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Ivankovic F, Johnson S, Shen J, Scharf JM, Mathews CA. Optimization of self- or parent-reported psychiatric phenotypes in longitudinal studies. J Child Psychol Psychiatry 2024. [PMID: 39246252 DOI: 10.1111/jcpp.14054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/01/2024] [Indexed: 09/10/2024]
Abstract
BACKGROUND The Adolescent Brain Cognitive Development (ABCD) study is a longitudinal study of US adolescents with a wide breadth of psychiatric, neuroimaging and genetic data that can be leveraged to better understand psychiatric diseases. The reliability and validity of the psychiatric data collected have not yet been examined. This study aims to explore and optimize the reliability/validity of psychiatric diagnostic constructs in the ABCD study. METHODS Parent-and-child-reported psychiatric data for 11,876 children (aged 9.5 ± 0.5 at first assessment) were examined over 4 years to derive specific constructs for psychiatric diagnoses using longitudinal information. Rates of psychiatric disorders were calculated and compared to those reported in the epidemiological literature. RESULTS The rates of self-reported psychiatric disorders at any single time point (broad diagnostic construct) were higher than indicated by epidemiological studies. Narrow diagnostic constructs, which required the endorsement of psychiatric disorders at a majority of longitudinal assessments, demonstrated a better rate approximation of literature-reported prevalences for most disorders (e.g. the prevalence of broad obsessive-compulsive disorder (OCD) was 13.3% compared to narrow OCD at 2.6% and a literature-reported prevalence of 2.3%). Analysis of comorbidity, using OCD as a representative example, also showed a better approximation of literature-reported comorbidity rates using the narrow construct, with some exceptions. CONCLUSIONS Self- or parent-report-based assessments tend to overestimate prevalences of psychiatric disorders in the ABCD Study, particularly when longitudinal data are summed to create lifetime prevalences. Such assessments should be accompanied by more in-depth assessments or clinician-administered structured interviews if using data where accurate disorder classifications are paramount.
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Affiliation(s)
- Franjo Ivankovic
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital Center for Genomic Medicine, Boston, MA, USA
- Genetics & Genomics Graduate Program, University of Florida Genetics Institute, Gainesville, FL, USA
- Department of Psychiatry, Center for OCD, Anxiety and Related Disorders, McKnight Brain Institute, University of Florida College of Medicine, Gainesville, FL, USA
| | - Sharon Johnson
- Department of Psychiatry, Center for OCD, Anxiety and Related Disorders, McKnight Brain Institute, University of Florida College of Medicine, Gainesville, FL, USA
| | - James Shen
- Department of Psychiatry, Center for OCD, Anxiety and Related Disorders, McKnight Brain Institute, University of Florida College of Medicine, Gainesville, FL, USA
| | - Jeremiah M Scharf
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital Center for Genomic Medicine, Boston, MA, USA
- Department of Neurology and Psychiatry, Massachusetts General Hospital Psychiatric and Neurodevelopmental Genetics Unit, Boston, MA, USA
- Department of Neurology, Center for Brain Mind Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Carol A Mathews
- Genetics & Genomics Graduate Program, University of Florida Genetics Institute, Gainesville, FL, USA
- Department of Psychiatry, Center for OCD, Anxiety and Related Disorders, McKnight Brain Institute, University of Florida College of Medicine, Gainesville, FL, USA
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2
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Wang C, Wang T, Kiryluk K, Wei Y, Aschard H, Ionita-Laza I. Genome-wide discovery for biomarkers using quantile regression at biobank scale. Nat Commun 2024; 15:6460. [PMID: 39085219 PMCID: PMC11291931 DOI: 10.1038/s41467-024-50726-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/18/2024] [Indexed: 08/02/2024] Open
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, NY, USA
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | | | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY, USA.
- Department of Statistics, Lund University, Lund, Sweden.
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3
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Lim AMW, Lim EU, Chen PL, Fann CSJ. Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks. iScience 2024; 27:109815. [PMID: 39040048 PMCID: PMC11260869 DOI: 10.1016/j.isci.2024.109815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/24/2024] Open
Abstract
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.
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Affiliation(s)
- Aylwin Ming Wee Lim
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- ASUS Intelligent Cloud Services (AICS), Taipei 112, Taiwan
| | - Evan Unit Lim
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
- Department of Medical Genetics, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Cathy Shen Jang Fann
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
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4
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Benstock SE, Weaver K, Hettema JM, Verhulst B. Using Alternative Definitions of Controls to Increase Statistical Power in GWAS. Behav Genet 2024; 54:353-366. [PMID: 38869698 DOI: 10.1007/s10519-024-10187-w] [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: 01/12/2024] [Accepted: 05/29/2024] [Indexed: 06/14/2024]
Abstract
Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.
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Affiliation(s)
- Sarah E Benstock
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Katherine Weaver
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - John M Hettema
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA.
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Henkel C, Erikstrup C, Ostrowski SR, Pedersen OB, Troelsen A. Genetics may affect the risk of undergoing surgery for rhizarthrosis. J Orthop Res 2024; 42:1001-1008. [PMID: 38263870 DOI: 10.1002/jor.25753] [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: 08/10/2023] [Revised: 10/25/2023] [Accepted: 11/28/2023] [Indexed: 01/25/2024]
Abstract
Osteoarthritis is a prevalent and severe disease. Involvement of the trapeziometacarpal joint is common and can lead to both pain and disability. Genetics are known to affect the risk of osteoarthritis, but it remains unclear how genetics affect disease trajectories. In this study, we investigated whether the genetic associations of trapeziometacarpal osteoarthritis (rhizarthrosis) vary with the need for surgical treatment. The study was conducted as a case-control genome-wide association study using individuals from the Copenhagen Hospital Biobank pain and degenerative musculoskeletal disease study and the Danish Blood Donor Study (N = 208,342). We identified patients diagnosed with rhizarthrosis and grouped them by treatment status, resulting in two case groups: surgical (N = 1083) and nonsurgical (N = 1888). The case groups were tested against osteoarthritis-free controls in two genome-wide association studies. We then compared variants suggestive of association (p < 10-6) in either of these analyses directly between the treatment groups (surgical vs. nonsurgical rhizarthrosis). We identified 10 variants suggestive of association with either surgical (seven variants) or nonsurgical (three variants) rhizarthrosis. None of the variants reached nominal significance in the opposite treatment group (p ≥ 0.14), and all 10 variants were significantly different between the treatment groups at a false discovery rate of 5%. These results suggest possible differences in the genetic associations of rhizarthrosis depending on surgical treatment. Clinical significance: Uncovering genetic differences between clinically distinct patient groups can reveal biological determinants of disease trajectories.
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Affiliation(s)
- Cecilie Henkel
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital Køge, Køge, Denmark
| | - Anders Troelsen
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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6
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Duffy A, Grof P. Longitudinal studies of bipolar patients and their families: translating findings to advance individualized risk prediction, treatment and research. Int J Bipolar Disord 2024; 12:12. [PMID: 38609722 PMCID: PMC11014837 DOI: 10.1186/s40345-024-00333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Bipolar disorder is a broad diagnostic construct associated with significant phenotypic and genetic heterogeneity challenging progress in clinical practice and discovery research. Prospective studies of well-characterized patients and their family members have identified lithium responsive (LiR) and lithium non-responsive (LiNR) subtypes that hold promise for advancement. METHOD In this narrative review, relevant observations from published longitudinal studies of well-characterized bipolar patients and their families spanning six decades are highlighted. DSM diagnoses based on SADS-L interviews were decided in blind consensus reviews by expert clinicians. Genetic, neurobiological, and psychosocial factors were investigated in subsets of well-characterized probands and adult relatives. Systematic maintenance trials of lithium, antipsychotics, and lamotrigine were carried out. Clinical profiles that included detailed histories of the clinical course, symptom sets and disorders segregating in families were documented. Offspring of LiR and LiNR families were repeatedly assessed up to 20 years using KSADS-PL format interviews and DSM diagnoses and sub-threshold symptoms were decided by expert clinicians in blind consensus reviews using all available clinical and research data. RESULTS A characteristic clinical profile differentiated bipolar patients who responded to lithium stabilization from those who did not. The LiR subtype was characterized by a recurrent fully remitting course predominated by depressive episodes and a positive family history of episodic remitting mood disorders, and not schizophrenia. Response to lithium clustered in families and the characteristic clinical profile predicted lithium response, with the episodic remitting course being a strong correlate. There is accumulating evidence that genetic and neurobiological markers differ between LiR and LiNR subtypes. Further, offspring of bipolar parents subdivided by lithium response differed in developmental history, clinical antecedents and early course of mood disorders. Moreover, the nature of the emergent course bred true from parent to offspring, independent of the nature of emergent psychopathology. CONCLUSIONS Bipolar disorders are heterogeneous and response to long-term lithium is associated with a familial subtype with characteristic course, treatment response, family history and likely pathogenesis. Incorporating distinctive clinical profiles that index valid bipolar subtypes into routine practice and research will improve patient outcomes and advance the development and translation of novel treatment targets to improve prevention and early intervention.
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Affiliation(s)
- Anne Duffy
- Department of Psychiatry, Queen's University, Kingston, ON, Canada.
- Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Paul Grof
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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7
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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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8
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Benstock SE, Weaver K, Hettema J, Verhulst B. Using Alternative Definitions of Controls to Increase Statistical Power in GWAS. RESEARCH SQUARE 2024:rs.3.rs-3858178. [PMID: 38352402 PMCID: PMC10862954 DOI: 10.21203/rs.3.rs-3858178/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Genome-wide association studies (GWAS) are underpowered due to small effect sizes of single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the most statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.
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9
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Kariotis S, Tan PF, Lu H, Rhodes CJ, Wilkins MR, Lawrie A, Wang D. Omada: robust clustering of transcriptomes through multiple testing. Gigascience 2024; 13:giae039. [PMID: 38991852 PMCID: PMC11238428 DOI: 10.1093/gigascience/giae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/09/2024] [Accepted: 06/17/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions. FINDINGS The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. CONCLUSIONS In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.
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Affiliation(s)
- Sokratis Kariotis
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, 117609, Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis St, Matrix, 138671, Singapore, Republic of Singapore
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, SW3 6LY, London, United Kingdom
| | - Pei Fang Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, 117609, Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis St, Matrix, 138671, Singapore, Republic of Singapore
| | - Haiping Lu
- Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, S1 4DP, Sheffield, United Kingdom
| | - Christopher J Rhodes
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, SW3 6LY, London, United Kingdom
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, SW3 6LY, London, United Kingdom
| | - Allan Lawrie
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, SW3 6LY, London, United Kingdom
| | - Dennis Wang
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, 117609, Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis St, Matrix, 138671, Singapore, Republic of Singapore
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, SW3 6LY, London, United Kingdom
- Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, S1 4DP, Sheffield, United Kingdom
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10
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Manchia M. The Relevance of Body Mass Index in Bipolar Disorder. Am J Psychiatry 2024; 181:4-6. [PMID: 38161308 DOI: 10.1176/appi.ajp.20230921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia
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11
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He Y, Koido M, Sutoh Y, Shi M, Otsuka-Yamasaki Y, Munter HM, Morisaki T, Nagai A, Murakami Y, Tanikawa C, Hachiya T, Matsuda K, Shimizu A, Kamatani Y. East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease. Nat Genet 2023; 55:2129-2138. [PMID: 38036781 PMCID: PMC10703676 DOI: 10.1038/s41588-023-01569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 10/12/2023] [Indexed: 12/02/2023]
Abstract
Peptic ulcer disease (PUD) refers to acid-induced injury of the digestive tract, occurring mainly in the stomach (gastric ulcer (GU)) or duodenum (duodenal ulcer (DU)). In the present study, we conducted a large-scale, cross-ancestry meta-analysis of PUD combining genome-wide association studies with Japanese and European studies (52,032 cases and 905,344 controls), and discovered 25 new loci highly concordant across ancestries. An examination of GU and DU genetic architecture demonstrated that GUs shared the same risk loci as DUs, although with smaller genetic effect sizes and higher polygenicity than DUs, indicating higher heterogeneity of GUs. Helicobacter pylori (HP)-stratified analysis found an HP-related host genetic locus. Integrative analyses using bulk and single-cell transcriptome profiles highlighted the genetic factors of PUD being enriched in the highly expressed genes in stomach tissues, especially in somatostatin-producing D cells. Our results provide genetic evidence that gastrointestinal cell differentiations and hormone regulations are critical in PUD etiology.
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Affiliation(s)
- Yunye He
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoichi Sutoh
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Mingyang Shi
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine and Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Takayuki Morisaki
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Akiko Nagai
- Department of Public Policy, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Hachiya
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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12
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Erawijantari PP, Kartal E, Liñares-Blanco J, Laajala TD, Feldman LE, Carmona-Saez P, Shigdel R, Claesson MJ, Bertelsen RJ, Gomez-Cabrero D, Minot S, Albrecht J, Chung V, Inouye M, Jousilahti P, Schultz JH, Friederich HC, Knight R, Salomaa V, Niiranen T, Havulinna AS, Saez-Rodriguez J, Levinson RT, Lahti L. Microbiome-based risk prediction in incident heart failure: a community challenge. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.12.23296829. [PMID: 37873403 PMCID: PMC10593042 DOI: 10.1101/2023.10.12.23296829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.
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Affiliation(s)
| | - Ece Kartal
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - José Liñares-Blanco
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración 114, 18016, Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Spain
| | - Teemu D Laajala
- Department of Mathematics and Statistics, Faculty of Science, University of Turku, Finland
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Lily Elizabeth Feldman
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Pedro Carmona-Saez
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración 114, 18016, Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Spain
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marcus Joakim Claesson
- APC Microbiome Ireland, University College Cork, T12 YT20 Cork, Ireland
- School of Microbiology, University College Cork, T12 YT20 Cork, Ireland
| | | | - David Gomez-Cabrero
- Translational Bioinformatics Unit, Navarrabiomed, Public University of Navarra, IDISNA, Pamplona, Spain
- Biological and Environmental Sciences & Engineering Division, King Abdullah University of Science & Technology, Thuwal, Kingdom of Saudi Arabia
| | - Samuel Minot
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
| | | | | | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, Cambridge University, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jobst-Hendrik Schultz
- Department of General Internal Medicine & Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Hans-Christoph Friederich
- Department of General Internal Medicine & Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Rob Knight
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA. USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA. USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA. USA
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA. USA
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Internal Medicine, University of Turku, Turku, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, Helsinki, Finland
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rebecca T Levinson
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of General Internal Medicine & Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Leo Lahti
- Department of Computing, Faculty of Technology, University of Turku, Turku, Finland
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13
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Elgaeva EE, Williams FMK, Zaytseva OO, Freidin MB, Aulchenko YS, Suri P, Tsepilov YA. Bidirectional Mendelian Randomization Study of Personality Traits Reveals a Positive Feedback Loop Between Neuroticism and Back Pain. THE JOURNAL OF PAIN 2023; 24:1875-1885. [PMID: 37270142 DOI: 10.1016/j.jpain.2023.05.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 04/11/2023] [Accepted: 05/24/2023] [Indexed: 06/05/2023]
Abstract
We conducted a bidirectional Mendelian randomization study to examine the causal effects of six personality traits (anxiety, neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness) on back pain associated with health care use and the causal effect of back pain on the same risk factors. Genetic instruments for the personality traits and back pain were obtained from the largest published genome-wide association studies conducted in individuals of European ancestry. We used inverse weighted variance meta-analysis and Causal Analysis Using Summary Effect for primary analyses and sensitivity analyses to examine evidence for causal associations. We interpreted exposure-outcome associations as being consistent with a causal relationship if results of at least one primary analysis were statistically significant after accounting for multiple statistical testing (P-value < .0042), and the direction and magnitude of effect estimates were concordant between primary and sensitivity analyses. We found evidence for statistically significant bidirectional causal associations between neuroticism and back pain, with odds ratio 1.51 (95% confidence interval 1.37; 1.67) of back pain per neuroticism sum score standard deviation, P-value = 7.80e-16; and beta = .12, se = .04 of neuroticism sum score standard deviation per log odds of back pain, P-value = 2.48e-03. Other relationships did not meet our predefined criteria for causal association. PERSPECTIVE: The significant positive feedback loop between neuroticism and back pain highlights the importance of considering neuroticism in the management of patients with back pain.
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Affiliation(s)
- Elizaveta E Elgaeva
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia; Institute of Cytology and Genetics, Novosibirsk, Russia.
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | | | - Maxim B Freidin
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yurii S Aulchenko
- Institute of Cytology and Genetics, Novosibirsk, Russia; PolyOmica, 's-Hertogenbosch, The Netherlands
| | - Pradeep Suri
- Division of Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, Washington, USA; Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA; Clinical Learning, Evidence, and Research (CLEAR) Center, University of Washington, Seattle, Washington, USA
| | - Yakov A Tsepilov
- Institute of Cytology and Genetics, Novosibirsk, Russia; Kurchatov Genomics Center, Institute of Cytology & Genetics, Novosibirsk, Russia
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14
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Roshandel D, Sanders EJ, Shakeshaft A, Panjwani N, Lin F, Collingwood A, Hall A, Keenan K, Deneubourg C, Mirabella F, Topp S, Zarubova J, Thomas RH, Talvik I, Syvertsen M, Striano P, Smith AB, Selmer KK, Rubboli G, Orsini A, Ng CC, Møller RS, Lim KS, Hamandi K, Greenberg DA, Gesche J, Gardella E, Fong CY, Beier CP, Andrade DM, Jungbluth H, Richardson MP, Pastore A, Fanto M, Pal DK, Strug LJ. SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy. NPJ Genom Med 2023; 8:28. [PMID: 37770509 PMCID: PMC10539321 DOI: 10.1038/s41525-023-00370-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023] Open
Abstract
Elevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and juvenile myoclonic epilepsy (JME). We performed a genome-wide association, colocalization, polygenic risk score, and pathway analysis of impulsivity in JME (n = 381). Results were followed up with functional characterisation using a drosophila model. We identified genome-wide associated SNPs at 8q13.3 (P = 7.5 × 10-9) and 10p11.21 (P = 3.6 × 10-8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (P = 9.5 × 10-3). SLCO5A1 codes for an organic anion transporter and upregulates synapse assembly/organisation genes. Pathway analysis demonstrates 12.7-fold enrichment for presynaptic membrane assembly genes (P = 0.0005) and 14.3-fold enrichment for presynaptic organisation genes (P = 0.0005) including NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila polypeptide with the highest homology to SLCO5A1, causes over-reactive startling behaviour (P = 8.7 × 10-3) and increased seizure-like events (P = 6.8 × 10-7). Polygenic risk score for ADHD genetically correlates with impulsivity scores in JME (P = 1.60 × 10-3). SLCO5A1 loss-of-function represents an impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease.
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Affiliation(s)
- Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Eric J Sanders
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada
| | - Amy Shakeshaft
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Naim Panjwani
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Fan Lin
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Amber Collingwood
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Anna Hall
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Katherine Keenan
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Celine Deneubourg
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Filippo Mirabella
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Simon Topp
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jana Zarubova
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Rhys H Thomas
- Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Marte Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Trust, Oslo, Norway
| | - Pasquale Striano
- IRCCS Istituto 'G. Gaslini', Genova, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Anna B Smith
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kaja K Selmer
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway
| | - Guido Rubboli
- Danish Epilepsy Centre, Dianalund, Denmark
- University of Copenhagen, Copenhagen, Denmark
| | - Alessandro Orsini
- Pediatric Neurology, Azienda Ospedaliero-Universitaria Pisana, Pisa University Hospital, Pisa, Italy
| | - Ching Ching Ng
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Rikke S Møller
- Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Kheng Seang Lim
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Khalid Hamandi
- The Welsh Epilepsy Unit, Department of Neurology Cardiff & Vale University Health Board, Cardiff, UK
- Department of Psychological Medicine and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | | | | | - Elena Gardella
- Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Choong Yi Fong
- Division of Paediatric Neurology, Department of Pediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Danielle M Andrade
- Adult Epilepsy Genetics Program, Krembil Research Institute, University of Toronto, Toronto, Canada
| | - Heinz Jungbluth
- Randall Centre for Cell and Molecular Biophysics, Muscle Signalling Section, Faculty of Life Sciences and Medicine, King's College London, London, UK
- Department of Paediatric Neurology, Neuromuscular Service, Evelina's Children Hospital, Guy's & St. Thomas' Hospital NHS Foundation Trust, London, UK
| | - Mark P Richardson
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- King's College Hospital, London, UK
| | - Annalisa Pastore
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Manolis Fanto
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Deb K Pal
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK.
- King's College Hospital, London, UK.
| | - Lisa J Strug
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.
- Division of Biostatistics, Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada.
- Departments of Statistical Sciences and Computer Science, The University of Toronto, Toronto, Canada.
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada.
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15
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Gloss AD, Steiner MC, Novembre J, Bergelson J. The design of mapping populations: Impacts of geographic scale on genetic architecture and mapping efficacy for defense and immunity. CURRENT OPINION IN PLANT BIOLOGY 2023; 74:102399. [PMID: 37307746 PMCID: PMC10441534 DOI: 10.1016/j.pbi.2023.102399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/29/2023] [Accepted: 05/15/2023] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities ("local" populations) could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations.
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Affiliation(s)
- Andrew D Gloss
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA.
| | | | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, USA; Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA
| | - Joy Bergelson
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA.
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16
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Miola A, Fountoulakis KN, Baldessarini RJ, Veldic M, Solmi M, Rasgon N, Ozerdem A, Perugi G, Frye MA, Preti A. Prevalence and outcomes of rapid cycling bipolar disorder: Mixed method systematic meta-review. J Psychiatr Res 2023; 164:404-415. [PMID: 37429185 DOI: 10.1016/j.jpsychires.2023.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/12/2023]
Abstract
Rapid-cycling in bipolar disorder (RC-BD) is associated with greater illness morbidity and inferior treatment response but many aspects remain unclear, prompting this systematic review of its definitions, prevalence, and clinical characteristics. We searched multiple literature databases through April 2022 for systematic reviews or meta-analyses on RC-BD and extracted associated definitions, prevalence, risk-factors, and clinical outcomes. We assessed study quality (NIH Quality Assessment Tool) and levels of evidence (Oxford criteria). Of 146 identified reviews, 22 fulfilling selection criteria were included, yielding 30 studies involving 13,698 BD patients, of whom 3777 (27.6% [CI: 26.8-28.3]) were considered RC-BD, as defined in 14 reports by ≥4 recurrences/year within the past 12 months or in any year, without considering responsiveness to treatment. Random-effects meta-analytically pooled one-year prevalence was 22.3% [CI: 14.4-32.9] in 12 reports and lifetime prevalence was 35.5% [27.6-44.3] in 18 heterogenous reports. Meta-regression indicated greater lifetime prevalence of RC-BD among women than men (p=0.003). Association of RC-BD with suicide attempts, and unsatisfactory response to mood-stabilizers was supported by strong evidence (Level 1); associations with childhood maltreatment, mixed-features, female sex, and type-II BD had moderate evidence (Level 2). Other factors: genetic predisposition, metabolic disturbances or hypothyroidism, antidepressant exposure, predominant depressive polarity (Level 3), along with greater illness duration and immune-inflammatory dysfunction (Level 4) require further study. RC-BD was consistently recognized as having high prevalence (22.3%-35.5% of BD cases) and inferior treatment response. Identified associated factors can inform clinical practice. Long-term illness-course, metabolic factors, and optimal treatment require further investigation.
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Affiliation(s)
- Alessandro Miola
- Department of Neuroscience, University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy; Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA; International Consortium for Mood & Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, MA, USA.
| | - Konstantinos N Fountoulakis
- Department of Psychiatry III, School of Medicine Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Ross J Baldessarini
- International Consortium for Mood & Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Marin Veldic
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ontario, Canada; Department of Mental Health, The Ottawa Hospital, Ontario, Canada; Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Natalie Rasgon
- Department of Psychiatry, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Aysegul Ozerdem
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Giulio Perugi
- Psychiatry Section, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Antonio Preti
- Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy; Eating Disorders Center, Azienda Ospedaliero-Universitaria, Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
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17
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Wang C, Wang T, Wei Y, Aschard H, Ionita-Laza I. Quantile Regression for biomarkers in the UK Biobank. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543699. [PMID: 37333162 PMCID: PMC10274625 DOI: 10.1101/2023.06.05.543699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. An alternative and easy to apply approach is quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest by modeling conditional quantiles within a regression framework. Quantile regression can be applied efficiently at biobank scale using standard statistical packages in much the same way as linear regression, while having some unique advantages such as identifying variants with heterogeneous effects across different quantiles, including non-additive effects and variants involved in gene-environment interactions; accommodating a wide range of phenotype distributions with invariance to trait transformation; and overall providing more detailed information about the underlying genotype-phenotype associations. Here, we demonstrate the value of quantile regression in the context of GWAS by applying it to 39 quantitative traits in the UK Biobank (n > 300 , 000 individuals). Across these 39 traits we identify 7,297 significant loci, including 259 loci only detected by quantile regression. We show that quantile regression can help uncover replicable but unmodelled gene-environment interactions, and can provide additional key insights into poorly understood genotype-phenotype correlations for clinically relevant biomarkers at minimal additional cost.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, USA
| | - Tianying Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, France
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18
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Henkel C, Styrkársdóttir U, Thorleifsson G, Stefánsdóttir L, Björnsdóttir G, Banasik K, Brunak S, Erikstrup C, Dinh KM, Hansen TF, Nielsen KR, Bruun MT, Dowsett J, Brodersen T, Thorgeirsson TE, Gromov K, Boesen MP, Ullum H, Ostrowski SR, Pedersen OB, Stefánsson K, Troelsen A. Genome-wide association meta-analysis of knee and hip osteoarthritis uncovers genetic differences between patients treated with joint replacement and patients without joint replacement. Ann Rheum Dis 2023; 82:384-392. [PMID: 36376028 DOI: 10.1136/ard-2022-223199] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/25/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Osteoarthritis is a common and severe, multifactorial disease with a well-established genetic component. However, little is known about how genetics affect disease progression, and thereby the need for joint placement. Therefore, we aimed to investigate whether the genetic associations of knee and hip osteoarthritis differ between patients treated with joint replacement and patients without joint replacement. METHODS We included knee and hip osteoarthritis cases along with healthy controls, altogether counting >700 000 individuals. The cases were divided into two groups based on joint replacement status (surgical vs non-surgical) and included in four genome-wide association meta-analyses: surgical knee osteoarthritis (N = 22 525), non-surgical knee osteoarthritis (N = 38 626), surgical hip osteoarthritis (N = 20 221) and non-surgical hip osteoarthritis (N = 17 847). In addition, we tested for genetic correlation between the osteoarthritis groups and the pain phenotypes intervertebral disc disorder, dorsalgia, fibromyalgia, migraine and joint pain. RESULTS We identified 52 sequence variants associated with knee osteoarthritis (surgical: 17, non-surgical: 3) or hip osteoarthritis (surgical: 34, non-surgical: 1). For the surgical phenotypes, we identified 10 novel variants, including genes involved in autophagy (rs2447606 in ATG7) and mechanotransduction (rs202127176 in PIEZO1). One variant, rs13107325 in SLC39A8, associated more strongly with non-surgical knee osteoarthritis than surgical knee osteoarthritis. For all other variants, significance and effect sizes were higher for the surgical phenotypes. In contrast, genetic correlations with pain phenotypes tended to be stronger in the non-surgical groups. CONCLUSIONS Our results indicate differences in genetic associations between knee and hip osteoarthritis depending on joint replacement status.
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Affiliation(s)
- Cecilie Henkel
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | | | | | | | | | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Khoa Manh Dinh
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Thomas Folkmann Hansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Danish Headache Center, Department of Neurology, Rigshospitalet Glostrup, Glostrup, Denmark
| | - Kaspar René Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Joseph Dowsett
- Department of Clinical Immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Thorsten Brodersen
- Department of Clinical Immunology, Zealand University Hospital Køge, Køge, Denmark
| | | | | | - Kirill Gromov
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.,Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
| | - Mikael Ploug Boesen
- Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark.,Department of Radiology, Bispebjerg Hospital, Copenhagen, Denmark
| | | | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Pedersen
- Department of Clinical Immunology, Zealand University Hospital Køge, Køge, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Anders Troelsen
- Clinical Orthopaedic Research Hvidovre (CORH), Department of Orthopaedic Surgery, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.,Clinical Academic Group: Research OsteoArthritis Denmark (CAG ROAD), Greater Copenhagen Health Science Partners, Copenhagen, Denmark
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19
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Hamilton F, Mitchell R, Ahmed H, Ghazal P, Timpson NJ. An observational and Mendelian randomisation study on iron status and sepsis. Sci Rep 2023; 13:2867. [PMID: 36808173 PMCID: PMC9938246 DOI: 10.1038/s41598-023-29641-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
Iron deficiency is associated with a substantial burden of morbidity. However, supplementation of iron has been linked to increased rates of serious infection in randomised trials of children in sub-Saharan Africa. Randomised trials in other settings have been inconclusive and it is unknown if changes in levels of iron biomarkers are linked to sepsis in these other settings. We used genetic variants associated with levels of iron biomarkers as instrumental variables in a Mendelian randomisation (MR) analysis to test the hypothesis that increasing levels of iron biomarkers increase the risk of sepsis. In observational and MR analyses we found that increases in iron biomarkers increase the odds of sepsis. In stratified analyses, we show that this risk may be larger in those with iron deficiency and/or anaemia. Taken together, results here suggest a required caution in supplementation of iron and underline the role of iron homeostasis in severe infection.
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Affiliation(s)
- Fergus Hamilton
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Infection Sciences, North Bristol NHS Trust, Bristol, UK.
| | - Ruth Mitchell
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Haroon Ahmed
- Division of Population Medicine, Cardiff University Medical School, Cardiff, UK
| | - Peter Ghazal
- System Immunity Research Institute, Division of Infection and Immunity, Cardiff University, Cardiff, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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20
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Barnard ME, Meeks H, Jarboe EA, Albro J, Camp NJ, Doherty JA. Familial risk of epithelial ovarian cancer after accounting for gynaecological surgery: a population-based study. J Med Genet 2023; 60:119-127. [PMID: 35534206 PMCID: PMC9643667 DOI: 10.1136/jmedgenet-2021-108402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/14/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Uptake of risk-reducing surgery has increased among women at high risk of epithelial ovarian cancer. We sought to characterise familial risk of epithelial ovarian cancer histotypes in a population-based study after accounting for gynaecological surgeries, including bilateral oophorectomy. METHODS We compared risk of epithelial ovarian cancer in relatives of 3536 epithelial ovarian cancer cases diagnosed in 1966-2016 and relatives of 35 326 matched controls. We used Cox competing risk models, incorporating bilateral oophorectomy as a competing risk, to estimate the relative risk of ovarian cancer in first-degree (FDR), second-degree (SDR) and third-degree (TDR) relatives from 1966 to 2016. We also estimated relative risks in time periods before (1966-1994, 1995-2004) and after (2005-2016) formal recommendations were made for prophylactic oophorectomy among women with pathogenic variants in BRCA1/2. RESULTS The relative risks of epithelial ovarian cancer in FDRs, SDRs and TDRs of cases versus controls were 1.68 (95% CI 1.39 to 2.04), 1.51 (95% CI 1.30 to 1.75) and 1.34 (95% CI 1.20 to 1.48), respectively. Relative risks were greatest for high-grade serous, mucinous and 'other epithelial' histotypes. Relative risks were attenuated for case FDRs, but not for SDRs or TDRs, from 2005 onwards, consistent with the timing of recommendations for prophylactic surgery. CONCLUSION Familial risk of epithelial ovarian cancer extends to TDRs, especially for high-grade serous and mucinous histotypes. Distant relatives share genes but minimal environment, highlighting the importance of germline inherited genetics in ovarian cancer aetiology. Increased ovarian cancer risk in distant relatives has implications for counselling and recommendations for prophylactic surgeries that, from our data, appear only to reach FDRs.
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Affiliation(s)
- Mollie E Barnard
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Huong Meeks
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Elke A Jarboe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Departments of Pathology and Obstetrics and Gynecology, University of Utah, Salt Lake City, Utah, USA
| | - James Albro
- Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, Utah, USA
- Department of Pathology, Intermountain Medical Center, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Nicola J Camp
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Jennifer A Doherty
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Departments of Population Health Sciences and Obstetrics and Gynecology, University of Utah, Salt Lake City, Utah, USA
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21
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Burstein D, Hoffman G, Mathur D, Venkatesh S, Therrien K, Fanous AH, Bigdeli TB, Harvey PD, Roussos P, Voloudakis G. Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284670. [PMID: 36711948 PMCID: PMC9882426 DOI: 10.1101/2023.01.17.23284670] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.
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Affiliation(s)
- David Burstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Gabriel Hoffman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Deepika Mathur
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sanan Venkatesh
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry, University of Arizona College of Medicine-Phoenix, Phoenix
- Carl T. Hayden Veterans Affairs Medical Center, Phoenix, Arizona
| | - Tim B Bigdeli
- VA New York Harbor Healthcare System, Brooklyn
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, New York
| | - Philip D Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, Florida
- University of Miami Miller School of Medicine, Miami, Florida
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Georgios Voloudakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
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22
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Gorla A, Sankararaman S, Burchard E, Flint J, Zaitlen N, Rahmani E. Phenotypic subtyping via contrastive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522921. [PMID: 36711575 PMCID: PMC9881932 DOI: 10.1101/2023.01.05.522921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on clinically observed heterogeneity or metadata clustering. However, they generally tend to capture the dominant sources of variation in the data, which often originate from variation that is not descriptive of the mechanistic heterogeneity of the phenotype of interest; in fact, such dominant sources of variation, such as population structure or technical variation, are, in general, expected to be independent of subphenotypic structure. We instead aim to find a subspace with signal that is unique to a group of samples for which we believe that subphenotypic variation exists (e.g., cases of a disease). To that end, we introduce Phenotype Aware Components Analysis (PACA), a contrastive learning approach leveraging canonical correlation analysis to robustly capture weak sources of subphenotypic variation. In the context of disease, PACA learns a gradient of variation unique to cases in a given dataset, while leveraging control samples for accounting for variation and imbalances of biological and technical confounders between cases and controls. We evaluated PACA using an extensive simulation study, as well as on various subtyping tasks using genotypes, transcriptomics, and DNA methylation data. Our results provide multiple strong evidence that PACA allows us to robustly capture weak unknown variation of interest while being calibrated and well-powered, far superseding the performance of alternative methods. This renders PACA as a state-of-the-art tool for defining de novo subtypes that are more likely to reflect molecular heterogeneity, especially in challenging cases where the phenotypic heterogeneity may be masked by a myriad of strong unrelated effects in the data.
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Affiliation(s)
- Aditya Gorla
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Esteban Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Behavioral Sciences, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Elior Rahmani
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
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23
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E96V Mutation in the Kdelr3 Gene Is Associated with Type 2 Diabetes Susceptibility in Obese NZO Mice. Int J Mol Sci 2023; 24:ijms24010845. [PMID: 36614300 PMCID: PMC9820861 DOI: 10.3390/ijms24010845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/16/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Type 2 diabetes (T2D) represents a multifactorial metabolic disease with a strong genetic predisposition. Despite elaborate efforts in identifying the genetic variants determining individual susceptibility towards T2D, the majority of genetic factors driving disease development remain poorly understood. With the aim to identify novel T2D risk genes we previously generated an N2 outcross population using the two inbred mouse strains New Zealand obese (NZO) and C3HeB/FeJ (C3H). A linkage study performed in this population led to the identification of the novel T2D-associated quantitative trait locus (QTL) Nbg15 (NZO blood glucose on chromosome 15, Logarithm of odds (LOD) 6.6). In this study we used a combined approach of positional cloning, gene expression analyses and in silico predictions of DNA polymorphism on gene/protein function to dissect the genetic variants linking Nbg15 to the development of T2D. Moreover, we have generated congenic strains that associated the distal sublocus of Nbg15 to mechanisms altering pancreatic beta cell function. In this sublocus, Cbx6, Fam135b and Kdelr3 were nominated as potential causative genes associated with the Nbg15 driven effects. Moreover, a putative mutation in the Kdelr3 gene from NZO was identified, negatively influencing adaptive responses associated with pancreatic beta cell death and induction of endoplasmic reticulum stress. Importantly, knockdown of Kdelr3 in cultured Min6 beta cells altered insulin granules maturation and pro-insulin levels, pointing towards a crucial role of this gene in islets function and T2D susceptibility.
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24
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Oliva V, Fanelli G, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, De Ronchi D, Fabbri C, Serretti A. Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns. J Affect Disord 2023; 320:534-543. [PMID: 36216191 DOI: 10.1016/j.jad.2022.10.003] [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: 02/14/2022] [Revised: 09/27/2022] [Accepted: 10/02/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly prevalent psychiatric condition characterised by a heterogeneous clinical presentation and an estimated twin-based heritability of ~40-50 %. Different clinical MDD subtypes might partly reflect distinctive underlying genetics. This study aims to investigate if polygenic risk scores (PRSs) for different psychiatric disorders, personality traits, and substance use-related traits may be associated with different clinical subtypes of MDD (i.e., MDD with melancholic or psychotic features), higher symptom severity, or different clusters of depressive symptoms (i.e., sadness symptoms, typical neurovegetative symptoms, detachment symptoms, and negative thoughts). METHODS The target sample included 1149 patients with MDD, recruited by the European Group for the Study of Resistant Depression. PRSs for 25 psychiatric disorders and traits were computed based on the most recent publicly available summary statistics of the largest genome-wide association studies. PRSs were then used as predictors in regression models, adjusting for age, sex, population stratification, and recruitment sites. RESULTS Patients with MDD having higher PRS for MDD and loneliness were more likely to exhibit melancholic features of MDD (p = 0.0009 and p = 0.005, respectively). Moreover, patients with higher PRS for alcohol intake and post-traumatic stress disorder were more likely to experience greater typical neurovegetative symptoms (p = 0.0012 and p = 0.0045, respectively). LIMITATIONS The proportion of phenotypic variance explained by the PRSs was limited. CONCLUSIONS This study suggests that melancholic features and typical neurovegetative symptoms of MDD may show distinctive underlying genetics. Our findings provide a new contribution to the understanding of the genetic heterogeneity of MDD.
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Affiliation(s)
- Vincenzo Oliva
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Joseph Zohar
- Psychiatric Division, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
| | - Daniel Souery
- School of Medicine, Free University of Brussels, Brussels, Belgium; Psy Pluriel-European Centre of Psychological Medicine, Brussels, Belgium
| | - Stuart Montgomery
- Imperial College School of Medicine, University of London, London, UK
| | - Diego Albani
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - Gianluigi Forloni
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | | | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Diana De Ronchi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
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25
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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26
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Dapas M, Dunaif A. Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. Endocr Rev 2022; 43:927-965. [PMID: 35026001 PMCID: PMC9695127 DOI: 10.1210/endrev/bnac001] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Indexed: 01/16/2023]
Abstract
Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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27
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Murillo-García N, Barrio-Martínez S, Setién-Suero E, Soler J, Papiol S, Fatjó-Vilas M, Ayesa-Arriola R. Overlap between genetic variants associated with schizophrenia spectrum disorders and intelligence quotient: a systematic review. J Psychiatry Neurosci 2022; 47:E393-E408. [PMID: 36414327 PMCID: PMC9710545 DOI: 10.1503/jpn.220026] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND To study whether there is genetic overlap underlying the risk for schizophrenia spectrum disorders (SSDs) and low intelligence quotient (IQ), we reviewed and summarized the evidence on genetic variants associated with both traits. METHODS We performed this review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and preregistered it in PROSPERO. We searched the Medline databases via PubMed, PsycInfo, Web of Science and Scopus. We included studies in adults with a diagnosis of SSD that explored genetic variants (single nucleotide polymorphisms [SNPs], copy number variants [CNVs], genomic insertions or genomic deletions), estimated IQ and studied the relationship between genetic variability and both traits (SSD and IQ). We synthesized the results and assessed risk of bias using the Quality of Genetic Association Studies (Q-Genie) tool. RESULTS Fifty-five studies met the inclusion criteria (45 case-control, 9 cross-sectional, 1 cohort), of which 55% reported significant associations for genetic variants involved in IQ and SSD. The SNPs more frequently explored through candidate gene studies were in COMT, DTNBP1, BDNF and TCF4. Through genome-wide association studies, 2 SNPs in CHD7 and GATAD2A were associated with IQ in patients with SSD. The studies on CNVs suggested significant associations between structural variants and low IQ in patients with SSD. LIMITATIONS Overall, primary studies used heterogeneous IQ measurement tools and had small samples. Grey literature was not screened. CONCLUSION Genetic overlap between SSD and IQ supports the neurodevelopmental hypothesis of schizophrenia. Most of the risk polymorphisms identified were in genes relevant to brain development, neural proliferation and differentiation, and synaptic plasticity.
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Affiliation(s)
| | | | | | | | | | | | - Rosa Ayesa-Arriola
- From the Research Unit in Mental Illness, Valdecilla Biomedical Research Institute, Santander, Cantabria, Spain (Murillo-García, Barrio-Martínez, Ayesa-Arriola); the Department of Molecular Biology, Faculty of Medicine, University of Cantabria, Santander, Cantabria, Spain (Murillo-García, Ayesa-Arriola); the Faculty of Psychology, University Complutense of Madrid, Madrid, Spain (Barrio-Martínez); the Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Basque Country, Spain (Setién-Suero); the Biomedical Research Networking Center for Mental Health (CIBERSAM), Madrid, Madrid, Spain (Soler, Papiol, Fatjó-Vilas, Ayesa-Arriola); the Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain (Soler, Fatjó-Vilas); the Institut de Biomedicina de la Universitat de Barcelona, Universitat de Barcelona, Barcelona, Spain (Soler); the Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany (Papiol); the Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany (Papiol); the FIDMAG Sisters Hospitallers Research Foundation, Sant Boi de Llobregat, Barcelona, Spain (Fatjó-Vilas)
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28
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Zamanpoor M, Austin NA, Ghaedi H, Nograles NH, Brown AE, Wilson AD, Merriman TR, Morison IM, Omrani MD. Association Analysis of CMYA5 rs4704591 Polymorphism with Rheumatoid
Arthritis in Caucasians. AKTUEL RHEUMATOL 2022. [DOI: 10.1055/a-1386-3344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Zusammenfassung
Einleitung Einzel nukleotid polymorphismen (SNPs) im
Cardiomyopathy-Associated Protein 5 (CMYA5)-Gen wurden in genomweiten
Assoziationsstudien mit rheumatoider Arthritis (RA) in Verbindung gebracht.
In dieser Studie wollten wir die Assoziation zwischen
CMYA5-Genpolymorphismen und RA in unabhängigen kaukasischen
Fall-Kontroll-Kohorten replizieren und eine Metaanalyse durchführen,
um die Assoziation von CMYA5-Genpolymorphismen mit RA in kaukasischen
Populationen zu untersuchen.
Methoden Wir analysierten 2731 RA-Fälle und 1783 gesunde
Kontrollen in vier unabhängigen kaukasischen Probensätzen.
rs4704591 im CMYA5-Gen wurden unter Verwendung des TaqMan
SNP-Genotypisierungsassays genotypisiert. Die Metaanalyse wurde über
kaukasische Kohorten hinweg durchgeführt.
Ergebnisse Unsere Analyse ergab keine Hinweise auf eine Assoziation
von rs4704591 mit RA in den Replikationsprobensätzen
(P=0,941, OR=0,997). Die Metaanalyse zeigte eine schwache
Assoziation zwischen dem kleinen Allel der CMYA5-Variante rs4704591 (C) und
RA in den kombinierten RA-Kohorten (P=0,023, OR=0,938) unter
Verwendung des logistischen Regressionsmodells in der
Matched-Case-Control-Studie.
Schlussfolgerung Unsere Studie war nicht erfolgreich darin, die
Assoziation der CMYA5-Variante rs4704591 mit RA zu replizieren. Daher
können wir die Assoziation zwischen CMYA5-Genpolymorphismen und RA
in der kaukasischen Bevölkerung nicht bestätigen.
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Affiliation(s)
- Mansour Zamanpoor
- Biochemistry, University of Otago, Dunedin, New
Zealand
- Medical Genetics, Shahid Beheshti University of Medical
Sciences, Tehran, Iran (the Islamic Republic of)
- Wellington Regional Genetics Laboratory, Wellington
Regional Hospital, Wellington, New Zealand
| | | | - Hamid Ghaedi
- Medical Genetics, Shahid Beheshti University of Medical
Sciences, Tehran, Iran (the Islamic Republic of)
| | - Nadine H. Nograles
- Biomedical Sciences, Newcastle University Medicine
Malaysia, Nusajaya, Malaysia
| | - Angela E. Brown
- Wellington Regional Genetics Laboratory, Wellington
Regional Hospital, Wellington, New Zealand
| | - Andrew D. Wilson
- Wellington Regional Genetics Laboratory, Wellington
Regional Hospital, Wellington, New Zealand
- Department of Pathology and Molecular Medicine,
University of Otago, Wellington, New Zealand
| | | | | | - Mir Davood Omrani
- Medical Genetics, Shahid Beheshti University of Medical
Sciences, Tehran, Iran (the Islamic Republic of)
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29
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Nunes A, Scott K, Alda M. Lessons from ecology for understanding the heterogeneity of bipolar disorder. J Psychiatry Neurosci 2022; 47:E359-E365. [PMID: 36257674 PMCID: PMC9584152 DOI: 10.1503/jpn.220172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Abraham Nunes
- From the Department of Psychiatry, Dalhousie University, Halifax, NS (Nunes, Scott, Alda); and the Faculty of Computer Science, Dalhousie University, Halifax, NS (Nunes)
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30
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Vihinen M. Individual Genetic Heterogeneity. Genes (Basel) 2022; 13:1626. [PMID: 36140794 PMCID: PMC9498725 DOI: 10.3390/genes13091626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022] Open
Abstract
Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden
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31
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Wang T, Ionita-Laza I, Wei Y. Integrated Quantile RAnk Test (iQRAT) for gene-level associations. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Tianying Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University
| | | | - Ying Wei
- Department of Biostatistics, Columbia University
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32
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Greenwood TA. Genetic Influences on Cognitive Dysfunction in Schizophrenia. Curr Top Behav Neurosci 2022; 63:291-314. [PMID: 36029459 DOI: 10.1007/7854_2022_388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case-control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.
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Affiliation(s)
- Tiffany A Greenwood
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
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33
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Juyal G, Pandey A, Garcia SL, Negi S, Gupta R, Kumar U, Bhat B, Juyal RC, Thelma BK. Stratification of rheumatoid arthritis cohort using Ayurveda based deep phenotyping approach identifies novel genes in a GWAS. J Ayurveda Integr Med 2022; 13:100578. [PMID: 35793592 PMCID: PMC9259475 DOI: 10.1016/j.jaim.2022.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background and aim Genome wide association studies have scaled up both in terms of sample size and range of complex disorders investigated, but these have explained relatively little phenotypic variance. Of the several reasons, phenotypic heterogeneity seems to be a likely contributor for missing out genetic associations of large effects. Ayurveda, the traditional Indian system of medicine is one such tool which adopts a holistic deep phenotyping approach and classifies individuals based on their body constitution/prakriti. We hypothesized that Ayurveda based phenotypic stratification of healthy and diseased individuals will allow us to achieve much desired homogeneous cohorts which would facilitate detection of genetic association of large effects. In this proof of concept study, we performed a genome wide association testing of clinically diagnosed rheumatoid arthritis patients and healthy controls, who were re-phenotyped into Vata, Pitta and Kapha predominant prakriti sub-groups. Experimental procedure Genotypes of rheumatoid arthritis cases (Vata = 49; Pitta = 117; Kapha = 78) and controls (Vata = 33; Pitta = 175; Kapha = 85) were retrieved from the total genotype data, used in a recent genome-wide association study performed in our laboratory. A total of 528461 SNPs were included after quality control. Prakriti-wise genome-wide association analysis was employed. Results and conclusion This study identified (i) prakriti-specific novel disease risk genes of high effect sizes; (ii) putative candidates of novel therapeutic potential; and (iii) a good correlation between genetic findings and clinical knowledge in Ayurveda. Adopting Ayurveda based deep phenotyping may facilitate explaining hitherto undiscovered heritability in complex traits and may propel much needed progress in personalized medicine.
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Affiliation(s)
- Garima Juyal
- School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India.
| | - Anuj Pandey
- Department of Genetics, University of Delhi South Campus, New Delhi 110021, India
| | - Sara L Garcia
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Sapna Negi
- National Institute of Pathology, Safdarjung Hospital Campus, New Delhi 110029, India
| | - Ramneek Gupta
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Uma Kumar
- Department of Rheumatology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Bheema Bhat
- Department of Ayurveda, Holy Family Hospital, New Delhi 110025, India
| | - Ramesh C Juyal
- National Institute of Immunology, New Delhi 110067, India
| | - B K Thelma
- Department of Genetics, University of Delhi South Campus, New Delhi 110021, India.
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34
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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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35
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Paribello P, Manchia M, Bosia M, Pinna F, Carpiniello B, Comai S. Melatonin and aggressive behavior: A systematic review of the literature on preclinical and clinical evidence. J Pineal Res 2022; 72:e12794. [PMID: 35192237 PMCID: PMC9285357 DOI: 10.1111/jpi.12794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/04/2022] [Accepted: 02/18/2022] [Indexed: 11/27/2022]
Abstract
The melatonin system and circadian disruption have well-established links with aggressive behaviors; however, the biological underpinnings have not been thoroughly investigated. Here, we aimed at examining the current knowledge regarding the neurobiological and psychopharmacological involvement of the melatonin system in aggressive/violent behaviors. To this end, we performed a systematic review on Embase and Pubmed/MEDLINE of preclinical and clinical evidence linking the melatonin system, melatonin, and melatoninergic drugs with aggressive/violent behaviors. Two blinded raters performed an independent screening of the relevant literature. Overall, this review included 38 papers distributed between clinical and preclinical models. Eleven papers specifically addressed the existing evidence in rodent models, five in fish models, and 21 in humans. The data indicate that depending on the species, model, and timing of administration, melatonin may exert a complex influence on aggressive/violent behaviors. Particularly, the apparent contrasting findings on the link between the melatonin system and aggression/violence (with either increased, no, or decreased effect) shown in preclinical models underscore the need for further research to develop more accurate and fruitful translational models. Likewise, the significant heterogeneity found in the results of clinical studies does not allow yet to draw any firm conclusion on the efficacy of melatonin or melatonergic drugs on aggressive/violent behaviors. However, findings in children and in traits associated with aggressive/violent behavior, including irritability and anger, are emerging and deserve empirical attention given the low toxicity of melatonin and melatonergic drugs.
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Affiliation(s)
- Pasquale Paribello
- Section of Psychiatry, Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
- Unit of Clinical PsychiatryUniversity Hospital Agency of CagliariCagliariItaly
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
- Unit of Clinical PsychiatryUniversity Hospital Agency of CagliariCagliariItaly
- Department of PharmacologyDalhousie UniversityHalifaxNova ScotiaCanada
| | - Marta Bosia
- Division of NeuroscienceSan Raffaele Scientific InstituteMilanItaly
- School of MedicineVita Salute San Raffaele UniversityMilanItaly
| | - Federica Pinna
- Section of Psychiatry, Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
- Unit of Clinical PsychiatryUniversity Hospital Agency of CagliariCagliariItaly
| | - Bernardo Carpiniello
- Section of Psychiatry, Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
- Unit of Clinical PsychiatryUniversity Hospital Agency of CagliariCagliariItaly
| | - Stefano Comai
- Division of NeuroscienceSan Raffaele Scientific InstituteMilanItaly
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
- Department of Pharmaceutical and Pharmacological SciencesUniversity of PaduaPaduaItaly
- Department of Biomedical SciencesUniversity of PaduaPaduaItaly
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36
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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37
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Basavaraju P, Balasubramani R, Kathiresan DS, Devaraj I, Babu K, Alagarsamy V, Puthamohan VM. Genetic Regulatory Networks of Apolipoproteins and Associated Medical Risks. Front Cardiovasc Med 2022; 8:788852. [PMID: 35071357 PMCID: PMC8770923 DOI: 10.3389/fcvm.2021.788852] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Apolipoproteins (APO proteins) are the lipoprotein family proteins that play key roles in transporting lipoproteins all over the body. There are nearly more than twenty members reported in the APO protein family, among which the A, B, C, E, and L play major roles in contributing genetic risks to several disorders. Among these genetic risks, the single nucleotide polymorphisms (SNPs), involving the variation of single nucleotide base pairs, and their contributing polymorphisms play crucial roles in the apolipoprotein family and its concordant disease heterogeneity that have predominantly recurred through the years. In this review, we have contributed a handful of information on such genetic polymorphisms that include APOE, ApoA1/B ratio, and A1/C3/A4/A5 gene cluster-based population genetic studies carried throughout the world, to elaborately discuss the effects of various genetic polymorphisms in imparting various medical conditions, such as obesity, cardiovascular, stroke, Alzheimer's disease, diabetes, vascular complications, and other associated risks.
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Affiliation(s)
- Preethi Basavaraju
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Rubadevi Balasubramani
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Divya Sri Kathiresan
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Ilakkiyapavai Devaraj
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Kavipriya Babu
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Vasanthakumar Alagarsamy
- Biomaterials and Nano-Medicine Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
| | - Vinayaga Moorthi Puthamohan
- Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, India
- *Correspondence: Vinayaga Moorthi Puthamohan
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38
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Dupuis A, Mudiyanselage P, Burton CL, Arnold PD, Crosbie J, Schachar RJ. Hyperfocus or flow? Attentional strengths in autism spectrum disorder. Front Psychiatry 2022; 13:886692. [PMID: 36276327 PMCID: PMC9579965 DOI: 10.3389/fpsyt.2022.886692] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/30/2022] [Indexed: 12/03/2022] Open
Abstract
The comorbidity of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) diagnoses is well established. An ASD diagnosis is associated with elevated ADHD traits and symptoms, as well as strengths in attention. In the ASD literature, attentional strengths have been described as maladaptive (e.g., hyperfocus), in contrast with positive portrayals in the typically developing population (e.g., flow). The objective of this study was to (1) compare profiles of attentional strengths and weaknesses in ASD and ADHD and (2) determine whether attentional strengths in ASD are associated with impairment, poorer cognitive flexibility, and perseveration/perfectionism. In a community sample of 5,744 children and youth, 131 children were reported as having a diagnosis of ASD (mean age 10.3 years) and 346 children were reported as having a diagnosis of ADHD (mean age 10.7 years). We used the Strengths and Weaknesses of Attention-Deficit/Hyperactivity-symptoms and Normal-behaviors (SWAN) rating scale to calculate attentional and hyperactive/impulse control strength and weakness counts and scores. The Autism-Spectrum Quotient Switching factor served as a measure of cognitive flexibility. Impairment was assessed with the Columbia Impairment Scale. We used the symmetry/ordering factor on the Toronto Obsessive-Compulsive Scale as a measure of perseveration/perfectionism. No differences were found between the ADHD and ASD groups in SWAN weakness scores, symptoms, or hyperactive/impulse control strengths; however, autistic children had higher rates of attentional strengths [odds ratio: 5.7, 95% CI (2.8, 11.6), p < 0.0001]. Post-hoc pairwise testing identified four attentional strengths with significantly higher rates in ASD than in ADHD. Attentional strength scores were not associated with impairment or poor cognitive flexibility, but predicted levels of perseveration/perfectionism. The effect of attentional strengths on impairment and cognitive flexibility did not differ between autistic and Control children, but the higher perseveration/perfectionism scores seen in ASD were not found in Control children. ASD is associated with a pattern of attentional strengths that is not found in ADHD Characterizing the full range of attentional abilities in autistic children may explain variability in outcomes such as quality-of-life indicators and identify protective factors, providing targets for strength-based behavioral interventions. The clinical and etiological implications of the subgroup of autistic children with attentional strengths require further investigation.
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Affiliation(s)
- Annie Dupuis
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Piyumi Mudiyanselage
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Christie L Burton
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Paul D Arnold
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,The Mathison Centre for Mental Health Research and Education, Calgary, AB, Canada.,Hotchkiss Brain Centre, University of Calgary, Calgary, AB, Canada
| | - Jennifer Crosbie
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Russell J Schachar
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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39
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Davies MR, Buckman JEJ, Adey BN, Armour C, Bradley JR, Curzons SCB, Davies HL, Davis KAS, Goldsmith KA, Hirsch CR, Hotopf M, Hübel C, Jones IR, Kalsi G, Krebs G, Lin Y, Marsh I, McAtarsney-Kovacs M, McIntosh AM, Mundy J, Monssen D, Peel AJ, Rogers HC, Skelton M, Smith DJ, Ter Kuile A, Thompson KN, Veale D, Walters JTR, Zahn R, Breen G, Eley TC. Comparison of symptom-based versus self-reported diagnostic measures of anxiety and depression disorders in the GLAD and COPING cohorts. J Anxiety Disord 2022; 85:102491. [PMID: 34775166 DOI: 10.1016/j.janxdis.2021.102491] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 10/13/2021] [Accepted: 10/20/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Understanding and improving outcomes for people with anxiety or depression often requires large sample sizes. To increase participation and reduce costs, such research is typically unable to utilise "gold-standard" methods to ascertain diagnoses, instead relying on remote, self-report measures. AIMS Assess the comparability of remote diagnostic methods for anxiety and depression disorders commonly used in research. METHOD Participants from the UK-based GLAD and COPING NBR cohorts (N = 58,400) completed an online questionnaire between 2018 and 2020. Responses to detailed symptom reports were compared to DSM-5 criteria to generate symptom-based diagnoses of major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Participants also self-reported any prior diagnoses from health professionals, termed self-reported diagnoses. "Any anxiety" included participants with at least one anxiety disorder. Agreement was assessed by calculating accuracy, Cohen's kappa, McNemar's chi-squared, sensitivity, and specificity. RESULTS Agreement between diagnoses was moderate for MDD, any anxiety, and GAD, but varied by cohort. Agreement was slight to fair for the phobic disorders. Many participants with self-reported GAD did not receive a symptom-based diagnosis. In contrast, symptom-based diagnoses of the phobic disorders were more common than self-reported diagnoses. CONCLUSIONS Agreement for MDD, any anxiety, and GAD was higher for cases in the case-enriched GLAD cohort and for controls in the general population COPING NBR cohort. For anxiety disorders, self-reported diagnoses classified most participants as having GAD, whereas symptom-based diagnoses distributed participants more evenly across the anxiety disorders. Further validation against gold standard measures is required.
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Affiliation(s)
- Molly R Davies
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Joshua E J Buckman
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational and Health Psychology, University College London, Gower Street, London WC1E 7HB, UK; iCope - Camden and Islington Psychological Therapies Services, Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK
| | - Brett N Adey
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Chérie Armour
- Stress, Trauma & Related Conditions (STARC) research lab, School of Psychology, Queens University Belfast (QUB), Belfast, Northern Ireland, UK
| | - John R Bradley
- NIHR BioResource, Cambridge University Hospitals, Cambridge Biomedical Campus, Cambridge, UK
| | - Susannah C B Curzons
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Helena L Davies
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Katrina A S Davis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Kimberley A Goldsmith
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Colette R Hirsch
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Christopher Hübel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; National Centre for Register-based Research, Aarhus Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | - Ian R Jones
- National Centre for Mental Health, Division of Psychiatry and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Gursharan Kalsi
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Georgina Krebs
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Yuhao Lin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Ian Marsh
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Monika McAtarsney-Kovacs
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinurgh, UK
| | - Jessica Mundy
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Dina Monssen
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Alicia J Peel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK
| | - Henry C Rogers
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Megan Skelton
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Abigail Ter Kuile
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Katherine N Thompson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - David Veale
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - James T R Walters
- National Centre for Mental Health, Division of Psychiatry and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Roland Zahn
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Gerome Breen
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Thalia C Eley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK.
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Malanchini M, Rimfeld K, Gidziela A, Cheesman R, Allegrini AG, Shakeshaft N, Schofield K, Packer A, Ogden R, McMillan A, Ritchie SJ, Dale PS, Eley TC, von Stumm S, Plomin R. Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. Mol Psychiatry 2021; 26:7823-7837. [PMID: 34599278 PMCID: PMC8872983 DOI: 10.1038/s41380-021-01300-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/17/2021] [Accepted: 09/08/2021] [Indexed: 02/03/2023]
Abstract
Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier to finding more of this 'missing heritability' is assessment--the use of diverse measures across GWA studies as well as time and the cost of assessment. In a series of four studies, we created a 15-min (40-item), online, gamified measure of g that is highly reliable (alpha = 0.78; two-week test-retest reliability = 0.88), psychometrically valid and scalable; we called this new measure Pathfinder. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields reliable verbal and nonverbal scores, correlated substantially with standard measures of g collected at previous ages (r ranging from 0.42 at age 7 to 0.57 at age 16). Pathfinder showed substantial twin heritability (0.57, 95% CIs = 0.43, 0.68) and SNP heritability (0.37, 95% CIs = 0.04, 0.70). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical, and behavioural sciences.
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Affiliation(s)
- Margherita Malanchini
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Kaili Rimfeld
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Agnieszka Gidziela
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Rosa Cheesman
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Andrea G Allegrini
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nicholas Shakeshaft
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- QuodIt Ltd, London, UK
| | - Kerry Schofield
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- QuodIt Ltd, London, UK
| | - Amy Packer
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rachel Ogden
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrew McMillan
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Stuart J Ritchie
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Philip S Dale
- Department of Speech and Hearing Science, University of New Mexico, Albuquerque, NM, USA
| | - Thalia C Eley
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Robert Plomin
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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41
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Verdi S, Marquand AF, Schott JM, Cole JH. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain 2021; 144:2946-2953. [PMID: 33892488 PMCID: PMC8634113 DOI: 10.1093/brain/awab165] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/24/2021] [Accepted: 04/08/2021] [Indexed: 11/25/2022] Open
Abstract
Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - James H Cole
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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42
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Morneau-Vaillancourt G, Andlauer TFM, Ouellet-Morin I, Paquin S, Brendgen MR, Vitaro F, Gouin JP, Séguin JR, Gagnon É, Cheesman R, Forget-Dubois N, Rouleau GA, Turecki G, Tremblay RE, Côté SM, Dionne G, Boivin M. Polygenic scores differentially predict developmental trajectories of subtypes of social withdrawal in childhood. J Child Psychol Psychiatry 2021; 62:1320-1329. [PMID: 34085288 DOI: 10.1111/jcpp.13459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/09/2021] [Accepted: 04/15/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Children who consistently withdraw from social situations face increased risk for later socioemotional difficulties. Twin studies indicate that genetic factors substantially account for the persistence of social withdrawal over time. However, the molecular genetic etiology of chronic courses of social wariness and preference for solitude, two dimensions of social withdrawal, remains undocumented. The objectives of the present study were (a) to identify high-risk trajectories for social wariness and preference for solitude in childhood and (b) to examine whether falling on these high-risk trajectories can be predicted by specific polygenic scores for mental health traits and disorders and by a general polygenic predisposition to these traits. METHODS Teachers evaluated 971 genotyped children at five occasions (age 6 to 12 years) from two prospective longitudinal studies, the Quebec Newborn Twin Study and the Quebec Longitudinal Study of Child Development. Developmental trajectories for social wariness and preference for solitude were identified. We tested whether polygenic scores for attention deficit hyperactivity disorder, autism spectrum disorder, depression, loneliness, and subjective well-being, as well as a general mental health genetic risk score derived across these traits, were associated with the developmental trajectories. RESULTS Polygenic scores differentially predicted social wariness and preference for solitude. Only the loneliness polygenic score significantly predicted the high trajectory for social wariness. By contrast, the general mental health genetic risk score factor was associated with the trajectory depicting high-chronic preference for solitude. CONCLUSIONS Distinct associations were uncovered between the polygenic scores, social wariness, and preference for solitude.
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Affiliation(s)
| | - Till F M Andlauer
- Department of Neurology, Technical University of Munich, Munich, Germany
| | | | - Stéphane Paquin
- Department of Psychology, The Pennsylvania State University, State College, PA, USA
| | - Mara R Brendgen
- Département de psychologie, Université du Québec à Montréal, Montreal, QC, Canada
| | - Frank Vitaro
- École de psychoéducation, Université de Montréal, Montreal, QC, Canada
| | | | - Jean R Séguin
- Département de psychiatrie et d'addictologie, Université de Montréal, Montreal, QC, Canada.,Centre de recherche du CHU Sainte-Justine, Montreal, QC, Canada
| | - Éloi Gagnon
- École de psychologie, Université Laval, Quebec City, QC, Canada
| | - Rosa Cheesman
- Promenta Research Centre, University of Oslo, Oslo, Norway
| | | | - Guy A Rouleau
- Institut-hôpital neurologique de Montréal, McGill University, Montreal, QC, Canada
| | - Gustavo Turecki
- Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - Richard E Tremblay
- Départements de pédiatrie et de psychologie, Université de Montréal, Montreal, QC, Canada
| | - Sylvana M Côté
- Département de médecine sociale et préventive, Université de Montréal, Montreal, QC, Canada
| | - Ginette Dionne
- École de psychologie, Université Laval, Quebec City, QC, Canada
| | - Michel Boivin
- École de psychologie, Université Laval, Quebec City, QC, Canada
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43
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Jin Q, Shi G. Meta-Analysis of Joint Test of SNP and SNP-Environment Interaction with Heterogeneity. Hum Hered 2021; 86:1-9. [PMID: 34700323 DOI: 10.1159/000519098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 07/29/2021] [Indexed: 12/13/2022] Open
Abstract
Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.
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Affiliation(s)
- Qinqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.,Applied Science College, Taiyuan University of Science and Technology, Taiyuan, China
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China
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44
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de Jorge Martínez C, Rukh G, Williams MJ, Gaudio S, Brooks S, Schiöth HB. Genetics of anorexia nervosa: an overview of genome-wide association studies and emerging biological links. J Genet Genomics 2021; 49:1-12. [PMID: 34634498 DOI: 10.1016/j.jgg.2021.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/29/2022]
Abstract
Anorexia nervosa (AN) is a complex disorder with a strong genetic component. Comorbidities are frequent and there is substantial overlap with other disorders. The lack of understanding of the molecular and neuroanatomical causes has made it difficult to develop effective treatments and it is often difficult to treat in clinical practice. Recent advances in genetics have changed our understanding of polygenic diseases, increasing the possibility of understanding better how molecular pathways are intertwined. This review synthetizes the current state of genetic research providing an overview of genome-wide association studies (GWAS) findings in AN as well as overlap with other disorders, traits, pathways, and imaging results. This paper also discusses the different putative global pathways that are contributing to the disease including the evidence for metabolic and psychiatric origin of the disease.
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Affiliation(s)
| | - Gull Rukh
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden.
| | - Michael J Williams
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Santino Gaudio
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Samantha Brooks
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden; School of Psychology, Faculty of Health, Liverpool John Moores University, UK; Department of Psychology, School of Human and Community Development, University of the Witwatersrand, Johannesburg, South Africa
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden; Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
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45
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Boer CG, Hatzikotoulas K, Southam L, Stefánsdóttir L, Zhang Y, Coutinho de Almeida R, Wu TT, Zheng J, Hartley A, Teder-Laving M, Skogholt AH, Terao C, Zengini E, Alexiadis G, Barysenka A, Bjornsdottir G, Gabrielsen ME, Gilly A, Ingvarsson T, Johnsen MB, Jonsson H, Kloppenburg M, Luetge A, Lund SH, Mägi R, Mangino M, Nelissen RRGHH, Shivakumar M, Steinberg J, Takuwa H, Thomas LF, Tuerlings M, Babis GC, Cheung JPY, Kang JH, Kraft P, Lietman SA, Samartzis D, Slagboom PE, Stefansson K, Thorsteinsdottir U, Tobias JH, Uitterlinden AG, Winsvold B, Zwart JA, Davey Smith G, Sham PC, Thorleifsson G, Gaunt TR, Morris AP, Valdes AM, Tsezou A, Cheah KSE, Ikegawa S, Hveem K, Esko T, Wilkinson JM, Meulenbelt I, Lee MTM, van Meurs JBJ, Styrkársdóttir U, Zeggini E. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell 2021; 184:4784-4818.e17. [PMID: 34450027 PMCID: PMC8459317 DOI: 10.1016/j.cell.2021.07.038] [Citation(s) in RCA: 159] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 03/26/2021] [Accepted: 07/30/2021] [Indexed: 12/19/2022]
Abstract
Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.
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Affiliation(s)
- Cindy G Boer
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Rodrigo Coutinho de Almeida
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Tian T Wu
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - April Hartley
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK; Musculoskeletal Research Unit, Translation Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol BS10 5NB, UK
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
| | - Eleni Zengini
- 4(th) Psychiatric Department, Dromokaiteio Psychiatric Hospital, 12461 Athens, Greece
| | - George Alexiadis
- 1(st) Department of Orthopaedics, KAT General Hospital, 14561 Athens, Greece
| | - Andrei Barysenka
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | | | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Thorvaldur Ingvarsson
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland; Department of Orthopedic Surgery, Akureyri Hospital, 600 Akureyri, Iceland
| | - Marianne B Johnsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0316 Oslo, Norway; Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, 0424 Oslo, Norway
| | - Helgi Jonsson
- Department of Medicine, Landspitali The National University Hospital of Iceland, 108 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Margreet Kloppenburg
- Departments of Rheumatology and Clinical Epidemiology, Leiden University Medical Center, 9600, 23OORC Leiden, the Netherlands
| | - Almut Luetge
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | | | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Rob R G H H Nelissen
- Department of Orthopaedics, Leiden University Medical Center, 9600, 23OORC Leiden, the Netherlands
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Julia Steinberg
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, NSW 1340, Australia
| | - Hiroshi Takuwa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo 108-8639, Japan; Department of Orthopedic Surgery, Shimane University, Shimane 693-8501, Japan
| | - Laurent F Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway; BioCore-Bioinformatics Core Facility, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Margo Tuerlings
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - George C Babis
- 2(nd) Department of Orthopaedics, National and Kapodistrian University of Athens, Medical School, Nea Ionia General Hospital Konstantopouleio, 14233 Athens, Greece
| | - Jason Pui Yin Cheung
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jae Hee Kang
- Department of Medicine, Brigham and Women's Hospital, 181 Longwood Ave, Boston, MA 02115, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
| | - Steven A Lietman
- Musculoskeletal Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Dino Samartzis
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, China; Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc., 102 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen Inc., 102 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Jonathan H Tobias
- Musculoskeletal Research Unit, Translation Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol BS10 5NB, UK; MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | - Bendik Winsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway; Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - John-Anker Zwart
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Pak Chung Sham
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | | | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester M13 9LJ, UK
| | - Ana M Valdes
- Faculty of Medicine and Health Sciences, School of Medicine, University of Nottingham, Nottingham, Nottinghamshire NG5 1PB, UK
| | - Aspasia Tsezou
- Laboratory of Cytogenetics and Molecular Genetics, Faculty of Medicine, University of Thessaly, Larissa 411 10, Greece
| | - Kathryn S E Cheah
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo 108-8639, Japan
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - J Mark Wilkinson
- Department of Oncology and Metabolism and Healthy Lifespan Institute, University of Sheffield, Sheffield S10 2RX, UK
| | - Ingrid Meulenbelt
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Ming Ta Michael Lee
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA; Institute of Biomedical Sciences, Academia Sinica, 115 Taipei, Taiwan
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | | | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, 81675 Munich, Germany.
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Nosková A, Hiltpold M, Janett F, Echtermann T, Fang ZH, Sidler X, Selige C, Hofer A, Neuenschwander S, Pausch H. Infertility due to defective sperm flagella caused by an intronic deletion in DNAH17 that perturbs splicing. Genetics 2021; 217:6041611. [PMID: 33724408 DOI: 10.1093/genetics/iyaa033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 12/08/2020] [Indexed: 12/30/2022] Open
Abstract
Artificial insemination in pig (Sus scrofa domesticus) breeding involves the evaluation of the semen quality of breeding boars. Ejaculates that fulfill predefined quality requirements are processed, diluted and used for inseminations. Within short time, eight Swiss Large White boars producing immotile sperm that had multiple morphological abnormalities of the sperm flagella were noticed at a semen collection center. The eight boars were inbred on a common ancestor suggesting that the novel sperm flagella defect is a recessive trait. Transmission electron microscopy cross-sections revealed that the immotile sperm had disorganized flagellar axonemes. Haplotype-based association testing involving microarray-derived genotypes at 41,094 SNPs of six affected and 100 fertile boars yielded strong association (P = 4.22 × 10-15) at chromosome 12. Autozygosity mapping enabled us to pinpoint the causal mutation on a 1.11 Mb haplotype located between 3,473,632 and 4,587,759 bp. The haplotype carries an intronic 13-bp deletion (Chr12:3,556,401-3,556,414 bp) that is compatible with recessive inheritance. The 13-bp deletion excises the polypyrimidine tract upstream exon 56 of DNAH17 (XM_021066525.1: c.8510-17_8510-5del) encoding dynein axonemal heavy chain 17. Transcriptome analysis of the testis of two affected boars revealed that the loss of the polypyrimidine tract causes exon skipping which results in the in-frame loss of 89 amino acids from DNAH17. Disruption of DNAH17 impairs the assembly of the flagellar axoneme and manifests in multiple morphological abnormalities of the sperm flagella. Direct gene testing may now be implemented to monitor the defective allele in the Swiss Large White population and prevent the frequent manifestation of a sterilizing sperm tail disorder in breeding boars.
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Affiliation(s)
- Adéla Nosková
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Maya Hiltpold
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Fredi Janett
- Clinic of Reproductive Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | - Thomas Echtermann
- Division of Swine Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | - Zih-Hua Fang
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Xaver Sidler
- Division of Swine Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | | | | | - Stefan Neuenschwander
- Animal Genetics, Institute of Agricultural Science, ETH Zürich, 8092 Zürich, Switzerland
| | - Hubert Pausch
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
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Suri P, Stanaway IB, Zhang Y, Freidin MB, Tsepilov YA, Carrell DS, Williams FM, Aulchenko YS, Hakonarson H, Namjou B, Crosslin DR, Jarvik GP, Lee MT. Genome-wide association studies of low back pain and lumbar spinal disorders using electronic health record data identify a locus associated with lumbar spinal stenosis. Pain 2021; 162:2263-2272. [PMID: 33729212 PMCID: PMC8277660 DOI: 10.1097/j.pain.0000000000002221] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
ABSTRACT Identifying genetic risk factors for lumbar spine disorders may lead to knowledge regarding underlying mechanisms and the development of new treatments. We conducted a genome-wide association study involving 100,811 participants with genotypes and longitudinal electronic health record data from the Electronic Medical Records and Genomics Network and Geisinger Health. Cases and controls were defined using validated algorithms and clinical diagnostic codes. Electronic health record-defined phenotypes included low back pain requiring healthcare utilization (LBP-HC), lumbosacral radicular syndrome (LSRS), and lumbar spinal stenosis (LSS). Genome-wide association study used logistic regression with additive genetic effects adjusting for age, sex, site-specific factors, and ancestry (principal components). A fixed-effect inverse-variance weighted meta-analysis was conducted. Genetic variants of genome-wide significance (P < 5 × 10-8) were carried forward for replication in an independent sample from UK Biobank. Phenotype prevalence was 48.8% for LBP-HC, 19.8% for LSRS, and 7.9% for LSS. No variants were significantly associated with LBP-HC. One locus was associated with LSRS (lead variant rs146153280:C>G, odds ratio [OR] = 1.17 for G, P = 2.1 × 10-9), but was not replicated. Another locus on chromosome 2 spanning GFPT1, NFU1, and AAK1 was associated with LSS (lead variant rs13427243:G>A, OR = 1.10 for A, P = 4.3 × 10-8) and replicated in UK Biobank (OR = 1.11, P = 5.4 × 10-5). This was the first genome-wide association study meta-analysis of lumbar spinal disorders using electronic health record data. We identified 2 novel associations with LSRS and LSS; the latter was replicated in an independent sample.
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Affiliation(s)
- Pradeep Suri
- Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, USA
- Division of Rehabilitation Care Services, 1660 S. Columbian Way, Seattle, WA 98108, USA
- Clinical Learning, Evidence, and Research Center, University of Washington, 325 Ninth Avenue, Box 359612 Seattle, WA 98104, USA
- Department of Rehabilitation Medicine, University of Washington, 325 Ninth Avenue, Box 359612 Seattle, WA 98104, USA
| | - Ian B. Stanaway
- Department of Medicine (Medical Genetics), University of Washington Medical Center, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger, 100 N. Academy Avenue, Danville, PA 17822, USA
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, SE1 7EH, UK
| | - Yakov A. Tsepilov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, 1 Pirogova Street, Novosibirsk, 630090, Russia
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 10 Lavrentiev Avenue, Novosibirsk, 630090, Russia
- PolyOmica, s’-Hetogenbosch,5237 PA, The Netherlands
| | - David S. Carrell
- Kaiser Permante Washington Health Research Institute, 1700 Minor Ave, Suite 1600, Seattle, WA 98101, USA
| | - Frances M.K. Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, SE1 7EH, UK
| | - Yurii S. Aulchenko
- PolyOmica, s’-Hetogenbosch,5237 PA, The Netherlands
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
| | - Hakon Hakonarson
- Department of Pediatrics, Children’s Hospital of Philadelphia, 3615 Civic Center Blvd.Philadelphia, PA 19104, USA
| | - Bahram Namjou
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, USA
| | - David R. Crosslin
- Department of Biomedical Informatics and Education, University of Washington, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Gail P. Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Ming Ta Lee
- Genomic Medicine Institute, Geisinger, 100 N. Academy Avenue, Danville, PA 17822, USA
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Kelly KM, Smith JA, Mezuk B. Depression and interleukin-6 signaling: A Mendelian Randomization study. Brain Behav Immun 2021; 95:106-114. [PMID: 33631287 PMCID: PMC11081733 DOI: 10.1016/j.bbi.2021.02.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 01/19/2021] [Accepted: 02/18/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND A large body of research has reported associations between depression and elevated interleukin-6 (IL-6), a cytokine with several roles including pro-inflammatory signaling. The nature and directionality of this relationship are not yet clear. In this study we use Mendelian Randomization to examine the possibility of a causal relationship between IL-6 and depressive symptoms, and to explore multiple signaling pathways that could serve as mechanisms for this relationship. METHODS This study uses a two-sample Mendelian Randomization design. Data come from the UK Biobank (n = 89,119) and published summary statistics from six existing GWAS analyses. The primary analysis focuses on the soluble interleukin-6 receptor (sIL-6R), which is involved in multiple signaling pathways. Exploratory analyses use C-reactive protein (CRP) and soluble glycoprotein 130 (sgp130) to further examine potential underlying mechanisms. RESULTS Results are consistent with a causal effect of sIL-6R on depression (PCA-IVW Odds Ratio: 1.023 (95% Confidence Interval: 1.006-1.039), p = 0.006). Exploratory analyses demonstrate that the relationship could be consistent with either decreased classical signaling or increased trans signaling as the underlying mechanism. DISCUSSION These results strengthen the body evidence implicating IL-6 signaling in depression. When compared with existing observational and animal findings, the direction of these results suggests involvement of IL-6 trans signaling. Further study is needed to examine whether IL6R genetic variants might influence IL-6 trans signaling in the brain, as well as to explore other potential pathways linking depression and inflammation.
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Affiliation(s)
- Kristen M Kelly
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, The Netherlands.
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Institute for Social Research, University of Michigan, United States
| | - Briana Mezuk
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Institute for Social Research, University of Michigan, United States
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Mariam A, Miller-Atkins G, Pantalone KM, Zimmerman RS, Barnard J, Kattan MW, Shah H, McLeod HL, Doria A, Wagner MJ, Buse JB, Motsinger-Reif AA, Rotroff DM. A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment. Diabetes Care 2021; 44:1410-1418. [PMID: 33863751 PMCID: PMC8247498 DOI: 10.2337/dc20-2700] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/23/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Current type 2 diabetes (T2D) management contraindicates intensive glycemia treatment in patients with high cardiovascular disease (CVD) risk and is partially motivated by evidence of harms in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Heterogeneity in response to intensive glycemia treatment has been observed, suggesting potential benefit for some individuals. RESEARCH DESIGN AND METHODS ACCORD was a randomized controlled trial that investigated whether intensively treating glycemia in individuals with T2D would reduce CVD outcomes. Using a novel approach to cluster HbA1c trajectories, we identified groups in the intensive glycemia arm with modified CVD risk. Genome-wide analysis and polygenic score (PS) were developed to predict group membership. Mendelian randomization was performed to infer causality. RESULTS We identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD (hazard ratio [HR] 0.34; P = 2.01 × 10-3) and microvascular outcomes (HR 0.86; P = 0.015) than those receiving standard treatment. A single-nucleotide polymorphism, rs220721, in MAS1 reached suggestive significance in C4 (P = 4.34 × 10-7). PS predicted C4 with high accuracy (area under the receiver operating characteristic curve 0.98), and this predicted C4 displayed reduced CVD risk with intensive versus standard glycemia treatment (HR 0.53; P = 4.02 × 10-6), but not reduced risk of microvascular outcomes (P < 0.05). Mendelian randomization indicated causality between PS, on-trial HbA1c, and reduction in CVD outcomes (P < 0.05). CONCLUSIONS We found evidence of a T2D clinical group in ACCORD that benefited from intensive glycemia treatment, and membership in this group could be predicted using genetic variants. This study generates new hypotheses with implications for precision medicine in T2D and represents an important development in this landmark clinical trial warranting further investigation.
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Affiliation(s)
- Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Galen Miller-Atkins
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Kevin M Pantalone
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH
| | | | - John Barnard
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Hetal Shah
- Joslin Diabetes Center and Harvard Medical School, Boston, MA
| | - Howard L McLeod
- Taneja College of Pharmacy, University of South Florida, Tampa, FL
| | | | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - John B Buse
- Division of Endocrinology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
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50
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Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B. Genome-wide association mapping in maize: status and prospects. 3 Biotech 2021; 11:244. [PMID: 33968587 PMCID: PMC8085158 DOI: 10.1007/s13205-021-02799-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association study (GWAS) provides a robust and potent tool to retrieve complex phenotypic traits back to their underlying genetics. Maize is an excellent crop for performing GWAS due to diverse genetic variability, rapid decay of linkage disequilibrium, availability of distinct sub-populations and abundant SNP information. The application of GWAS in maize has resulted in successful identification of thousands of genomic regions associated with many abiotic and biotic stresses. Many agronomic and quality traits of maize are severely affected by such stresses and, significantly affecting its growth and productivity. To improve productivity of maize crop in countries like India which contribute only 2% to the world's total production in 2019-2020, it is essential to understand genetic complexity of underlying traits. Various DNA markers and trait associations have been revealed using conventional linkage mapping methods. However, it has achieved limited success in improving polygenic complex traits due to lower resolution of trait mapping. The present review explores the prospects of GWAS in improving yield, quality and stress tolerance in maize besides, strengths and challenges of using GWAS for molecular breeding and genomic selection. The information gathered will facilitate elucidation of genetic mechanisms of complex traits and improve efficiency of marker-assisted selection in maize breeding. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13205-021-02799-4.
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Affiliation(s)
- Kumari Shikha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - J. P. Shahi
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - M. T. Vinayan
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - P. H. Zaidi
- International Maize and Wheat Improvement Centre (CIMMYT)-Asia, ICRISAT Campus, Patancheru, Hyderabad, Telangana India
| | - A. K. Singh
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
| | - B. Sinha
- Department of Genetics and Plant Breeding, Institute of Agriculltural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh India
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