T61SUICIDE DEATHS SELECTED FOR GENETIC RISK: POLYGENIC RISK SCORE CHARACTERISTICS AND HIGH-IMPACT SEQUENCE VARIANTS

2019 ◽  
Vol 29 ◽  
pp. S248
Author(s):  
Hilary Coon ◽  
Andrey Shabalin ◽  
Emily DiBlasi ◽  
Brooks Keeshin ◽  
Amanda Bakian ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Risk ◽  
High Impact ◽  
Polygenic Risk Score ◽  
Sequence Variants ◽  
Polygenic Risk

Effects of genetic risk for alcohol dependence and onset of regular drinking on the progression to alcohol dependence: A polygenic risk score approach

2021 ◽  
pp. 109117
Author(s):  
Ellen W. Yeung ◽  
Kellyn M. Spychala ◽  
Alex P. Miller ◽  
Jacqueline M. Otto ◽  
Joseph D. Deak ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Risk Score ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals Diet quality indices, genetic risk and risk of cardiovascular disease and mortality: a longitudinal analysis of 77 004 UK Biobank participants

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e045362
Author(s):  
Katherine M Livingstone ◽  
Gavin Abbott ◽  
Steven J Bowe ◽  
Joey Ward ◽  
Catherine Milte ◽  
...  
Keyword(s):  
Risk Score ◽  
Diet Quality ◽  
Genetic Risk ◽  
Lower Risk ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Quality Indices ◽  
Polygenic Risk ◽  
All Cause Mortality ◽  
Cvd Mortality

ObjectivesTo examine associations of three diet quality indices and a polygenic risk score with incidence of all-cause mortality, cardiovascular disease (CVD) mortality, myocardial infarction (MI) and stroke.DesignProspective cohort study.SettingUK Biobank, UK.Participants77 004 men and women (40–70 years) recruited between 2006 and 2010.Main outcome measuresA polygenic risk score was created from 300 single nucleotide polymorphisms associated with CVD. Cox proportional HRs were used to estimate independent effects of diet quality and genetic risk on all-cause mortality, CVD mortality, MI and stroke risk. Dietary intake (Oxford WebQ) was used to calculate Recommended Food Score (RFS), Healthy Diet Indicator (HDI) and Mediterranean Diet Score (MDS).ResultsNew all-cause (n=2409) and CVD (n=364) deaths and MI (n=1141) and stroke (n=748) events were identified during mean follow-ups of 7.9 and 7.8 years, respectively. The adjusted HR associated with one-point higher RFS for all-cause mortality was 0.96 (95% CI: 0.94 to 0.98), CVD mortality was 0.94 (95% CI: 0.90 to 0.98), MI was 0.97 (95% CI: 0.95 to 1.00) and stroke was 0.94 (95% CI: 0.91 to 0.98). The adjusted HR for all-cause mortality associated with one-point higher HDI and MDS was 0.97 (95% CI: 0.93 to 0.99) and 0.95 (95% CI: 0.91 to 0.98), respectively. The adjusted HR associated with one-point higher MDS for stroke was 0.93 (95% CI: 0.87 to 1.00). There was little evidence of associations between HDI and risk of CVD mortality, MI or stroke. There was evidence of an interaction between diet quality and genetic risk score for MI.ConclusionHigher diet quality predicted lower risk of all-cause mortality, independent of genetic risk. Higher RFS was also associated with lower risk of CVD mortality and MI. These findings demonstrate the benefit of following a healthy diet, regardless of genetic risk.

scholarly journals Capturing additional genetic risk from family history for improved polygenic risk prediction

2022 ◽  
Author(s):  
Tianyuan Lu ◽  
Vincenzo Forgetta ◽  
J Brent Richards ◽  
Celia MT Greenwood
Keyword(s):  
Family History ◽  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Adult Height ◽  
Prediction Models ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Parental History ◽  
Polygenic Risk

Family history of complex traits may reflect transmitted rare pathogenic variants, intrafamilial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. We applied our model to predict adult height for 941 children in the Avon Longitudinal Study of Parents and Children cohort as well as 11 complex diseases for ~400,000 European ancestry participants in the UK Biobank. Parental history brought consistent significant improvements in the predictive power of polygenic risk prediction. For instance, a joint predictor was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Our work showcases an innovative paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.

The BARCODE1 pilot study targeting men with increased genetic risk of developing prostate cancer: Examining the feasibility of a community-based screening program using polygenic risk score to target screening.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 227-227
Author(s):  
Jana Kathlyn McHugh ◽  
Sarah Benafif ◽  
Holly ni Raghallaigh ◽  
Elizabeth Bancroft ◽  
Zsofia Kote-Jarai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Primary Care ◽  
Dna Extraction ◽  
Risk Score ◽  
Genetic Risk ◽  
Population Screening ◽  
Low Risk ◽  
Polygenic Risk Score ◽  
Genetic Profiling ◽  
Polygenic Risk

227 Background: A significant proportion of Prostate cancer (PrCa) risk is attributable to heritable risk factors of which only a minority are high risk Mendelian traits. A greater proportion of PrCa is due to the combined effect of multiple low risk variants. There have been approximately 170 single nucleotide polymorphisms (SNPs) identified that are associated with PrCa risk in Europeans. Although each of these confer a low to moderate risk of PrCa, the cumulative risk (polygenic risk score, PRS) of increasing numbers of these risk alleles may confer a substantial relative risk. In PrCa genetic profiling, using PRS, could be used to target population screening to those at highest risk. BARCODE1 is the first study to prospectively review the use of a genetic profile in PrCa screening in the general population in the UK. Methods: Our study invited healthy males aged 55-69 to participate through their Primary Care physicians. Collection kits were mailed to retrieve saliva samples. Genotyping was carried out after DNA extraction using a study specific assay and the PRS was calculated for each participant using the sum of weighted alleles for 130 risk loci. Prostate MRI and Biopsy were then offered to men in the top 10% of the genetic risk profile. Results: 1434 men were invited by letter to participate. The uptake was 26%, of whom 87% of men were eligible for inclusion. Following DNA extraction, genotyping, and quality control checks, data were available for 297 men. 25 participants had PRS in the top 10% and were invited for screening; 19 underwent a prostate MRI, and 18 went on to have a systematic (+/- targeted prostate biopsy. There were 7 diagnoses of PrCa (38.9%). The cancers detected were low-risk and are being managed with Active Surveillance (AS). Results of the first year of follow up will be presented and an update of the main study which aims to recruit 5000 men. Conclusions: The BARCODE1 pilot has shown the feasibility of this population-based study, with an overall uptake of 26% and a cancer incidence of nearly 40%. We have identified approximately 70 Primary care providers who have contributed to the transition to the full BARCODE1 study, which will aim to recruit 5,000 men. The BARCODE1 study results will be important in defining the role of PRS genetic profiling in targeted PrCa population screening. Clinical trial information: IRAS257684.

scholarly journals A weighted burden test using logistic regression for integrated analysis of sequence variants, copy number variants and polygenic risk score

2018 ◽  
Author(s):  
David Curtis
Keyword(s):  
Logistic Regression ◽  
Risk Score ◽  
Principal Components ◽  
Copy Number ◽  
Copy Number Variants ◽  
Integrated Analysis ◽  
Polygenic Risk Score ◽  
Sequence Variants ◽  
Polygenic Risk ◽  
Functional Variants

AbstractPreviously described methods of analysis allow variants in a gene to be weighted more highly according to rarity and/or predicted function and then for the variant contributions to be summed into a gene-wise risk score which can be compared between cases and controls using a t test. However this does not allow incorporating covariates into the analysis. Schizophrenia is an example of an illness where there is evidence that different kinds of genetic variation can contribute to risk, including common variants contributing to a polygenic risk score (PRS), very rare copy number variants (CNVs) and sequence variants. A logistic regression approach has been implemented to compare the gene-wise risk scores between cases and controls while incorporating as covariates population principal components, the PRS and the presence of pathogenic CNVs and sequence variants. A likelihood ratio test is performed comparing the likelihoods of logistic regression models with and without this score. The method was applied to an ethnically heterogeneous exome-sequenced sample of 6000 controls and 5000 schizophrenia cases. In the raw analysis the test statistic is inflated but inclusion of principal components satisfactorily controls for this. In this dataset the inclusion of the PRS and effect from CNVs and sequence variants had only small effects. The set of genes which are FMRP targets showed some evidence for enrichment of rare, functional variants among cases (p=0.0005). This approach can be applied to any disease in which different kinds of genetic and non-genetic risk factors make contributions to risk.

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