scholarly journals Investigation of Target Sequencing of SARS-CoV-2 and Immunogenic Profiling in Host Cells of COVID-19 in Vietnam

2021 ◽  
Author(s):  
Tham Hoang ◽  
Giang Vu ◽  
Mai Tran ◽  
Nam Vo ◽  
Quang Le ◽  
...  
Keyword(s):  
Host Cells ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Target Sequence ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Target Sequencing ◽  
Global Pandemic ◽  
Downstream Analysis ◽  
Public Datasets

Abstract Background: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. Method: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients in Vietnam. We provided summary statistics of a target sequence of SARS-CoV-2 for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. Result: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models’ predictive capabilities. Conclusion: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.

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

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.

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

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.

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

Hypertension ◽  
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.

scholarly journals An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

2018 ◽  
Author(s):  
Tom G. Richardson ◽  
Sean Harrison ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

scholarly journals Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

2019 ◽  
Author(s):  
Zijie Zhao ◽  
Yanyao Yi ◽  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yupei Lin ◽  
...  
Keyword(s):  
Association Studies ◽  
Fine Tuning ◽  
Risk Scores ◽  
Training Dataset ◽  
Validation Dataset ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Model Tuning

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require individual-level genetic data as the training dataset or as a validation dataset independent from both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS (Parameter-tuning Using Marginal Association Statistics), a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. On average, PUMAS improves the predictive R2 by 205.6% and 62.5% compared to PRSs with arbitrary p-value cutoffs of 0.01 and 1, respectively. Applied to 211 neuroimaging traits and Alzheimer’s disease, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

scholarly journals Multiplex melanoma families are enriched for polygenic risk

2020 ◽  
Vol 29 (17) ◽  
pp. 2976-2985
Author(s):  
Matthew H Law ◽  
Lauren G Aoude ◽  
David L Duffy ◽  
Georgina V Long ◽  
Peter A Johansson ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Cumulative Effect ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Single Family ◽  
Polygenic Risk ◽  
Complex Disorders ◽  
Risk Variants ◽  
High Penetrance ◽  
Genetic Risks

Abstract Cancers, including cutaneous melanoma, can cluster in families. In addition to environmental etiological factors such as ultraviolet radiation, cutaneous melanoma has a strong genetic component. Genetic risks for cutaneous melanoma range from rare, high-penetrance mutations to common, low-penetrance variants. Known high-penetrance mutations account for only about half of all densely affected cutaneous melanoma families, and the causes of familial clustering in the remainder are unknown. We hypothesize that some clustering is due to the cumulative effect of a large number of variants of individually small effect. Common, low-penetrance genetic risk variants can be combined into polygenic risk scores. We used a polygenic risk score for cutaneous melanoma to compare families without known high-penetrance mutations with unrelated melanoma cases and melanoma-free controls. Family members had significantly higher mean polygenic load for cutaneous melanoma than unrelated cases or melanoma-free healthy controls (Bonferroni-corrected t-test P = 1.5 × 10−5 and 6.3 × 10−45, respectively). Whole genome sequencing of germline DNA from 51 members of 21 families with low polygenic risk for melanoma identified a CDKN2A p.G101W mutation in a single family but no other candidate high-penetrance melanoma susceptibility genes. This work provides further evidence that melanoma, like many other common complex disorders, can arise from the joint action of multiple predisposing factors, including rare high-penetrance mutations, as well as via a combination of large numbers of alleles of small effect.

scholarly journals Jumping to conclusions, general intelligence, and psychosis liability: findings from the multi-centre EU-GEI case-control study

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Giada Tripoli ◽  
Diego Quattrone ◽  
Laura Ferraro ◽  
Charlotte Gayer-Anderson ◽  
Victoria Rodriguez ◽  
...  
Keyword(s):  
Cognitive Biases ◽  
Population Based ◽  
Case Control ◽  
Risk Scores ◽  
First Episode ◽  
Polygenic Risk Score ◽  
General Intelligence ◽  
Polygenic Risk ◽  
Jumping To Conclusions ◽  
Episode Psychosis

Abstract Background The ‘jumping to conclusions’ (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ. Methods A total of 817 first episode psychosis patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC, and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia. Results The estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia polygenic risk score was non-significantly associated with a higher number of beads drawn (B = 0.47, 95% CI −0.21 to 1.16, p = 0.17); whereas IQ PRS (B = 0.51, 95% CI 0.25–0.76, p < 0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with the higher level of psychotic-like experiences (PLEs) in controls, including after controlling for IQ (B = −1.7, 95% CI −2.8 to −0.5, p = 0.006), but did not relate to delusions in patients. Conclusions Our findings suggest that the JTC reasoning bias in psychosis might not be a specific cognitive deficit but rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to PLEs, independent of IQ. The work has the potential to inform interventions targeting cognitive biases in early psychosis.

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 Polygenic Risk Scores Augment Stroke Subtyping

2021 ◽  
Vol 7 (2) ◽  
pp. e560
Author(s):  
Jiang Li ◽  
Durgesh P. Chaudhary ◽  
Ayesha Khan ◽  
Christoph Griessenauer ◽  
David J. Carey ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Genetic Correlation ◽  
Genome Wide Association Study ◽  
Low Frequency ◽  
European Ancestry ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Large Artery ◽  
Polygenic Risk ◽  
Gene Sets

ObjectiveTo determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.MethodsControls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.ResultsA moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R2 increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R2 = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.ConclusionsWe provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

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