scholarly journals Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

2018 ◽  
Vol 213 (3) ◽  
pp. 535-541 ◽  
Author(s):  
Maria Stella Calafato ◽  
Johan H. Thygesen ◽  
Siri Ranlund ◽  
Eirini Zartaloudi ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Predictive Models ◽  
Genetic Variants ◽  
Psychotic Disorders ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Early Interventions ◽  
Polygenic Risk ◽  
Increases In Risk ◽  
Shared Genetic

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.

Performance of polygenic risk scores for cancer prediction in an academic biobank.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1528-1528
Author(s):  
Heena Desai ◽  
Anh Le ◽  
Ryan Hausler ◽  
Shefali Verma ◽  
Anurag Verma ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Variants ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
European Americans ◽  
Genome Wide ◽  
Common Genetic Variants

1528 Background: The discovery of rare genetic variants associated with cancer have a tremendous impact on reducing cancer morbidity and mortality when identified; however, rare variants are found in less than 5% of cancer patients. Genome wide association studies (GWAS) have identified hundreds of common genetic variants significantly associated with a number of cancers, but the clinical utility of individual variants or a polygenic risk score (PRS) derived from multiple variants is still unclear. Methods: We tested the ability of polygenic risk score (PRS) models developed from genome-wide significant variants to differentiate cases versus controls in the Penn Medicine Biobank. Cases for 15 different cancers and cancer-free controls were identified using electronic health record billing codes for 11,524 European American and 5,994 African American individuals from the Penn Medicine Biobank. Results: The discriminatory ability of the 15 PRS models to distinguish their respective cancer cases versus controls ranged from 0.68-0.79 in European Americans and 0.74-0.93 in African Americans. Seven of the 15 cancer PRS trended towards an association with their cancer at a p<0.05 (Table), and PRS for prostate, thyroid and melanoma were significantly associated with their cancers at a bonferroni corrected p<0.003 with OR 1.3-1.6 in European Americans. Conclusions: Our data demonstrate that common variants with significant associations from GWAS studies can distinguish cancer cases versus controls for some cancers in an unselected biobank population. Given the small effects, future studies are needed to determine how best to incorporate PRS with other risk factors in the precision prediction of cancer risk. [Table: see text]

scholarly journals Genetic risk for bipolar disorder and schizophrenia predicts structure and function of the ventromedial prefrontal cortex

2021 ◽  
Vol 46 (4) ◽  
pp. E441-E450
Author(s):  
Christoph Abé ◽  
Predrag Petrovic ◽  
William Ossler ◽  
William H. Thompson ◽  
Benny Liberg ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Prefrontal Cortex ◽  
Genetic Risk ◽  
Psychotic Disorders ◽  
Structure And Function ◽  
Ventromedial Prefrontal Cortex ◽  
Risk Scores ◽  
Polygenic Risk ◽  
And Function ◽  
Structural Brain

Background: Bipolar disorder is highly heritable and polygenic. The polygenic risk for bipolar disorder overlaps with that of schizophrenia, and polygenic scores are normally distributed in the population. Bipolar disorder has been associated with structural brain abnormalities, but it is unknown how these are linked to genetic risk factors for psychotic disorders. Methods: We tested whether polygenic risk scores for bipolar disorder and schizophrenia predict structural brain alterations in 98 patients with bipolar disorder and 81 healthy controls. We derived brain cortical thickness, surface area and volume from structural MRI scans. In post-hoc analyses, we correlated polygenic risk with functional hub strength, derived from resting-state functional MRI and brain connectomics. Results: Higher polygenic risk scores for both bipolar disorder and schizophrenia were associated with a thinner ventromedial prefrontal cortex (vmPFC). We found these associations in the combined group, and separately in patients and drug-naive controls. Polygenic risk for bipolar disorder was correlated with the functional hub strength of the vmPFC within the default mode network. Limitations: Polygenic risk is a cumulative measure of genomic burden. Detailed genetic mechanisms underlying brain alterations and their cognitive consequences still need to be determined. Conclusion: Our multimodal neuroimaging study linked genomic burden and brain endophenotype by demonstrating an association between polygenic risk scores for bipolar disorder and schizophrenia and the structure and function of the vmPFC. Our findings suggest that genetic factors might confer risk for psychotic disorders by influencing the integrity of the vmPFC, a brain region involved in self-referential processes and emotional regulation. Our study may also provide an imaging–genetics vulnerability marker that can be used to help identify individuals at risk for developing bipolar disorder.

The use of Polygenic Risk Scores to Inform Aetiology of Mood and Psychotic Disorders

2017 ◽  
Vol 41 (S1) ◽  
pp. S166-S166
Author(s):  
J. Harrison ◽  
S. Mistry
Keyword(s):  
Bipolar Disorder ◽  
Genetic Basis ◽  
Psychotic Disorders ◽  
Mental Illnesses ◽  
Risk Scores ◽  
Psychotic Symptoms ◽  
Polygenic Risk ◽  
Genetic Overlap ◽  
Competing Interest

IntroductionPolygenic risk scores (PRS) incorporate many small genetic markers that are associated with conditions. This technique was first used to investigate mental illnesses in 2009. Since then, it has been widely used.ObjectivesWe wanted to explore how PRS have been used to the study the aetiology of psychosis, schizophrenia, bipolar disorder and depression.AimsWe aimed to conduct a systematic review, identifying studies that have examined associations between PRS for bipolar disorder, schizophrenia/psychosis and depression and psychopathology-related outcome measures.MethodsWe searched EMBASE, Medline and PsychInfo from 06/08/2009 to 14/03/2016. We hand-searched the reference lists of related papers.ResultsAfter removing duplicates, the search yielded 1043 publications. When irrelevant articles were excluded, 33 articles remained. We found 24 studies using schizophrenia PRS, three using bipolar PRS and nine using depression PRS. Many studies successfully used PRS to predict case/control status. Some studies showed associations between PRS and diagnostic sub-categories. A range of clinical phenotypes and symptoms has been explored. For example, specific PRS are associated with cognitive performance in schizophrenia, psychotic symptoms in bipolar disorder, and frequency of episodes of depression. PRS have also demonstrated genetic overlap between mental illnesses. It was difficult to assess the quality of some studies as not all reported sufficient methodological detail.ConclusionsPRS have enabled us to explore the polygenic architecture of mental illness and demonstrate a genetic basis for some observed features. However, they have yet to give insights into the biology, which underpin mental illnesses.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Significant Sparse Polygenic Risk Scores across 428 traits in UK Biobank

2021 ◽  
Author(s):  
Yosuke Tanigawa ◽  
Junyang Qian ◽  
Guhan Ram Venkataraman ◽  
Johanne M. Justesen ◽  
Ruilin Li ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Quantitative Traits ◽  
Predictive Performance ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Systematic Assessment ◽  
Phenotype Data ◽  
The Uk

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,600 traits using genetic and phenotype data in the UK Biobank. We report 428 sparse PRS models with significant (p < 2.5e-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, and the genotype principal components. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance in quantitative traits (Spearman's ρ = 0.54, p = 1.4e-15), but not in binary traits (ρ = 0.059, p = 0.35). The sparse PRS model trained on European individuals showed limited transferability when evaluated on individuals from non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

scholarly journals Association between substance use disorder and polygenic liability to schizophrenia

2017 ◽  
Author(s):  
Sarah M. Hartz ◽  
Amy Horton ◽  
Mary Oehlert ◽  
Caitlin E. Carey ◽  
Arpana Agrawal ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorder ◽  
Meta Analysis ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Individual Substance ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Individual Dataset ◽  
Shared Genetic

AbstractBackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.MethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.ResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4×10-23).ConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

scholarly journals Neuroticism as a predictor of frailty in old age: a genetically informative approach

2019 ◽  
Author(s):  
Hilda Bjork Danielsdottir ◽  
Juulia Jylhävä ◽  
Sara Hägg ◽  
Yi Lu ◽  
Lucía Colodro-Conde ◽  
...  
Keyword(s):  
Old Age ◽  
Genetic Variants ◽  
Control Method ◽  
Frailty Index ◽  
Genetic Influences ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Common Genetic Variants ◽  
Shared Genetic

ABSTRACTObjectiveNeuroticism is associated with poor health outcomes, but its contribution to the accumulation of health deficits in old age, i.e. frailty, is largely unknown. We aimed to explore associations between neuroticism and frailty cross-sectionally and over up to 29 years, and to investigate the contribution of shared genetic influences.MethodData were derived from the UK Biobank (UKB, n=502,631), the Australian Over 50’s Study (AO50, n=3,011) and the Swedish Twin Registry (SALT n=23,744, SATSA n=1,637). Associations between neuroticism and the Frailty Index were investigated using regression analysis cross-sectionally in UKB, AO50 and SATSA, and longitudinally in SALT (25-29y follow-up) and SATSA (6 and 23y follow-up). The co-twin control method was applied to explore the contribution of underlying shared familial factors (SALT, SATSA, AO50). Genome-wide polygenic risk scores for neuroticism in all samples were used to further assess whether common genetic variants associated with neuroticism predict frailty.ResultsHigh neuroticism was consistently associated with greater frailty cross-sectionally (adjusted β, 95% confidence intervals in UKB= 0.32, 0.32-0.33; AO50= 0.35, 0.31-0.39; SATSA= 0.33, 0.27-0.39) and longitudinally up to 29 years (SALT= 0.24; 0.22-0.25; SATSA 6y= 0.31, 0.24-0.38; SATSA 23y= 0.16, 0.07-0.25). When controlling for underlying shared genetic and environmental factors the neuroticism-frailty association remained significant, although decreased. Polygenic risk scores for neuroticism significantly predicted frailty in the two larger samples (meta-analyzed total β= 0.06, 0.05-0.06).ConclusionHigh neuroticism is associated with the development and course of frailty. Both environmental and genetic influences, including neuroticism-associated genetic variants, contribute to this relationship.

scholarly journals Genome-wide Association Study of Pediatric Obsessive-Compulsive Traits: Shared Genetic Risk between Traits and Disorder

2019 ◽  
Author(s):  
Christie L. Burton ◽  
Mathieu Lemire ◽  
Bowei Xiao ◽  
Elizabeth C. Corfield ◽  
Lauren Erdman ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Case Control ◽  
Genome Wide Association ◽  
Polygenic Risk Score ◽  
Obsessive Compulsive ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Shared Genetic

AbstractObjectiveTo identify genetic variants associated with obsessive-compulsive (OC) traits and test for sharing of genetic risks between OC traits and obsessive-compulsive disorder (OCD).MethodsWe conducted a genome-wide association analysis of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of three OCD case-controls samples (cases=3384, controls=8363). Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses.ResultsA locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p=2.48×10−8). The rs7856850 locus was also associated with OCD in a meta-analysis of three independent OCD case/control genome-wide datasets (p=0.0069). Polygenic risk scores derived from OC traits were significantly associated with OCD in a sample of childhood-onset OCD and vice versa (p’s<0.01). OC traits were highly but not significantly genetically correlated with OCD (rg=0.83, p=0.07).ConclusionsWe report the first validated genome-wide significant variant for OC traits. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the importance of the type of measure used to measure traits as well as the feasibility and power of using trait-based approaches in community samples for genetic discovery.

scholarly journals Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brandon J. Coombes ◽  
Matej Markota ◽  
J. John Mann ◽  
Colin Colby ◽  
Eli Stahl ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Suicide Attempts ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Rapid Cycling ◽  
Clinical Heterogeneity ◽  
Polygenic Risk ◽  
Clinical Observations ◽  
History Of

Abstract Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.

scholarly journals Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes

2017 ◽  
Author(s):  
Adam Socrates ◽  
Tom Bond ◽  
Ville Karhunen ◽  
Juha Auvinen ◽  
Cornelius A. Rietveld ◽  
...  
Keyword(s):  
Epidemiological Study ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Convincing Evidence ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Associations ◽  
Polygenic Risk ◽  
Moderate Sample Size ◽  
Shared Genetic

AbstractBackgroundThere is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.Methods and findingsHere we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study – the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimer’s disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel significant genetic associations between traits.ConclusionsThis study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

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