Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A–C Covariance

AbstractThe assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, it would be useful to be able to estimate these parameters in the twin model. Here we consider the possibility of relaxing this assumption by adding polygenic score to the (univariate) twin model. We demonstrated numerically and analytically this extension renders the additive genetic (A) – unshared environmental correlation (E) and the additive genetic (A) - shared environmental (C) correlations simultaneously identified. We studied the statistical power to detect A-C and A-E correlations in the ACE model, and to detect A-E correlation in the AE model. The results showed that the power to detect these covariance terms, given 1000 MZ and 1000 DZ twin pairs (α=0.05), depends greatly on the parameter settings of the model. We show fixing the estimated percentage of variance in the outcome trait that is due to the polygenic scores greatly increases statistical power.

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Gamete simulation improves polygenic transmission disequilibrium analysis

10.1101/2020.10.26.355602 ◽

2020 ◽

Author(s):

Jiawen Chen ◽

Jing You ◽

Zijie Zhao ◽

Zheng Ni ◽

Kunling Huang ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Autism Spectrum ◽

Genetic Maps ◽

Risk Scores ◽

Parental Genotype ◽

Genome Wide Association Studies ◽

Transmission Disequilibrium ◽

Polygenic Risk

AbstractPolygenic risk scores (PRS) derived from summary statistics of genome-wide association studies (GWAS) have enjoyed great popularity in human genetics research. Applied to population cohorts, PRS can effectively stratify individuals by risk group and has promising applications in early diagnosis and clinical intervention. However, our understanding of within-family polygenic risk is incomplete, in part because the small samples per family significantly limits power. Here, to address this challenge, we introduce ORIGAMI, a computational framework that uses parental genotype data to simulate offspring genomes. ORIGAMI uses state-of-the-art genetic maps to simulate realistic recombination events on phased parental genomes and allows quantifying the prospective PRS variability within each family. We quantify and showcase the substantially reduced yet highly heterogeneous PRS variation within families for numerous complex traits. Further, we incorporate within-family PRS variability to improve polygenic transmission disequilibrium test (pTDT). Through simulations, we demonstrate that modeling within-family risk substantially improves the statistical power of pTDT. Applied to 7,805 trios of autism spectrum disorder (ASD) probands and healthy parents, we successfully replicated previously reported over-transmission of ASD, educational attainment, and schizophrenia risk, and identified multiple novel traits with significant transmission disequilibrium. These results provided novel etiologic insights into the shared genetic basis of various complex traits and ASD.

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The power of pathway-based polygenic risk scores

10.21203/rs.3.rs-643696/v1 ◽

2021 ◽

Author(s):

Paul O’Reilly ◽

Shing Choi ◽

Judit Garcia-Gonzalez ◽

Yunfeng Ruan ◽

Hei Man Wu ◽

...

Keyword(s):

Complex Disease ◽

Predictive Accuracy ◽

Risk Scores ◽

Distinct Advantage ◽

Genetic Profile ◽

Genetic Liability ◽

Polygenic Risk ◽

Individual Level ◽

Genome Wide ◽

Polygenic Scores

Abstract Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, all of these distil genetic liability to a single number based on aggregation of an individual’s genome-wide alleles. This results in a key loss of information about an individual’s genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. Here we evaluate the performance of pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual, and we introduce a software, PRSet, for computing and analysing pathway PRSs. We find that pathway PRSs have similar power for evaluating pathway enrichment of GWAS signal as the leading methods, with the distinct advantage of providing estimates of pathway genetic liability at the individual-level. Exemplifying their utility, we demonstrate that pathway PRSs can stratify diseases into subtypes in the UK Biobank with substantially greater power than genome-wide PRSs. Compared to genome-wide PRSs, we expect pathway-based PRSs to offer greater insights into the heterogeneity of complex disease and treatment response, generate more biologically tractable therapeutic targets, and provide a more powerful path to precision medicine.

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Stressful Life Events and Self-Control: Testing Gene x Environment Interaction with a Polygenic Approach

10.31234/osf.io/jhbqd ◽

2019 ◽

Author(s):

Yayouk Willems ◽

Jouke-Jan Hottenga ◽

Lannie Ligthart ◽

Gonneke WIllemsen ◽

Dorret Boomsma ◽

...

Keyword(s):

Complex Traits ◽

Large Population ◽

Self Control ◽

Risk Scores ◽

Life Stressors ◽

Control Problems ◽

Environment Interaction ◽

Polygenic Risk ◽

Polygenic Scores ◽

Low Self Control

Background: Ill decisions and reckless behaviors due to low self-control are concurrently and longitudinally costly, and revealing possible factors contributing to individual differences in self-control is necessary. It is hypothesized that genetically sensitivity interacts with life stressors in the prediction of the development of low self-control (gene environment interaction), yet attempts to test this hypothesis mostly concern candidate gene studies yielding inconclusive results. The goal of this research was to bring findings from large scale gene identification studies into the developmental psychology framework, taking the polygenic nature of complex traits into account. Methods: Using data of a large population-based twin sample, we tested whether polygenic risk scores for self-control problems – based on the most recent ADHD GWAS – predict self-control problems in adults, and whether this polygenic risk scores interact with the presence of environmental stressors. Results: While polygenic scores and life stressors significantly predicted low self-control, we did not find a significant interaction effect. Conclusions: Empirically, finding statistical evidence for this hypothesis remains a challenge, and more research is needed to investigate how to better detect G x E.

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Mapping the genetic architecture of suicide attempt and suicide death using polygenic risk scores for clinically-related psychiatric disorders and traits

Psychological Medicine ◽

10.1017/s0033291721004700 ◽

2021 ◽

pp. 1-9

Author(s):

Ikuo Otsuka ◽

Hanga Galfalvy ◽

Jia Guo ◽

Masato Akiyama ◽

Dan Rujescu ◽

...

Keyword(s):

Suicide Attempt ◽

Psychiatric Disorders ◽

Suicidal Behavior ◽

Statistical Power ◽

Small Sample ◽

European Ancestry ◽

Risk Scores ◽

Suicide Attempters ◽

Polygenic Risk ◽

Suicide Death

Abstract Background Suicidal behavior is moderately heritable and a consequence of a combination of the diathesis traits for suicidal behavior and suicide-related major psychiatric disorders. Here, we sought to examine shared polygenic effects between various psychiatric disorders/traits and suicidal behavior and to compare the shared polygenic effects of various psychiatric disorders/traits on non-fatal suicide attempt and suicide death. Methods We used our genotyped European ancestry sample of 260 non-fatal suicide attempters, 317 suicide decedents and 874 non-psychiatric controls to test whether polygenic risk scores (PRSs) obtained from large GWASs for 22 suicide-related psychiatric disorders/traits were associated with suicidal behavior. Results were compared between non-fatal suicide attempt and suicide death in a sensitivity analysis. Results PRSs for major depressive disorder, bipolar disorder, schizophrenia, ADHD, alcohol dependence, sensitivity to environmental stress and adversity, educational attainment, cognitive performance, and IQ were associated with suicidal behavior (Bonferroni-corrected p < 2.5 × 10−4). The polygenic effects of all 22 psychiatric disorders/traits had the same direction (p for binomial tests = 4.8 × 10−7) and were correlated (Spearman's ρ = 0.85) between non-fatal suicide attempters and suicide decedents. Conclusions We found that polygenic effects for major psychiatric disorders and diathesis-related traits including stress responsiveness and intellect/cognitive function contributed to suicidal behavior. While we found comparable polygenic architecture between non-fatal suicide attempters and suicide decedents based on correlations with PRSs of suicide-related psychiatric disorders/traits, our analyses are limited by small sample size resulting in low statistical power to detect difference between non-fatal suicide attempt and suicide death.

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A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE

Brain Communications ◽

10.1093/braincomms/fcz047 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Andre Altmann ◽

Marzia A Scelsi ◽

Maryam Shoai ◽

Eric de Silva ◽

Leon M Aksman ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Diagnosis ◽

Risk Scores ◽

Imaging Biomarkers ◽

Cross Sectional ◽

Polygenic Risk ◽

Apoe Ε4 ◽

Polygenic Scores ◽

Apoe Locus

Abstract Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer’s disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer’s disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer’s disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer’s disease and their association (beyond the APOE locus) with a broad range of Alzheimer’s disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.

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Genetic risk for neurodegenerative disorders, and its overlap with cognitive ability and physical function

10.1101/219345 ◽

2017 ◽

Author(s):

Saskia P. Hagenaars ◽

Ratko Radakovic ◽

Christopher Crockford ◽

Chloe Fawns-Ritchie ◽

Sarah E. Harris ◽

...

Keyword(s):

Physical Health ◽

Physical Function ◽

Cognitive Performance ◽

Neurodegenerative Disorders ◽

Forced Expiratory Volume ◽

Risk Scores ◽

Significant Group ◽

Polygenic Risk ◽

Health Group ◽

Polygenic Scores

AbstractINTRODUCTIONIt is unclear whether polygenic risk for neurodegenerative disease is associated with cognitive performance and physical health.METHODSThis study tested whether polygenic scores for Alzheimer’s disease (AD), Amyotrophic Lateral Sclerosis (ALS), or frontotemporal dementia (FTD) are associated with cognitive performance and physical health. Group-based analyses were performed to compare associations with cognitive and physical function outcomes in the top and bottom 10% for the three neurodegenerative polygenic risk scores.RESULTSHigher polygenic risk scores for AD, ALS, and FTD were associated with lower cognitive performance. Higher polygenic risk scores for FTD was also associated with increased forced expiratory volume in 1s and peak expiratory flow. A significant group difference was observed on the symbol digit substitution task between individuals with high polygenic risk for FTD and high polygenic risk for ALS.DISCUSSIONOur results suggest overlap between polygenic risk for neurodegenerative disorders, cognitive function and physical health.

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Extending causality tests with genetic instruments: an integration of Mendelian Randomization and the Classical Twin Design

10.1101/134585 ◽

2017 ◽

Cited By ~ 3

Author(s):

Camelia C. Minică ◽

Conor V. Dolan ◽

Dorret I. Boomsma ◽

Eco de Geus ◽

Michael C. Neale

Keyword(s):

Statistical Power ◽

Mendelian Randomization ◽

Causal Effect ◽

Twin Design ◽

Twin Model ◽

Polygenic Scores ◽

Exposure Variable ◽

Causality Tests ◽

Instrument Bias ◽

Genetic Instruments

ABSTRACTMendelian Randomization (MR) is an important approach to modelling causality in non-experimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR.We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable.MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

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Improving reporting standards for polygenic scores in risk prediction studies

10.1101/2020.04.23.20077099 ◽

2020 ◽

Author(s):

Hannah Wand ◽

Samuel A. Lambert ◽

Cecelia Tamburro ◽

Michael A. Iacocca ◽

Jack W. O’Sullivan ◽

...

Keyword(s):

Complex Disease ◽

Public Health Practice ◽

Internal Validity ◽

Risk Scores ◽

Polygenic Risk Score ◽

Reporting Standards ◽

Polygenic Risk ◽

Genome Wide ◽

Polygenic Scores ◽

Study Population

SummaryIn recent years, polygenic risk scores (PRS) have become an increasingly studied tool to capture the genome-wide liability underlying many human traits and diseases, hoping to better inform an individual’s genetic risk. However, a lack of adherence to previous reporting standards has hindered the translation of this important tool into clinical and public health practice with the heterogeneous underreporting of details necessary for benchmarking and reproducibility. To address this gap, the ClinGen Complex Disease Working Group and Polygenic Score (PGS) Catalog have collaborated to develop the 33-item Polygenic Risk Score Reporting Statement (PRS-RS). This framework provides the minimal information expected of authors to promote the internal validity, transparency, and reproducibility of PRS by requiring authors to detail the study population, statistical methods, and clinical utility of a published score. The widespread adoption of this framework will encourage rigorous methodological consideration and facilitate benchmarking to ensure high quality scores are translated into the clinic.

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Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease

Genome Medicine ◽

10.1186/s13073-020-00820-8 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Jessica K. Dennis ◽

Julia M. Sealock ◽

Peter Straub ◽

Younga H. Lee ◽

Donald Hucks ◽

...

Keyword(s):

Large Scale ◽

Complex Disease ◽

Clinical Laboratory ◽

Population Level ◽

Risk Scores ◽

Polygenic Risk ◽

Polygenic Scores ◽

Large Populations ◽

Novel Biomarkers ◽

Artery Disease

Abstract Background Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. Methods A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center’s (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. Results Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. Conclusions Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

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