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Woolway GE, Legge SE, Lynham A, Smart SE, Hubbard L, Daniel ER, Pardiñas AF, Escott-Price V, O'Donovan MC, Owen MJ, Jones IR, Walters JT. Assessing the validity of a self-reported clinical diagnosis of schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299622. [PMID: 38106032 PMCID: PMC10723562 DOI: 10.1101/2023.12.06.23299622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia. Methods The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC). Results In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts. Conclusions Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.
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Affiliation(s)
- Grace E Woolway
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sophie E Legge
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Amy Lynham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sophie E Smart
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Leon Hubbard
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Ellie R Daniel
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Ian R Jones
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - James Tr Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
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Owen MJ, Legge SE, Rees E, Walters JTR, O'Donovan MC. Genomic findings in schizophrenia and their implications. Mol Psychiatry 2023; 28:3638-3647. [PMID: 37853064 PMCID: PMC10730422 DOI: 10.1038/s41380-023-02293-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023]
Abstract
There has been substantial progress in understanding the genetics of schizophrenia over the past 15 years. This has revealed a highly polygenic condition with the majority of the currently explained heritability coming from common alleles of small effect but with additional contributions from rare copy number and coding variants. Many specific genes and loci have been implicated that provide a firm basis upon which mechanistic research can proceed. These point to disturbances in neuronal, and particularly synaptic, functions that are not confined to a small number of brain regions and circuits. Genetic findings have also revealed the nature of schizophrenia's close relationship to other conditions, particularly bipolar disorder and childhood neurodevelopmental disorders, and provided an explanation for how common risk alleles persist in the population in the face of reduced fecundity. Current genomic approaches only potentially explain around 40% of heritability, but only a small proportion of this is attributable to robustly identified loci. The extreme polygenicity poses challenges for understanding biological mechanisms. The high degree of pleiotropy points to the need for more transdiagnostic research and the shortcomings of current diagnostic criteria as means of delineating biologically distinct strata. It also poses challenges for inferring causality in observational and experimental studies in both humans and model systems. Finally, the Eurocentric bias of genomic studies needs to be rectified to maximise benefits and ensure these are felt across diverse communities. Further advances are likely to come through the application of new and emerging technologies, such as whole-genome and long-read sequencing, to large and diverse samples. Substantive progress in biological understanding will require parallel advances in functional genomics and proteomics applied to the brain across developmental stages. For these efforts to succeed in identifying disease mechanisms and defining novel strata they will need to be combined with sufficiently granular phenotypic data.
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Affiliation(s)
- Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
| | - Sophie E Legge
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Elliott Rees
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
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Zeng L, He Z, Liu D, Li K, Gu K, Sun Q, Mei G, Zhang Y, Yan S, Zhang F. Genetic analysis of a large Han Chinese family line with schizoaffective psychosis. Heliyon 2023; 9:e14943. [PMID: 37025789 PMCID: PMC10070140 DOI: 10.1016/j.heliyon.2023.e14943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
To locate the specific susceptibility genes of a high incidence of schizoaffective disease (SAD) with autonomic dominant inheritance, we recruited a family group from Henan Province with a high incidence of SAD, including 19 individuals sampled from five generations. We used a genome-wide high-density SNP chip to perform genotype detection. The LINKAGE package and MENDEL programs were used for. The two-point and multipoint analyses were calculated by Merlin and SimWalk2 software to obtain the nonparametric linkage (NPL) value, corresponding P value, and parameter linkage limit of detection (LOD) value. Genome-wide linkage analysis yielded a significant linkage signal located on the short arm of chromosome 19. In the dominant genetic model, the LOD of the multipoint parametric analysis was 2.5, and the nonparametric analysis was 19.4 (P < 0.00001). Further haploid genotype analysis localized the candidate region in the 19p13.3-13.2 region, beginning at rs178414 and ending at rs11668751 with a physical length of approximately 4.9 Mb. We believe that the genes responsible for SAD are in this region.
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Affiliation(s)
- Liping Zeng
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
- Corresponding author. NO.984 Hospital of the People’s Liberation Army, Beijing, China
| | - Ziyun He
- College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563006, China
| | - Di Liu
- The 3rd People's Hospital of Heilongjiang Province-Qinhuangdao Branch, Qinhuangdao, 066001,China
| | - Kai Li
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
| | - Kesheng Gu
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
| | - Qi Sun
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
| | - Guisen Mei
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
| | - Yingxue Zhang
- The Clinical Laboratory of No.984 Hospital of the People's Liberation Army, Beijing, 100094, China
| | - Shengkai Yan
- College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563006, China
| | - Feng Zhang
- College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563006, China
- Beijing Institute of Genomics, Chinese Academy of Sciences No. 1 Beichen West Road, Chaoyang District, Beijing, 100800, China
- Ori-Gene (ShangDong)Science and Technology Co., Ltd, 261000, China
- Corresponding author. College of Laboratory Medicine, Zunyi Medical University, Zunyi, China.
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Chauvière L. Early cognitive comorbidities before disease onset: A common symptom towards prevention of related brain diseases? Heliyon 2022; 8:e12259. [PMID: 36590531 PMCID: PMC9800323 DOI: 10.1016/j.heliyon.2022.e12259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Brain diseases are very heterogeneous; however they also display multiple common risk factors and comorbidities. With a paucity of disease-modifying therapies, prevention became a health priority. Towards prevention, one strategy is to focus on similar symptoms of brain diseases occurring before disease onset. Cognitive deficits are a promising candidate as they occur across brain diseases before disease onset. Based on recent research, this review highlights the similarity of brain diseases and discusses how early cognitive deficits can be exploited to tackle disease prevention. After briefly introducing common risk factors, I review common comorbidities across brain diseases, with a focus on cognitive deficits before disease onset, reporting both experimental and clinical findings. Next, I describe network abnormalities associated with early cognitive deficits and discuss how these abnormalities can be targeted to prevent disease onset. A scenario on brain disease etiology with the idea that early cognitive deficits may constitute a common symptom of brain diseases is proposed.
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