scholarly journals An analysis on Dual Diagnosis Revealed a Possible deviation of Schizophrenia Polygenic Risk Score by Mexican Amerindian Ancestry

2018 ◽  
Author(s):  
Jose Jaime Martínez-Magaña ◽  
Thelma Beatriz Gonzalez-Castro ◽  
Alma Delia Genis-Mendoza ◽  
Carlos Alfonso Tovilla-Zarate ◽  
Isela Juarez-Rojop ◽  
...  
Keyword(s):  
Dual Diagnosis ◽  
Risk Score ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Psychiatric Diseases ◽  
Polygenic Risk ◽  
Asian Populations ◽  
Genome Wide ◽  
Highly Correlated ◽  
Larger Sample

In order to summarized the polygenic background of psychiatric diseases, polygenic risk scores (PRS) have been developed. Recently, PRS have been use to predict patients with higher comorbidities in psychiatric diseases, like dual diagnosis. PRS are principally derived in analysis of Caucasian and Asian populations, we are not aware of how this PRS could be applied in populations with high admixture. In order to explored this, the present work has the aim to analyzed if previous calculated PRS for psychiatric diseases could predict dual diagnosis in Mexican population, and also, if PRS calculation could be influenced by Mexican Amerindian (MA) global ancestry. We performed PRS calculation, using PRSice, with summary genome-wide association statistics previously published for psychiatric diseases, and also, performed Nagelkerke correlation test in order to established if PRS are correlated with dual diagnosis. We found that dual diagnosis could be predicted by major depressive disorder polygenic risk score. Nevertheless, schizophrenia polygenic risk score is highly correlated with global MA ancestry, independently of the schizophrenia diagnosis. Our results reinforced the notion that PRS calculation could be deviated by the MA global ancestry, nevertheless analysis on larger sample sizes are required in order to clarified this issue.

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]

Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction

2021 ◽  
Author(s):  
Minta Thomas ◽  
Lori C Sakoda ◽  
Jeffrey K Lee ◽  
Mark A Jenkins ◽  
Andrea Burnett-Hartman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Colorectal Cancer Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide

scholarly journals Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia

2018 ◽  
Vol 201 ◽  
pp. 393-399 ◽  
Author(s):  
Soichiro Nakahara ◽  
Sarah Medland ◽  
Jessica A. Turner ◽  
Vince D. Calhoun ◽  
Kelvin O. Lim ◽  
...  
Keyword(s):  
Risk Score ◽  
Genome Wide Association ◽  
Cognitive Domain ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Gene Set ◽  
Genome Wide

Abstract PO-163: Genome-wide polygenic risk score of prostate cancer in African and European ancestry men

2022 ◽  
Author(s):  
Burcu F. Darst ◽  
Ravi K Madduri ◽  
Alexis A. Rodriguez ◽  
Xin Sheng ◽  
Rosalind A. Eeles ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide

Genome-wide association study (GWAS) of chemotherapy-induced Raynaud's phenomenon (RP) to reveal shared pathways with cardiovascular disease (CVD).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18162-e18162
Author(s):  
Omar El Charif ◽  
Heather E. Wheeler ◽  
Matthew Trendowski ◽  
Eric R Gamazon ◽  
Shirin Ardeshirrouhanifard ◽  
...  
Keyword(s):  
Risk Score ◽  
Cellular Response ◽  
Genome Wide Association Study ◽  
Significant Snps ◽  
Case Control ◽  
Gene Set Enrichment Analysis ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Patient Reported

e18162 Background: RP is an adverse drug reaction characterized by reduced blood flow to the extremities causing pain and sensations of cold. Few studies have examined the genetic basis for RP, although family studies suggest a heritable component to primary RP. Methods: Eligible testicular cancer survivors (TCS) were < 55 y at diagnosis, treated with first line cisplatin-based chemotherapy, and completed questionnaires. Genotyping with standard quality control and imputation were performed. A case-control RP phenotype was derived from patient-reported outcomes and associations were computed by logistic regression. GWAS used cumulative bleomycin dose and 10 genetic principal components as covariates. Gene set enrichment analysis (GSEA) utilized genes ranked by the most significant GWAS SNP in/within 20 kilobases. A polygenic risk score for CVD derived from four prior independent GWAS (Khera et al. NEJM 2016) was assessed for association with RP. Results: Of 749 patients (median age 38 y, median time since chemotherapy 5 y), 38% reported RP. Bleomycin dose was the most significant predictor of RP (OR100 mg/m2 = 1.25, p < 0.0001). Number of years smoking also correlated with RP (ORyear = 1.05, p = 0.002). Age and hypertension showed no significant correlation with RP. GSEA revealed several significant pathways (FDR q < 0.1), including “ cellular response to VEGF stimulus” (q = 0.05) and “ cardiac muscle cell action potential” (q = 0.09). We hypothesized that RP may share genetic architecture with CVD. Deriving a polygenic risk score from genome-wide significant SNPs in prior CVD GWAS (n = 4260-22,389), we showed nearly significant case-control differences in CVD polygenic risk score (two-tailed t-test, p = 0.053). RP frequency significantly increased with polygenic risk score quartile (OR = 1.19, p = 0.008). Conclusions: Over one third of TCS report RP, with greater frequency among bleomycin-treated patients and smokers. Implicated genetic pathways include ones established in CVD. Although shared genetic risk between chemotherapy-induced RP and CVD may be possible, further investigation is required. Primary RP has been inconsistently linked with CVD.

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 The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia

2019 ◽  
Author(s):  
Alexander L Richards ◽  
Antonio F Pardiñas ◽  
Aura Frizzati ◽  
Katherine E Tansey ◽  
Amy J Lynham ◽  
...  
Keyword(s):  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Related Factors ◽  
Polygenic Risk ◽  
Genome Wide ◽  
A Genome ◽  
Genetic Overlap ◽  
Individual Variant

Abstract Background Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases. Methods We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. Results PRS for both population IQ (P = 4.39 × 10–28) and EA (P = 1.27 × 10–26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. Conclusions Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

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