Polygenic risk modeling with latent trait-related genetic components

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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Inclusion of Variants Discovered from Diverse Populations Improves Polygenic Risk Score Transferability

10.1101/2020.05.21.108845 ◽

2020 ◽

Cited By ~ 1

Author(s):

Taylor B. Cavazos ◽

John S. Witte

Keyword(s):

Genetic Risk ◽

African Ancestry ◽

European Ancestry ◽

Risk Scores ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Genetic Risk Prediction ◽

Causal Variants ◽

The Uk ◽

The Relationship

ABSTRACTThe majority of polygenic risk scores (PRS) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRS that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRS developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of HbA1c, asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across populations.

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The Polygenic Risk Score Knowledge Base: A Centralized Online Repository for Calculating and Contextualizing Polygenic Risk Scores

10.21203/rs.3.rs-799235/v1 ◽

2021 ◽

Author(s):

Madeline Page ◽

Elizabeth Vance ◽

Matthew Cloward ◽

Ed Ringger ◽

Louisa Dayton ◽

...

Keyword(s):

Knowledge Base ◽

Risk Score ◽

Genetic Risk ◽

Data Availability ◽

Risk Scores ◽

Polygenic Risk Score ◽

Analysis Tool ◽

Web Interface ◽

Polygenic Risk ◽

Gwa Studies

Abstract Introduction: Genome-wide association (GWA) studies identify correlation between genetic variants and phenotypes. GWA findings can be used to calculate polygenic risk scores, which represent the aggregate genetic risk across all associated loci. Methods: We developed a centralized polygenic risk score calculator containing over 2,300 GWA studies from the NHGRI-EBI GWAS Catalog. Polygenic risk scores are calculated from user-uploaded data using various user-defined parameters across any disease(s) or studies. Results: The Polygenic Risk Score Knowledge Base (https://prs.byu.edu) and command-line interface facilitate user-specific polygenic risk score calculations. We report study-specific polygenic risk scores across the U.K. Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and identify potentially confounding genetic risk factors in ADNI.Discussion: We introduce the first streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies. We anticipate that the PRSKB will facilitate a wider adaptation and innovative use of polygenic risk scores in disease research. Data Availability: This project is documented online at https://polyriskscore.readthedocs.io/en/latest/, and all programs are publicly available at https://github.com/kauwelab/PolyRiskScore. A web interface is also available at https://prs.byu.edu/.

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Proof of concept: Molecular prediction of schizophrenia risk

10.1101/129213 ◽

2017 ◽

Cited By ~ 1

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.

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

10.1101/203257 ◽

2017 ◽

Cited By ~ 9

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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Genetic Risk for Rheumatoid Arthritis is Associated with Increased Striatal Volume in Healthy Young Adults

10.1101/519132 ◽

2019 ◽

Author(s):

Reut Avinun ◽

Adam Nevo ◽

Ahmad R. Hariri

Keyword(s):

Rheumatoid Arthritis ◽

Genetic Risk ◽

Genome Wide Association Study ◽

Morphological Changes ◽

Disease Onset ◽

Risk Scores ◽

Intracranial Volume ◽

Polygenic Risk ◽

Genetic Components ◽

A Genome

AbstractRheumatoid arthritis (RA), an autoimmune disease, has recently been associated with increased striatal volume and decreased intracranial volume (ICV) in longstanding patients. As inflammation has been shown to precede the clinical diagnosis of RA and it is a known moderator of neuro- and gliogenesis, we were interested in testing whether these brain morphological changes appear before the clinical onset of disease in healthy young adult volunteers, as a function of relative genetic risk for RA. Genetic and structural MRI data were available for 516 healthy non-Hispanic Caucasian university students (275 women, mean age 19.78±1.24 years). Polygenic risk scores were computed for each individual based on a genome-wide association study of RA, so that higher scores indicated higher risk. Striatal volume (sum of caudate, putamen, and nucleus accumbens volumes) and ICV were derived for each individual from high-resolution T1-weighted images. After controlling for sex, age, genetic components of ethnicity, socioeconomic status, and depressive symptoms, we found that higher RA polygenic risk scores were associated with increased striatal volume, but not decreased ICV. Our findings suggest that increased striatal volume may be linked to processes that precede disease onset, such as inflammation, while decreased ICV may relate to disease progression.

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Symptom dimensions of major depression in a large community-based cohort

Psychological Medicine ◽

10.1017/s0033291721001707 ◽

2021 ◽

pp. 1-8

Author(s):

Michael Wainberg ◽

Peter Zhukovsky ◽

Sean L. Hill ◽

Daniel Felsky ◽

Aristotle Voineskos ◽

...

Keyword(s):

Primary Care ◽

Major Depression ◽

Risk Scores ◽

Polygenic Risk Score ◽

Psychiatric Diagnoses ◽

Uk Biobank ◽

Community Based ◽

Symptom Dimensions ◽

Polygenic Risk ◽

Large Community

Abstract Background Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community. Methods This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or ‘symptom dimensions’ via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records. Results Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations. Conclusions An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

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Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa289 ◽

2020 ◽

Author(s):

Niccolo’ Tesi ◽

Sven J van der Lee ◽

Marc Hulsman ◽

Iris E Jansen ◽

Najada Stringa ◽

...

Keyword(s):

Older Adults ◽

Cellular Response ◽

Molecular Mechanisms ◽

Association Studies ◽

Risk Scores ◽

Human Longevity ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Age Related

Abstract Studying the genome of centenarians may give insights into the molecular mechanisms underlying extreme human longevity and the escape of age-related diseases. Here, we set out to construct polygenic risk scores (PRSs) for longevity and to investigate the functions of longevity-associated variants. Using a cohort of centenarians with maintained cognitive health (N = 343), a population-matched cohort of older adults from 5 cohorts (N = 2905), and summary statistics data from genome-wide association studies on parental longevity, we constructed a PRS including 330 variants that significantly discriminated between centenarians and older adults. This PRS was also associated with longer survival in an independent sample of younger individuals (p = .02), leading up to a 4-year difference in survival based on common genetic factors only. We show that this PRS was, in part, able to compensate for the deleterious effect of the APOE-ε4 allele. Using an integrative framework, we annotated the 330 variants included in this PRS by the genes they associate with. We find that they are enriched with genes associated with cellular differentiation, developmental processes, and cellular response to stress. Together, our results indicate that an extended human life span is, in part, the result of a constellation of variants each exerting small advantageous effects on aging-related biological mechanisms that maintain overall health and decrease the risk of age-related diseases.

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

Psychological Medicine ◽

10.1017/s0033291721001082 ◽

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.

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

Nature Communications ◽

10.1038/s41467-021-24082-z ◽

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.

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Abstract P256: Genetic Determinants Of Blood Pressure Associate With Apparent Treatment Resistant Hypertension

Hypertension ◽

10.1161/hyp.78.suppl_1.p256 ◽

2021 ◽

Vol 78 (Suppl_1) ◽

Author(s):

Joseph H Breeyear ◽

Megan M Shuey ◽

Todd L Edwards ◽

Jacklyn Hellwege

Keyword(s):

Blood Pressure ◽

Risk Scores ◽

Uncontrolled Hypertension ◽

Polygenic Risk Score ◽

Genetic Determinants ◽

Treatment Resistant ◽

Polygenic Risk ◽

Blood Pressures ◽

The Uk ◽

Hispanic White

Hypertension is estimated to affect more than 49.6% of US adults 20 years and older. Of those individuals with hypertension, more than ten million are classified as apparent treatment resistant hypertensive (aTRH). The attributable risk of uncontrolled hypertension was estimated to be 49% for cardiovascular disease and 62% for stroke. We developed a polygenic risk score (PRS) for systolic (SBP) and diastolic (DBP) blood pressure to examine the association between the genetic determinants of blood pressure and aTRH with the goal of identifying high risk individuals. The meta-analyzed transethnic results of Giri et al., Biobank Japan, and Liang et al. were used to generate a PRS with PRS-CS followed by p -value thresholding, and validation in the UK Biobank (n max =341,930). Associations were modeled with logistic regression adjusted for age, age-squared, BMI, sex, and ten principal components of ancestry in BioVU’s transethnic population (n max =37,978), as well as non-Hispanic Black (n max =5,026) and non-Hispanic White (n max =28,545) subsets. The SBP PRS was significantly associated with an increased aTRH risk in the non-Hispanic White subset (1.08 (1.04 - 1.12), p = 0.00037) and transethnic (1.08 (1.04 - 1.13), p = 0.00020) populations, but not the non-Hispanic Black subset. The DBP PRS was not associated with aTRH in any population. Our findings present evidence that individuals with a higher genetic predisposition towards hypertension are at higher risk of aTRH. By integrating polygenic risk scores and clinical covariates in prediction of aTRH, individuals’ therapeutic regimens may be tailored to help maintain stable blood pressures, therefore reducing their risk of comorbidities.

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