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Yang H, Li Z, Zhang Y, Chang Q, Jiang J, Liu Y, Ji C, Chen L, Xia Y, Zhao Y. Associations between frailty, genetic predisposition, and chronic kidney disease risk in middle-aged and older adults: A prospective cohort study. Maturitas 2024; 187:108059. [PMID: 38941958 DOI: 10.1016/j.maturitas.2024.108059] [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: 02/06/2024] [Revised: 04/09/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024]
Abstract
OBJECTIVES Cross-sectional evidence has shown that frailty is highly prevalent in patients with chronic kidney disease (CKD). However, there is limited evidence of the longitudinal associations between frailty, genetic predisposition to CKD, and the risk of CKD in the general population. Therefore, this study aimed to examine such associations among participants in the UK Biobank. STUDY DESIGN This is a prospective cohort study included 370,965 middle-aged and older adults from the UK Biobank. Physical frailty was assessed using a modified version of the Fried phenotype classification. A weighted genetic risk score was built using 263 variants associated with estimated glomerular filtration rate. MAIN OUTCOME MEASURES Incident CKD was identified from hospital inpatient records. RESULTS Over a median follow-up of 12.3 years, we documented a total of 11,121 incident CKD cases. Time-dependent Cox proportional hazards regression models indicated that individuals with frailty (hazard ratio [HR]: 1.94, 95 % confidence interval [CI]: 1.81-2.08) and pre-frailty (HR: 1.27, 95 % CI: 1.22-1.33) had an increased risk of developing CKD, compared with non-frail individuals. No significant interaction between frailty and genetic risk score was observed (P for interaction = 0.41). The highest risk was observed among the individuals with high genetic risk and frailty (HR: 2.31, 95 % CI: 2.00-2.68). CONCLUSION Our results demonstrated that frailty and pre-frailty were associated with increased risk of incident CKD in middle-age and older adults, regardless of genetic risk of CKD. Our study underscores the importance of frailty screening and intervention as a potential strategy to prevent CKD. Future clinical trials are needed to validate our findings.
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Affiliation(s)
- Honghao Yang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Zhenhua Li
- Department of Urology Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yixiao Zhang
- Department of Urology Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Jinguo Jiang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Yashu Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Chao Ji
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China.
| | - Yuhong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China.
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Liu R, Li D, Ma Y, Tang L, Chen R, Tian Y. Air pollutants, genetic susceptibility and the risk of schizophrenia: large prospective study. Br J Psychiatry 2024:1-9. [PMID: 39117363 DOI: 10.1192/bjp.2024.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
BACKGROUND Evidence linking air pollutants and the risk of schizophrenia remains limited and inconsistent, and no studies have investigated the joint effect of air pollutant exposure and genetic factors on schizophrenia risk. AIMS To investigate how exposure to air pollution affects schizophrenia risk and the potential effect modification of genetic susceptibility. METHOD Our study was conducted using data on 485 288 participants from the UK Biobank. Cox proportional hazards models were used to estimate the schizophrenia risk as a function of long-term air pollution exposure presented as a time-varying variable. We also derived the schizophrenia polygenic risk score (PRS) utilising data provided by the UK Biobank, and investigated the modification effect of genetic susceptibility. RESULTS During a median follow-up period of 11.9 years, 417 individuals developed schizophrenia (mean age 55.57 years, s.d. = 8.68; 45.6% female). Significant correlations were observed between long-term exposure to four air pollutants (PM2.5; PM10; nitrogen oxides, NOx; nitrogen dioxide, NO2) and the schizophrenia risk in each genetic risk group. Interactions between genetic factors and the pollutants NO2 and NOx had an effect on schizophrenia events. Compared with those with low PRS and low air pollution, participants with high PRS and high air pollution had the highest risk of incident schizophrenia (PM2.5: hazard ratio = 6.25 (95% CI 5.03-7.76); PM10: hazard ratio = 7.38 (95% CI 5.86-9.29); NO2: hazard ratio = 6.31 (95% CI 5.02-7.93); NOx: hazard ratio = 6.62 (95% CI 5.24-8.37)). CONCLUSIONS Long-term exposure to air pollutants was positively related to the schizophrenia risk. Furthermore, high genetic susceptibility could increase the effect of NO2 and NOx on schizophrenia risk.
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Affiliation(s)
- Run Liu
- Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dankang Li
- Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yudiyang Ma
- Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lingxi Tang
- Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruiqi Chen
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; and Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yaohua Tian
- Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Geraghty R, Lovegrove C, Howles S, Sayer JA. Role of Genetic Testing in Kidney Stone Disease: A Narrative Review. Curr Urol Rep 2024:10.1007/s11934-024-01225-5. [PMID: 39096463 DOI: 10.1007/s11934-024-01225-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE OF REVIEW Kidney stone disease (KSD) is a common and potentially life-threatening condition, and half of patients experience a repeat kidney stone episode within 5-10 years. Despite the ~50% estimate heritability of KSD, international guidelines have not kept up with the pace of discovery of genetic causes of KSD. The European Association of Urology guidelines lists 7 genetic causes of KSD as 'high risk'. RECENT FINDINGS There are currently 46 known monogenic (single gene) causes of kidney stone disease, with evidence of association in a further 23 genes. There is also evidence for polygenic risk of developing KSD. Evidence is lacking for recurrent disease, and only one genome wide association study has investigated this phenomenon, identifying two associated genes (SLC34A1 and TRPV5). However, in the absence of other evidence, patients with genetic predisposition to KSD should be treated as 'high risk'. Further studies are needed to characterize both monogenic and polygenic associations with recurrent disease, to allow for appropriate risk stratification. Durability of test result must be balanced against cost. This would enable retrospective analysis if no genetic cause was found initially. We recommend genetic testing using a gene panel for all children, adults < 25 years, and older patients who have factors associated with high risk disease within the context of a wider metabolic evaluation. Those with a genetic predisposition should be managed via a multi-disciplinary team approach including urologists, radiologists, nephrologists, clinical geneticists and chemical pathologists. This will enable appropriate follow-up, counselling and potentially prophylaxis.
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Affiliation(s)
- Robert Geraghty
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
- Department of Urology, The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK.
| | - Catherine Lovegrove
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Sarah Howles
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - John A Sayer
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Renal Services, The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
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Jansen PR, Vos N, van Uhm J, Dekkers IA, van der Meer R, Mannens MMAM, van Haelst MM. The utility of obesity polygenic risk scores from research to clinical practice: A review. Obes Rev 2024:e13810. [PMID: 39075585 DOI: 10.1111/obr.13810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 06/13/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024]
Abstract
Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.
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Affiliation(s)
- Philip R Jansen
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Niels Vos
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Jorrit van Uhm
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rieneke van der Meer
- Netherlands Obesity Clinic, Huis ter Heide, Netherlands
- Amsterdam UMC, Department of Endocrinology and Metabolism, University of Amsterdam, Amsterdam, Netherlands
| | - Marcel M A M Mannens
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Mieke M van Haelst
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Amsterdam UMC, Emma Center for Personalized Medicine, University of Amsterdam, Amsterdam, Netherlands
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Jiang D, Shi Z, Wei J, Tran H, Zheng SL, Xu J, Lee CJ. Polygenic risk score informed clinical model for improving abdominal aortic aneurysm screening. Ann Vasc Surg 2024:S0890-5096(24)00473-4. [PMID: 39067852 DOI: 10.1016/j.avsg.2024.06.036] [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: 02/18/2024] [Revised: 05/24/2024] [Accepted: 06/02/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Abdominal aortic aneurysm (AAA) is a complex disease with environmental and genetic risk factors. Polygenic risk scores (PRS) based on disease-specific risk-associated single nucleotide variants (SNVs) have demonstrated effectiveness in stratifying individual-level disease risk for cardiovascular diseases. This prospective cohort study assessed associations of PRS of AAA (PRSAAA) with risk of incident AAA, analyzed the effectiveness of a combined clinical-genetic risk model, and explored the clinical utility of the model in identifying high-risk individuals for AAA screening. METHODS PRSAAA was calculated using 911,440 SNVs and PRSCAD was calculated using 2,324,683 SNVs derived from mixed ancestry genome wide association studies. The UK Biobank was used as the study cohort. All individuals with complete genetic data available and no diagnosis of AAA at time of recruitment were included in the analysis and followed prospectively to assess for incident AAA. A PRS informed clinical model, Prob-AAA, was developed using clinically significant variables and PRSAAA. RESULTS 481,105 individuals were included in the analysis with 2,668 incident AAA cases. Incident AAA increased from 0.30% to 0.93% between the lowest and highest decile of PRSAAA; similarly, severe AAA, requiring surgery and/or presenting with rupture, increased from 23% to 39% of incident AAA cases across deciles. PRSAAA was a predictor of incident AAA diagnosis (HR 2.06 [1.70 - 2.48]) independent of other clinical risk factors including male sex, older age, and smoking history. Prob-AAA was an independent predictor of incident AAA (HR 1.92 [1.69 - 2.20]), and identified 9.6% of cases of incident AAA compared to only 4.2% by PRSAAA. Current screening guidelines captured 5.7% of the overall cohort, with an incident AAA rate of approximately 3.2%. Amongst males not included by current guidelines, Prob-AAA identified an additional cohort, approximately 2% of the overall cohort, with a similar rate of incident AAA. CONCLUSIONS Prob-AAA, a PRS informed clinical model for AAA, improved upon the predictive power of current, clinical risk factor informed, screening guidelines for AAA.
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Affiliation(s)
- David Jiang
- Department of Surgery, University of Chicago Medicine, Chicago, Illinois, USA.
| | - Zhuqing Shi
- Program for Personalized Cancer Care, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
| | - Jun Wei
- Program for Personalized Cancer Care, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
| | - Huy Tran
- Program for Personalized Cancer Care, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
| | - Jianfeng Xu
- Department of Surgery, University of Chicago Medicine, Chicago, Illinois, USA; Program for Personalized Cancer Care, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA; Department of Surgery, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
| | - Cheong J Lee
- Department of Surgery, Endeavor Health (formerly NorthShore University HealthSystem), Evanston, Illinois, USA
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6
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Zhang S, Zhu D, Wu Z, Yang S, Liu Y, Kang X, Chen X, Zhu Z, Dong Q, Suo C, Han X. GWAS-based polygenic risk scoring for predicting cerebral artery dissection in the Chinese population. BMC Neurol 2024; 24:258. [PMID: 39054468 PMCID: PMC11271197 DOI: 10.1186/s12883-024-03759-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 07/12/2024] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVE Cerebral artery dissection (CeAD) is a rare but serious disease. Genetic risk assessment for CeAD is lacking in Chinese population. We performed genome-wide association study (GWAS) and computed polygenic risk score (PRS) to explore genetic susceptibility factors and prediction model of CeAD based on patients in Huashan Hospital. METHODS A total of 210 CeAD patients and 280 controls were enrolled from June 2017 to September 2022 in Department of Neurology, Huashan Hospital, Fudan University. We performed GWAS to identify genetic variants associated with CeAD in 140 CeAD patients and 210 control individuals according to a case and control 1:1.5 design rule in the training dataset, while the other 70 patients with CeAD and 70 controls were used as validation. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were utilized to identify the significant pathways. We constructed a PRS by capturing all independent GWAS SNPs in the analysis and explored the predictivity of PRS, age, and sex for CeAD. RESULTS Through GWAS analysis of the 140 cases and 210 controls in the training dataset, we identified 13 leading SNPs associated with CeAD at a genome-wide significance level of P < 5 × 10- 8. Among them, 10 SNPs were annotated in or near (in the upstream and downstream regions of ± 500Kb) 10 functional genes. rs34508376 (OR2L13) played a suggestive role in CeAD pathophysiology which was in line with previous observations in aortic aneurysms. The other nine genes were first-time associations in CeAD cases. GO enrichment analyses showed that these 10 genes have known roles in 20 important GO terms clustered into two groups: (1) cellular biological processes (BP); (2) molecular function (MF). We used genome-wide association data to compute PRS including 32 independent SNPs and constructed predictive model for CeAD by using age, sex and PRS as predictors both in training and validation test. The area under curve (AUC) of PRS predictive model for CeAD reached 99% and 95% in the training test and validation test respectively, which were significantly larger than the age and sex models of 83% and 86%. CONCLUSIONS Our study showed that ten risk loci were associated with CeAD susceptibility, and annotated functional genes had roles in 20 important GO terms clustered into biological process and molecular function. The PRS derived from risk variants was associated with CeAD incidence after adjusting for age and sex both in training test and validation.
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Grants
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- No. 8227052180 National Natural Science Foundation of China
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan university.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- none The Cerebrovascular Disease Management Project of Sailing Foundation of China Stroke Association.
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
- NO. HIGHER2022107 Heart and Brain Health Public Welfare Project of Buchang Zhiyuan Foundation
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Affiliation(s)
- Shufan Zhang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Dongliang Zhu
- State Key Laboratory of Genetic Engineering, School of life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Zhengyu Wu
- Department of Geriatrics, Huashan Hospital, Fudan University, Shanghai, China
| | - Shilin Yang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuanzeng Liu
- Gu Mei Community Health Service Center of Minhang District, Shanghai, China
| | - Xiaocui Kang
- Department of Neurology, Shanghai Shidong Hospital, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Zhu Zhu
- Department of Neurology, Indianan University Health, Bloomington, IN, USA
| | - Qiang Dong
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chen Suo
- State Key Laboratory of Genetic Engineering, School of life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
| | - Xiang Han
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
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Heyne HO, Pajuste FD, Wanner J, Daniel Onwuchekwa JI, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. Nat Commun 2024; 15:6277. [PMID: 39054313 PMCID: PMC11272783 DOI: 10.1038/s41467-024-50295-z] [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: 09/14/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 700k Finns and Estonians. We found that a high genetic generalized epilepsy PRS (PRSGGE) increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.73 per PRSGGE standard deviation [SD]) across lifetime and within 10 years after an unspecified seizure event. The effect of PRSGGE was significantly larger on idiopathic generalized epilepsies, in females and for earlier epilepsy onset. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). Here, we outline the potential of epilepsy specific PRSs to serve as biomarkers after a first seizure event.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
- Hasso Plattner Institute, Mount Sinai School of Medicine, New York, NY, US.
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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Müller S, Lieb K, Streit F, Awasthi S, Wagner S, Frank J, Müller MB, Tadic A, Heilmann-Heimbach S, Hoffmann P, Mavarani L, Schmidt B, Rietschel M, Witt SH, Zillich L, Engelmann J. Common polygenic variation in the early medication change (EMC) cohort affects disorder risk, but not the antidepressant treatment response. J Affect Disord 2024; 363:542-551. [PMID: 39038621 DOI: 10.1016/j.jad.2024.07.138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/08/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND Given the great interest in identifying reliable predictors of the response to antidepressant drugs, the present study investigated whether polygenic scores (PGS) for Major Depressive Disorder (MDD) and antidepressant treatment response (ADR) were related to the complex trait of antidepressant response in the Early Medication Change (EMC) cohort. METHODS In this secondary analysis of the EMC trial (N = 889), 481 MDD patients were included and compared to controls from a population-based cohort. Patients were treated over eight weeks within a pre-defined treatment-algorithm. We investigated patients' genetic variation associated with MDD and ADR, using PGS and examined the association of PGS with treatment outcomes (early improvement, response, remission). Additionally, the influence of two cytochrome P450 drug-metabolizing enzymes (CYP2C19, CYP2D6) was determined. RESULTS PGS for MDD was significantly associated with disorder status (NkR2 = 2.48 %, p < 1*10-12), with higher genetic burden in EMC patients compared to controls. The PGS for ADR did not explain remission status. The PGS for MDD and ADR were also not associated with treatment outcomes. In addition, there were no effects of common CYP450 gene variants on ADR. LIMITATIONS The study was limited by variability in the outcome parameters due to differences in treatment and insufficient sample size in the used ADR genome-wide association study (GWAS). CONCLUSIONS The present study confirms a polygenic contribution to MDD burden in the EMC patients. Larger GWAS with homogeneity in antidepressant treatments are needed to explore the genetic variation associated with ADR and realize the potential of PGS to contribute to specific response subtypes.
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Affiliation(s)
- Svenja Müller
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; German Center for Mental Health (DZPG), Partner site Mannheim/Heidelberg/Ulm, Germany.
| | - Klaus Lieb
- Department of Psychiatry and Psychotherapy, University Medical Center, Mainz, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; German Center for Mental Health (DZPG), Partner site Mannheim/Heidelberg/Ulm, Germany; Hector Institute for Artificial Intelligence in Psychiatry (HITKIP), Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Swapnil Awasthi
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Stefanie Wagner
- Department of Psychiatry and Psychotherapy, University Medical Center, Mainz, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marianne B Müller
- Department of Psychiatry and Psychotherapy, University Medical Center, Mainz, Germany; Translational Psychiatry, Department of Psychiatry and Psychotherapy & Focus Program Translational Neuroscience, University Medical Center, Mainz, Germany
| | - André Tadic
- Department of Psychiatry and Psychotherapy, University Medical Center, Mainz, Germany; Department of Psychiatry, Psychosomatics, and Psychotherapy, DR. FONTHEIM Mentale Gesundheit, Liebenburg, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Laven Mavarani
- Institute for Medical Informatics, Biometry and Epidemiology, University Duisburg-Essen, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University Duisburg-Essen, Essen, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; German Center for Mental Health (DZPG), Partner site Mannheim/Heidelberg/Ulm, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; German Center for Mental Health (DZPG), Partner site Mannheim/Heidelberg/Ulm, Germany
| | - Jan Engelmann
- Department of Psychiatry and Psychotherapy, University Medical Center, Mainz, Germany; Translational Psychiatry, Department of Psychiatry and Psychotherapy & Focus Program Translational Neuroscience, University Medical Center, Mainz, Germany.
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9
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Kincses B, Forkmann K, Schlitt F, Jan Pawlik R, Schmidt K, Timmann D, Elsenbruch S, Wiech K, Bingel U, Spisak T. An externally validated resting-state brain connectivity signature of pain-related learning. Commun Biol 2024; 7:875. [PMID: 39020002 PMCID: PMC11255216 DOI: 10.1038/s42003-024-06574-y] [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: 10/09/2023] [Accepted: 07/10/2024] [Indexed: 07/19/2024] Open
Abstract
Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.
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Affiliation(s)
- Balint Kincses
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany.
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany.
| | - Katarina Forkmann
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Frederik Schlitt
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Robert Jan Pawlik
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Katharina Schmidt
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Dagmar Timmann
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Sigrid Elsenbruch
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Katja Wiech
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ulrike Bingel
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Tamas Spisak
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
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10
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Treccani M, Veschetti L, Patuzzo C, Malerba G, Vaglio A, Martorana D. Genetic and Non-Genetic Contributions to Eosinophilic Granulomatosis with Polyangiitis: Current Knowledge and Future Perspectives. Curr Issues Mol Biol 2024; 46:7516-7529. [PMID: 39057087 PMCID: PMC11275403 DOI: 10.3390/cimb46070446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
In this work, we present a comprehensive overview of the genetic and non-genetic complexity of eosinophilic granulomatosis with polyangiitis (EGPA). EGPA is a rare complex systemic disease that occurs in people presenting with severe asthma and high eosinophilia. After briefly introducing EGPA and its relationship with the anti-neutrophil cytoplasmic autoantibodies (ANCA)-associated vasculitis (AAVs), we delve into the complexity of this disease. At first, the two main biological actors, ANCA and eosinophils, are presented. Biological and clinical phenotypes related to ANCA positivity or negativity are explained, as well as the role of eosinophils and their pathological subtypes, pointing out their intricate relations with EGPA. Then, the genetics of EGPA are described, providing an overview of the research effort to unravel them. Candidate gene studies have investigated biologically relevant candidate genes; the more recent genome-wide association studies and meta-analyses, able to analyze the whole genome, have confirmed previous associations and discovered novel risk loci; in the end, family-based studies have dissected the contribution of rare variants and the heritability of EGPA. Then, we briefly present the environmental contribution to EGPA, reporting seasonal events and pollutants as triggering factors. In the end, the latest omic research is discussed and the most recent epigenomic, transcriptomic and microbiome studies are presented, highlighting the current challenges, open questions and suggesting approaches to unraveling this complex disease.
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Affiliation(s)
- Mirko Treccani
- GM Lab, Department of Surgery, Dentistry, Gynaecology and Paediatrics, University of Verona, 37134 Verona, Italy;
| | - Laura Veschetti
- Infections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy;
- Vita-Salute San Raffaele University, 20132 Milano, Italy
| | - Cristina Patuzzo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy;
| | - Giovanni Malerba
- GM Lab, Department of Surgery, Dentistry, Gynaecology and Paediatrics, University of Verona, 37134 Verona, Italy;
| | - Augusto Vaglio
- Nephrology and Dialysis Unit, Meyer Children’s Hospital IRCCS, 50139 Florence, Italy;
- Department of Biomedical Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50121 Florence, Italy
| | - Davide Martorana
- Medical Genetics Unit, Department of Onco-Hematology, University Hospital of Parma, 43126 Parma, Italy;
- CoreLab Unit, Research Center, University Hospital of Parma, 43126 Parma, Italy
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11
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Tubbs JD, Chen Y, Duan R, Huang H, Ge T. Real-time dynamic polygenic prediction for streaming data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.12.24310357. [PMID: 39040195 PMCID: PMC11261927 DOI: 10.1101/2024.07.12.24310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies (GWASs), which are often updated at lengthy intervals. As genetic data and health outcomes are continuously being generated at an ever-increasing pace, the current PRS training and deployment paradigm is suboptimal in maximizing the prediction accuracy of PRSs for incoming patients in healthcare settings. Here, we introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and calibration of PRS as each new sample is collected, without the need to perform intermediate GWASs. Through extensive simulation studies, we evaluate the performance of rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from the Mass General Brigham Biobank and UK Biobank, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts in 7 Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically predicting and stratifying disease risk across diverse genetic ancestries.
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Affiliation(s)
- Justin D. Tubbs
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Yu Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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12
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Dybing KM, McAllister TW, Wu YC, McDonald BC, Broglio SP, Mihalik JP, Guskiewicz KM, Goldman JT, Jackson JC, Risacher SL, Saykin AJ, Nudelman KNH. Association of Alzheimer's disease polygenic risk score with concussion severity and recovery metrics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.10.24309042. [PMID: 39040205 PMCID: PMC11261937 DOI: 10.1101/2024.07.10.24309042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Identification of genetic alleles associated with both Alzheimer's disease (AD) and concussion severity/recovery could help explain the association between concussion and elevated dementia risk. However, there has been little investigation into whether AD risk genes associate with concussion severity/recovery, and the limited findings are mixed. We used AD polygenic risk scores (PRS) and APOE genotypes to investigate any such associations in the NCAA-DoD Grand Alliance CARE Consortium (CARE) dataset. We assessed six outcomes in 931 total participants. The outcomes were two concussion recovery measures (number of days to asymptomatic status, number of days to return to play (RTP)) and four concussion severity measures (scores on SAC and BESS, SCAT symptom severity, and total number of symptoms). We calculated PRS using a published score [1] and performed multiple linear regression (MLR) to assess the relationship of PRS with the outcomes. We also used t-tests and chi-square tests to examine outcomes by APOE genotype, and MLR to analyze outcomes in European and African genetic ancestry subgroups. Higher PRS was associated with longer injury to RTP in the normal RTP (<24 days) subgroup ( p = 0.024), and one standard deviation increase in PRS resulted in a 9.89 hour increase to the RTP interval. There were no other consistently significant effects, suggesting that high AD genetic risk is not strongly associated with more severe concussions or poor recovery in young adults. Future studies should attempt to replicate these findings in larger samples with longer follow-up using PRS calculated from diverse populations.
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13
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [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: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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14
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Vinci M, Treccarichi S, Galati Rando R, Musumeci A, Todaro V, Federico C, Saccone S, Elia M, Calì F. A de novo ARIH2 gene mutation was detected in a patient with autism spectrum disorders and intellectual disability. Sci Rep 2024; 14:15848. [PMID: 38982159 PMCID: PMC11233510 DOI: 10.1038/s41598-024-66475-2] [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: 02/20/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024] Open
Abstract
E3 ubiquitin protein ligase encoded by ARIH2 gene catalyses the ubiquitination of target proteins and plays a crucial role in posttranslational modifications across various cellular processes. As prior documented, mutations in genes involved in the ubiquitination process are often associated with autism spectrum disorder (ASD) and/or intellectual disability (ID). In the current study, a de novo heterozygous mutation was identified in the splicing intronic region adjacent to the last exon of the ARIH2 gene using whole exome sequencing (WES). We hypothesize that this mutation, found in an ASD/ID patient, disrupts the protein Ariadne domain which is involved in the autoinhibition of ARIH2 enzyme. Predictive analyses elucidated the implications of the novel mutation in the splicing process and confirmed its autosomal dominant inheritance model. Nevertheless, we cannot exclude the possibility that other genetic factors, undetectable by WES, such as mutations in non-coding regions and polygenic risk in inter-allelic complementation, may contribute to the patient's phenotype. This work aims to suggest potential relationship between the detected mutation in ARIH2 gene and both ASD and ID, even though functional studies combined with new sequencing approaches will be necessary to validate this hypothesis.
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Affiliation(s)
| | | | | | | | - Valeria Todaro
- Department of Medical and Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Catania, Italy
| | - Concetta Federico
- Department of Biological, Geological and Environmental Sciences, University of Catania, Via Androne 81, 95124, Catania, Italy
| | - Salvatore Saccone
- Department of Biological, Geological and Environmental Sciences, University of Catania, Via Androne 81, 95124, Catania, Italy.
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15
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Rozanov V, Mazo G. Using the Strategy of Genome-Wide Association Studies to Identify Genetic Markers of Suicidal Behavior: A Narrative Review. CONSORTIUM PSYCHIATRICUM 2024; 5:63-77. [PMID: 39072004 PMCID: PMC11272302 DOI: 10.17816/cp15495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/10/2024] [Indexed: 07/30/2024] Open
Abstract
BACKGROUND Several studies involving various suicidal phenotypes based on the strategy of the search of genome-wide associations with single nucleotide polymorphisms have been performed recently. These studies need to be generalized. AIM To systematize the findings of a number of genome-wide association studies (GWAS) for suicidal phenotypes, annotate the identified markers, analyze their functionality, and possibly substantiate the hypothesis holding that these phenotypes reflect a nonspecific set of gene variants that are relevant as relates to stress-vulnerability as a key endophenotype of suicidal behavior (SB). METHODS A search on the PubMed and related resources using the combinations "suicide AND GWAS" and "suicidal behavior AND GWAS" was performed. It yielded a total of 34 independent studies and meta-analyses. RESULTS For the 10 years since such studies emerged, they have undergone significant progress. Estimates of the SNP heritability of SB in some cases are comparable with estimates of heritability based on the twin method. Many studies show a high genetic correlation with the genomic markers of the most common mental disorders (depression, bipolar disorder, schizophrenia, post-traumatic stress disorder). At the same time, a genomic architecture specific to SB is also encountered. Studies utilizing the GWAS strategy have not revealed any associations of SB with candidate genes that had been previously studied in detail (different neurotransmitters, stress response system, polyamines, etc.). Frequently reported findings from various studies belong in three main groups: 1) genes involved in cell interactions, neurogenesis, the development of brain structures, inflammation, and the immune responses; 2) genes encoding receptors for neurotrophins and various components of the intracellular signaling systems involved in synaptic plasticity, embryonic development, and carcinogenesis; and 3) genes encoding various neuro-specific proteins and regulators. CONCLUSION In general, GWAS in the field of suicidology mainly serve the purpose of a deeper understanding of the pathophysiology of suicidal behavior. However, they also demonstrate growing capability in terms of predicting and preventing suicide, especially when calculating the polygenic risk score among certain populations (psychiatric patients) and in combination with tests of different modalities. From our point of view, there exists a set of markers revealed by the GWAS strategy that seems to point to a leading role played by stress vulnerability, an endophenotype that is formed during early development and which subsequently comes to play the role of key pathogenetic mechanism in SB.
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16
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Forer L, Taliun D, LeFaive J, Smith AV, Boughton A, Coassin S, Lamina C, Kronenberg F, Fuchsberger C, Schönherr S. Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers. Nucleic Acids Res 2024; 52:W70-W77. [PMID: 38709879 PMCID: PMC11223871 DOI: 10.1093/nar/gkae331] [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: 02/21/2024] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.
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Affiliation(s)
- Lukas Forer
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montreal, Québec, Canada
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
| | - Jonathon LeFaive
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Albert V Smith
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew P Boughton
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stefan Coassin
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Claudia Lamina
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Biostatistics and the Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
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17
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Biagetti G, Thompson E, O'Brien C, Damrauer S. The Role of Genetics in Managing Peripheral Arterial Disease. Ann Vasc Surg 2024; 108:279-286. [PMID: 38960093 DOI: 10.1016/j.avsg.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Genome wide association studies (GWAS) have allowed for a rapid increase in our understanding of the underlying genetics and biology of many diseases. By capitalizing on common genetic variation between individuals, GWAS can identify DNA variants associated with diseases of interest. A variety of statistical methods can be applied to GWAS results which allows for risk factor identification, stratification, and to identify potential treatments. Peripheral artery disease (PAD) is a common vascular disease that has been shown to have a strong genetic component. This article provides a review of the modern literature and our current understanding of the role of genetics in PAD. METHODS All available GWAS studies on PAD were reviewed. A literature search involving these studies was conducted and relevant articles applying the available GWAS data were summarized to provide a comprehensive review of our current understanding of the genetic component in PAD. RESULTS The largest available GWAS on PAD has identified 19 genome wide significant loci, with factor V Leiden and genes responsible for circulating lipoproteins being implicated in the development of PAD. Mendelian randomization (MR) studies have identified risk factors and causal associations with smoking, diabetes, and obesity and many other traits; protein-based MR has also identified circulating lipid and clotting factor levels associated with the incidence of PAD. Polygenic risk scores may allow for improved prediction of disease incidence and allow for early identification of at-risk patients but more work needs to be done to validate this approach. CONCLUSIONS Genetic epidemiology has allowed for an increased understanding of PAD in the past decade. Genome-wide association studies have led to improved detection of genetic contributions to PAD, and further genetic analyses have validated risk factors and may provide options for improved screening in at-risk populations. Ongoing biobank studies of chronic limb threatening ischemia patients and the increasing ancestral diversity in biobank enrollment will allow for even further exploration into the pathogenesis and progression of PAD.
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Affiliation(s)
- Gina Biagetti
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Elizabeth Thompson
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ciaran O'Brien
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Scott Damrauer
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael Crescenz VA Medical Center, Philadelphia, PA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
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18
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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19
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Caparali EB, De Gregorio V, Barua M. Genetic Causes of Nephrotic Syndrome and Focal and Segmental Glomerulosclerosis. ADVANCES IN KIDNEY DISEASE AND HEALTH 2024; 31:309-316. [PMID: 39084756 DOI: 10.1053/j.akdh.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 08/02/2024]
Abstract
The field of nephrology has a long-standing interest in deciphering the genetic basis of nephrotic syndrome (NS), motivated by the mechanistic insights it provides in chronic kidney disease. The initial era of genetic studies solidified NS and the focal segmental glomerulosclerosis lesion as podocyte disorders. The likelihood of identifying a single gene (called monogenic) cause is higher if certain factors are present such as positive family history. Obtaining a monogenic diagnosis enables reproductive counseling and screening of family members. Now, with a new era of genomic studies facilitated by technological advances and the emergence of large genetically characterized cohorts, more insights are apparent. This includes the phenotypic breadth associated with disease genes, as evidenced in Alport syndrome and congenital NS of the Finnish type. Moreover, the underlying genetic architecture is more complex than previously appreciated, as shown by genome-wide association studies, suggesting that variants in multiple genes collectively influence risk. Achieving molecularly informed diagnoses also holds substantial potential for personalizing medicine, including the development of targeted therapeutics. Illustrative examples include coenzyme Q10 for ADCK4-associated NS and inaxaplin, a small molecule that inhibits apolipoprotein L1 channel activity, though larger studies are required to confirm benefit.
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Affiliation(s)
- Emine Bilge Caparali
- Division of Nephrology, University Health Network, Toronto, Ontario, Canada; Division of Nephrology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Vanessa De Gregorio
- Division of Nephrology, University Health Network, Toronto, Ontario, Canada; Division of Nephrology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Moumita Barua
- Division of Nephrology, University Health Network, Toronto, Ontario, Canada; Division of Nephrology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
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20
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Siermann M, Vermeesch JR, Raivio T, Tšuiko O, Borry P. Polygenic embryo screening: quo vadis? J Assist Reprod Genet 2024; 41:1719-1726. [PMID: 38879662 PMCID: PMC11263429 DOI: 10.1007/s10815-024-03169-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 06/06/2024] [Indexed: 07/23/2024] Open
Abstract
Recently, the use of polygenic risk scores in embryo screening (PGT-P) has been introduced on the premise of reducing polygenic disease risk through embryo selection. However, it has been met with extensive critique: considered "technology-driven" rather than "evidence-based", concerns exist about its validity, utility, ethics, and societal effects. Its scientific foundations and criticisms thus need to be carefully considered. However, seeing as PGT-P is already offered in some settings, further questions need to be addressed, in order to give due diligence to various aspects of PGT-P. By examining the complexities of clinical introduction of PGT-P, we discuss whether PGT-P could be responsibly implemented in the first place, what elements need to be addressed if PGT-P is clinically implemented, and subsequently how counselling and decision-making of its users could be envisaged. By dissecting these elements, we provide an overview of important practical questions of PGT-P and emphasize elements of PGT-P that we think have yet to be given sufficient attention. These questions and elements are for example related to the potential target group, scope, and decision-making possibilities of PGT-P. The aspects we raise are crucial to consider by the scientific community and policy makers for the development of guidelines and/or an ethical framework for PGT-P.
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Affiliation(s)
- Maria Siermann
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Box 7001, 3000, Leuven, Belgium.
- Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, P.O. Box 63, 00014, Helsinki, Finland.
| | | | - Taneli Raivio
- Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, P.O. Box 63, 00014, Helsinki, Finland
| | - Olga Tšuiko
- Center for Human Genetics, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Pascal Borry
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Box 7001, 3000, Leuven, Belgium
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21
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Gadd DA, Hillary RF, Kuncheva Z, Mangelis T, Cheng Y, Dissanayake M, Admanit R, Gagnon J, Lin T, Ferber KL, Runz H, Foley CN, Marioni RE, Sun BB. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. NATURE AGING 2024; 4:939-948. [PMID: 38987645 PMCID: PMC11257969 DOI: 10.1038/s43587-024-00655-7] [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: 03/15/2023] [Accepted: 05/22/2024] [Indexed: 07/12/2024]
Abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.
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Affiliation(s)
- Danni A Gadd
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Manju Dissanayake
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Romi Admanit
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Jake Gagnon
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Tinchi Lin
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Christopher N Foley
- Optima Partners, Edinburgh, UK.
- Bayes Centre, University of Edinburgh, Edinburgh, UK.
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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22
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Mckinnon K, Conole ELS, Vaher K, Hillary RF, Gadd DA, Binkowska J, Sullivan G, Stevenson AJ, Corrigan A, Murphy L, Whalley HC, Richardson H, Marioni RE, Cox SR, Boardman JP. Epigenetic scores derived in saliva are associated with gestational age at birth. Clin Epigenetics 2024; 16:84. [PMID: 38951914 PMCID: PMC11218140 DOI: 10.1186/s13148-024-01701-2] [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: 12/21/2023] [Accepted: 06/22/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins. RESULTS In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10-3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10-3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES-EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis. CONCLUSIONS Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.
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Affiliation(s)
- Katie Mckinnon
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Eleanor L S Conole
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kadi Vaher
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Justyna Binkowska
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Gemma Sullivan
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Amy Corrigan
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Boardman
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, 4-5 Little France Drive, Edinburgh, EH16 4UU, UK.
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
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23
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Høberg A, Solberg BS, Hegvik TA, Haavik J. Using polygenic scores in combination with symptom rating scales to identify attention-deficit/hyperactivity disorder. BMC Psychiatry 2024; 24:471. [PMID: 38937684 PMCID: PMC11210094 DOI: 10.1186/s12888-024-05925-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 06/20/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. METHODS Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. RESULTS The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2ASRS = 61.11%, R2ASRS + PGS=61.69%), the WURS by 0.61pp (R2WURS = 77.33%, R2WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2ASRS + WURS=80.84%, R2ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2family = 28.06%, R2family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. CONCLUSION We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined.
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Affiliation(s)
- André Høberg
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway.
| | - Berit Skretting Solberg
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway
- Child- and adolescent psychiatric outpatient unit, Hospital Betanien, Bergen, Norway
| | - Tor-Arne Hegvik
- Clinic of Surgery, St. Olavs Hospital, Trondheim, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway
- Bergen Center for Brain Plasticity, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
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24
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Chen T, Pham G, Fox L, Adler N, Wang X, Zhang J, Byun J, Han Y, Saunders GRB, Liu D, Bray MJ, Ramsey AT, McKay J, Bierut L, Amos CI, Hung RJ, Lin X, Zhang H, Chen LS. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304556. [PMID: 38562690 PMCID: PMC10984046 DOI: 10.1101/2024.03.19.24304556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.
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25
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Momin MM, Wray NR, Lee SH. R2ROC: an efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores. Hum Genet 2024:10.1007/s00439-024-02682-1. [PMID: 38902498 DOI: 10.1007/s00439-024-02682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/29/2024] [Indexed: 06/22/2024]
Abstract
Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong's method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and R 2 . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong's method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and R 2 values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European individuals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.
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Affiliation(s)
- Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
| | - Naomi R Wray
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxford, OX3 7JX, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
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26
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Houtz C. Weighty matters: Considering the ethics of genetic risk scores for obesity. Genet Med 2024; 26:101196. [PMID: 38907622 DOI: 10.1016/j.gim.2024.101196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 06/14/2024] [Accepted: 06/15/2024] [Indexed: 06/24/2024] Open
Affiliation(s)
- Cassie Houtz
- University of Pennsylvania Perelman School of Medicine, Department of Medical Ethics and Health Policy, Philadelphia, PA.
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27
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Ekberg KM, Michelini G, Schneider KL, Docherty AR, Shabalin AA, Perlman G, Kotov R, Klein DN, Waszczuk MA. Associations between polygenic risk scores for cardiometabolic phenotypes and adolescent depression and body dissatisfaction. Pediatr Res 2024:10.1038/s41390-024-03323-z. [PMID: 38879627 DOI: 10.1038/s41390-024-03323-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND Adolescents with elevated body mass index (BMI) are at an increased risk for depression and body dissatisfaction. Type 2 diabetes (T2D) is an established risk factor for depression. However, shared genetic risk between cardiometabolic conditions and mental health outcomes remains understudied in youth. METHODS The current study examined associations between polygenic risk scores (PRS) for BMI and T2D, and symptoms of depression and body dissatisfaction, in a sample of 827 community adolescents (Mage = 13.63, SDage = 1.01; 76% girls). BMI, depressive symptoms, and body dissatisfaction were assessed using validated self-report questionnaires. RESULTS BMI-PRS was associated with phenotypic BMI (β = 0.24, p < 0.001) and body dissatisfaction (β = 0.17, p < 0.001), but not with depressive symptoms. The association between BMI-PRS and body dissatisfaction was significantly mediated by BMI (indirect effect = 0.10, CI [0.07-0.13]). T2D-PRS was not associated with depression or body dissatisfaction. CONCLUSIONS The results suggest phenotypic BMI may largely explain the association between genetic risk for elevated BMI and body dissatisfaction in adolescents. Further research on age-specific genetic effects is needed, as summary statistics from adult discovery samples may have limited utility in youth. IMPACT The association between genetic risk for elevated BMI and body dissatisfaction in adolescents may be largely explained by phenotypic BMI, indicating a potential pathway through which genetic predisposition influences body image perception. Furthermore, age-specific genetic research is needed to understand the unique influences on health outcomes during adolescence. By identifying BMI as a potential mediator in the association between genetic risk for elevated BMI and body dissatisfaction, the current findings offer insights that could inform interventions targeting body image concerns and mental health in this population.
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Affiliation(s)
- Krista M Ekberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.
| | - Giorgia Michelini
- Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
| | - Kristin L Schneider
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Anna R Docherty
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Andrey A Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Greg Perlman
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
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28
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Tiezzi F, Goda K, Morgante F. Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597631. [PMID: 38895222 PMCID: PMC11185601 DOI: 10.1101/2024.06.05.597631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.
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Affiliation(s)
- Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Khushi Goda
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Fabio Morgante
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
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29
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Fabbri C, Lewis CM, Serretti A. Polygenic risk scores for mood and related disorders and environmental factors: Interaction effects on wellbeing in the UK biobank. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110972. [PMID: 38367896 DOI: 10.1016/j.pnpbp.2024.110972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/15/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions may affect wellbeing measures, which go beyond categorical diagnoses and reflect the health-disease continuum. We evaluated GxE effects in the UK Biobank, considering as outcomes subjective wellbeing (feeling good and functioning well) and objective measures (education and income). We estimated the polygenic risk scores (PRSs) of major depressive disorder, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder. Stressful/traumatic events during adulthood or childhood were considered as E variables, as well as social support. The addition of the PRSxE interaction to PRS and E variables was tested in linear or multinomial regression models, adjusting for confounders. We included 33 k-380 k participants, depending on the variables considered. Most PRSs and E factors showed additive effects on outcomes, with effect sizes generally 3-5 times larger for E variables than PRSs. We found some interaction effects, particularly when considering recent stress, history of a long illness/disability/infirmity, and social support. Higher PRSs increased the negative effects of stress on wellbeing, but they also increased the positive effects of social support, with interaction effects particularly for the outcomes health satisfaction, loneliness, and income (p < Bonferroni corrected threshold of 1.92e-4). PRSxE terms usually added ∼0.01-0.02% variance explained to the corresponding additive model. PRSxE effects on wellbeing involve both positive and negative E factors. Despite small variance explained at the population level, preventive/therapeutic interventions that modify E factors could be beneficial at the individual level.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy.
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Department of Medicine and Surgery, Kore University of Enna, Enna, Italy
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30
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Kranzler HR, Davis CN, Feinn R, Jinwala Z, Khan Y, Oikonomou A, Silva-Lopez D, Burton I, Dixon M, Milone J, Ramirez S, Shifman N, Levey D, Gelernter J, Hartwell EE, Kember RL. Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence. Nat Hum Behav 2024:10.1038/s41562-024-01885-w. [PMID: 38834750 DOI: 10.1038/s41562-024-01885-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/10/2024] [Indexed: 06/06/2024]
Abstract
Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear. We examined associations among ACEs, mood/anxiety disorders and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/white). Using latent variables for each phenotype, we modelled direct and indirect associations of ACEs with substance dependence, mediated by mood/anxiety disorders (the forward or 'self-medication' model) and of ACEs with mood/anxiety disorders, mediated by substance dependence (the reverse or 'substance-induced' model). In a subsample, we tested polygenic scores for the substance dependence and mood/anxiety disorder factors as moderators in the mediation models. Although there were significant indirect paths in both directions, mediation by mood/anxiety disorders (the forward model) was greater than that by substance dependence (the reverse model). Greater genetic risk for substance use disorders was associated with a weaker direct association between ACEs and substance dependence in both ancestry groups (reflecting gene × environment interactions) and a weaker indirect association in European-ancestry individuals (reflecting moderated mediation). We found greater evidence that substance dependence reflects self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence, ACEs were less associated with that outcome. Following exposure to ACEs, multiple pathways appear to underlie the associations between mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.
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Affiliation(s)
- Henry R Kranzler
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
| | - Christal N Davis
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Richard Feinn
- Department of Medical Sciences, Frank H. Netter School of Medicine at Quinnipiac University, North Haven, CT, USA
| | - Zeal Jinwala
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Yousef Khan
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ariadni Oikonomou
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Damaris Silva-Lopez
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Isabel Burton
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Morgan Dixon
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jackson Milone
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sarah Ramirez
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Naomi Shifman
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Daniel Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Departments of Genetics and Neurobiology, Yale University School of Medicine, New Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Emily E Hartwell
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Rachel L Kember
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
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31
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [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: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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32
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Krug A, Stein F, David FS, Schmitt S, Brosch K, Pfarr JK, Ringwald KG, Meller T, Thomas-Odenthal F, Meinert S, Thiel K, Winter A, Waltemate L, Lemke H, Grotegerd D, Opel N, Repple J, Hahn T, Streit F, Witt SH, Rietschel M, Andlauer TFM, Nöthen MM, Philipsen A, Nenadić I, Dannlowski U, Kircher T, Forstner AJ. Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample. Transl Psychiatry 2024; 14:235. [PMID: 38830892 PMCID: PMC11148082 DOI: 10.1038/s41398-024-02936-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/05/2024] Open
Abstract
There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.
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Affiliation(s)
- Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany.
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- German Centre for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Jena, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Frankfurt, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159, Mannheim, Germany
| | - Till F M Andlauer
- Department of Neurology, Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Alexandra Philipsen
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
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33
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Arango NK, Morgante F. Comparing statistical learning methods for complex trait prediction from gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596951. [PMID: 38895364 PMCID: PMC11185554 DOI: 10.1101/2024.06.01.596951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
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Affiliation(s)
- Noah Klimkowski Arango
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Fabio Morgante
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
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34
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Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
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Affiliation(s)
- Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dezheng Ding
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yuxin Guo
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gideon Nave
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael L. Platt
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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35
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Topriceanu CC, Chaturvedi N, Mathur R, Garfield V. Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations. Eur J Hum Genet 2024; 32:697-707. [PMID: 38182743 PMCID: PMC11153583 DOI: 10.1038/s41431-023-01517-3] [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: 06/10/2023] [Revised: 10/29/2023] [Accepted: 11/28/2023] [Indexed: 01/07/2024] Open
Abstract
Polygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Nish Chaturvedi
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Rohini Mathur
- Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Victoria Garfield
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
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Patel KHS, Walters GB, Stefánsson H, Stefánsson K, Degenhardt F, Nothen M, Van Der Veen T, Demontis D, Borglum A, Kristiansen M, Bass NJ, McQuillin A. Predicting ADHD in alcohol dependence using polygenic risk scores for ADHD. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32967. [PMID: 37946686 DOI: 10.1002/ajmg.b.32967] [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: 02/02/2023] [Revised: 09/15/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with a high degree of comorbidity, including substance misuse. We aimed to assess whether ADHD polygenic risk scores (PRS) could predict ADHD diagnosis in alcohol dependence (AD). ADHD PRS were generated for 1223 AD subjects with ADHD diagnosis information and 1818 healthy controls. ADHD PRS distributions were compared to evaluate the differences between healthy controls and AD cases with and without ADHD. We found increased ADHD PRS means in the AD cohort with ADHD (mean 0.30, standard deviation (SD) 0.92; p = 3.9 × 10-6); and without ADHD (mean - 0.00, SD 1.00; p = 5.2 × 10-5) compared to the healthy control subjects (mean - 0.17, SD 0.99). The ADHD PRS means differed within the AD group with a higher ADHD PRS mean in those with ADHD, odds ratio (OR) 1.34, confidence interval (CI) 1.10 to 1.65; p = 0.002. This study showed a positive relationship between ADHD PRS and risk of ADHD in individuals with co-occurring AD indicating that ADHD PRS may have utility in identifying individuals that are at a higher or lower risk of ADHD. Further larger studies need to be conducted to confirm the reliability of the results before ADHD PRS can be considered as a robust biomarker for diagnosis.
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Affiliation(s)
- Kejal H S Patel
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - G Bragi Walters
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | | | - Kári Stefánsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Franziska Degenhardt
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, LVR Klinikum Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital, Bonn, Germany
| | - Markus Nothen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital, Bonn, Germany
| | - Tracey Van Der Veen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Ditte Demontis
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders Borglum
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Mark Kristiansen
- University College London Genomics, Institute of Child Health, University College London, London, UK
| | - Nicholas J Bass
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
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Lteif C, Huang Y, Guerra LA, Gawronski BE, Duarte JD. Using Omics to Identify Novel Therapeutic Targets in Heart Failure. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004398. [PMID: 38766848 PMCID: PMC11187651 DOI: 10.1161/circgen.123.004398] [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] [Indexed: 05/22/2024]
Abstract
Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.
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Affiliation(s)
- Christelle Lteif
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Yimei Huang
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Leonardo A Guerra
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Brian E Gawronski
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Julio D Duarte
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
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38
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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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39
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Henry J, Lin Y, Bouatia-Naji N. Enhancing the Prediction Power of Polygenic Risk Scores in Genetically Diverse Coronary Heart Disease. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004610. [PMID: 38586952 DOI: 10.1161/circgen.124.004610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Joséphine Henry
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
| | - Yilong Lin
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
| | - Nabila Bouatia-Naji
- Université Paris Cité, Paris-Cardiovascular Research Center, Institut National de la Sante et de la Recherche Medicale, France
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40
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Flint JP, Welstead M, Cox SR, Russ TC, Marshall A, Luciano M. Validation of a polygenic risk score for frailty in the Lothian Birth Cohort 1936 and English longitudinal study of ageing. Sci Rep 2024; 14:12586. [PMID: 38822050 PMCID: PMC11143351 DOI: 10.1038/s41598-024-63229-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/24/2024] [Indexed: 06/02/2024] Open
Abstract
Frailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from a Genome Wide Association Study on the Frailty Index to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples - the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67-84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty, measured via the FI, at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085-0.21) and 1.8% (β = 0.14, 95%CI, 0.10-0.17) of the variance, respectively, at age ~ 68/ ~ 70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~ 68/ ~ 70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.
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Affiliation(s)
- J P Flint
- Advanced Care Research Centre, School of Engineering, College of Science and Engineering, The University of Edinburgh, Edinburgh, UK.
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK.
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.
| | - M Welstead
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - S R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - T C Russ
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A Marshall
- Advanced Care Research Centre, School of Engineering, College of Science and Engineering, The University of Edinburgh, Edinburgh, UK
- School of Social and Political Science, University of Edinburgh, Edinburgh, UK
| | - M Luciano
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
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41
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Schwantes-An TH, Whitfield JB, Aithal GP, Atkinson SR, Bataller R, Botwin G, Chalasani NP, Cordell HJ, Daly AK, Darlay R, Day CP, Eyer F, Foroud T, Gawrieh S, Gleeson D, Goldman D, Haber PS, Jacquet JM, Lammert CS, Liang T, Liangpunsakul S, Masson S, Mathurin P, Moirand R, McQuillin A, Moreno C, Morgan MY, Mueller S, Müllhaupt B, Nagy LE, Nahon P, Nalpas B, Naveau S, Perney P, Pirmohamed M, Seitz HK, Soyka M, Stickel F, Thompson A, Thursz MR, Trépo E, Morgan TR, Seth D. A polygenic risk score for alcohol-associated cirrhosis among heavy drinkers with European ancestry. Hepatol Commun 2024; 8:e0431. [PMID: 38727677 PMCID: PMC11093576 DOI: 10.1097/hc9.0000000000000431] [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: 07/10/2023] [Accepted: 11/01/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Polygenic Risk Scores (PRS) based on results from genome-wide association studies offer the prospect of risk stratification for many common and complex diseases. We developed a PRS for alcohol-associated cirrhosis by comparing single-nucleotide polymorphisms among patients with alcohol-associated cirrhosis (ALC) versus drinkers who did not have evidence of liver fibrosis/cirrhosis. METHODS Using a data-driven approach, a PRS for ALC was generated using a meta-genome-wide association study of ALC (N=4305) and an independent cohort of heavy drinkers with ALC and without significant liver disease (N=3037). It was validated in 2 additional independent cohorts from the UK Biobank with diagnosed ALC (N=467) and high-risk drinking controls (N=8981) and participants in the Indiana Biobank Liver cohort with alcohol-associated liver disease (N=121) and controls without liver disease (N=3239). RESULTS A 20-single-nucleotide polymorphisms PRS for ALC (PRSALC) was generated that stratified risk for ALC comparing the top and bottom deciles of PRS in the 2 validation cohorts (ORs: 2.83 [95% CI: 1.82 -4.39] in UK Biobank; 4.40 [1.56 -12.44] in Indiana Biobank Liver cohort). Furthermore, PRSALC improved the prediction of ALC risk when added to the models of clinically known predictors of ALC risk. It also stratified the risk for metabolic dysfunction -associated steatotic liver disease -cirrhosis (3.94 [2.23 -6.95]) in the Indiana Biobank Liver cohort -based exploratory analysis. CONCLUSIONS PRSALC incorporates 20 single-nucleotide polymorphisms, predicts increased risk for ALC, and improves risk stratification for ALC compared with the models that only include clinical risk factors. This new score has the potential for early detection of heavy drinking patients who are at high risk for ALC.
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Affiliation(s)
- Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis IN, USA
| | - John B. Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Queensland 4029, Australia
| | - Guruprasad P. Aithal
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals and the University of Nottingham, Nottingham NG7 2UH, UK
| | - Stephen R. Atkinson
- Department of Metabolism, Digestion & Reproduction, Imperial College London, UK
| | - Ramon Bataller
- Center for Liver Diseases, University of Pittsburgh Medical Center, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Greg Botwin
- Department of Veterans Affairs, VA Long Beach Healthcare System, 5901 East Seventh Street, Long Beach, CA 90822, USA
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California CA 90048, USA
| | - Naga P. Chalasani
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Ann K. Daly
- Faculty of Medical Sciences, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Rebecca Darlay
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Christopher P. Day
- Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Florian Eyer
- Division of Clinical Toxicology, Department of Internal Medicine 2, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis IN, USA
| | - Samer Gawrieh
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Dermot Gleeson
- Liver Unit, Sheffield Teaching Hospitals, AO Floor Robert Hadfield Building, Northern General Hospital, Sheffield S5 7AU, UK
| | - David Goldman
- Office of the Clinical Director and Laboratory of Neurogenetics, NIAAA, Bethesda, MD 20952, USA
| | - Paul S. Haber
- Edith Collins Centre (Translational Research in Alcohol Drugs and Toxicology), Sydney Local Health District, Missenden Road, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2006, Australia
| | | | - Craig S. Lammert
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Tiebing Liang
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Suthat Liangpunsakul
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University and Roudebush Veterans Administration Medical Center, Indianapolis, USA
| | - Steven Masson
- Faculty of Medical Sciences, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Philippe Mathurin
- CHRU de Lille, Hôpital Claude Huriez, Rue M. Polonovski CS 70001, 59 037 Lille Cedex, France
| | - Romain Moirand
- Univ Rennes, INRA, INSERM, CHU Rennes, Institut NUMECAN (Nutrition Metabolisms and Cancer), F-35000 Rennes, France
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London WC1E 6DE, UK
| | - Christophe Moreno
- CUB Hôpital Erasme, Université Libre de Bruxelles, clinique d’Hépatologie, Brussels, Belgium; Laboratory of Experimental Gastroenterology, Université Libre de Bruxelles, Brussels, Belgium
| | - Marsha Y. Morgan
- UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London NW3 2PF, UK
| | - Sebastian Mueller
- Department of Internal Medicine, Salem Medical Center and Center for Alcohol Research, University of Heidelberg, Zeppelinstraße 11-33, 69121 Heidelberg, Germany
| | - Beat Müllhaupt
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Rämistrasse 100, CH-8901 Zurich, Switzerland
| | - Laura E. Nagy
- Lerner Research Institute, 9500 Euclid Avenue, Cleveland, Ohio, OH 44195, USA
| | - Pierre Nahon
- Service d'Hépatologie, APHP Hôpital Avicenne et Université Paris 13, Bobigny, France
- University Paris 13, Bobigny, France
- Inserm U1162 Génomique fonctionnelle des tumeurs solides, Paris, France
| | - Bertrand Nalpas
- Service Addictologie, CHRU Caremeau, 30029 Nîmes, France
- DISC, Inserm, 75013 Paris, France
| | - Sylvie Naveau
- Hôpital Antoine-Béclère, 157 Rue de la Porte de Trivaux, 92140 Clamart, France
| | - Pascal Perney
- Hôpital Universitaire Caremeau, Place du Pr. Robert Debre, 30029 Nîmes, France
| | - Munir Pirmohamed
- MRC Centre for Drug Safety Science, Liverpool Centre for Alcohol Research, University of Liverpool, The Royal Liverpool and Broadgreen University Hospitals NHS Trust, and Liverpool Health Partners, Liverpool, L69 3GL, UK
| | - Helmut K. Seitz
- Department of Internal Medicine, Salem Medical Center and Center for Alcohol Research, University of Heidelberg, Zeppelinstraße 11-33, 69121 Heidelberg, Germany
| | - Michael Soyka
- Psychiatric Hospital University of Munich, Nussbaumsstr.7, 80336 Munich, Germany
| | - Felix Stickel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Rämistrasse 100, CH-8901 Zurich, Switzerland
| | - Andrew Thompson
- MRC Centre for Drug Safety Science, Liverpool Centre for Alcohol Research, University of Liverpool, The Royal Liverpool and Broadgreen University Hospitals NHS Trust, and Liverpool Health Partners, Liverpool, L69 3GL, UK
- Health Analytics, Lane Clark & Peacock LLP, London, UK
| | - Mark R. Thursz
- Department of Metabolism, Digestion & Reproduction, Imperial College London, UK
| | - Eric Trépo
- CUB Hôpital Erasme, Université Libre de Bruxelles, clinique d’Hépatologie, Brussels, Belgium; Laboratory of Experimental Gastroenterology, Université Libre de Bruxelles, Brussels, Belgium
| | - Timothy R. Morgan
- Department of Medicine, University of California, Irvine, USA
- Department of Veterans Affairs, VA Long Beach Healthcare System, 5901 East Seventh Street, Long Beach, CA 90822, USA
| | - Devanshi Seth
- Edith Collins Centre (Translational Research in Alcohol Drugs and Toxicology), Sydney Local Health District, Missenden Road, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2006, Australia
- Centenary Institute of Cancer Medicine and Cell Biology, the University of Sydney, Sydney, NSW 2006, Australia
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42
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Xiao Y, Yi Y, Jing D, Yang S, Guo Y, Xiao H, Kuang Y, Zhu W, Zhao J, Li Y, Liu H, Li J, Chen X, Shen M. Age at Natural Menopause, Reproductive Lifespan, and the Risk of Late-Onset Psoriasis and Psoriatic Arthritis in Women: A Prospective Cohort Study. J Invest Dermatol 2024; 144:1273-1281.e5. [PMID: 38081449 DOI: 10.1016/j.jid.2023.11.010] [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: 09/25/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
Although a peak incidence of psoriasis in women aged around 60 years has been observed, the link between reproductive lifespan and late-onset psoriatic diseases is underexplored. This study aims to elucidate the association between reproductive lifespan and the risk of late-onset psoriasis and psoriatic arthritis (PsA). Utilizing the UK Biobank data, we conducted a prospective cohort study in postmenopausal women without baseline psoriatic diseases. The exposure variables included age at natural menopause (ANM) and duration from menarche to menopause, termed reproductive years. The outcome variables were incident psoriasis and PsA. We employed Cox regression analysis, factoring in polygenic risk scores for psoriatic diseases and recognized risk factors. We found that later ANM and longer reproductive years were significantly associated with decreased risks of late-onset psoriasis and PsA in a dose-dependent manner (P<.05). ANM after age 55 years led to a 34 and 46% risk reduction in late-onset psoriasis and PsA, respectively, compared with ANM before age 45 years (P<.001). The population-attributable risks of ANM were 17.4% for late-onset psoriasis and 21.6% for PsA. In conclusion, reproductive lifespan, with its inherent homeostasis, plays a pivotal yet overlooked role in late-onset psoriatic diseases. Investigations into estrogen-centric causes and sex-specific interventions are imperative.
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Affiliation(s)
- Yi Xiao
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, China
| | - Yan Yi
- Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Danrong Jing
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Songchun Yang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Yeye Guo
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Hui Xiao
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Yehong Kuang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Wu Zhu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Jing Zhao
- Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Yanping Li
- Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Hong Liu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, China; Bioinformatics Center, Xiangya Hospital, Central South University, Changsha, China; Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Xiang Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, China.
| | - Minxue Shen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China; Furong Laboratory, Changsha, China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China; Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China.
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Hu L, Wu S, Shu Y, Su K, Wang C, Wang D, He Q, Chen X, Li W, Mi N, Xie P, Zhao J, Zhang S, Yuan J, Xiang J, Xia B. Impact of Maternal Smoking, Offspring Smoking, and Genetic Susceptibility on Crohn's Disease and Ulcerative Colitis. J Crohns Colitis 2024; 18:671-678. [PMID: 38038665 DOI: 10.1093/ecco-jcc/jjad200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND AND AIMS The long-term impact of maternal smoking during pregnancy [MSDP] on the risk of Crohn's disease [CD] and ulcerative colitis [UC] in adult offspring remains uncertain. The present study aimed to investigate the individual and combined effects of early life exposure [MSDP], offspring personal behaviour [smoking], and genetic risk on the development of CD and UC in adult offspring. METHODS We conducted a prospective cohort study using UK Biobank data, including 334 083 participants recruited between 2006 and 2010, with follow-up until December 31, 2021. Multivariable Cox regression models were used to evaluate the associations of genetic factors, maternal and personal smoking, and their combination with CD and UC. RESULTS Participants exposed to MSDP had an 18% increased risk of CD compared to those without MSDP (hazard ratio [HR] = 1.18, 95% confidence interval [CI] = 1.01-1.39). However, no significant association was found between MSDP and UC risk [HR = 1.03, 95% CI = 0.92-1.16]. Personal smoking increased the risk of CD and UC, and had a numerically amplified effect with MSDP. Participants with high genetic risk and MSDP had a 2.01-fold [95% CI = 1.53-2.65] and a 2.45-fold [95% CI = 2.00-2.99] increased risk of CD and UC, respectively, compared to participants without MSDP and with low genetic risk. CONCLUSIONS Our prospective cohort study provides evidence that MSDP increases the risk of CD in adult offspring, whereas no evidence supports their causal association. Additionally, smoking and genetic susceptibility had a numerically amplified effect with MSDP on CD and UC, but the interaction lacked statistical significance.
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Affiliation(s)
- Linmin Hu
- School of Public Health [Shenzhen], Sun Yat-sen University, Shenzhen 518107, China
| | - Siqing Wu
- School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Yuelong Shu
- School of Public Health [Shenzhen], Sun Yat-sen University, Shenzhen 518107, China
- Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Pekina Union Medical School, Beijing, China
| | - Kai Su
- School of Public Health [Shenzhen], Sun Yat-sen University, Shenzhen 518107, China
| | - Chunliang Wang
- School of Public Health [Shenzhen], Sun Yat-sen University, Shenzhen 518107, China
| | - Danni Wang
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Qiangsheng He
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
- Chinese Health Risk Management Collaboration [CHRIMAC], Shenzhen, Guangdong 518107, China
| | - Xinyu Chen
- Department of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-sen University, No. 628, Zhenyuan Road, Xinhu Street, Guangming District, Shenzhen 518107, China
| | - Wenjing Li
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Ningning Mi
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Peng Xie
- Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Jinyu Zhao
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Shiyong Zhang
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080China
| | - Jinqiu Yuan
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
- Chinese Health Risk Management Collaboration [CHRIMAC], Shenzhen, Guangdong 518107, China
- Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Jianbang Xiang
- School of Public Health [Shenzhen], Sun Yat-sen University, Shenzhen 518107, China
| | - Bin Xia
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China
- Chinese Health Risk Management Collaboration [CHRIMAC], Shenzhen, Guangdong 518107, China
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Ho PJ, Khng A, Tan BKT, Khor CC, Tan EY, Lim GH, Yuan JM, Tan SM, Chang X, Tan VKM, Sim X, Dorajoo R, Koh WP, Hartman M, Li J. Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants. Cancers (Basel) 2024; 16:2072. [PMID: 38893190 PMCID: PMC11171367 DOI: 10.3390/cancers16112072] [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: 04/22/2024] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. METHODS Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases. RESULTS We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS. CONCLUSIONS While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore 138672, Singapore; (P.J.H.)
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Alexis Khng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore 138672, Singapore; (P.J.H.)
| | - Benita Kiat-Tee Tan
- Department of General Surgery, Sengkang General Hospital, Singapore 544886, Singapore
- Department of Breast Surgery, Singapore General Hospital, Singapore 544886, Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Chiea Chuen Khor
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore 138672, Singapore; (P.J.H.)
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore 639798, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), 61 Biopolis Street, Singapore 138673, Singapore
| | - Geok Hoon Lim
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
| | - Jian-Min Yuan
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Su-Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore 529889, Singapore
| | - Xuling Chang
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Khoo Teck Puat—National University Children’s Medical Institute, National University Health System, Singapore 119074, Singapore
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Veronique Kiak Mien Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore 544886, Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Rajkumar Dorajoo
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore 138672, Singapore; (P.J.H.)
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117545, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore 117609, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore 119074, Singapore
| | - Jingmei Li
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore 138672, Singapore; (P.J.H.)
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
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Socrates AJ, Mullins N, Gur RC, Gur RE, Stahl E, O'Reilly PF, Reichenberg A, Jones H, Zammit S, Velthorst E. Polygenic risk of social isolation behavior and its influence on psychopathology and personality. Mol Psychiatry 2024:10.1038/s41380-024-02617-2. [PMID: 38811692 DOI: 10.1038/s41380-024-02617-2] [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: 02/22/2023] [Revised: 05/02/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders.
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Affiliation(s)
- Adam J Socrates
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA.
| | - Niamh Mullins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine and the Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, 3400 Spruce, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine and the Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, 3400 Spruce, Philadelphia, PA, 19104, USA
| | - Eli Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Regeneron Genetics Centre, Tarrytown, NY, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Hannah Jones
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2PR, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
| | - Stanley Zammit
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
- Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Eva Velthorst
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Department of Research, Mental Health Organization "GGZ Noord-Holland-Noord,", Heerhugowaard, The Netherlands
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46
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Ohta R, Tanigawa Y, Suzuki Y, Kellis M, Morishita S. A polygenic score method boosted by non-additive models. Nat Commun 2024; 15:4433. [PMID: 38811555 DOI: 10.1038/s41467-024-48654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
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Affiliation(s)
- Rikifumi Ohta
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
| | - Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Yuta Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Shinichi Morishita
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
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Gerring ZF, Thorp JG, Treur JL, Verweij KJH, Derks EM. The genetic landscape of substance use disorders. Mol Psychiatry 2024:10.1038/s41380-024-02547-z. [PMID: 38811691 DOI: 10.1038/s41380-024-02547-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 05/31/2024]
Abstract
Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.
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Affiliation(s)
- Zachary F Gerring
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
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Li L, Pang S, Starnecker F, Mueller-Myhsok B, Schunkert H. Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease. Eur Heart J 2024; 45:1843-1852. [PMID: 38551411 PMCID: PMC11129792 DOI: 10.1093/eurheartj/ehae048] [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: 02/19/2023] [Revised: 12/20/2023] [Accepted: 01/18/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND AND AIMS It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2. METHODS A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification. RESULTS In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC. CONCLUSIONS This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.
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Affiliation(s)
- Ling Li
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- School of Computation, Information and Technology, Technische Universität München, Munich, Germany
| | - Shichao Pang
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
| | - Fabian Starnecker
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Bertram Mueller-Myhsok
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
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Wimberley T, Brikell I, Astrup A, Larsen JT, Petersen LV, Albiñana C, Vilhjálmsson BJ, Bulik CM, Chang Z, Fanelli G, Bralten J, Mota NR, Salas-Salvadó J, Fernandez-Aranda F, Bulló M, Franke B, Børglum A, Mortensen PB, Horsdal HT, Dalsgaard S. Shared familial risk for type 2 diabetes mellitus and psychiatric disorders: a nationwide multigenerational genetics study. Psychol Med 2024:1-10. [PMID: 38801094 DOI: 10.1017/s0033291724001053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Psychiatric disorders and type 2 diabetes mellitus (T2DM) are heritable, polygenic, and often comorbid conditions, yet knowledge about their potential shared familial risk is lacking. We used family designs and T2DM polygenic risk score (T2DM-PRS) to investigate the genetic associations between psychiatric disorders and T2DM. METHODS We linked 659 906 individuals born in Denmark 1990-2000 to their parents, grandparents, and aunts/uncles using population-based registers. We compared rates of T2DM in relatives of children with and without a diagnosis of any or one of 11 specific psychiatric disorders, including neuropsychiatric and neurodevelopmental disorders, using Cox regression. In a genotyped sample (iPSYCH2015) of individuals born 1981-2008 (n = 134 403), we used logistic regression to estimate associations between a T2DM-PRS and these psychiatric disorders. RESULTS Among 5 235 300 relative pairs, relatives of individuals with a psychiatric disorder had an increased risk for T2DM with stronger associations for closer relatives (parents:hazard ratio = 1.38, 95% confidence interval 1.35-1.42; grandparents: 1.14, 1.13-1.15; and aunts/uncles: 1.19, 1.16-1.22). In the genetic sample, one standard deviation increase in T2DM-PRS was associated with an increased risk for any psychiatric disorder (odds ratio = 1.11, 1.08-1.14). Both familial T2DM and T2DM-PRS were significantly associated with seven of 11 psychiatric disorders, most strongly with attention-deficit/hyperactivity disorder and conduct disorder, and inversely with anorexia nervosa. CONCLUSIONS Our findings of familial co-aggregation and higher T2DM polygenic liability associated with psychiatric disorders point toward shared familial risk. This suggests that part of the comorbidity is explained by shared familial risks. The underlying mechanisms still remain largely unknown and the contributions of genetics and environment need further investigation.
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Affiliation(s)
- Theresa Wimberley
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Centre for Integrated Register-based Research (CIRRAU), Aarhus University, Aarhus, Denmark
| | - Isabell Brikell
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Aske Astrup
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | - Janne T Larsen
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
| | - Liselotte V Petersen
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
| | - Clara Albiñana
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Zheng Chang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Giuseppe Fanelli
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Nina R Mota
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jordi Salas-Salvadó
- Department of Biochemistry & Biotechnology, School of Medicine, IISPV, Rovira i Virgili University. Reus, Spain
- Institute of Health Pere Virgili (IISPV), Reus, Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII). Madrid, Spain
| | - Fernando Fernandez-Aranda
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII). Madrid, Spain
- Clinical Psychology Unit, University Hospital Bellvitge, Hospitalet del Llobregat, Spain
- Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Hospitalet del Llobregat, Spain
- Psychoneurobiology of Eating and Addictive Behaviours Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet del Llobregat, Spain
| | - Monica Bulló
- Department of Biochemistry & Biotechnology, School of Medicine, IISPV, Rovira i Virgili University. Reus, Spain
- Institute of Health Pere Virgili (IISPV), Reus, Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII). Madrid, Spain
- Center of Environmental, Food and Toxicological Technology - TecnATox, Rovira i Virgili University, 43201 Reus, Spain
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anders Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Preben B Mortensen
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Centre for Integrated Register-based Research (CIRRAU), Aarhus University, Aarhus, Denmark
| | - Henriette T Horsdal
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | - Søren Dalsgaard
- The National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Child and Adolescent Psychiatry Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Department of Clinical Medicine, Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Step K, Ndong Sima CAA, Mata I, Bardien S. Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction. Front Neurosci 2024; 18:1380860. [PMID: 38859922 PMCID: PMC11163124 DOI: 10.3389/fnins.2024.1380860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Affiliation(s)
- Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
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