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Gao S, Zhu P, Wang T, Han Z, Xue Y, Zhang Y, Wang L, Zhang H, Chen Y, Liu G. Alzheimer's disease genome-wide association studies in the context of statistical heterogeneity. Mol Psychiatry 2024:10.1038/s41380-024-02654-x. [PMID: 38965422 DOI: 10.1038/s41380-024-02654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Affiliation(s)
- Shan Gao
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, 100069, Beijing, China
| | - Ping Zhu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, 100069, Beijing, China
| | - Tao Wang
- Chinese Institute for Brain Research, 102206, Beijing, China
| | - Zhifa Han
- Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Acadamy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, 100029, Beijing, China
| | - Yanli Xue
- School of Biomedical Engineering, Capital Medical University, 10069, Beijing, China
| | - Yan Zhang
- Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Longcai Wang
- Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Haihua Zhang
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, 100069, Beijing, China
| | - Yan Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu, 241002, Anhui, China
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No.22, Wenchang Road, Wuhu, 241002, Anhui, China
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, 100069, Beijing, China.
- Chinese Institute for Brain Research, 102206, Beijing, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu, 241002, Anhui, China.
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No.22, Wenchang Road, Wuhu, 241002, Anhui, China.
- Beijing Key Laboratory of Hypoxia Translational Medicine, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Diseases, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, China.
- Brain Hospital, Shengli Oilfield Central Hospital, Dongying, 257000, Shandong, China.
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He J, Cabrera-Mendoza B, Friligkou E, Mecca AP, van Dyck CH, Pathak GA, Polimanti R. Sex differences in the associations of socioeconomic factors and cognitive performance with family history of Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.12.24308850. [PMID: 38947007 PMCID: PMC11213115 DOI: 10.1101/2024.06.12.24308850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
INTRODUCTION While higher socioeconomic factors (SEF) and cognitive performance (CP) have been associated with reduced Alzheimer's disease (AD) risk, recent evidence highlighted that these factors may have opposite effects on family history of AD (FHAD). METHODS Leveraging data from the UK Biobank (N=448,100) and the All of Us Research Program (N=240,319), we applied generalized linear regression models, polygenic risk scoring (PRS), and one-sample Mendelian randomization (MR) to test the sex-specific SEF and CP associations with AD and FHAD. RESULTS Observational and genetically informed analyses highlighted that higher SEF and CP were associated with reduced AD and sibling-FHAD, while these factors were associated with increased parent-FHAD. We also observed that population minorities may present different patterns with respect to sibling-FHAD vs. parent-FHAD. Sex differences in FHAD associations were identified in ancestry-specific and SEF PRS and MR results. DISCUSSION This study contributes to understanding the sex-specific relationships linking SEF and CP to FHAD, highlighting the potential role of reporting, recall, and surviving-related dynamics.
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Luo R, Zeraatkar D, Glymour M, Ellis RJ, Estiri H, Patel CJ. Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank. BMC Med 2024; 22:216. [PMID: 38807092 PMCID: PMC11134914 DOI: 10.1186/s12916-024-03424-w] [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: 01/05/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND In 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes. METHODS We evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors). RESULTS SCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes. CONCLUSIONS We observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.
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Affiliation(s)
- Renhao Luo
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Dena Zeraatkar
- Department of Anesthesia and Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - Maria Glymour
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Randall J Ellis
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hossein Estiri
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Qiao Y, Jewett EM, McManus KF, Freyman WA, Curran JE, Williams-Blangero S, Blangero J, Williams AL. Reconstructing parent genomes using siblings and other relatives. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593578. [PMID: 38798596 PMCID: PMC11118276 DOI: 10.1101/2024.05.10.593578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Reconstructing the DNA of ancestors from their descendants has the potential to empower phenotypic analyses (including association and genetic nurture studies), improve pedigree reconstruction, and shed light on the ancestral population and phenotypes of ancestors. We developed HAPI-RECAP, a method that reconstructs the DNA of parents from full siblings and their relatives. This tool leverages HAPI2's output, a new phasing approach that applies to siblings (and optionally one or both parents) and reliably infers parent haplotypes but does not link the ungenotyped parents' DNA across chromosomes or between segments flanking ambiguities. By combining IBD between the reconstructed parents and the relatives, HAPI-RECAP resolves the source parent of these segments. Moreover, the method exploits crossovers the children inherited and sex-specific genetic maps to infer the reconstructed parents' sexes. We validated these methods on research participants from both 23andMe, Inc. and the San Antonio Mexican American Family Studies. Given data for one parent, HAPI2 reconstructs large fractions of the missing parent's DNA, between 77.6% and 99.97% among all families, and 90.3% on average in three- and four-child families. When reconstructing both parents, HAPI-RECAP inferred between 33.2% and 96.6% of the parents' genotypes, averaging 70.6% in four-child families. Reconstructed genotypes have average error rates < 10-3, or comparable to those from direct genotyping. HAPI-RECAP inferred the parent sexes 100% correctly given IBD-linked segments and can also reconstruct parents without any IBD. As datasets grow in size, more families will be implicitly collected; HAPI-RECAP holds promise to enable high quality parent genotype reconstruction.
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Affiliation(s)
- Ying Qiao
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
| | | | | | | | - Joanne E. Curran
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - Sarah Williams-Blangero
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | | | - Amy L. Williams
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- 23andMe, Inc., Sunnyvale, CA 94086, USA
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Duchateau L, Wawrzyniak N, Sleegers K. The ABC's of Alzheimer risk gene ABCA7. Alzheimers Dement 2024; 20:3629-3648. [PMID: 38556850 PMCID: PMC11095487 DOI: 10.1002/alz.13805] [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: 01/03/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2024]
Abstract
Alzheimer's disease (AD) is a growing problem worldwide. Since ABCA7's identification as a risk gene, it has been extensively researched for its role in the disease. We review its recently characterized structure and what the mechanistic insights teach us about its function. We furthermore provide an overview of identified ABCA7 mutations, their presence in different ancestries and protein domains and how they might cause AD. For ABCA7 PTC variants and a VNTR expansion, haploinsufficiency is proposed as the most likely mode-of-action, although splice events could further influence disease risk. Overall, the need to better understand expression of canonical ABCA7 and its isoforms in disease is indicated. Finally, ABCA7's potential functions in lipid metabolism, phagocytosis, amyloid deposition, and the interplay between these three, is described. To conclude, in this review, we provide a comprehensive overview and discussion about the current knowledge on ABCA7 in AD, and what research questions remain. HIGHLIGHTS: Alzheimer's risk-increasing variants in ABCA7 can be found in up to 7% of AD patients. We review the recently characterized protein structure of ABCA7. We present latest insights in genetics, expression patterns, and functions of ABCA7.
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Affiliation(s)
- Lena Duchateau
- Complex Genetics of Alzheimer's Disease group, VIB‐UAntwerp Center for Molecular NeurologyWilrijkAntwerpBelgium
- Department of Biomedical SciencesUniversity of AntwerpWilrijkAntwerpBelgium
| | - Nicole Wawrzyniak
- Complex Genetics of Alzheimer's Disease group, VIB‐UAntwerp Center for Molecular NeurologyWilrijkAntwerpBelgium
- Chávez‐Gutiérrez Lab, VIB‐KU Leuven Center for Brain and Disease Research, VIBLeuvenBelgium
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease group, VIB‐UAntwerp Center for Molecular NeurologyWilrijkAntwerpBelgium
- Department of Biomedical SciencesUniversity of AntwerpWilrijkAntwerpBelgium
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Mews MA, Naj AC, Griswold AJ, Below JE, Bush WS. Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305737. [PMID: 38699333 PMCID: PMC11065015 DOI: 10.1101/2024.04.17.24305737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
INTRODUCTION Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS. METHODS We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944). RESULTS We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 . DISCUSSION Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.
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Gao S, Wang T, Han Z, Hu Y, Zhu P, Xue Y, Huang C, Chen Y, Liu G. Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity. Brief Bioinform 2024; 25:bbae140. [PMID: 38711368 PMCID: PMC11074593 DOI: 10.1093/bib/bbae140] [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: 08/21/2023] [Revised: 02/22/2024] [Accepted: 03/14/2024] [Indexed: 05/08/2024] Open
Abstract
Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.
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Affiliation(s)
- Shan Gao
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, No. 10, Xitoutiao, You’an Men Wai, Fengtai District, Beijing 100069, China
| | - Tao Wang
- Chinese Institute for Brain Research, No. 26, Kexueyuan Road, Changping District, Beijing 102206, China
| | - Zhifa Han
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, No. 5, Dongdan Santichao, Dongcheng District, Beijing 100193, China
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin 150006, China
| | - Ping Zhu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, No. 10, Xitoutiao, You’an Men Wai, Fengtai District, Beijing 100069, China
| | - Yanli Xue
- School of Biomedical Engineering, Capital Medical University, No. 10 Xitoutiao, You'an Men Wai, Fengtai District, Beijing 100069, China
| | - Chen Huang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida WaiLong, Taipa 999078, Macao SAR, China
| | - Yan Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
| | - Guiyou Liu
- Chinese Institute for Brain Research, No. 26, Kexueyuan Road, Changping District, Beijing 102206, China
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian 271000, Shandong, China
- Beijing Key Laboratory of Hypoxia Translational Medicine, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Road, Xicheng District, Beijing 100053, China
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Ma X, Thela SR, Zhao F, Yao B, Wen Z, Jin P, Zhao J, Chen L. Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583444. [PMID: 38496575 PMCID: PMC10942288 DOI: 10.1101/2024.03.04.583444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
5-hydroxymethylcytosine (5hmC), a critical epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and the histone modification information to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four time points during forebrain organoid development and across 17 human tissues. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions in a case-control study of Alzheimer's disease.
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Affiliation(s)
- Xin Ma
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
| | - Sai Ritesh Thela
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
| | - Fengdi Zhao
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
| | - Bing Yao
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Zhexing Wen
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jinying Zhao
- Department of Epidemiology, University of Florida, Gainesville, FL, 32603, USA
| | - Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Zhao F, Ma X, Yao B, Chen L. scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.21.576570. [PMID: 38328112 PMCID: PMC10849518 DOI: 10.1101/2024.01.21.576570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study, regions which are most enriched in GO terms related to neurogenesis, the clinical phenotype of AD, and SNPs identified in AD-associated GWAS.
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Affiliation(s)
- Fengdi Zhao
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Xin Ma
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Bing Yao
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
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11
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Zhu J, Liu S, Walker KA, Zhong H, Ghoneim DH, Zhang Z, Surendran P, Fahle S, Butterworth A, Alam MA, Deng HW, Wu C, Wu L. Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models. Alzheimers Res Ther 2024; 16:8. [PMID: 38212844 PMCID: PMC10782590 DOI: 10.1186/s13195-023-01378-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 12/30/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. METHODS We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. RESULTS We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. CONCLUSIONS Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.
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Affiliation(s)
- Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute On Aging, Intramural Research Program, Baltimore, MD, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Dalia H Ghoneim
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Praveen Surendran
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sarah Fahle
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Adam Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics., Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, 1440 Canal Street, New Orleans, LA, 70112, USA
- Center for Outcomes Research, Ochsner Clinic Foundation, New Orleans, LA, 70121, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics., Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, 1440 Canal Street, New Orleans, LA, 70112, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.
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12
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Mentrup T, Leinung N, Patel M, Fluhrer R, Schröder B. The role of SPP/SPPL intramembrane proteases in membrane protein homeostasis. FEBS J 2024; 291:25-44. [PMID: 37625440 DOI: 10.1111/febs.16941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/03/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023]
Abstract
Signal peptide peptidase (SPP) and the four SPP-like proteases SPPL2a, SPPL2b, SPPL2c and SPPL3 constitute a family of aspartyl intramembrane proteases with homology to presenilins. The different members reside in distinct cellular localisations within the secretory pathway and the endo-lysosomal system. Despite individual cleavage characteristics, they all cleave single-span transmembrane proteins with a type II orientation exhibiting a cytosolic N-terminus. Though the identification of substrates is not complete, SPP/SPPL-mediated proteolysis appears to be rather selective. Therefore, according to our current understanding cleavage by SPP/SPPL proteases rather seems to serve a regulatory function than being a bulk proteolytic pathway. In the present review, we will summarise our state of knowledge on SPP/SPPL proteases and in particular highlight recently identified substrates and the functional and/or (patho)-physiological implications of these cleavage events. Based on this, we aim to provide an overview of the current open questions in the field. These are connected to the regulation of these proteases at the cellular level but also in context of disease and patho-physiological processes. Furthermore, the interplay with other proteostatic systems capable of degrading membrane proteins is beginning to emerge.
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Affiliation(s)
- Torben Mentrup
- Institute for Physiological Chemistry, Technische Universität Dresden, Germany
| | - Nadja Leinung
- Institute for Physiological Chemistry, Technische Universität Dresden, Germany
| | - Mehul Patel
- Institute for Physiological Chemistry, Technische Universität Dresden, Germany
| | - Regina Fluhrer
- Biochemistry and Molecular Biology, Institute of Theoretical Medicine, Faculty of Medicine, University of Augsburg, Germany
- Center for Interdisciplinary Health Research, University of Augsburg, Germany
| | - Bernd Schröder
- Institute for Physiological Chemistry, Technische Universität Dresden, Germany
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13
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Dahl A, Thompson M, An U, Krebs M, Appadurai V, Border R, Bacanu SA, Werge T, Flint J, Schork AJ, Sankararaman S, Kendler KS, Cai N. Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder. Nat Genet 2023; 55:2082-2093. [PMID: 37985818 PMCID: PMC10703686 DOI: 10.1038/s41588-023-01559-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/18/2023] [Indexed: 11/22/2023]
Abstract
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
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Affiliation(s)
- Andrew Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
| | - Michael Thompson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ulzee An
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Richard Border
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany.
- School of Medicine, Technical University of Munich, Munich, Germany.
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14
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Jowell AR, Bhattacharya R, Marnell C, Wong M, Haidermota S, Trinder M, Fahed AC, Peloso GM, Honigberg MC, Natarajan P. Genetic and clinical factors underlying a self-reported family history of heart disease. Eur J Prev Cardiol 2023; 30:1571-1579. [PMID: 37011137 PMCID: PMC10545808 DOI: 10.1093/eurjpc/zwad096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 04/05/2023]
Abstract
AIMS To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors. METHODS AND RESULTS Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27-1.33) and HeFH (OR 1.31, 1.11-1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15-1.21), lipoprotein(a) (OR 1.17, CI 1.14-1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10-1.16), and triglycerides (OR 1.07, CI 1.04-1.10). For the PAR analyses: 21.9% (CI 18.19-25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44-23.88) is attributed to genetic factors, and 36.0% (CI 33.31-38.68) is attributed to genetic and clinical factors combined. CONCLUSIONS A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.
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Affiliation(s)
- Amanda R Jowell
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Romit Bhattacharya
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
| | - Christopher Marnell
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Cardiology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY 10029, USA
| | - Megan Wong
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
| | - Sara Haidermota
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
| | - Mark Trinder
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
- Department of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Akl C Fahed
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02115, USA
| | - Michael C Honigberg
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 320, Boston, MA 02114, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Merkin Building, 415 Main Street, Cambridge, MA 02142, USA
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15
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Wu Y, Sun Z, Zheng Q, Miao J, Dorn S, Mukherjee S, Fletcher JM, Lu Q. Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562272. [PMID: 37904974 PMCID: PMC10614766 DOI: 10.1101/2023.10.13.562272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.
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Affiliation(s)
- Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA
| | - Zhongxuan Sun
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Qinwen Zheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Stephen Dorn
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Jason M. Fletcher
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA
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16
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Gouilly D, Rafiq M, Nogueira L, Salabert AS, Payoux P, Péran P, Pariente J. Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology. Rev Neurol (Paris) 2023; 179:812-830. [PMID: 36906457 DOI: 10.1016/j.neurol.2022.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/02/2022] [Accepted: 12/02/2022] [Indexed: 03/13/2023]
Abstract
Alzheimer's disease (AD) is a multi-etiology disease. The biological system of AD is associated with multidomain genetic, molecular, cellular, and network brain dysfunctions, interacting with central and peripheral immunity. These dysfunctions have been primarily conceptualized according to the assumption that amyloid deposition in the brain, whether from a stochastic or a genetic accident, is the upstream pathological change. However, the arborescence of AD pathological changes suggests that a single amyloid pathway might be too restrictive or inconsistent with a cascading effect. In this review, we discuss the recent human studies of late-onset AD pathophysiology in an attempt to establish a general updated view focusing on the early stages. Several factors highlight heterogenous multi-cellular pathological changes in AD, which seem to work in a self-amplifying manner with amyloid and tau pathologies. Neuroinflammation has an increasing importance as a major pathological driver, and perhaps as a convergent biological basis of aging, genetic, lifestyle and environmental risk factors.
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Affiliation(s)
- D Gouilly
- Toulouse Neuroimaging Center, Toulouse, France.
| | - M Rafiq
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France
| | - L Nogueira
- Department of Cell Biology and Cytology, CHU Toulouse Purpan, France
| | - A-S Salabert
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France
| | - P Payoux
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
| | - P Péran
- Toulouse Neuroimaging Center, Toulouse, France
| | - J Pariente
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
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17
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Soh PXY, Khatkar MS, Williamson P. Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis. Vet Sci 2023; 10:581. [PMID: 37756103 PMCID: PMC10536503 DOI: 10.3390/vetsci10090581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
There has been considerable interest in studying cancer in dogs and its potential as a model system for humans. One area of research has been the search for genetic risk variants in canine lymphoma, which is amongst the most common canine cancers. Previous studies have focused on a limited number of breeds, but none have included Border Collies. The aims of this study were to identify relationships between Border Collie lymphoma cases through an extensive pedigree investigation and to utilise relationship information to conduct genome-wide association study (GWAS) analyses to identify risk regions associated with lymphoma. The expanded pedigree analysis included 83,000 Border Collies, with 71 identified lymphoma cases. The analysis identified affected close relatives, and a common ancestor was identified for 54 cases. For the genomic study, a GWAS was designed to incorporate lymphoma cases, putative "carriers", and controls. A case-control GWAS was also conducted as a comparison. Both analyses showed significant SNPs in regions on chromosomes 18 and 27. Putative top candidate genes from these regions included DLA-79, WNT10B, LMBR1L, KMT2D, and CCNT1.
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Affiliation(s)
- Pamela Xing Yi Soh
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia;
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Mehar Singh Khatkar
- Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia;
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA 5371, Australia
| | - Peter Williamson
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia;
- Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia;
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18
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Wilcox N, Dumont M, González-Neira A, Carvalho S, Joly Beauparlant C, Crotti M, Luccarini C, Soucy P, Dubois S, Nuñez-Torres R, Pita G, Gardner EJ, Dennis J, Alonso MR, Álvarez N, Baynes C, Collin-Deschesnes AC, Desjardins S, Becher H, Behrens S, Bolla MK, Castelao JE, Chang-Claude J, Cornelissen S, Dörk T, Engel C, Gago-Dominguez M, Guénel P, Hadjisavvas A, Hahnen E, Hartman M, Herráez B, Jung A, Keeman R, Kiechle M, Li J, Loizidou MA, Lush M, Michailidou K, Panayiotidis MI, Sim X, Teo SH, Tyrer JP, van der Kolk LE, Wahlström C, Wang Q, Perry JRB, Benitez J, Schmidt MK, Schmutzler RK, Pharoah PDP, Droit A, Dunning AM, Kvist A, Devilee P, Easton DF, Simard J. Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk. Nat Genet 2023; 55:1435-1439. [PMID: 37592023 PMCID: PMC10484782 DOI: 10.1038/s41588-023-01466-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/05/2023] [Indexed: 08/19/2023]
Abstract
Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2.5 × 10-6): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1. Associations were also observed for LZTR1, ATR and BARD1 with P < 1 × 10-4. Associations between predicted deleterious rare missense or protein-truncating variants and breast cancer were additionally identified for CDKN2A at exome-wide significance. The overall contribution of coding variants in genes beyond the previously known genes is estimated to be small.
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Affiliation(s)
- Naomi Wilcox
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martine Dumont
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Charles Joly Beauparlant
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Marco Crotti
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Penny Soucy
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Stéphane Dubois
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Rocio Nuñez-Torres
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Eugene J Gardner
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - M Rosario Alonso
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Nuria Álvarez
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Caroline Baynes
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Annie Claude Collin-Deschesnes
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Sylvie Desjardins
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sten Cornelissen
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE-Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Manuela Gago-Dominguez
- Cancer Genetics and Epidemiology Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) Foundation, Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Pascal Guénel
- Team 'Exposome and Heredity,' CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France
| | - Andreas Hadjisavvas
- Department of Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore City, Singapore
- Department of Surgery, National University Health System, Singapore City, Singapore
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
| | - Belén Herráez
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Marion Kiechle
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Jingmei Li
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore City, Singapore.
| | - Maria A Loizidou
- Department of Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Mihalis I Panayiotidis
- Department of Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore City, Singapore
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, UM Cancer Research Institute, Kuala Lumpur, Malaysia
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Lizet E van der Kolk
- Family Cancer Clinic, The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands
| | - Cecilia Wahlström
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Javier Benitez
- Human Genetics Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands
| | - Rita K Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Arnaud Droit
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
- Département de Médecine Moléculaire, Faculté de Médecine, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Quebec, Canada
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Anders Kvist
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
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19
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Hwang LD, Cuellar-Partida G, Yengo L, Zeng J, Beaumont RN, Freathy RM, Moen GH, Warrington NM, Evans DM. Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.22.23294446. [PMID: 37693475 PMCID: PMC10491281 DOI: 10.1101/2023.08.22.23294446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared across the individual GWAS. In this manuscript we investigate the performance of three strategies to detect loci in maternal and fetal GWAS of the same trait: (i) the traditional strategy of analysing maternal and fetal GWAS separately; (ii) a novel two degree of freedom test which combines information from maternal and fetal GWAS; and (iii) a novel one degree of freedom test where signals from maternal and fetal GWAS are meta-analysed together conditional on the estimated sample overlap. We demonstrate through a combination of analytical formulae and data simulation that the optimal strategy depends on the extent of sample overlap/relatedness between the maternal and fetal GWAS, the correlation between own and offspring phenotypes, whether loci jointly exhibit fetal and maternal effects, and if so, whether these effects are directionally concordant. We apply our methods to summary results statistics from a recent GWAS meta-analysis of birth weight from deCODE, the UK Biobank and the Early Growth Genetics (EGG) consortium. Both the two degree of freedom (213 loci) and meta-analytic approach (226 loci) dramatically increase the number of robustly associated genetic loci for birth weight relative to separately analysing the scans (183 loci). Our best strategy identifies an additional 62 novel loci compared to the most recent published meta-analysis of birth weight and implicates both known and new biological pathways in the aetiology of the trait. We implement our methods in the online DINGO (Direct and INdirect effects analysis of Genetic lOci) software package, which allows users to perform one and/or two degree of freedom tests easily and computationally efficiently across the genome. We conclude that whilst the novel two degree of freedom test may be particularly useful for the analysis of certain perinatal phenotypes where many loci exhibit discordant maternal and fetal genetic effects, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWAS only partially overlap.
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Affiliation(s)
- Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | | | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- The Frazer Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia
| | - Nicole M Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- The Frazer Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia
| | - David M Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- The Frazer Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia
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20
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Rego S, Sanchez G, Da Mesquita S. Current views on meningeal lymphatics and immunity in aging and Alzheimer's disease. Mol Neurodegener 2023; 18:55. [PMID: 37580702 PMCID: PMC10424377 DOI: 10.1186/s13024-023-00645-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023] Open
Abstract
Alzheimer's disease (AD) is an aging-related form of dementia associated with the accumulation of pathological aggregates of amyloid beta and neurofibrillary tangles in the brain. These phenomena are accompanied by exacerbated inflammation and marked neuronal loss, which altogether contribute to accelerated cognitive decline. The multifactorial nature of AD, allied to our still limited knowledge of its etiology and pathophysiology, have lessened our capacity to develop effective treatments for AD patients. Over the last few decades, genome wide association studies and biomarker development, alongside mechanistic experiments involving animal models, have identified different immune components that play key roles in the modulation of brain pathology in AD, affecting its progression and severity. As we will relay in this review, much of the recent efforts have been directed to better understanding the role of brain innate immunity, and particularly of microglia. However, and despite the lack of diversity within brain resident immune cells, the brain border tissues, especially the meninges, harbour a considerable number of different types and subtypes of adaptive and innate immune cells. Alongside microglia, which have taken the centre stage as important players in AD research, there is new and exciting evidence pointing to adaptive immune cells, namely T and B cells found in the brain and its meninges, as important modulators of neuroinflammation and neuronal (dys)function in AD. Importantly, a genuine and functional lymphatic vascular network is present around the brain in the outermost meningeal layer, the dura. The meningeal lymphatics are directly connected to the peripheral lymphatic system in different mammalian species, including humans, and play a crucial role in preserving a "healthy" immune surveillance of the CNS, by shaping immune responses, not only locally at the meninges, but also at the level of the brain tissue. In this review, we will provide a comprehensive view on our current knowledge about the meningeal lymphatic vasculature, emphasizing its described roles in modulating CNS fluid and macromolecule drainage, meningeal and brain immunity, as well as glial and neuronal function in aging and in AD.
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Affiliation(s)
- Shanon Rego
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
- Post-baccalaureate Research Education Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Guadalupe Sanchez
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
- Neuroscience Ph.D. Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Sandro Da Mesquita
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.
- Post-baccalaureate Research Education Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Jacksonville, FL, 32224, USA.
- Neuroscience Ph.D. Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Jacksonville, FL, 32224, USA.
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21
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Lona-Durazo F, Reynolds RH, Scholz SW, Ryten M, Gagliano Taliun SA. Regional genetic correlations highlight relationships between neurodegenerative disease loci and the immune system. Commun Biol 2023; 6:729. [PMID: 37454237 PMCID: PMC10349864 DOI: 10.1038/s42003-023-05113-5] [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: 12/14/2022] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
Neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are devastating complex diseases resulting in physical and psychological burdens on patients and their families. There have been important efforts to understand their genetic basis leading to the identification of disease risk-associated loci involved in several molecular mechanisms, including immune-related pathways. Regional, in contrast to genome-wide, genetic correlations between pairs of immune and neurodegenerative traits have not been comprehensively explored, but could uncover additional immune-mediated risk-associated loci. Here, we systematically assess the role of the immune system in five neurodegenerative diseases by estimating regional genetic correlations between these diseases and immune-cell-derived single-cell expression quantitative trait loci (sc-eQTLs). We also investigate correlations between diseases and protein levels. We observe significant (FDR < 0.01) correlations between sc-eQTLs and neurodegenerative diseases across 151 unique genes, spanning both the innate and adaptive immune systems, across most diseases tested. With Parkinson's, for instance, RAB7L1 in CD4+ naïve T cells is positively correlated and KANSL1-AS1 is negatively correlated across all adaptive immune cell types. Follow-up colocalization highlight candidate causal risk genes. The outcomes of this study will improve our understanding of the immune component of neurodegeneration, which can warrant repurposing of existing immunotherapies to slow disease progression.
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Affiliation(s)
- Frida Lona-Durazo
- Montréal Heart Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Regina H Reynolds
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Sarah A Gagliano Taliun
- Montréal Heart Institute, Montréal, QC, Canada.
- Department of Medicine & Department of Neurosciences, Université de Montréal, Montréal, QC, Canada.
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22
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Lambert JC, Ramirez A, Grenier-Boley B, Bellenguez C. Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease. Mol Psychiatry 2023; 28:2716-2727. [PMID: 37131074 PMCID: PMC10615767 DOI: 10.1038/s41380-023-02076-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 05/04/2023]
Abstract
Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.
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Affiliation(s)
- Jean-Charles Lambert
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France.
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurodegenerative diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Benjamin Grenier-Boley
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France
| | - Céline Bellenguez
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France
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23
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Logue MW, Miller MW, Sherva R, Zhang R, Harrington KM, Fonda JR, Merritt VC, Panizzon MS, Hauger RL, Wolf EJ, Neale Z, Gaziano JM. Alzheimer's disease and related dementias among aging veterans: Examining gene-by-environment interactions with post-traumatic stress disorder and traumatic brain injury. Alzheimers Dement 2023; 19:2549-2559. [PMID: 36546606 PMCID: PMC10271966 DOI: 10.1002/alz.12870] [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: 05/24/2022] [Revised: 10/03/2022] [Accepted: 10/17/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) confer risk for Alzheimer's disease and related dementias (ADRD). METHODS This study from the Million Veteran Program (MVP) evaluated the impact of apolipoprotein E (APOE) ε4, PTSD, and TBI on ADRD prevalence in veteran cohorts of European ancestry (EA; n = 11,112 ADRD cases, 170,361 controls) and African ancestry (AA; n = 1443 ADRD cases, 16,191 controls). Additive-scale interactions were estimated using the relative excess risk due to interaction (RERI) statistic. RESULTS PTSD, TBI, and APOE ε4 showed strong main-effect associations with ADRD. RERI analysis revealed significant additive APOE ε4 interactions with PTSD and TBI in the EA cohort and TBI in the AA cohort. These additive interactions indicate that ADRD prevalence associated with PTSD and TBI increased with the number of inherited APOE ε4 alleles. DISCUSSION PTSD and TBI history will be an important part of interpreting the results of ADRD genetic testing and doing accurate ADRD risk assessment.
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Affiliation(s)
- Mark W Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Mark W Miller
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Richard Sherva
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, Massachusetts, USA
| | - Rui Zhang
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kelly M Harrington
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Jennifer R Fonda
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Educational and Clinical Center (GRECC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Victoria C Merritt
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, California, USA
- Division of Aging, Harvard Medical School, Brigham & Women's Hospital, Boston, Massachusetts, USA
| | - Richard L Hauger
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, California, USA
| | - Erika J Wolf
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Zoe Neale
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Division of Aging, Harvard Medical School, Brigham & Women's Hospital, Boston, Massachusetts, USA
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24
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Gorelik AJ, Paul SE, Karcher NR, Johnson EC, Nagella I, Blaydon L, Modi H, Hansen IS, Colbert SMC, Baranger DAA, Norton SA, Spears I, Gordon B, Zhang W, Hill PL, Oltmanns TF, Bijsterbosch JD, Agrawal A, Hatoum AS, Bogdan R. A Phenome-Wide Association Study (PheWAS) of Late Onset Alzheimer Disease Genetic Risk in Children of European Ancestry at Middle Childhood: Results from the ABCD Study. Behav Genet 2023; 53:249-264. [PMID: 37071275 PMCID: PMC10309061 DOI: 10.1007/s10519-023-10140-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/08/2023] [Indexed: 04/19/2023]
Abstract
Genetic risk for Late Onset Alzheimer Disease (AD) has been associated with lower cognition and smaller hippocampal volume in healthy young adults. However, whether these and other associations are present during childhood remains unclear. Using data from 5556 genomically-confirmed European ancestry youth who completed the baseline session of the ongoing the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®), our phenome-wide association study estimating associations between four indices of genetic risk for late-onset AD (i.e., AD polygenic risk scores (PRS), APOE rs429358 genotype, AD PRS with the APOE region removed (ADPRS-APOE), and an interaction between ADPRS-APOE and APOE genotype) and 1687 psychosocial, behavioral, and neural phenotypes revealed no significant associations after correction for multiple testing (all ps > 0.0002; all pfdr > 0.07). These data suggest that AD genetic risk may not phenotypically manifest during middle-childhood or that effects are smaller than this sample is powered to detect.
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Affiliation(s)
- Aaron J Gorelik
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Sarah E Paul
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Nicole R Karcher
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Isha Nagella
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Lauren Blaydon
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Hailey Modi
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Isabella S Hansen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Sarah M C Colbert
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - David A A Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Sara A Norton
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Isaiah Spears
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Brian Gordon
- Department of Radiology, Washington University in Saint Louis, 660 South Euclid Ave, Box 8225, St. Louis, MO, 63110, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St Louis, MO, USA
| | - Wei Zhang
- Department of Radiology, Washington University in Saint Louis, 660 South Euclid Ave, Box 8225, St. Louis, MO, 63110, USA
| | - Patrick L Hill
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Thomas F Oltmanns
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University in Saint Louis, 660 South Euclid Ave, Box 8225, St. Louis, MO, 63110, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, One Booking Drive, St. Louis, MO, 63130, USA.
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25
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Dai J, Xu Y, Wang T, Zeng P. Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: From genetic correlation to causality. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110700. [PMID: 36566903 DOI: 10.1016/j.pnpbp.2022.110700] [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: 04/10/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Patients with Alzheimer's disease (AD) are markedly increasing as population aging and no disease-modifying therapies are currently available for AD. Previous studies suggested a broad link between socioeconomic status and a variety of disorders, including mental illness and cognitive abilities. However, the association between socioeconomic deprivation and AD has been unknown. We here employed Townsend deprivation index (TDI) to explore such relation and found a positive genetic correlation (r̂g=0.211, P = 8.00 × 10-4) between the two traits with summary statistics data (N = 455,258 for TDI and N = 455,815 for AD). Then, we performed pleiotropy analysis at both variant and gene levels using a powerful method called PLACO and detected 87 distinct pleiotropic genes. Functional analysis demonstrated these genes were significantly enriched in pancreas, liver, heart, blood, brain, and muscle tissues. Using Mendelian randomization methods, we further found that one genetically predicted standard deviation elevation in TDI could lead to approximately 18.5% (95% confidence intervals 1.6- 38.2%, P = 0.031) increase of AD risk, and that the identified causal association was robust against used MR approaches, horizontal pleiotropy, and instrumental selection. Overall, this study provides deep insight into common genetic components underlying TDI and AD, and further reveals causal connection between them. It is also helpful to develop a more suitable plan for ameliorating inequities, hardship, and disadvantage, with the hope of improving health outcomes among economically disadvantaged people.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yue Xu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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26
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Barry CJ, Carslake D, Wade KH, Sanderson E, Davey Smith G. Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank. Int J Epidemiol 2023; 52:545-561. [PMID: 35947758 PMCID: PMC10114047 DOI: 10.1093/ije/dyac159] [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: 09/28/2021] [Accepted: 07/25/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants. METHODS In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation. RESULTS Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter). CONCLUSION Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.
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Affiliation(s)
- Ciarrah-Jane Barry
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - David Carslake
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - Kaitlin H Wade
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1156] [Impact Index Per Article: 1156.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Wightman DP, Savage JE, de Leeuw CA, Jansen IE, Posthuma D. Rare variant aggregation in 148,508 exomes identifies genes associated with proxy dementia. Sci Rep 2023; 13:2179. [PMID: 36750708 PMCID: PMC9905079 DOI: 10.1038/s41598-023-29108-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Proxy phenotypes allow for the utilization of genetic data from large population cohorts to analyze late-onset diseases by using parental diagnoses as a proxy for genetic disease risk. Proxy phenotypes based on parental diagnosis status have been used in previous studies to identify common variants associated with Alzheimer's disease. As of yet, proxy phenotypes have not been used to identify genes associated with Alzheimer's disease through rare variants. Here we show that a proxy Alzheimer's disease/dementia phenotype can capture known Alzheimer's disease risk genes through rare variant aggregation. We generated a proxy Alzheimer's disease/dementia phenotype for 148,508 unrelated individuals of European ancestry in the UK biobank in order to perform exome-wide rare variant aggregation analyses to identify genes associated with proxy Alzheimer's disease/dementia. We identified four genes significantly associated with the proxy phenotype, three of which were significantly associated with proxy Alzheimer's disease/dementia in an independent replication cohort consisting of 197,506 unrelated individuals of European ancestry in the UK biobank. All three of the replicated genes have been previously associated with clinically diagnosed Alzheimer's disease (SORL1, TREM2, and TOMM40/APOE). We show that proxy Alzheimer's disease/dementia can be used to identify genes associated with Alzheimer's disease through rare variant aggregation.
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Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands.
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
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Abdellaoui A, Yengo L, Verweij KJH, Visscher PM. 15 years of GWAS discovery: Realizing the promise. Am J Hum Genet 2023; 110:179-194. [PMID: 36634672 PMCID: PMC9943775 DOI: 10.1016/j.ajhg.2022.12.011] [Citation(s) in RCA: 75] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.
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Affiliation(s)
- Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Leyden GM, Greenwood MP, Gaborieau V, Han Y, Amos CI, Brennan P, Murphy D, Davey Smith G, Richardson TG. Disentangling the aetiological pathways between body mass index and site-specific cancer risk using tissue-partitioned Mendelian randomisation. Br J Cancer 2023; 128:618-625. [PMID: 36434155 PMCID: PMC9938133 DOI: 10.1038/s41416-022-02060-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Body mass index (BMI) is known to influence the risk of various site-specific cancers, however, dissecting which subcomponents of this heterogenous risk factor are predominantly responsible for driving disease effects has proven difficult to establish. We have leveraged tissue-specific gene expression to separate the effects of distinct phenotypes underlying BMI on the risk of seven site-specific cancers. METHODS SNP-exposure estimates were weighted in a multivariable Mendelian randomisation analysis by their evidence for colocalization with subcutaneous adipose- and brain-tissue-derived gene expression using a recently developed methodology. RESULTS Our results provide evidence that brain-tissue-derived BMI variants are predominantly responsible for driving the genetically predicted effect of BMI on lung cancer (OR: 1.17; 95% CI: 1.01-1.36; P = 0.03). Similar findings were identified when analysing cigarettes per day as an outcome (Beta = 0.44; 95% CI: 0.26-0.61; P = 1.62 × 10-6), highlighting a possible shared aetiology or mediator effect between brain-tissue BMI, smoking and lung cancer. Our results additionally suggest that adipose-tissue-derived BMI variants may predominantly drive the effect of BMI and increased risk for endometrial cancer (OR: 1.71; 95% CI: 1.07-2.74; P = 0.02), highlighting a putatively important role in the aetiology of endometrial cancer. CONCLUSIONS The study provides valuable insight into the divergent underlying pathways between BMI and the risk of site-specific cancers.
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Affiliation(s)
- Genevieve M Leyden
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK.
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK.
| | - Michael P Greenwood
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - Valérie Gaborieau
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Paul Brennan
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - David Murphy
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK.
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31
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Hayes JP, Pierce ME, Brown E, Salat D, Logue MW, Constantinescu J, Valerio K, Miller MW, Sherva R, Huber BR, Milberg W, McGlinchey R. Genetic Risk for Alzheimer Disease and Plasma Tau Are Associated With Accelerated Parietal Cortex Thickness Change in Middle-Aged Adults. Neurol Genet 2023; 9:e200053. [PMID: 36742995 PMCID: PMC9893442 DOI: 10.1212/nxg.0000000000200053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/21/2022] [Indexed: 02/04/2023]
Abstract
Background and Objectives Neuroimaging and biomarker studies in Alzheimer disease (AD) have shown well-characterized patterns of cortical thinning and altered biomarker concentrations of tau and β-amyloid (Aβ). However, earlier identification of AD has great potential to advance clinical care and determine candidates for drug trials. The extent to which AD risk markers relate to cortical thinning patterns in midlife is unknown. The first objective of this study was to examine cortical thickness change associated with genetic risk for AD among middle-aged military veterans. The second objective was to determine the relationship between plasma tau and Aβ and change in brain cortical thickness among veterans stratified by genetic risk for AD. Methods Participants consisted of post-9/11 veterans (N = 155) who were consecutively enrolled in the Translational Research Center for TBI and Stress Disorders prospective longitudinal cohort and were assessed for mild traumatic brain injury (TBI) and posttraumatic disorder (PTSD). Genome-wide polygenic risk scores (PRSs) for AD were calculated using summary results from the International Genomics of Alzheimer's Disease Project. T-tau and Aβ40 and Aβ42 plasma assays were run using Simoa technology. Whole-brain MRI cortical thickness change estimates were obtained using the longitudinal stream of FreeSurfer. Follow-up moderation analyses examined the AD PRS × plasma interaction on change in cortical thickness in AD-vulnerable regions. Results Higher AD PRS, signifying greater genetic risk for AD, was associated with accelerated cortical thickness change in a right hemisphere inferior parietal cortex cluster that included the supramarginal gyrus, angular gyrus, and intraparietal sulcus. Higher tau, but not Aβ42/40 ratio, was associated with greater cortical thickness change among those with higher AD PRS. Mild TBI and PTSD were not associated with cortical thickness change. Discussion Plasma tau, particularly when combined with genetic stratification for AD risk, can be a useful indicator of brain change in midlife. Accelerated inferior parietal cortex changes in midlife may be an important factor to consider as a marker of AD-related brain alterations.
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Affiliation(s)
- Jasmeet Pannu Hayes
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Meghan E Pierce
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Emma Brown
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - David Salat
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Mark W Logue
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Julie Constantinescu
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Kate Valerio
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Mark W Miller
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Richard Sherva
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Bertrand Russell Huber
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - William Milberg
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Regina McGlinchey
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
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Green ZD, Vidoni ED, Swerdlow RH, Burns JM, Morris JK, Honea RA. Increased Functional Connectivity of the Precuneus in Individuals with a Family History of Alzheimer's Disease. J Alzheimers Dis 2023; 91:559-571. [PMID: 36463439 PMCID: PMC9912732 DOI: 10.3233/jad-210326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND First-degree relatives of individuals with late-onset Alzheimer's disease (AD) have increased risk for AD, with children of affected parents at an especially high risk. OBJECTIVE We aimed to investigate default mode network connectivity, medial temporal cortex volume, and cognition in cognitively healthy (CH) individuals with (FH+) and without (FH-) a family history of AD, alongside amnestic mild cognitive impairment (aMCI) and AD individuals, to determine the context and directionality of dysfunction in at-risk individuals. Our primary hypothesis was that there would be a linear decline (CH FH- > CH FH+ > aMCI > AD) within the risk groups on all measures of AD risk. METHODS We used MRI and fMRI to study cognitively healthy individuals (n = 28) with and without AD family history (FH+ and FH-, respectively), those with aMCI (n = 31) and early-stage AD (n = 25). We tested connectivity within the default mode network, as well as measures of volume and thickness within the medial temporal cortex and selected seed regions. RESULTS As expected, we identified decreased medial temporal cortex volumes in the aMCI and AD groups compared to cognitively healthy groups. We also observed patterns of connectivity across risk groups that suggest a nonlinear relationship of change, such that the FH+ group showed increased connectivity compared to the FH- and AD groups (CH FH+ > CH FH- > aMCI > AD). This pattern emerged primarily in connectivity between the precuneus and frontal regions. CONCLUSION These results add to a growing literature that suggests compensatory brain function in otherwise cognitively healthy individuals with a family history of AD.
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Affiliation(s)
- Zachary D. Green
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Eric D. Vidoni
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Russell H. Swerdlow
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jeffrey M. Burns
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jill K. Morris
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Robyn A. Honea
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA,Correspondence to: Robyn A. Honea, University of Kansas School of Medicine, Department of Neurology, University of Kansas Alzheimer’s Disease Research Center, 4350 Shawnee Mission Parkway, Fairway, KS, 66205, USA. Tel.: +1 913 588 5514; E-mail:
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Yang L, Sadler MC, Altman RB. Genetic association studies using disease liabilities from deep neural networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.18.23284383. [PMID: 36712099 PMCID: PMC9882423 DOI: 10.1101/2023.01.18.23284383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The case-control study is a widely used method for investigating the genetic landscape of binary traits. However, the health-related outcome or disease status of participants in long-term, prospective cohort studies such as the UK Biobank are subject to change. Here, we develop an approach for the genetic association study leveraging disease liabilities computed from a deep patient phenotyping framework (AI-based liability). Analyzing 44 common traits in 261,807 participants from the UK Biobank, we identified novel loci compared to the conventional case-control (CC) association studies. Our results showed that combining liability scores with CC status was more powerful than the CC-GWAS in detecting independent genetic loci across different diseases. This boost in statistical power was further reflected in increased SNP-based heritability estimates. Moreover, polygenic risk scores calculated from AI-based liabilities better identified newly diagnosed cases in the 2022 release of the UK Biobank that served as controls in the 2019 version (6.2% percentile rank increase on average). These findings demonstrate the utility of deep neural networks that are able to model disease liabilities from high-dimensional phenotypic data in large-scale population cohorts. Our pipeline of genome-wide association studies with disease liabilities can be applied to other biobanks with rich phenotype and genotype data.
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Affiliation(s)
- Lu Yang
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Marie C. Sadler
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- University Center for Primary Care and Public Health, Lausanne, 1010, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
- Department of Medicine, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
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He R, Mo J, Zhu K, Luo Q, Liu X, Huang H, Sheng J. The early life course-related traits with three psychiatric disorders: A two-sample Mendelian randomization study. Front Psychiatry 2023; 14:1098664. [PMID: 37025349 PMCID: PMC10070876 DOI: 10.3389/fpsyt.2023.1098664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/23/2023] [Indexed: 04/08/2023] Open
Abstract
Objectives Several studies have indicated a potential association between early life course-related traits and neurological and psychiatric disorders in adulthood, but the causal link remains unclear. Methods Instrumental variables (IVs) that have been shown to be strongly associated with exposure were obtained from summary data of genome-wide association studies (GWASs). Four early life course-related traits [i.e., birthweight (BW), childhood body mass index (BMI), early body size, and age at first birth (AFB)] were used as exposure IVs to estimate their causal associations with three neurological and psychiatric diseases [i.e., Alzheimer's disease (AD), major depressive disorder (MDD), and attention-deficit hyperactivity disorder (ADHD)]. Four different statistical methods, i.e., inverse-variance weighting (IVW), MR-Egger (MRE), weighted median (WM), and weighted mode (Wm), were performed in our MR analysis. Sensitivity analysis was performed by using the leave-one-out method, and horizontal pleiotropy was assessed using the MR-PRESSO package. Results There was evidence suggesting that BW has a causal effect on AD (ORMR-PRESSO = 1.05, p = 1.14E-03), but this association was not confirmed via multivariable Mendelian randomization (MVMR) (ORMVMR = 0.97, 95% CI 0.92-1.02, p = 3.00E-01). A strong relationship was observed between childhood BMI and ADHD among both sexes; a 1-SD increase in BMI significantly predicted a 1.46-fold increase in the OR for ADHD (p = 9.13E-06). In addition, a similar relationship was found between early life body size and ADHD (ORMR-PRESSO = 1.47, p = 9.62E-05), and this effect was mainly driven by male participants (ORMR-PRESSO = 1.50, p = 1.28E-3). Earlier AFB could significantly predict a higher risk of MDD (ORMR-PRESSO = 1.19, p = 1.96E-10) and ADHD (ORMR-PRESSO = 1.45, p = 1.47E-15). No significant causal associations were observed between the remaining exposures and outcomes. Conclusion Our results reveal the adverse effects of childhood obesity and preterm birth on the risk of ADHD later in life. The results of MVMR also show that lower BW may have no direct relationship with AD after adjusting for BMI. Furthermore, AFB may predict a higher risk of MDD.
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Affiliation(s)
- Renke He
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Jiaying Mo
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Kejing Zhu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Qinyu Luo
- Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xueying Liu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Hefeng Huang
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
- Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Reproductive Genetics, Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, China
- Shanghai Frontiers Science Center of Reproduction and Development, Shanghai, China
- Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
- *Correspondence: Hefeng Huang,
| | - Jianzhong Sheng
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
- Jianzhong Sheng,
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Lai D, Zhang M, Li R, Zhang C, Zhang P, Liu Y, Gao S, Foroud T. Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score. J Alzheimers Dis 2023; 96:1639-1649. [PMID: 38007651 DOI: 10.3233/jad-230510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
BACKGROUND Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes. OBJECTIVE The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies. METHODS We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions. RESULTS Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks. CONCLUSIONS Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.
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Affiliation(s)
- Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Michael Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rudong Li
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
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Wang Y, Chen H, Peloso GM, DeStefano AL, Dupuis J. Exploiting family history in aggregation unit-based genetic association tests. Eur J Hum Genet 2022; 30:1355-1362. [PMID: 34690355 PMCID: PMC9712547 DOI: 10.1038/s41431-021-00980-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022] Open
Abstract
The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.
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Affiliation(s)
- Yanbing Wang
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA.
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Gina M Peloso
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Anita L DeStefano
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA.
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Savignac C, Villeneuve S, Badhwar A, Saltoun K, Shafighi K, Zajner C, Sharma V, Gagliano Taliun SA, Farhan S, Poirier J, Bzdok D. APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia. PLoS Biol 2022; 20:e3001863. [PMID: 36512526 PMCID: PMC9747055 DOI: 10.1371/journal.pbio.3001863] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/30/2022] [Indexed: 12/15/2022] Open
Abstract
Alzheimer's disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in the default network (DN). Here, we examined codependent structural variations between the MTL's most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer's disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer's disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix's fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.
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Affiliation(s)
- Chloé Savignac
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Centre for Studies in the Prevention of Alzheimer’s Disease, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
- Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM), Montreal, Quebec, Canada
| | - Karin Saltoun
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Kimia Shafighi
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Chris Zajner
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Vaibhav Sharma
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Sarah A. Gagliano Taliun
- Department of Neurosciences & Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
- Montreal Heart Institute, Montréal, Quebec, Canada
| | - Sali Farhan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Centre for Studies in the Prevention of Alzheimer’s Disease, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- School of Computer Science, McGill University, Montreal, Quebec, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
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Romero-Molina C, Garretti F, Andrews SJ, Marcora E, Goate AM. Microglial efferocytosis: Diving into the Alzheimer's disease gene pool. Neuron 2022; 110:3513-3533. [PMID: 36327897 DOI: 10.1016/j.neuron.2022.10.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies and functional genomics studies have linked specific cell types, genes, and pathways to Alzheimer's disease (AD) risk. In particular, AD risk alleles primarily affect the abundance or structure, and thus the activity, of genes expressed in macrophages, strongly implicating microglia (the brain-resident macrophages) in the etiology of AD. These genes converge on pathways (endocytosis/phagocytosis, cholesterol metabolism, and immune response) with critical roles in core macrophage functions such as efferocytosis. Here, we review these pathways, highlighting relevant genes identified in the latest AD genetics and genomics studies, and describe how they may contribute to AD pathogenesis. Investigating the functional impact of AD-associated variants and genes in microglia is essential for elucidating disease risk mechanisms and developing effective therapeutic approaches.
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Affiliation(s)
- Carmen Romero-Molina
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Garretti
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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The HUNT study: A population-based cohort for genetic research. CELL GENOMICS 2022; 2:100193. [PMID: 36777998 PMCID: PMC9903730 DOI: 10.1016/j.xgen.2022.100193] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/10/2022] [Accepted: 09/13/2022] [Indexed: 11/06/2022]
Abstract
The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.
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Cognitive performance protects against Alzheimer's disease independently of educational attainment and intelligence. Mol Psychiatry 2022; 27:4297-4306. [PMID: 35840796 DOI: 10.1038/s41380-022-01695-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/21/2022] [Accepted: 06/30/2022] [Indexed: 02/07/2023]
Abstract
Mendelian-randomization (MR) studies using large-scale genome-wide association studies (GWAS) have identified causal association between educational attainment and Alzheimer's disease (AD). However, the underlying mechanisms are still required to be explored. Here, we conduct univariable and multivariable MR analyses using large-scale educational attainment, cognitive performance, intelligence and AD GWAS datasets. In stage 1, we found significant causal effects of educational attainment on cognitive performance (beta = 0.907, 95% confidence interval (CI): 0.884-0.930, P < 1.145E-299), and vice versa (beta = 0.571, 95% CI: 0.557-0.585, P < 1.145E-299). In stage 2, we found that both increase in educational attainment (odds ratio (OR) = 0.72, 95% CI: 0.66-0.78, P = 1.39E-14) and cognitive performance (OR = 0.69, 95% CI: 0.64-0.75, P = 1.78E-20) could reduce the risk of AD. In stage 3, we found that educational attainment may protect against AD dependently of cognitive performance (OR = 1.07, 95% CI: 0.90-1.28, P = 4.48E-01), and cognitive performance may protect against AD independently of educational attainment (OR = 0.69, 95% CI: 0.53-0.89, P = 5.00E-03). In stage 4, we found significant causal effects of cognitive performance on intelligence (beta = 0.907, 95% CI: 0.877-0.938, P < 1.145E-299), and vice versa (beta = 0.957, 95% CI: 0.937-0.978, P < 1.145E-299). In stage 5, we identified that cognitive performance may protect against AD independently of intelligence (OR = 0.74, 95% CI: 0.61-0.90, P = 2.00E-03), and intelligence may protect against AD dependently of cognitive performance (OR = 1.17, 95% CI: 0.40-3.43, P = 4.48E-01). Collectively, our univariable and multivariable MR analyses highlight the protective role of cognitive performance in AD independently of educational attainment and intelligence. In addition to the intelligence, we extend the mechanisms underlying the associations of educational attainment with AD.
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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Zhuang Y, Wolford BN, Nam K, Bi W, Zhou W, Willer CJ, Mukherjee B, Lee S. Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies. Bioinformatics 2022; 38:4337-4343. [PMID: 35876838 PMCID: PMC9477535 DOI: 10.1093/bioinformatics/btac459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 05/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution. RESULTS We propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations. AVAILABILITY AND IMPLEMENTATION The summary statistics and code generated in this study are available at https://github.com/styvon/TAPE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yongwen Zhuang
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Brooke N Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Wenjian Bi
- Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Wei Zhou
- Massachusetts General Hospital, Broad Institute, Boston, MA, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Michigan Institute of Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, Korea
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Gabriel AAG, Atkins JR, Penha RCC, Smith-Byrne K, Gaborieau V, Voegele C, Abedi-Ardekani B, Milojevic M, Olaso R, Meyer V, Boland A, Deleuze JF, Zaridze D, Mukeriya A, Swiatkowska B, Janout V, Schejbalová M, Mates D, Stojšić J, Ognjanovic M, Witte JS, Rashkin SR, Kachuri L, Hung RJ, Kar S, Brennan P, Sertier AS, Ferrari A, Viari A, Johansson M, Amos CI, Foll M, McKay JD. Genetic Analysis of Lung Cancer and the Germline Impact on Somatic Mutation Burden. J Natl Cancer Inst 2022; 114:1159-1166. [PMID: 35511172 PMCID: PMC9360465 DOI: 10.1093/jnci/djac087] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/31/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Germline genetic variation contributes to lung cancer (LC) susceptibility. Previous genome-wide association studies (GWAS) have implicated susceptibility loci involved in smoking behaviors and DNA repair genes, but further work is required to identify susceptibility variants. METHODS To identify LC susceptibility loci, a family history-based genome-wide association by proxy (GWAx) of LC (48 843 European proxy LC patients, 195 387 controls) was combined with a previous LC GWAS (29 266 patients, 56 450 controls) by meta-analysis. Colocalization was used to explore candidate genes and overlap with existing traits at discovered susceptibility loci. Polygenic risk scores (PRS) were tested within an independent validation cohort (1 666 LC patients vs 6 664 controls) using variants selected from the LC susceptibility loci and a novel selection approach using published GWAS summary statistics. Finally, the effects of the LC PRS on somatic mutational burden were explored in patients whose tumor resections have been profiled by exome (n = 685) and genome sequencing (n = 61). Statistical tests were 2-sided. RESULTS The GWAx-GWAS meta-analysis identified 8 novel LC loci. Colocalization implicated DNA repair genes (CHEK1), metabolic genes (CYP1A1), and smoking propensity genes (CHRNA4 and CHRNB2). PRS analysis demonstrated that these variants, as well as subgenome-wide significant variants related to expression quantitative trait loci and/or smoking propensity, assisted in LC genetic risk prediction (odds ratio = 1.37, 95% confidence interval = 1.29 to 1.45; P < .001). Patients with higher genetic PRS loads of smoking-related variants tended to have higher mutation burdens in their lung tumors. CONCLUSIONS This study has expanded the number of LC susceptibility loci and provided insights into the molecular mechanisms by which these susceptibility variants contribute to LC development.
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Affiliation(s)
- Aurélie A G Gabriel
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Joshua R Atkins
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Ricardo C C Penha
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Karl Smith-Byrne
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Valerie Gaborieau
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Catherine Voegele
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Behnoush Abedi-Ardekani
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Maja Milojevic
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Robert Olaso
- Université Paris-Saclay, The French Alternative Energies and Atomic Energy Commission (CEA), Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
| | - Vincent Meyer
- Université Paris-Saclay, The French Alternative Energies and Atomic Energy Commission (CEA), Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
| | - Anne Boland
- Université Paris-Saclay, The French Alternative Energies and Atomic Energy Commission (CEA), Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
| | - Jean François Deleuze
- Université Paris-Saclay, The French Alternative Energies and Atomic Energy Commission (CEA), Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France
| | - David Zaridze
- Russian N.N. Blokhin Cancer Research Centre, Moscow, Russian Federation
| | - Anush Mukeriya
- Russian N.N. Blokhin Cancer Research Centre, Moscow, Russian Federation
| | - Beata Swiatkowska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Vladimir Janout
- Faculty of Medicine, Palacky University, Olomouc, Czech Republic
| | | | - Dana Mates
- National Institute of Public Health, Bucharest, Romania
| | - Jelena Stojšić
- Department of Thoracic Pathology, Service of Pathology, University Clinical Centre of Serbia, Belgrade, Serbia
| | - Miodrag Ognjanovic
- International Organisation for Cancer Prevention and Research, Belgrade, Serbia
| | | | - John S Witte
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Sara R Rashkin
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Linda Kachuri
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Anne-Sophie Sertier
- Fondation Synergie Lyon Cancer, Plateforme de bioinformatique Gilles Thomas, Lyon, France
| | - Anthony Ferrari
- Fondation Synergie Lyon Cancer, Plateforme de bioinformatique Gilles Thomas, Lyon, France
| | - Alain Viari
- Fondation Synergie Lyon Cancer, Plateforme de bioinformatique Gilles Thomas, Lyon, France
- Inria Centre de Recherche Grenoble Rhone-Alpes, Grenoble, France
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, USA
| | - Matthieu Foll
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
| | - James D McKay
- Genomic Epidemiology Branch, International Agency for Research on Cancer/World Health Organization (IARC/WHO), Lyon, France
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Rodríguez-Fernández B, Domingo Gispert J, Guigo R, Navarro A, Vilor-Tejedor N, Crous-Bou M. Genetically predicted Telomere Length and its relationship with Neurodegenerative diseases and Life Expectancy. Comput Struct Biotechnol J 2022; 20:4251-4256. [PMID: 36051868 PMCID: PMC9399257 DOI: 10.1016/j.csbj.2022.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/03/2022] Open
Abstract
Inheriting longer telomere length (TL) increases life expectancy. TL is associated with Alzheimer’s disease but no other neurodegenerative diseases. Diverse biological aging mechanisms might be involved in neurodegenerative diseases.
Telomere length (TL) is a biomarker of biological aging. Shorter telomeres have been associated with mortality and increased rates of age-related diseases. However, observational studies are unable to conclude whether TL is causally associated with those outcomes. Mendelian randomization (MR) was developed for assessing causality using genetic variants in epidemiological research. The objective of this study was to test the potential causal role of TL in neurodegenerative disorders and life expectancy through MR analysis. Summary level data were extracted from the most recent genome-wide association studies for TL, Alzheimer’s disease (AD), Parkinson’s disease, Frontotemporal dementia, Amyotrophic Lateral Sclerosis, Progressive Supranuclear Palsy and life expectancy. MR estimates revealed that longer telomeres inferred a protective effect on risk of AD (OR = 0.964; adjusted p-value = 0.039). Moreover, longer telomeres were significantly associated with increased life expectancy (βIVW = 0.011; adjusted p-value = 0.039). Sensitivity analyses suggested evidence for directional pleiotropy in AD analyses. Our results showed that genetically predicted longer TL may increase life expectancy and play a protective causal effect on AD. We did not observe significant causal relationships between longer TL and other neurodegenerative diseases. This suggests that the involvement of TL on specific biological mechanisms might differ between AD and life expectancy, with respect to that in other neurodegenerative diseases. Moreover, the presence of pleiotropy may reflect the complex interplay between TL homeostasis and AD pathophysiology. Further observational studies are needed to confirm these results.
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45
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Hujoel ML, Loh PR, Neale BM, Price AL. Incorporating family history of disease improves polygenic risk scores in diverse populations. CELL GENOMICS 2022; 2:100152. [PMID: 35935918 PMCID: PMC9351615 DOI: 10.1016/j.xgen.2022.100152] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 01/04/2023]
Abstract
Polygenic risk scores (PRSs) derived from genotype data and family history (FH) of disease provide valuable information for predicting disease risk, but PRSs perform poorly when applied to diverse populations. Here, we explore methods for combining both types of information (PRS-FH) in UK Biobank data. PRSs were trained using all British individuals (n = 409,000), and target samples consisted of unrelated non-British Europeans (n = 42,000), South Asians (n = 7,000), or Africans (n = 7,000). We evaluated PRS, FH, and PRS-FH using liability-scale R 2, primarily focusing on 3 well-powered diseases (type 2 diabetes, hypertension, and depression). PRS attained average prediction R 2s of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R 2s of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans. In conclusion, including family history improves the accuracy of polygenic risk scores, particularly in diverse populations.
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Affiliation(s)
- Margaux L.A. Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Liu W, Zhang L, Fang H, Gao Y, Liu K, Li S, Liu H, Wang X, Liu C, Song B, Xia Z, Xu Y. Genetically predicted frailty index and risk of stroke and Alzheimer's disease. Eur J Neurol 2022; 29:1913-1921. [PMID: 35318774 DOI: 10.1111/ene.15332] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/18/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE Previous studies have reported the association between frailty and stroke or Alzheimer's disease (AD). However, the causality remains unclear. The aim of the present study was to evaluate whether genetically predicted frailty is associated with the risk of stroke or AD by a Mendelian randomization (MR) study. METHODS Genetic variants associated with the frailty index (FI) were obtained from a large genome-wide association study (GWAS). Summary-level data for stroke and AD were adopted from the corresponding large GWAS of individuals of European ancestry. The inverse variance weighted method was used for estimating causal effects. Multivariable analysis was performed for further adjustment. RESULTS The present MR study indicated a suggestive association between genetically predicted FI and a higher risk of any stroke (odds ratio 1.360, 95% confidence interval 1.006-1.838, p = 0.046). Regarding the subtypes of stroke, genetically predicted FI was associated with a higher risk of large artery atherosclerosis stroke (LAS) (odds ratio 2.487, 95% confidence interval 1.282-4.826, p = 0.007). No causal links were identified between genetically predicted FI and any ischaemic stroke, intracranial haemorrhage, cardioembolic stroke, small artery stroke, AD or AD-by-proxy. Multivariable MR analysis indicated that the association of genetically predicted FI with LAS was attenuated after adjustment for inflammatory bowel disease (p = 0.114). CONCLUSIONS The MR study suggested that genetically predicted FI may be associated with an increased risk of any stroke. Subgroup analysis indicated a suggestive association between genetically predicted FI and the risk of LAS. The underlying mechanisms need further investigation.
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Affiliation(s)
- Weishi Liu
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luyang Zhang
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Fang
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuan Gao
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Liu
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shen Li
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hongbing Liu
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Wang
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chen Liu
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Song
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zongping Xia
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Clinical Systems Biology Laboratories, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuming Xu
- Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Ji Y, Wei Q, Chen R, Wang Q, Tao R, Li B. Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery. PLoS Genet 2022; 18:e1009814. [PMID: 35771864 PMCID: PMC9278751 DOI: 10.1371/journal.pgen.1009814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 07/13/2022] [Accepted: 05/26/2022] [Indexed: 12/30/2022] Open
Abstract
A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.
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Affiliation(s)
- Ying Ji
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Qiang Wei
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Rui Chen
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Quan Wang
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail: (RT); (BL)
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail: (RT); (BL)
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de la Fuente J, Grotzinger AD, Marioni RE, Nivard MG, Tucker-Drob EM. Integrated analysis of direct and proxy genome wide association studies highlights polygenicity of Alzheimer's disease outside of the APOE region. PLoS Genet 2022; 18:e1010208. [PMID: 35658006 PMCID: PMC9200312 DOI: 10.1371/journal.pgen.1010208] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 06/15/2022] [Accepted: 04/19/2022] [Indexed: 11/19/2022] Open
Abstract
Recent meta-analyses combining direct genome-wide association studies (GWAS) with those of family history (GWAX) have indicated very low SNP heritability of Alzheimer's disease (AD). These low estimates may call into question the prospects of continued progress in genetic discovery for AD within the spectrum of common variants. We highlight dramatic downward biases in previous methods, and we validate a novel method for the estimation of SNP heritability via integration of GWAS and GWAX summary data. We apply our method to investigate the genetic architecture of AD using GWAX from UK Biobank and direct case-control GWAS from the International Genomics of Alzheimer's Project (IGAP). We estimate the liability scale common variant SNP heritability of Clinical AD outside of APOE region at ~7-11%, and we project the corresponding estimate for AD pathology to be up to approximately 23%. We estimate that nearly 90% of common variant SNP heritability of Clinical AD exists outside the APOE region. Rare variants not tagged in standard GWAS may account for additional variance. Our results indicate that, while GWAX for AD in UK Biobank may result in greater attenuation of genetic effects beyond that conventionally assumed, it does not introduce appreciable contamination of signal by genetically distinct traits relative to direct case-control GWAS in IGAP. Genetic risk for AD represents a strong effect of APOE superimposed upon a highly polygenic background.
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Affiliation(s)
- Javier de la Fuente
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Texas, United States of America
- * E-mail: (JF); (EMT-D)
| | - Andrew D. Grotzinger
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, United Kingdom
| | - Michel G. Nivard
- Department of Biological Psychology, VU University Amsterdam, the Netherlands
| | - Elliot M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Texas, United States of America
- * E-mail: (JF); (EMT-D)
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Liu A, Manuel AM, Dai Y, Fernandes BS, Enduru N, Jia P, Zhao Z. Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data. Hum Mol Genet 2022; 31:3341-3354. [PMID: 35640139 PMCID: PMC9523561 DOI: 10.1093/hmg/ddac124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/04/2022] [Accepted: 05/24/2022] [Indexed: 02/02/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.
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Affiliation(s)
- Andi Liu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA,Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Brisa S Fernandes
- Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Nitesh Enduru
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA,Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Zhongming Zhao
- To whom correspondence should be addressed at: Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA. Tel: +1 7135003631;
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50
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Dauyey K, Saitou N. Inferring intelligence of ancient people based on modern genomic studies. J Hum Genet 2022; 67:527-532. [PMID: 35534677 PMCID: PMC9402434 DOI: 10.1038/s10038-022-01039-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 11/26/2022]
Abstract
Quantification of ancient human intelligence has become possible with recent advances in polygenic prediction. Intelligence is a complex trait that has both environmental and genetic components and high heritability. Large-scale genome-wide association studies based on ~270,000 individuals have demonstrated highly significant single-nucleotide polymorphisms (SNPs) associated with intelligence in present-day humans. We utilized those previously reported 12,037 SNPs to estimate a genetic component of intelligence in ancient Funadomari Jomon individual from 3700 years BP as well as four individuals of Afanasievo nuclear family from about 4100 years BP and who are considered anatomically modern humans. We have demonstrated that ancient individuals could have been not inferior in intelligence compared to present-day humans through assessment of the genetic component of intelligence. We have also confirmed that alleles associated with intelligence tend to spread equally between ancestral and derived origin suggesting that intelligence may be a neutral trait in human evolution.
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