scholarly journals S174. POLYENVIRONMENTAL AND POLYGENIC RISK SCORES AND THE EMERGENCE OF PSYCHOTIC EXPERIENCES IN CHILDREN AND ADOLESCENTS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S103-S104
Author(s):  
Sintia Belangero ◽  
Gabrielle Navarro ◽  
Lais Fonseca ◽  
Marcos Leite Santoro ◽  
Adrielle Oliveira ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children And Adolescents ◽  
Risk Score ◽  
Negative Correlation ◽  
Paternal Age ◽  
Childhood And Adolescence ◽  
Polygenic Risk Score ◽  
Psychotic Experiences ◽  
Psychotic Experience ◽  
Polygenic Risk

Abstract Background Psychotic experiences (PE) include subliminal hallucinations and delusions without the characteristic functional impairment that constitutes a psychotic disorder. PE are prevalent during childhood and adolescence and studies show a clear link with higher risk to clinical psychosis and schizophrenia. The persistence and accumulation of psychosocial problems are also well established risk factors, but how they interplay with genetic risk is still unclear, especially during developmental stages. Polygenic risk score to schizophrenia (PRS-SZ) and the polyenviromic risk score (PERS) are two validated measures created to assess the contribution of each factor on the development of such psychopathology. Our aim was to verify if PRS and PERS jointly are able to predict psychotic experiences in a cohort of children and adolescents, considering two time-points. Methods We analyzed data from the High Risk Cohort (HRC) for Psychiatric Disorders, composed of 2511 children and adolescents from São Paulo and Porto Alegre. PRS-SZ was calculated using summary statistics from the PGC and corrected for the ten first principal components (PC) of the GWAS. In order to calculate the PERS, we used data corresponding to the nine variables that are consider on the score, being respectively, winter or spring birth, urbanicity, cannabis use, advanced paternal age, obstetric and perinatal complications, physical and sexual abuse, neglect and paternal death, therefore if the person is exposed to one or more enviromic factor the odds ratio corresponding to that factor are added up and divided by all factors considered on the calculation, generating the final score. PE was assessed through the Community Assessment of Psychotic Experiences (CAPE) and a latent variable was generated through confirmatory factor analysis producing a good model fit. The prediction model was performed using different linear regressions where the clinical outcome was the CAPE score and PRS and PERS as independent variables. We performed Spearman’s correlations in order to observe possible correlation between our variables. Results Our sample varied from 9 to 18 years old (Mean: 13.49, SD: 1.9, 53.9% male) and a total of 1704 individuals provided available CAPE scores, PRS and PERS. When Spearman’s correlations were performed, we observed a non-significant weak positive correlation between PERS x CAPE (R2 = 0.0118, p = 0.623) and between PRS x CAPE (R2 = 0.0292, p = 0.228) and a non-significant negative correlation between PERS x PRS (R2 = -0.03051, p = 0.207). Lastly, we perform a multiple linear regression and used in the model the ten first PC as covariables and, we observed that with an increase in one unit in the PRS, the model explain positively about 8% of the PE variance (R2 = 0.007986 (F(12;1691) = 2.143, p = 0.01225). When we used the PRS already adjusted by ten first PC in the model, this significance is lost (R2=0.0008381 F(2);1701, p=0.1804 (PC2 and PC8 explaining the most of variance). Discussion Previous studies have shown a lack of significant association between PRS-SZ and PE for youth samples. Our results are in line with such results, but also depict a trend direction for those variables. Although all correlations were non-significant statically, they show us their direction as discussed below. The higher PERS, higher the psychotic experience, suggesting known environment risk factors for psychosis play a role in the report of PE as well. The higher PRS, the higher psychotic experience also. On the other hand, we found a negative correlation between PERS and PRS. In addition, PERS and PRS jointly were not able to predict psychotic experience. Although non-significant, our results may shed light on knowledge of disease.

scholarly journals S24. PATHWAYS LINKING ENVIRONMENTAL RISKS, POLYGENIC RISK SCORE TO SCHIZOPHRENIA AND PSYCHOTIC EXPERIENCES IN A BRAZILIAN COHORT OF CHILDREN AND ADOLESCENTS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S39-S40
Author(s):  
Lais Fonseca ◽  
Gabrielle de Oliveira S V Navarro ◽  
Marcos Leite Santoro ◽  
Pedro M Pan ◽  
Rodrigo Bressan ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Risk Score ◽  
Moderated Mediation ◽  
Polygenic Risk Score ◽  
Psychotic Experiences ◽  
Mediation Model ◽  
Polygenic Risk ◽  
Moderated Mediation Model ◽  
P Factor

Abstract Background Polygenic risk score to schizophrenia (PRS-SZ) provides a liability measure summarizing each genetic risk variant and the polyenviromic risk score (PERS) proposes the same regarding exposure factors to psychosis, yet few studies addressed how both scopes interplay, especially in early developmental stages. Psychotic experiences (PE) rest on the lower range of psychosis spectrum, representing an important asset to study psychotic disorders, ie. schizophrenia. However, investigators failed to find significant associations between PRS-SZ and PE in children. We hypothesize that unspecific psychopathology – also previously linked to PE – can mediate the effects of higher risk load for psychosis during neurodevelopment. Thus, our aim is to test a moderated mediation model in which PERS and general psychopathology in youths can lead to PE, prospectively, through SZ genetic liability. Methods We analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders, a youth community sample with 2 time-points: baseline (w0) and 3year follow-up (w1), from São Paulo and Porto Alegre, both urban centers. PRS-SZ was calculated using summary statistics from the PGC and corrected for the 10 principal components of the GWAS. PE were assessed at w0 and w1 with the Community Assessment Psychotic Experiences – CAPE and trained psychologists rated the reliability of students’ answers. The Development and Well-Being Assessment – DAWBA, a structured interview with a transdiagnostic approach, was used to extract a general factor for psychopathology (P-factor) on w0. Latent variables for PE and P-factor were generated through confirmatory factor analysis yielding good model fits. We calculated PERS on w0, as validated, with birth season, urbanicity, cannabis use, paternal age, obstetric/perinatal complications and physical/sexual abuse, neglect or parental loss/separation. Last, we built a moderated mediation diagram based on model 15 of Haye’s PROCESS builder on SPSS: (X) PERS > (M) P-factor > (Y) PE w1, with (V) PRS-SZ as a moderator for PERS > PE and P-factor > PE. Age, sex, site and PE w0 were covariates. Results 2,511 students (6–14 y/o, mean=10.2 ± 1.9, 53% male) completed the w0 assessment and 2,010 the follow-up (mean=13.5 y/o ± 1.9). In our moderated mediation model, P-factor emerged as a full mediator between PERS and PE w1 (B=.324, BootLL–UL CI=.138 to .553). We found PRS-SZ provided a significant moderation effect on the P-factor > PE relation (M*V=.053, R2-chng=.003, p=.037), with the moderator effects of the focal predictor rising considerably according to values of PRS-SZ: p16 (B=.047, p=.192), p50 (B=.099, p=.000) and p84 (B=.153, p=.000). PRS-SZ did not moderate PERS > PE separately (X*V=.016, R2-chng=.001, p=.974). However, conditional indirect coefficients for the complete model were also significant for higher PRS-SZ levels: p16 (B=.143, BootLL–UL CI=-.072 to .389), p50 (B=.304, BootLL–UL CI=.126 to .529) and p84 (B=.470, BootLL–UL CI=.197 to .814). Discussion Our findings suggest environmental risk factors and intermediate phenotypes – namely unspecific non-psychotic psychopathology – can play crucial and intertwined roles in children and adolescents with higher genetic liability to SZ. Moreover, the moderation effects of PRS-SZ imply the existence of thresholds for those relations. The non-clinical nature and age of our sample could explain the low effect sizes. Next steps would include additional phenotypic tracks, such as cognition and social functionality – both previously connected to PRS-SZ as well. We hope our results can help disentangle the genetic and environmental trajectories bonding SZ proneness and PE, and possibly contribute to risk assessment in youths, especially among vulnerable populations.

scholarly journals Polygenic risk score, psychosocial environment and the risk of attention-deficit/hyperactivity disorder

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Søren D. Østergaard ◽  
Betina B. Trabjerg ◽  
Thomas D. Als ◽  
Clara Albiñana Climent ◽  
Florian Privé ◽  
...  
Keyword(s):  
Risk Factors ◽  
Attention Deficit Hyperactivity Disorder ◽  
Risk Score ◽  
Attention Deficit ◽  
Polygenic Risk Score ◽  
Environment Impact ◽  
Psychosocial Environment ◽  
Polygenic Risk ◽  
Hyperactivity Disorder ◽  
Main Effects

Abstract The objective of the present study was to investigate whether the polygenic liability for attention-deficit/hyperactivity disorder (ADHD) and the psychosocial environment impact the risk of ADHD in interaction or independently of each other. We conducted a register- and biobank-based cohort study of 13,725 individuals with ADHD and 20,147 randomly drawn population-based controls. These 33,872 cohort members were genotyped on the Infinium PsychChip v1.0 array (Illumina). Subsequently, we calculated the polygenic risk score (PRS) for ADHD and extracted register data regarding the following risk factors pertaining to the psychosocial environment for each cohort member at the time of birth: maternal/paternal history of mental disorders, maternal/paternal education, maternal/paternal work status, and maternal/paternal income. We used logistic regression analyses to assess the main effects of the PRS for ADHD and the psychosocial environment on the risk of ADHD. Subsequently, we evaluated whether the effect of the PRS and the psychosocial environment act independently or in interaction upon the risk of ADHD. We found that ADHD was strongly associated with the PRS (odds ratio: 6.03, 95%CI: 4.74–7.70 for highest vs. lowest 2% liability). All risk factors pertaining to the psychosocial environment were associated with an increased risk of ADHD. These associations were only slightly attenuated after mutual adjustments. We found no statistically significant interaction between the polygenic liability and the psychosocial environment upon the risk of ADHD. In conclusion, we found main effects of both polygenic liability and risk factors pertaining to the psychosocial environment on the risk of ADHD—in the expected direction.

scholarly journals Polygenic risk score validation using Korean genomes of 265 early-onset acute myocardial infarction patients and 636 healthy controls

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246538
Author(s):  
Youngjune Bhak ◽  
Yeonsu Jeon ◽  
Sungwon Jeon ◽  
Changhan Yoon ◽  
Min Kim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Early Onset ◽  
Area Under The Curve ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Repeat Revascularization ◽  
Artery Disease ◽  
Conventional Risk Factors

Background The polygenic risk score (PRS) developed for coronary artery disease (CAD) is known to be effective for classifying patients with CAD and predicting subsequent events. However, the PRS was developed mainly based on the analysis of Caucasian genomes and has not been validated for East Asians. We aimed to evaluate the PRS in the genomes of Korean early-onset AMI patients (n = 265, age ≤50 years) following PCI and controls (n = 636) to examine whether the PRS improves risk prediction beyond conventional risk factors. Results The odds ratio of the PRS was 1.83 (95% confidence interval [CI]: 1.69–1.99) for early-onset AMI patients compared with the controls. For the classification of patients, the area under the curve (AUC) for the combined model with the six conventional risk factors (diabetes mellitus, family history of CAD, hypertension, body mass index, hypercholesterolemia, and current smoking) and PRS was 0.92 (95% CI: 0.90–0.94) while that for the six conventional risk factors was 0.91 (95% CI: 0.85–0.93). Although the AUC for PRS alone was 0.65 (95% CI: 0.61–0.69), adding the PRS to the six conventional risk factors significantly improved the accuracy of the prediction model (P = 0.015). Patients with the upper 50% of PRS showed a higher frequency of repeat revascularization (hazard ratio = 2.19, 95% CI: 1.47–3.26) than the others. Conclusions The PRS using 265 early-onset AMI genomes showed improvement in the identification of patients in the Korean population and showed potential for genomic screening in early life to complement conventional risk prediction.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Contribution of Genetic Background and Data Collection on Adverse Events of Anti–human Immunodeficiency Virus (HIV) Drugs (D:A:D) Clinical Risk Score to Chronic Kidney Disease in Swiss HIV-infected Persons With Normal Baseline Estimated Glomerular Filtration Rate

2019 ◽  
Author(s):  
Léna G Dietrich ◽  
Catalina Barceló ◽  
Christian W Thorball ◽  
Lene Ryom ◽  
Felix Burkhalter ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Genetic Background ◽  
Filtration Rate ◽  
Estimated Glomerular Filtration Rate ◽  
Polygenic Risk Score ◽  
Genetic Score ◽  
Polygenic Risk ◽  
Immunodeficiency Virus ◽  
Hiv Drugs

Abstract Background In human immunodeficiency virus (HIV), the relative contribution of genetic background, clinical risk factors, and antiretrovirals to chronic kidney disease (CKD) is unknown. Methods We applied a case-control design and performed genome-wide genotyping in white Swiss HIV Cohort participants with normal baseline estimated glomerular filtration rate (eGFR >90 mL/minute/1.73 m2). Univariable and multivariable CKD odds ratios (ORs) were calculated based on the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) score, which summarizes clinical CKD risk factors, and a polygenic risk score that summarizes genetic information from 86 613 single-nucleotide polymorphisms. Results We included 743 cases with confirmed eGFR drop to <60 mL/minute/1.73 m2 (n = 144) or ≥25% eGFR drop to <90 mL/minute/1.73 m2 (n = 599), and 322 controls (eGFR drop <15%). Polygenic risk score and D:A:D score contributed to CKD. In multivariable analysis, CKD ORs were 2.13 (95% confidence interval [CI], 1.55–2.97) in participants in the fourth (most unfavorable) vs first (most favorable) genetic score quartile; 1.94 (95% CI, 1.37–2.65) in the fourth vs first D:A:D score quartile; and 2.98 (95% CI, 2.02–4.66), 1.70 (95% CI, 1.29–2.29), and 1.83 (95% CI, 1.45–2.40), per 5 years of exposure to atazanavir/ritonavir, lopinavir/ritonavir, and tenofovir disoproxil fumarate, respectively. Participants in the first genetic score quartile had no increased CKD risk, even if they were in the fourth D:A:D score quartile. Conclusions Genetic score increased CKD risk similar to clinical D:A:D score and potentially nephrotoxic antiretrovirals. Irrespective of D:A:D score, individuals with the most favorable genetic background may be protected against CKD.

scholarly journals Contribution of genetic ancestry and polygenic risk score in meeting vitamin B12 needs in healthy Brazilian children and adolescents

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carlos Alessandro Fuzo ◽  
Fábio da Veiga Ued ◽  
Sofia Moco ◽  
Ornella Cominetti ◽  
Sylviane Métairon ◽  
...  
Keyword(s):  
Native American ◽  
Vitamin B12 ◽  
Children And Adolescents ◽  
Risk Score ◽  
Genetic Ancestry ◽  
Polygenic Risk Score ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
Local Ancestry ◽  
Brazilian Children

AbstractPolymorphisms in genes related to the metabolism of vitamin B12 haven’t been examined in a Brazilian population. To (a) determine the correlation between the local genetic ancestry components and vitamin B12 levels using ninety B12-related genes; (b) determine associations between these genes and their SNPs with vitamin B12 levels; (c) determine a polygenic risk score (PRS) using significant variants. This cross-sectional study included 168 children and adolescents, aged 9–13 years old. Total cobalamin was measured in plasma. Genotyping arrays and whole exome data were combined to yield ~ 7000 SNPs in 90 genes related to vitamin B12. The Efficient Local Ancestry Inference was used to estimate local ancestry for African (AFR), Native American, and European (EUR). The association between the genotypes and vitamin B12 levels were determined with generalized estimating equation. Vitamin B12 levels were driven by positive (EUR) and negative (AFR, AMR) correlations with genetic ancestry. A set of 36 variants were used to create a PRS that explained 42% of vitamin level variation. Vitamin B12 levels are influenced by genetic ancestry and a PRS explained almost 50% of the variation in plasma cobalamin in Brazilian children and adolescents.

scholarly journals Transforming summary statistics from logistic regression to the liability scale: application to genetic and environmental risk scores

2018 ◽  
Author(s):  
Alexandra C. Gillett ◽  
Evangelos Vassos ◽  
Cathryn M. Lewis
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Environmental Risk ◽  
Effect Size ◽  
Association Studies ◽  
Risk Scores ◽  
Model Parameters ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Size Estimates

1.Abstract1.1.ObjectiveStratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies and models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies however the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates.1.2.MethodsWe present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales.1.3.ResultsThese equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors.1.4.ConclusionThis straightforward method allows conversion of model parameters between the logit and liability scales, and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.

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