09485-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/24/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Although polygenic scores (PGS) for autism have been related to various psychiatric and medical conditions, most studies to date have been conducted in research ascertained populations. We aimed to identify the psychiatric and physical conditions associated with autism PGS in a health care setting. METHODS We computed PGS for 12,383 unrelated participants of African genetic ancestry (AF) and 65,363 unrelated participants of European genetic ancestry (EU) from Vanderbilt's de-identified biobank. Next, we performed phenome wide association studies of the autism PGS within these two genetic ancestries. RESULTS Seven associations surpassed the Bonferroni adjusted threshold for statistical significance (p = 0.05/1374 = 3.6 × 10-5) in the EU participants, including mood disorders (OR (95%CI) = 1.08(1.05 to 1.10), p = 1.0 × 10-10), autism (OR (95%CI) = 1.34(1.24 to 1.43), p = 1.2 × 10-9), and breast cancer (OR (95%CI) = 1.09(1.05 to 1.14), 2.6 × 10-5). There was no statistical evidence for PGS-phenotype associations in the AF participants. Conditioning on the diagnosis of autism or on median body mass index (BMI) did not impact the strength of the reported associations. Although we observed some sex differences in the pattern of associations, there was no significant interaction between sex and autism PGS. Finally, the associations between autism PGS and autism diagnosis were stronger in childhood and adolescence, while the associations with mood disorders and breast cancer were stronger in adulthood. DISCUSSION Our findings indicate that autism PGS is not only related to autism diagnosis but may also be related to adult-onset conditions, including mood disorders and some cancers. CONCLUSIONS Our study raises the hypothesis that genes associated with autism may also increase the risk for cancers later in life. Future studies are necessary to replicate and extend our findings.
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Affiliation(s)
- Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Tyne Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Beth A Malow
- Sleep Disorders Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Neurology, Pharmacology and Special Education, Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
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32
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Ahmad A, Imran M, Ahsan H. Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics 2023; 15:1630. [PMID: 37376078 DOI: 10.3390/pharmaceutics15061630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.
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Affiliation(s)
- Anas Ahmad
- Julia McFarlane Diabetes Research Centre (JMDRC), Department of Microbiology, Immunology and Infectious Diseases, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, Cumming School of Medicine, Foothills Medical Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Mohammad Imran
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane 4102, Australia
| | - Haseeb Ahsan
- Department of Biochemistry, Faculty of Dentistry, Jamia Millia Islamia, New Delhi 110025, India
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Drouet DE, Liu S, Crawford DC. Assessment of multi-population polygenic risk scores for lipid traits in African Americans. PeerJ 2023; 11:e14910. [PMID: 37214096 PMCID: PMC10198155 DOI: 10.7717/peerj.14910] [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: 08/22/2022] [Accepted: 01/25/2023] [Indexed: 05/24/2023] Open
Abstract
Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.
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Affiliation(s)
- Domenica E. Drouet
- Department of Medicine, Case Western Reserve University, Cleveland, OH, United States of America
| | - Shiying Liu
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
- Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States of America
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Actkins KV, Jean-Pierre G, Aldrich MC, Velez Edwards DR, Davis LK. Sex modifies the effect of genetic risk scores for polycystic ovary syndrome on metabolic phenotypes. PLoS Genet 2023; 19:e1010764. [PMID: 37256887 PMCID: PMC10259776 DOI: 10.1371/journal.pgen.1010764] [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: 10/23/2021] [Revised: 06/12/2023] [Accepted: 04/26/2023] [Indexed: 06/02/2023] Open
Abstract
Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as insulin resistance, obesity, and type 2 diabetes (T2D). While only diagnosable in females, males with a family history of PCOS can also exhibit a poor cardiometabolic profile. Therefore, we aimed to elucidate the role of sex in the cardiometabolic comorbidities observed in PCOS by conducting bidirectional genetic risk score analyses in both sexes. We first conducted a phenome-wide association study (PheWAS) using PCOS polygenic risk scores (PCOSPRS) to identify potential pleiotropic effects of PCOSPRS across 1,380 medical conditions recorded in the Vanderbilt University Medical Center electronic health record (EHR) database, in females and males. After adjusting for age and genetic ancestry, we found that European (EUR)-ancestry males with higher PCOSPRS were significantly more likely to develop hypertensive diseases than females at the same level of genetic risk. We performed the same analysis in an African (AFR)-ancestry population, but observed no significant associations, likely due to poor trans-ancestry performance of the PRS. Based on observed significant associations in the EUR-ancestry population, we then tested whether the PRS for comorbid conditions (e.g., T2D, body mass index (BMI), hypertension, etc.) also increased the odds of a PCOS diagnosis. Only BMIPRS and T2DPRS were significantly associated with a PCOS diagnosis in EUR-ancestry females. We then further adjusted the T2DPRS for measured BMI and BMIresidual (regressed on the BMIPRS and enriched for the environmental contribution to BMI). Results demonstrated that genetically regulated BMI primarily accounted for the relationship between T2DPRS and PCOS. Overall, our findings show that the genetic architecture of PCOS has distinct sex differences in associations with genetically correlated cardiometabolic traits. It is possible that the cardiometabolic comorbidities observed in PCOS are primarily explained by their shared genetic risk factors, which can be further influenced by biological variables including sex and BMI.
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Affiliation(s)
- Ky’Era V. Actkins
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Genevieve Jean-Pierre
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Melinda C. Aldrich
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Digna R. Velez Edwards
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Epidemiology Center, Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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35
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Wang Z, Shi W, Carroll RJ, Chatterjee N. Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528572. [PMID: 36824704 PMCID: PMC9948994 DOI: 10.1101/2023.02.14.528572] [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: 06/18/2023]
Abstract
Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.
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Affiliation(s)
- Ziqiao Wang
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Wen Shi
- McKusick-Nathans Institute, Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Raymond J. Carroll
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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36
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Wendt FR, Pathak GA, Singh K, Stein MB, Koenen KC, Krystal JH, Gelernter J, Davis LK, Polimanti R. Sex-Specific Genetic and Transcriptomic Liability to Neuroticism. Biol Psychiatry 2023; 93:243-252. [PMID: 36244801 PMCID: PMC10508260 DOI: 10.1016/j.biopsych.2022.07.019] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/08/2022] [Accepted: 07/13/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND The presentation, etiology, and relative risk of psychiatric disorders are strongly influenced by biological sex. Neuroticism is a transdiagnostic feature of psychiatric disorders displaying prominent sex differences. We performed genome-wide association studies of neuroticism separately in males and females to identify sex-specific genetic and transcriptomic profiles. METHODS Neuroticism scores were derived from the Eysenck Personality Inventory Neuroticism scale. Genome-wide association studies were performed in 145,669 females and 129,229 males from the UK Biobank considering autosomal and X chromosomal variation. Two-sided z tests were used to test for sex-specific effects of discovered loci, genetic correlates (n = 673 traits), tissue and gene transcriptomic profiles, and polygenic associations across health outcomes in the Vanderbilt University Biobank (39,692 females and 31,268 males). RESULTS The single nucleotide polymorphism heritability of neuroticism was not statistically different between males (h2 = 10.6%) and females (h2 = 11.85%). Four female-specific (rs10736549-CNTN5, rs6507056-ASXL3, rs2087182-MMS22L, and rs72995548-HSPB2) and 2 male-specific (rs10507274-MED13L and rs7984597) neuroticism risk loci reached genome-wide significance. Male- and female-specific neuroticism polygenic scores were most significantly associated with mood disorders (males: odds ratio = 1.11, p = 1.40 × 10-9; females: odds ratio = 1.14, p = 6.05 × 10-22). They also associated with sex-specific laboratory measurements related to erythrocyte count, distribution, and hemoglobin concentration. Gene expression variation in the pituitary was enriched for neuroticism loci in males (male: b = 0.026, p = .002), and genetically regulated transcriptomic changes highlighted the effect of SHISHA9, TEX26, and NCOA6. CONCLUSIONS Through a comprehensive assessment of genetic risk for neuroticism and the associated biological processes, this study identified several molecular pathways that can partially explain the known sex differences in neurotic symptoms and their psychiatric comorbidities.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA CT Healthcare System, West Haven, Connecticut; Department of Anthropology, University of Toronto, Mississauga, Ontario, Canada; Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA CT Healthcare System, West Haven, Connecticut
| | - Kritika Singh
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, California; Department of Psychiatry, University of California, San Diego, San Diego, California; Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, California
| | - Karestan C Koenen
- Stanley Center for Psychiatry Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Psychiatry and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; Department of Genetics, Yale School of Medicine, New Haven, Connecticut; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut; VA CT Healthcare System, West Haven, Connecticut
| | - Lea K Davis
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA CT Healthcare System, West Haven, Connecticut.
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37
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Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, Atabaki-Pasdar N, Kalamajski S, Poveda A, Miller-Fleming TW, Zhong X, Giordano GN, Pearson ER, Cox NJ, Franks PW. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab 2023; 5:237-247. [PMID: 36703017 PMCID: PMC9970876 DOI: 10.1038/s42255-022-00731-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2022] [Indexed: 01/27/2023]
Abstract
Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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Affiliation(s)
- Daniel E Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
| | - Juan Fernandez-Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Pomares-Millan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tyne W Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ewan R Pearson
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Population Health and Genomics, University of Dundee, Dundee, UK
| | - Nancy J Cox
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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38
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Fu L, Wang Y, Hu YQ. Bi-directional causal effect between vitamin B12 and non-alcoholic fatty liver disease: Inferring from large population data. Front Nutr 2023; 10:1015046. [PMID: 36950332 PMCID: PMC10025356 DOI: 10.3389/fnut.2023.1015046] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/16/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Many observational studies evaluate the association between vitamin B12 and non-alcoholic fatty liver disease (NAFLD). However, the causality of this association remains uncertain, especially in European populations. We conducted a bidirectional Mendelian randomization study to explore the association between vitamin B12 and NAFLD. Methods Two-sample Mendelian randomization study was conducted. Summary statistics for vitamin B12 were acquired from a genome-wide association studies (GWAS) meta-analysis including 45,576 subjects. Summary-level data for NAFLD was obtained from a GWAS meta-analysis of 8,434 cases and 770,180 non-cases and another GWAS meta-analysis of 1,483 cases and 17,781 non-cases. Summary-level data for 4 enzymes including alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma glutamyltransferase (GGT), was available from the UK Biobank. Inverse variance weighting (as main analysis), weighted median estimate, robust adjusted profile score, MR-Egger, and MR-PRESSO (sensitivity analyses) were performed to calculate causal estimates. Results Genetically predicted higher vitamin B12 concentrations were consistently associated with an increased NAFLD in two sources. The combined odds ratio (OR) of NAFLD was 1.30 (95% confidence interval (CI), 1.13 to 1.48; p < 0.001) per SD-increase in vitamin B12 concentrations. Genetic liability to NAFLD was also positively associated with vitamin B12 concentrations (Beta 0.08, 95%CI, 0.01 to 0.16; p = 0.034). Sensitivity analyses also revealed consistent results. Genetically predicted vitamin B12 concentrations showed no significant association with liver enzymes. Conclusion The present study indicates that increased serum vitamin B12 concentrations may play a role in NAFLD risk. NAFLD also has a causal impact on elevated vitamin B12 concentrations in the circulation. Notably, vitamin B12 concentrations imply the levels of vitamin B12 in the circulation, and higher intake of vitamin B12 may not directly lead to higher levels of serum vitamin B12, instead the higher levels of vitamin B12 in the circulation may be caused by the dysregulation of the metabolism of this vitamin in this study. There exist bidirectional causal effects between serum vitamin B12 concentrations and risk of NAFLD in European individuals.
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Affiliation(s)
- Liwan Fu
- 1Center for Non-Communicable Disease Management, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
- *Correspondence: Liwan Fu,
| | - Yuquan Wang
- 2State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- 2State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Institute of Biostatistics, Fudan University, Shanghai, China
- 3Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- Yue-Qing Hu,
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39
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Faucon A, Samaroo J, Ge T, Davis LK, Cox NJ, Tao R, Shuey MM. Improving the computation efficiency of polygenic risk score modeling: faster in Julia. Life Sci Alliance 2022; 5:5/12/e202201382. [PMID: 35851544 PMCID: PMC9297586 DOI: 10.26508/lsa.202201382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/24/2022] Open
Abstract
To enable computationally efficient polygenic risk score (PRS) calculations, PRS.jl translates a field standard PRS construction method, PRS-CS, to the Julia programming language. To enable large-scale application of polygenic risk scores (PRSs) in a computationally efficient manner, we translate a widely used PRS construction method, PRS–continuous shrinkage, to the Julia programming language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average runtime by 5.5×. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes usage of PRSs by lowering the computational burden of the PRS–continuous shrinkage method.
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Affiliation(s)
- Annika Faucon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julian Samaroo
- JuliaLab, Massachusetts Institute of Technology, Boston, MA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Megan M Shuey
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA .,Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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40
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Kember RL, Vickers-Smith R, Xu H, Toikumo S, Niarchou M, Zhou H, Hartwell EE, Crist RC, Rentsch CT, Davis LK, Justice AC, Sanchez-Roige S, Kampman KM, Gelernter J, Kranzler HR. Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction. Nat Neurosci 2022; 25:1279-1287. [PMID: 36171425 PMCID: PMC9682545 DOI: 10.1038/s41593-022-01160-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 08/11/2022] [Indexed: 11/09/2022]
Abstract
Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.
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Affiliation(s)
- Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel Vickers-Smith
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Epidemiology, University of Kentucky College of Public Health, Lexington, KY, USA
- Center on Drug and Alcohol Research, Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Maria Niarchou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hang Zhou
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Emily E Hartwell
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Richard C Crist
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Christopher T Rentsch
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Kyle M Kampman
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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41
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Niarchou M, Singer EV, Straub P, Malow BA, Davis LK. Investigating the genetic pathways of insomnia in Autism Spectrum Disorder. RESEARCH IN DEVELOPMENTAL DISABILITIES 2022; 128:104299. [PMID: 35820265 PMCID: PMC10068748 DOI: 10.1016/j.ridd.2022.104299] [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: 02/02/2022] [Revised: 05/11/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Sleep problems are common in children with autism spectrum disorder (autism). There is sparse research to date to examine whether insomnia in people with autism is related to autism genetics or insomnia genetics. Moreover, there is a lack of research examining whether circadian-rhythm related genes share potential pathways with autism. AIMS To address this research gap, we tested whether polygenic scores of insomnia or autism are related to risk of insomnia in people with autism, and whether the circadian genes are associated with insomnia in people with autism. METHODS AND PROCEDURES We tested these questions using the phenotypically and genotypically rich MSSNG dataset (N = 1049) as well as incorporating in the analyses data from the Vanderbilt University Biobank (BioVU) (N = 349). OUTCOMES AND RESULTS In our meta-analyzed sample, there was no evidence of associations between the polygenic scores (PGS) for insomnia and a clinical diagnosis of insomnia, or between the PGS of autism and insomnia. We also did not find evidence of a greater burden of rare and disruptive variation in the melatonin and circadian genes in individuals with autism and insomnia compared to individuals with autism without insomnia. CONCLUSIONS AND IMPLICATIONS Overall, we did not find evidence for strong effects of genetic scores influencing sleep in people with autism, however, we cannot rule out the possibility that smaller genetic effects may play a role in sleep problems. Our study indicated the need for a larger collection of data on sleep problems and sleep quality among people with autism.
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Affiliation(s)
- Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Emily V Singer
- Sleep Disorders Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Straub
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Beth A Malow
- Sleep Disorders Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
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42
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Niarchou M, Sealock JM, Straub P, Sanchez‐Roige S, Sutcliffe JS, Davis LK. A phenome-wide association study of polygenic scores for attention deficit hyperactivity disorder across two genetic ancestries in electronic health record data. Am J Med Genet B Neuropsychiatr Genet 2022; 189:185-195. [PMID: 35841203 PMCID: PMC9378640 DOI: 10.1002/ajmg.b.32911] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 05/10/2022] [Accepted: 06/14/2022] [Indexed: 11/24/2022]
Abstract
Testing the association between genetic scores for Attention Deficit Hyperactivity Disorder (ADHD) and health conditions, can help us better understand its complex etiology. Electronic health records linked to genetic data provide an opportunity to test whether genetic scores for ADHD correlate with ADHD and additional health outcomes in a health care context across different age groups. We generated polygenic scores (ADHD-PGS) trained on summary statistics from the latest genome-wide association study of ADHD (N = 55,374) and applied them to genome-wide data from 12,383 unrelated individuals of African-American ancestry and 66,378 unrelated individuals of European ancestry from the Vanderbilt Biobank. Overall, only Tobacco use disorder (TUD) was associated with ADHD-PGS in the African-American ancestry group (Odds ratio [95% confidence intervals] = 1.23[1.16-1.31], p = 9.3 × 10-09 ). Eighty-six phenotypes were associated with ADHD-PGS in the European ancestry individuals, including ADHD (OR[95%CIs] = 1.22[1.16-1.29], p = 3.6 × 10-10 ), and TUD (OR[95%CIs] = 1.22[1.19-1.25], p = 2.8 × 10-46 ). We then stratified outcomes by age (ages 0-11, 12-18, 19-25, 26-40, 41-60, and 61-100). Our results suggest that ADHD polygenic scores are associated with ADHD diagnoses early in life and with an increasing number of health conditions throughout the lifespan (even in the absence of ADHD diagnosis). This study reinforces the utility of applying trait-specific PGSs to biobank data, and performing exploratory sensitivity analyses, to probe relationships among clinical conditions.
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Affiliation(s)
- Maria Niarchou
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Division of Genetic Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Julia M. Sealock
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Peter Straub
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Sandra Sanchez‐Roige
- Division of Genetic Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
| | - James S. Sutcliffe
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Psychiatry and Behavioral SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityNashvilleTennesseeUSA
| | - Lea K. Davis
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- Division of Genetic Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Psychiatry and Behavioral SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Molecular Physiology and BiophysicsVanderbilt UniversityNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
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43
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Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5:293-320. [PMID: 35576555 PMCID: PMC9828290 DOI: 10.1146/annurev-biodatasci-111721-074830] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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McNew KL, Abraham A, Sack DE, Smart CD, Pettway YD, Falk AC, Lister RL, Faucon AB, Bejan CA, Capra JA, Aronoff DM, Boyd KL, Moore DJ. Vascular alterations impede fragile tolerance to pregnancy in type 1 diabetes. F&S SCIENCE 2022; 3:148-158. [PMID: 35560012 PMCID: PMC9850286 DOI: 10.1016/j.xfss.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 01/22/2023]
Abstract
OBJECTIVE To determine the impact of autoimmunity in the absence of glycemic alterations on pregnancy in type 1 diabetes (T1D). DESIGN Because nonobese diabetic (NOD) mice experience autoimmunity before the onset of hyperglycemia, we studied pregnancy outcomes in prediabetic NOD mice using flow cytometry and enzyme-linked immunosorbent assays. Once we determined that adverse events in pregnancy occurred in euglycemic mice, we performed an exploratory study using electronic health records to better understand pregnancy complications in humans with T1D and normal hemoglobin A1c levels. SETTING University Medical Center. PATIENT(S)/ANIMAL(S) Nonobese diabetic mice and electronic health records from Vanderbilt University Medical Center. INTERVENTION(S) Nonobese diabetic mice were administered 200 μg of an anti-interleukin 6 (IL-6) antibody every other day starting on day 5 of gestation. MAIN OUTCOME MEASURE(S) Changes in the number of abnormal and reabsorbed pups in NOD mice and odds of vascular complications in pregnancy in T1D in relation to A1c. RESULT(S) Prediabetic NOD mice had increased adverse pregnancy outcomes compared with nonautoimmune mice; blockade of IL-6, which was secreted by endothelial cells, decreased the number of reabsorbed and abnormal fetuses. Similarly, vascular complications were increased in pregnant patients with T1D across all A1c values. CONCLUSION(S) The vascular secretion of IL-6 drives adverse pregnancy outcomes in prediabetic NOD mice. Pregnant patients with T1D have increased vascular complications even with normal hemoglobin A1cs, indicating a potential effect of autoimmunity on the placental vasculature.
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Affiliation(s)
- Kelsey L. McNew
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt University Medical Scientist Training Program, Nashville, Tennessee
| | - Abin Abraham
- Vanderbilt University Medical Scientist Training Program, Nashville, Tennessee,Vanderbilt University School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Daniel E. Sack
- Vanderbilt University Medical Scientist Training Program, Nashville, Tennessee,Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Charles Duncan Smart
- Vanderbilt University Medical Scientist Training Program, Nashville, Tennessee,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Yasminye D. Pettway
- Vanderbilt University Medical Scientist Training Program, Nashville, Tennessee,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Alexander C. Falk
- Division of Pediatric Endocrinology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rolanda L. Lister
- Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Annika B. Faucon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee
| | - Cosmin A. Bejan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John A. Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, California
| | - David M. Aronoff
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee,Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee,Division of Infectious Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kelli L. Boyd
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee,Gilead Science, Inc., Foster, California
| | - Daniel J. Moore
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee,Division of Pediatric Endocrinology, Vanderbilt University Medical Center, Nashville, Tennessee
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45
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Goldberg A, Sucic JF, Talley SA. The angiotensin-converting enzyme gene insertion/deletion polymorphism interacts with fear of falling in relation to stepping speed in community-dwelling older adults. Physiother Theory Pract 2022:1-12. [PMID: 35383515 DOI: 10.1080/09593985.2022.2056861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Despite the association of genetic factors with falls, balance, and lower extremity functioning, interaction of the angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism with fear of falling (FOF) in relation to stepping performance has, to the best of our knowledge, not been investigated in older adults. OBJECTIVE The purpose of this study was to examine the interaction effects of the ACE I/D polymorphism with FOF in relation to stepping performance in older adults. METHODS Eighty-eight community-dwelling adults 60 years or older participated in a cross-sectional observational study. Participants completed tests of rapid and distance stepping, and self-reported FOF (yes/no). Participants provided saliva for ACE genotyping. General linear models evaluated ACE genotype × FOF interaction effects in relation to stepping performance. The α level was set at 0.05. RESULTS The ACE I/D polymorphism exhibited significant interaction effects (p for interactions 0.002 ≤ p ≤ .04) with FOF in relation to stepping speed. Relationships between FOF and stepping speed varied among ACE genotypes. The insertion/insertion (II) genotype was significantly associated (p = .01) with slow stepping in individuals with, but not without FOF (p > .05). CONCLUSION Variation in relationships between FOF and stepping speed among ACE genotypes suggests a role for the ACE I/D polymorphism in modifying relationships between FOF and stepping speed in older adults. The association of the ACE II genotype with slow stepping performance in individuals with, but not without FOF, suggests that older adults with the ACE II genotype and FOF may be at increased risk for poor stepping performance and associated functional declines.
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Affiliation(s)
- Allon Goldberg
- Physical Therapy Department, College of Health Sciences, University of Michigan-Flint, Flint, MI, USA
| | - Joseph F Sucic
- Department of Natural Sciences, College of Arts and Sciences, University of Michigan-Flint, Flint, MI, USA
| | - Susan Ann Talley
- Physical Therapy Department, College of Health Sciences, University of Michigan-Flint, Flint, MI, USA
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46
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Rowland B, Sun Q, Wang W, Miller-Fleming T, Cox N, Graff M, Faucon A, Shuey MM, Blue EE, Auer P, Li Y, Sankaran VG, Reiner AP, Raffield LM. Genetic Examination of Hematological Parameters in SARS-CoV-2 Infection and COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.02.28.22271562. [PMID: 35262092 PMCID: PMC8902884 DOI: 10.1101/2022.02.28.22271562] [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] [Indexed: 01/08/2023]
Abstract
Background People hospitalized with COVID-19 often exhibit hematological alterations, such as lower lymphocyte and platelet counts, which have been reported to associate with disease prognosis. It is unclear whether inter-individual variability in baseline hematological parameters prior to acute infection influences risk of SARS-CoV-2 infection and progression to severe COVID-19. Methods We assessed the association of blood cell counts and indices with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we used summary statistics from genome-wide association studies to assess the shared genetic architecture of baseline blood cell counts and indices on COVID-19 outcomes. Results We observed inconsistent associations between measured blood cell indices and both SARS-CoV-2 infection and COVID-19 hospitalization in UK Biobank and VUMC SD. In Mendelian randomization analyses using genetic summary statistics, no putative causal relationships were identified between COVID-19 related outcomes and hematological indices after adjusting for multiple testing. We observed overlapping genetic association signals between hematological parameters and COVID-19 traits. For example, we observed overlap between infection susceptibility-associated variants at PPP1R15A and red blood cell parameters, and between disease severity-associated variants at TYK2 and lymphocyte and platelet phenotypes. Conclusions We did not find convincing evidence of a relationship between baseline hematological parameters and susceptibility to SARS-CoV-2 infection or COVID-19 severity, though this relationship should be re-examined as larger and better-powered genetic analyses of SARS-CoV-2 infection and severe COVID-19 become available.
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Affiliation(s)
- Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Wanjiang Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tyne Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Nancy Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Misa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Annika Faucon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Megan M. Shuey
- Department of Medicine Vanderbilt University Medical Center Nashville, TN
| | - Elizabeth E. Blue
- Department of Medical Genetics, University of Washington, Seattle, WA
| | - Paul Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Vijay G. Sankaran
- Division of Hematology/Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
| | | | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
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47
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Overway EM, Bosma KJ, Claxton DP, Oeser JK, Singh K, Breidenbach LB, Mchaourab HS, Davis LK, O'Brien RM. Nonsynonymous single-nucleotide polymorphisms in the G6PC2 gene affect protein expression, enzyme activity, and fasting blood glucose. J Biol Chem 2022; 298:101534. [PMID: 34954144 PMCID: PMC8800118 DOI: 10.1016/j.jbc.2021.101534] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022] Open
Abstract
G6PC2 encodes a glucose-6-phosphatase (G6Pase) catalytic subunit that modulates the sensitivity of insulin secretion to glucose and thereby regulates fasting blood glucose (FBG). A common single-nucleotide polymorphism (SNP) in G6PC2, rs560887 is an important determinant of human FBG variability. This SNP has a subtle effect on G6PC2 RNA splicing, which raises the question as to whether nonsynonymous SNPs with a major impact on G6PC2 stability or enzyme activity might have a broader disease/metabolic impact. Previous attempts to characterize such SNPs were limited by the very low inherent G6Pase activity and expression of G6PC2 protein in islet-derived cell lines. In this study, we describe the use of a plasmid vector that confers high G6PC2 protein expression in islet cells, allowing for a functional analysis of 22 nonsynonymous G6PC2 SNPs, 19 of which alter amino acids that are conserved in mouse G6PC2 and the human and mouse variants of the related G6PC1 isoform. We show that 16 of these SNPs markedly impair G6PC2 protein expression (>50% decrease). These SNPs have variable effects on the stability of human and mouse G6PC1, despite the high sequence homology between these isoforms. Four of the remaining six SNPs impaired G6PC2 enzyme activity. Electronic health record-derived phenotype analyses showed an association between high-impact SNPs and FBG, but not other diseases/metabolites. While homozygous G6pc2 deletion in mice increases the risk of hypoglycemia, these human data reveal no evidence that the beneficial use of partial G6PC2 inhibitors to lower FBG would be associated with unintended negative consequences.
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Affiliation(s)
- Emily M Overway
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Karin J Bosma
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Derek P Claxton
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - James K Oeser
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Kritika Singh
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Lindsay B Breidenbach
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Hassane S Mchaourab
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Lea K Davis
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Richard M O'Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
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48
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Sealock JM, Lee YH, Moscati A, Venkatesh S, Voloudakis G, Straub P, Singh K, Feng YCA, Ge T, Roussos P, Smoller JW, Chen G, Davis LK. Use of the PsycheMERGE Network to Investigate the Association Between Depression Polygenic Scores and White Blood Cell Count. JAMA Psychiatry 2021; 78:1365-1374. [PMID: 34668925 PMCID: PMC8529528 DOI: 10.1001/jamapsychiatry.2021.2959] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
IMPORTANCE Although depression is a common psychiatric disorder, its underlying biological basis remains poorly understood. Pairing depression polygenic scores with the results of clinical laboratory tests can reveal biological processes involved in depression etiology and in the physiological changes resulting from depression. OBJECTIVE To characterize the association between depression polygenic scores and an inflammatory biomarker, ie, white blood cell count. DESIGN, SETTING, AND PARTICIPANTS This genetic association study was conducted from May 19, 2019, to June 5, 2021, using electronic health record data from 382 452 patients across 4 health care systems. Analyses were conducted separately in each health care system and meta-analyzed across all systems. Primary analyses were conducted in Vanderbilt University Medical Center's biobank. Replication analyses were conducted across 3 other PsycheMERGE sites: Icahn School of Medicine at Mount Sinai, Mass General Brigham, and the Million Veteran Program. All patients with available genetic data and recorded white blood cell count measurements were included in the analyses. Primary analyses were conducted in individuals of European descent and then repeated in a population of individuals of African descent. EXPOSURES Depression polygenic scores. MAIN OUTCOMES AND MEASURES White blood cell count. RESULTS Across the 4 PsycheMERGE sites, there were 382 452 total participants of European ancestry (18.7% female; median age, 57.9 years) and 12 383 participants of African ancestry (61.1% female; median age, 39.0 [range, birth-90.0 years]). A laboratory-wide association scan revealed a robust association between depression polygenic scores and white blood cell count (β, 0.03; SE, 0.004; P = 1.07 × 10-17), which was replicated in a meta-analysis across the 4 health care systems (β, 0.03; SE, 0.002; P = 1.03 × 10-136). Mediation analyses suggested a bidirectional association, with white blood cell count accounting for 2.5% of the association of depression polygenic score with depression diagnosis (95% CI, 2.2%-20.8%; P = 2.84 × 10-70) and depression diagnosis accounting for 9.8% of the association of depression polygenic score with white blood cell count (95% CI, 8.4%-11.1%; P = 1.78 × 10-44). Mendelian randomization provided additional support for an association between increased white blood count and depression risk, but depression modeled as the exposure showed no evidence of an influence on white blood cell counts. CONCLUSIONS AND RELEVANCE This genetic association study found that increased depression polygenic scores were associated with increased white blood cell count, and suggests that this association may be bidirectional. These findings highlight the potential importance of the immune system in the etiology of depression and may motivate future development of clinical biomarkers and targeted treatment options for depression.
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Affiliation(s)
- Julia M. Sealock
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Younga H. Lee
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston,Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sanan Venkatesh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York,Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Georgios Voloudakis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York,Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Peter Straub
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yen-Chen A. Feng
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston,Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Tian Ge
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston,Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York,Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Jordan W. Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston,Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Guanhua Chen
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee,Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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49
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Pathak GA, Singh K, Miller-Fleming TW, Wendt FR, Ehsan N, Hou K, Johnson R, Lu Z, Gopalan S, Yengo L, Mohammadi P, Pasaniuc B, Polimanti R, Davis LK, Mancuso N. Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization. Nat Commun 2021; 12:4569. [PMID: 34315903 PMCID: PMC8316582 DOI: 10.1038/s41467-021-24824-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022] Open
Abstract
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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Affiliation(s)
- Gita A Pathak
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank R Wendt
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Ruth Johnson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shyamalika Gopalan
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA
| | - Bogdan Pasaniuc
- Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Renato Polimanti
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Actkins KV, Beasley HK, Faucon AB, Davis LK, Sakwe AM. Calcium-Sensing Receptor Polymorphisms at rs1801725 Are Associated with Increased Risk of Secondary Malignancies. J Pers Med 2021; 11:642. [PMID: 34357109 PMCID: PMC8304025 DOI: 10.3390/jpm11070642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 07/02/2021] [Indexed: 12/19/2022] Open
Abstract
Dysregulation of systemic calcium homeostasis during malignancy is common in most patients with high-grade tumors. However, it remains unclear whether single nucleotide polymorphisms (SNPs) that alter the sensitivity of the calcium-sensing receptor (CaSR) to circulating calcium are associated with primary and/or secondary neoplasms at specific pathological sites in patients of European and African ancestry. Multivariable logistic regression models were used to analyze the association of CASR SNPs with circulating calcium, parathyroid hormone, vitamin D, and primary and secondary neoplasms. Circulating calcium is associated with an increased risk for breast, prostate, and skin cancers. In patients of European descent, the rs1801725 CASR SNP is associated with bone-related cancer phenotypes, deficiency of humoral immunity, and a higher risk of secondary neoplasms in the lungs and bone. Interestingly, circulating calcium levels are higher in homozygous patients for the inactivating CASR variant at rs1801725 (TT genotype), and this is associated with a higher risk of secondary malignancies. Our data suggest that expression of CaSR variants at rs1801725 is associated with a higher risk of developing secondary neoplastic lesions in the lungs and bone, due in part to cancer-induced hypercalcemia and/or tumor immune suppression. Screening of patients for CASR variants at this locus may lead to improved management of high calcium associated tumor progression.
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Affiliation(s)
- Ky’Era V. Actkins
- Department of Microbiology, Immunology and Physiology, School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA;
| | - Heather K. Beasley
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA; (H.K.B.); (L.K.D.)
| | - Annika B. Faucon
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN 37232, USA;
| | - Lea K. Davis
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA; (H.K.B.); (L.K.D.)
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Amos M. Sakwe
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA; (H.K.B.); (L.K.D.)
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