scholarly journals The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: Systematic review

2018 ◽  
Vol 197 ◽  
pp. 2-8 ◽  
Author(s):  
Sumit Mistry ◽  
Judith R. Harrison ◽  
Daniel J. Smith ◽  
Valentina Escott-Price ◽  
Stanley Zammit
2018 ◽  
Vol 234 ◽  
pp. 148-155 ◽  
Author(s):  
Sumit Mistry ◽  
Judith R. Harrison ◽  
Daniel J. Smith ◽  
Valentina Escott-Price ◽  
Stanley Zammit

2021 ◽  
pp. 1-12
Author(s):  
Simon Schmitt ◽  
Tina Meller ◽  
Frederike Stein ◽  
Katharina Brosch ◽  
Kai Ringwald ◽  
...  

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.


2017 ◽  
Vol 20 (7) ◽  
pp. 836-842 ◽  
Author(s):  
Jorien L Treur ◽  
Karin J H Verweij ◽  
Abdel Abdellaoui ◽  
Iryna O Fedko ◽  
Eveline L de Zeeuw ◽  
...  

Abstract Introduction Classical twin studies show that smoking is heritable. To determine if shared family environment plays a role in addition to genetic factors, and if they interact (G×E), we use a children-of-twins design. In a second sample, we measure genetic influence with polygenic risk scores (PRS) and environmental influence with a question on exposure to smoking during childhood. Methods Data on smoking initiation were available for 723 children of 712 twins from the Netherlands Twin Register (64.9% female, median birth year 1985). Children were grouped in ascending order of risk, based on smoking status and zygosity of their twin-parent and his/her co-twin: never smoking twin-parent with a never smoking co-twin; never smoking twin-parent with a smoking dizygotic co-twin; never smoking twin-parent with a smoking monozygotic co-twin; and smoking twin-parent with a smoking or never smoking co-twin. For 4072 participants from the Netherlands Twin Register (67.3% female, median birth year 1973), PRS for smoking were computed and smoking initiation, smoking heaviness, and exposure to smoking during childhood were available. Results Patterns of smoking initiation in the four group children-of-twins design suggested shared familial influences in addition to genetic factors. PRS for ever smoking were associated with smoking initiation in all individuals. PRS for smoking heaviness were associated with smoking heaviness in individuals exposed to smoking during childhood, but not in non-exposed individuals. Conclusions Shared family environment influences smoking, over and above genetic factors. Genetic risk of smoking heaviness was only important for individuals exposed to smoking during childhood, versus those not exposed (G×E). Implications This study adds to the very few existing children-of-twins (CoT) studies on smoking and combines a CoT design with a second research design that utilizes polygenic risk scores and data on exposure to smoking during childhood. The results show that shared family environment affects smoking behavior over and above genetic factors. There was also evidence for gene–environment interaction (G×E) such that genetic risk of heavy versus light smoking was only important for individuals who were also exposed to (second-hand) smoking during childhood. Together, these findings give additional incentive to recommending parents not to expose their children to cigarette smoking.


Neurology ◽  
2018 ◽  
Vol 90 (18) ◽  
pp. e1605-e1612 ◽  
Author(s):  
Tian Ge ◽  
Mert R. Sabuncu ◽  
Jordan W. Smoller ◽  
Reisa A. Sperling ◽  
Elizabeth C. Mormino ◽  
...  

ObjectiveTo investigate the effects of genetic risk of Alzheimer disease (AD) dementia in the context of β-amyloid (Aβ) accumulation.MethodsWe analyzed data from 702 participants (221 clinically normal, 367 with mild cognitive impairment, and 114 with AD dementia) with genetic data and florbetapir PET available. A subset of 669 participants additionally had longitudinal MRI scans to assess hippocampal volume. Polygenic risk scores (PRSs) were estimated with summary statistics from previous large-scale genome-wide association studies of AD dementia. We examined relationships between APOE ε4 status and PRS with longitudinal Aβ and cognitive and hippocampal volume measurements.ResultsAPOE ε4 was strongly related to baseline Aβ, whereas only weak associations between PRS and baseline Aβ were present. APOE ε4 was additionally related to greater memory decline and hippocampal atrophy in Aβ+ participants. When APOE ε4 was controlled for, PRS was related to cognitive decline in Aβ+ participants. Finally, PRSs were associated with hippocampal atrophy in Aβ− participants and weakly associated with baseline hippocampal volume in Aβ+ participants.ConclusionsGenetic risk factors of AD dementia demonstrate effects related to Aβ, as well as synergistic interactions with Aβ. The specific effect of faster cognitive decline in Aβ+ individuals with higher genetic risk may explain the large degree of heterogeneity in cognitive trajectories among Aβ+ individuals. Consideration of genetic variants in conjunction with baseline Aβ may improve enrichment strategies for clinical trials targeting Aβ+ individuals most at risk for imminent cognitive decline.


2019 ◽  
Author(s):  
Matthew Aguirre ◽  
Yosuke Tanigawa ◽  
Guhan Ram Venkataraman ◽  
Rob Tibshirani ◽  
Trevor Hastie ◽  
...  

AbstractPolygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While models like polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits in the UK Biobank and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual’s genetic risk for a trait across DeGAs components, highlighting specific results for body mass index (BMI), myocardial infarction (heart attack), and gout in 337,151 white British individuals, with replication in a further set of 25,486 non-British white individuals from the Biobank. We find that BMI polygenic risk factorizes into components relating to fat-free mass, fat mass, and overall health indicators like physical activity measures. Most individuals with high dPRS for BMI have strong contributions from both a fat mass component and a fat-free mass component, whereas a few ‘outlier’ individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.


2021 ◽  
Author(s):  
Padraig Dixon ◽  
Edna Keeney ◽  
Jenny C Taylor ◽  
Sarah Wordsworth ◽  
Richard Martin

Polygenic risk is known to influence susceptibility to cancer. The use of data on polygenic risk, in conjunction with other predictors of future disease status, may offer significant potential for preventative care through risk-stratified screening programmes. An important element in the evaluation of screening programmes is their cost-effectiveness. We undertook a systematic review of papers evaluating the cost-effectiveness of screening interventions informed by polygenic risk scores compared to more conventional screening modalities. We included papers reporting cost-effectiveness outcomes in the English language published as articles or uploaded onto preprint servers with no restriction on date, type of cancer or form of polygenic risk modelled. We excluded papers evaluating screening interventions that did not report cost-effectiveness outcomes or which had a focus on monogenic risk. We evaluated studies using the Quality of Health Economic Studies checklist. Ten studies were included in the review, which investigated three cancers: prostate (n=5), colorectal (n=3) and breast (n=2). All study designs were cost-utility papers implemented as Markov models (n=6) or microsimulations (n=4). Nine of ten papers scored highly (score >75 on a 0-100) scale) when assessed using the Quality of Health Economic Studies checklist. Eight of ten studies concluded that polygenic risk informed cancer screening was likely to be more cost-effective than alternatives. However, the included studies lacked robust external data on the cost of polygenic risk stratification, did not account for how very large volumes of polygenic risk data on individuals would be collected and used, did not consider ancestry-related differences in polygenic risk, and did not fully account for downstream economic sequalae stemming from the use of polygenic risk data in these ways. These topics merit attention in future research on how polygenic risk data might contribute to cost-effective cancer screening.


2021 ◽  
Vol 46 (4) ◽  
pp. E441-E450
Author(s):  
Christoph Abé ◽  
Predrag Petrovic ◽  
William Ossler ◽  
William H. Thompson ◽  
Benny Liberg ◽  
...  

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.


2020 ◽  
Author(s):  
Clare E Palmer ◽  
Robert John Loughnan ◽  
Carolina Makowski ◽  
Wesley Thompson ◽  
Deanna Barch ◽  
...  

Psychiatric disorders place a huge burden on those affected and their families, as well as society. Nearly all psychiatric disorders have a heritable component and lifetime prevalence rates of several disorders are higher among first degree biological relatives of individuals with a diagnosis. Given that many psychiatric disorders have their onset in adolescence, estimating genetic risk during childhood may identify at-risk individuals for early intervention that can reduce this burden. Here we measured genetic risk for psychopathology using both polygenic risk scores (PRS) and family history in a large typically developing sample of 9-10 year old children from the Adolescent Brain and Cognitive Development (ABCD) StudySM and determined associations with a large battery of behavioural phenotypes. By including all genetic risk predictors in the same model, we were able to delineate unique behavioral associations across these measures. Polygenic risk for Attention Deficit Hyperactivity Disorder (ADHD) and depression (DEP) was associated with unique patterns of both externalizing and internalizing behaviors. Family history of conduct problems, depression and anxiety/stress additionally predicted unique behavioral variance across similar measures. These findings provide important insight into the potential predictive utility of PRS and family history in early adolescence and suggest that they may be signaling differential, additive information that could be useful for quantifying risk during development.


Sign in / Sign up

Export Citation Format

Share Document