Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia.
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ABSTRACT:
SUBMITTER: Dobrindt K
PROVIDER: S-EPMC7923934 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p = 0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p = 0.0099; PROCESSING SPEED, p = 0.0006; WORKING MEMORY, p = 0.0023; and REASONING, p = 0.0015). Class II had modest reduction of positive symptoms (p = 0.0492) and better PROCESSING SPEED (p = 0.0071). Class IV had a specific reduction of negative symptoms (p = 0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.
| S-EPMC7396092 | biostudies-literature
Project description:Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.
| S-EPMC4596916 | biostudies-literature
Project description:BackgroundCognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases.MethodsWe combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases.ResultsPRS for both population IQ (P = 4.39 × 10-28) and EA (P = 1.27 × 10-26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS.ConclusionsCognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.
| S-EPMC7442352 | biostudies-literature
Project description:AimsRecent studies have revealed that the interplay between polygenic risk scores (PRS) and large copy number variants (CNV; >500kb) is essential for the etiology of schizophrenia (SCZ). To replicate previous findings, including those for smaller CNV (>10kb), the PRS between SCZ patients with and without CNV were compared.MethodsThe PRS were calculated for 724 patients with SCZ and 1178 healthy controls (HC), genotyped using array-based comparative genomic hybridization and single nucleotide polymorphisms chips, and comparisons were made between cases and HC, or between subjects with and without 'clinically significant' CNV.ResultsFirst, we replicated the higher PRS in patients with SCZ compared to that in HC (without taking into account the CNV). For clinically significant CNV, as defined by the American College of Medical Genetics ('pathogenic' and 'uncertain clinical significance, likely pathogenic' CNV), 66 patients with SCZ carried clinically significant CNV, whereas 658 SCZ patients had no such CNV. In the comparison of PRS between cases with/without the CNV, despite no significant difference in PRS, significant enrichment of the well-established risk CNV (22q11.2 deletion and 47,XXY/47,XXX) was observed in the lowest decile of PRS in SCZ patients with the CNV.ConclusionAlthough the present study failed to replicate the significant difference in PRS between SCZ patients with and without clinically significant CNV, SCZ patients with well-established risk CNV tended to have a lower PRS. Therefore, we speculate that the CNV in SCZ patients with lower PRS may contain 'genuine' risk; PRS is a possible tool for prioritizing clinically significant CNV because the power of the CNV association analysis is limited due to their rarity.
| S-EPMC6973280 | biostudies-literature
Project description:Schizophrenia is a psychiatric disorder with high heritability. Recent genome-wide association studies have provided a list of risk loci reliably derived from unprecedentedly large samples. However, further delineation of the diagnosis-associated susceptibility variants is needed to better characterize the genetic architecture given the disease's complex nature. In this sense, a data-driven approach might hold promise for identifying functionally related clusters of genetic variants that might not be captured by hypothesis-based models. In the current study, independent component analysis (ICA) was applied to the Psychiatric Genomics Consortium's schizophrenia-related single nucleotide polymorphisms (SNPs) in 104 schizophrenia patients and 142 healthy controls of European Ancestry. We found that, for 13 out of 16 extracted independent components, the associated loadings correlated highly (r>0.5) with the polygenic risk scores for SZ of the corresponding SNPs. These correlations were likely not inflated by the linkage disequilibrium structure (permutation p<0.001). In brief, we demonstrate an example of ICA analysis on SNP data yielding functionally meaningful clusters, which motivates further application of data-driven approaches as a complimentary tool for hypothesis-based methods to enrich our knowledge on the genetic basis of complex disorders.
| S-EPMC5348276 | biostudies-literature
Project description:We use polygenic risk scores (PRSs) for schizophrenia (SCZ) and bipolar disorder (BPD) to predict smoking, and addiction to nicotine, alcohol or drugs in individuals not diagnosed with psychotic disorders. Using PRSs for 144 609 subjects, including 10 036 individuals admitted for in-patient addiction treatment and 35 754 smokers, we find that diagnoses of various substance use disorders and smoking associate strongly with PRSs for SCZ (P = 5.3 × 10-50 -1.4 × 10-6 ) and BPD (P = 1.7 × 10-9 -1.9 × 10-3 ), showing shared genetic etiology between psychosis and addiction. Using standardized scores for SCZ and BPD scaled to a unit increase doubling the risk of the corresponding disorder, the odds ratios for alcohol and substance use disorders range from 1.19 to 1.31 for the SCZ-PRS, and from 1.07 to 1.29 for the BPD-PRS. Furthermore, we show that as regular smoking becomes more stigmatized and less prevalent, these biological risk factors gain importance as determinants of the behavior.
| S-EPMC5811785 | biostudies-literature
Project description:Patients with schizophrenia display characteristic smoking-related behaviors and genetic correlations between smoking behaviors and schizophrenia have been identified in European individuals. However, the genetic etiology of the association remains to be clarified. The present study investigated transethnic genetic overlaps between European-based smoking behaviors and the risk of Japanese schizophrenia by conducting polygenic risk score (PRS) analyses. Large-scale European genome-wide association study (GWAS) datasets (n = 24,114-74,035) related to four smoking-related intermediate phenotypes [(i) smoking initiation, (ii) age at smoking initiation, (iii) smoking quantity, and (iv) smoking cessation] were utilized as discovery samples. PRSs derived from these discovery GWASs were calculated for 332 Japanese subjects [schizophrenia patients, their unaffected first-degree relatives (FRs), and healthy controls (HCs)] as a target sample. Based on GWASs of European smoking phenotypes, we investigated the effects of PRSs on smoking phenotypes and the risk of schizophrenia in the Japanese population. Of the four smoking-related behaviors, the PRSs for age at smoking initiation in Europeans significantly predicted the age at smoking initiation (R2 = 0.049, p = 0.026) and the PRSs for smoking cessation significantly predicted the smoking cessation (R2 = 0.092, p = 0.027) in Japanese ever-smokers. Furthermore, the PRSs related to age at smoking initiation in Europeans were higher in Japanese schizophrenia patients than in the HCs and those of the FRs were intermediate between those of patients with schizophrenia and those of the HCs (R2 = 0.015, p = 0.015). In our target subjects, patients with schizophrenia had a higher mean age at smoking initiation (p = 0.018) and rate of daily smoking initiation after age 20 years (p = 0.023) compared with the HCs. A total of 60.6% of the patients started to smoke before the onset of schizophrenia. These findings suggest that genetic factors affecting late smoking initiation are associated with the risk of schizophrenia.
| S-EPMC7684279 | biostudies-literature
Project description:Patient samples play an important role in the study of inherited metabolic disorders. Open-access biorepositories distribute such samples. Unfortunately, not all clinically-characterized samples come with reliable genotype information. During studies directed toward population frequency assessments of cystinosis, a rare heritable disorder, we sequenced the CTNS gene from 14 cystinosis-related samples obtained from the Coriell Cell Repository. As a result, the disease genotypes of 7 samples were determined for the first time. The reported disease genotypes of 2 additional samples were found to be incorrect. Furthermore, we identified and experimentally confirmed a novel mutation, c.225 + 5G > A, which causes skipping of the 5th exon and is associated with infantile nephropathic cystinosis.
| S-EPMC5471396 | biostudies-literature
Project description:Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P < 10(-16)), the R(2) for TA increased by 123% (0.0154 to 0.0344; P < 10(-16)), and the liability-scale R(2) for BCC increased by 68% (0.0138-0.0232; P < 10(-16)) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.
| S-EPMC4734143 | biostudies-literature
Project description:BackgroundThere is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls.MethodUsing the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls.ResultsPatients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest.ConclusionsAlthough polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian.
| S-EPMC6130805 | biostudies-literature