scholarly journals Imputed Gene Expression Risk Scores: A Functionally Informed Component of Polygenic Risk

2020 ◽  
Author(s):  
Oliver Pain ◽  
Kylie P. Glanville ◽  
Saskia Hagenaars ◽  
Saskia Selzam ◽  
Anna Fürtjes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Risk ◽  
Elastic Net ◽  
Risk Scores ◽  
Future Research ◽  
Functional Genomic ◽  
Predictive Utility ◽  
Polygenic Risk ◽  
Regulated Gene Expression ◽  
Gene Expression Levels

AbstractBackgroundIntegration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results.MethodsThe predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study (TEDS). GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed SNP-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression.ResultsGeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for Height (p=0.023) to 4% for Rheumatoid Arthritis (p=5.9×10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalised genes.ConclusionGeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

scholarly journals Imputed gene expression risk scores: a functionally informed component of polygenic risk

2021 ◽  
Author(s):  
Oliver Pain ◽  
Kylie P Glanville ◽  
Saskia Hagenaars ◽  
Saskia Selzam ◽  
Anna Fürtjes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Risk ◽  
Elastic Net ◽  
Risk Scores ◽  
Future Research ◽  
Functional Genomic ◽  
Predictive Utility ◽  
Polygenic Risk ◽  
Regulated Gene Expression ◽  
Gene Expression Levels

Abstract Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression–genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10−8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

scholarly journals Polygenic Risk Scores for Cardio-renal-metabolic Diseases in the Penn Medicine Biobank

2019 ◽  
Author(s):  
R.L. Kember ◽  
A. Verma ◽  
S. Verma ◽  
A. Lucas ◽  
R. Judy ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Metabolic Diseases ◽  
Association Studies ◽  
African Ancestry ◽  
Treatment Strategies ◽  
European Ancestry ◽  
Risk Scores ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

AbstractCardio-renal-metabolic (CaReMe) conditions are common and the leading cause of mortality around the world. Genome-wide association studies have shown that these diseases are polygenic and share many genetic risk factors. Identifying individuals at high genetic risk will allow us to target prevention and treatment strategies. Polygenic risk scores (PRS) are aggregate weighted counts that can demonstrate an individual’s genetic liability for disease. However, current PRS are often based on European ancestry individuals, limiting the implementation of precision medicine efforts in diverse populations. In this study, we develop PRS for six diseases and traits related to cardio-renal-metabolic disease in the Penn Medicine Biobank. We investigate their performance in both European and African ancestry individuals, and identify genetic and phenotypic overlap within these conditions. We find that genetic risk is associated with the primary phenotype in both ancestries, but this does not translate into a model of predictive value in African ancestry individuals. We conclude that future research should prioritize genetic studies in diverse ancestries in order to address this disparity.

scholarly journals Proof of concept: Molecular prediction of schizophrenia risk

2017 ◽  
Author(s):  
Anna R. Docherty ◽  
Arden Moscati ◽  
Daniel E. Adkins ◽  
Gemma T. Wallace ◽  
Guarav Kumar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Risk ◽  
Complement Component ◽  
Classification And Regression Tree ◽  
Risk Scores ◽  
Prodromal Symptom ◽  
Polygenic Risk Score ◽  
Molecular Pathways ◽  
Proof Of Concept ◽  
Polygenic Risk

Key PointsQuestionTo what extent do global polygenic risk scores (PRS), molecular pathway-specific PRS, complement component (C4) gene expression, MHC loci, sex, and ancestry jointly contribute to risk for schizophrenia-spectrum disorders (SZ)?FindingsGlobal polygenic risk for schizophrenia, sex, and their interaction most robustly predict risk in a classification and regression tree model, with highest risk groups having 50/50 chance of SZ.MeaningPsychometric risk indicators, such as prodromal symptom assessments, may be enhanced by the examination of genetic risk metrics. Preliminary results suggest that of genetic risk metrics, global polygenic information has the most potential to significantly aide in the prediction of SZ.AbstractImportanceSchizophrenia (SZ) has a complex, heterogeneous symptom presentation with limited established associations between biological markers and illness onset. Many (gene) molecular pathways (MPs) are enriched for SZ signal, but it is still unclear how these MPs, global PRS, major histocompatibility complex (MHC) complement component (C4) gene expression, and MHC loci might jointly contribute to SZ and its clinical presentation. It is also unclear whether sex or ancestry interacts with these metrics to increase risk in certain individuals.ObjectiveTo examine multiple genetic metrics, sex, and their interactions as possible predictors of SZ risk. Genetic information could aid in the clinical prediction of risk, but it is still unclear which genetic metrics are most promising, and how sex interacts with genetic risk metrics.Design, Setting, and ParticipantsTo examine molecular risk in a proof-of-concept study, we used the Wellcome Trust case-control cohort and classified cases as a function of 1) polygenic risk score (PRS) for both whole genome and for 345 implicated molecular pathways, 2) predicted C4 expression, 3) SZ-relevant MHC loci, 4) sex, and 5) ancestry.Main Outcomes and MeasuresPRSs, C4 expression, SZ-relevant MHC loci, sex, and ancestry as joint risk factors for SZ.ResultsRecursive partitioning yielded 15 molecular risk classes and retained as significant psychosis classifiers only sex, genome-wide SZ polygenic risk, and one MP PRS. Sex was the most robust classifier in a stepwise regression, and there was a significant interaction of sex with SZ PRS on case status, suggesting males have a lower polygenic risk threshold. By down-sampling case proportion to 1% and 1.4% population base rates in males and females, respectively, high-risk subtypes defined by this model had roughly a 52% odds of developing SZ (individuals with SZ PRS elevated by 2.6 SDs; incidence = 51.8%).Conclusions and RelevanceThis proof-of-concept suggests that global SZ PRS, sex, and their interaction are robust predictors of risk and that males have a lower PRS threshold for onset. Implications for the integration of these metrics with psychometrically-identified risk are discussed.

scholarly journals Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

2021 ◽  
pp. 1-12
Author(s):  
Simon Schmitt ◽  
Tina Meller ◽  
Frederike Stein ◽  
Katharina Brosch ◽  
Kai Ringwald ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorders ◽  
Cortical Thickness ◽  
Genetic Risk ◽  
Right Hemisphere ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Grey Matter Volume ◽  
Polygenic Risk ◽  
Risk For Depression

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.

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

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.

Data Mining in Gene Expression Analysis

2008 ◽  
pp. 1643-1673
Author(s):  
Jilin Han ◽  
Le Gruenwald ◽  
Tyrrell Conway
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Biological Information ◽  
Future Research ◽  
Gene Expression Data Analysis ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Important Approach ◽  
Future Research Directions ◽  
Gene Expression Levels

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

scholarly journals Testing Familial Transmission of Smoking With Two Different Research Designs

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 ◽  
...  
Keyword(s):  
The Netherlands ◽  
Family Environment ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Environmental Influence ◽  
Smoking Initiation ◽  
Risk Scores ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Children Of Twins

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.

scholarly journals Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition

Neurology ◽  
2018 ◽  
Vol 90 (18) ◽  
pp. e1605-e1612 ◽  
Author(s):  
Tian Ge ◽  
Mert R. Sabuncu ◽  
Jordan W. Smoller ◽  
Reisa A. Sperling ◽  
Elizabeth C. Mormino ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Genetic Risk ◽  
Large Scale ◽  
Association Studies ◽  
Specific Effect ◽  
Hippocampal Volume ◽  
Risk Scores ◽  
Hippocampal Atrophy ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

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.

scholarly journals Polygenic risk modeling with latent trait-related genetic components

2019 ◽  
Author(s):  
Matthew Aguirre ◽  
Yosuke Tanigawa ◽  
Guhan Ram Venkataraman ◽  
Rob Tibshirani ◽  
Trevor Hastie ◽  
...  
Keyword(s):  
Fat Mass ◽  
Genetic Risk ◽  
Complex Traits ◽  
Fat Free Mass ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Associations ◽  
Polygenic Risk ◽  
Genetic Components ◽  
Mass Component

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.

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