IgAN Genetic Risk Score in the Clinical Setting.
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ABSTRACT:
SUBMITTER: Schena FP
PROVIDER: S-EPMC7572307 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
IgAN Genetic Risk Score in the Clinical Setting.
Schena Francesco Paolo FP Cox Sharon Natasha SN
Kidney international reports 20200805 10
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Project description:Background and aimsA cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown.MethodsThe Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone.ResultsThe distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28×10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1×10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores.ConclusionsIn a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems.
| S-EPMC11379490 | biostudies-literature
Project description:We aimed to predict obesity risk with genetic data, specifically, obesity-associated gene expression profiles. Genetic risk score was computed. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used. Linear regression and built support vector machine models predicted obesity risk using gene expression profiles and the genetic risk score with a new mathematical method.
2018-01-25 | GSE109597 | GEO
Project description:Prediction of susceptibility to multiple sclerosis (MS) might have important clinical applications, either as part of a diagnostic algorithm or as a means to identify high-risk individuals for prospective studies. We investigated the usefulness of an aggregate measure of risk of MS that is based on genetic susceptibility loci. We also assessed the added effect of environmental risk factors that are associated with susceptibility for MS.We created a weighted genetic risk score (wGRS) that includes 16 MS susceptibility loci. We tested our model with data from 2215 individuals with MS and 2189 controls (derivation samples), a validation set of 1340 individuals with MS and 1109 controls taken from several MS therapeutic trials (TT cohort), and a second validation set of 143 individuals with MS and 281 controls from the US Nurses' Health Studies I and II (NHS/NHS II), for whom we also have data on smoking and immune response to Epstein-Barr virus (EBV).Individuals with a wGRS that was more than 1.25 SD from the mean had a significantly higher odds of MS in all datasets. In the derivation sample, the mean (SD) wGRS was 3.5 (0.7) for individuals with MS and 3.0 (0.6) for controls (p<0.0001); in the TT validation sample, the mean wGRS was 3.4 (0.7) for individuals with MS versus 3.1 (0.7) for controls (p<0.0001); and in the NHS/NHS II dataset, the mean wGRS was 3.4 (0.8) for individuals with MS versus 3.0 (0.7) for controls (p<0.0001). In the derivation cohort, the area under the receiver operating characteristic curve (C statistic; a measure of the ability of a model to discriminate between individuals with MS and controls) for the genetic-only model was 0.70 and for the genetics plus sex model was 0.74 (p<0.0001). In the TT and NHS cohorts, the C statistics for the genetic-only model were both 0.64; adding sex to the TT model increased the C statistic to 0.72 (p<0.0001), whereas adding smoking and immune response to EBV to the NHS model increased the C statistic to 0.68 (p=0.02). However, the wGRS does not seem to be correlated with the conversion of clinically isolated syndrome to MS.The inclusion of 16 susceptibility alleles into a wGRS can modestly predict MS risk, shows consistent discriminatory ability in independent samples, and is enhanced by the inclusion of non-genetic risk factors into the algorithm. Future iterations of the wGRS might therefore make a contribution to algorithms that can predict a diagnosis of MS in a clinical or research setting.
| S-EPMC3099419 | biostudies-literature
Project description:SEARCH for Diabetes in Youth Study - Genetic Risk Score
| PRJNA782026 | ENA
Project description:SEARCH for Diabetes in Youth Study - Genetic Risk Score
| PRJNA782027 | ENA
Project description:BackgroundCoronary artery disease (CAD) risk traditionally has been assessed using clinical risk factors. We evaluated whether molecular genetic markers for CAD risk could add information to traditional variables.MethodsWe developed a false discovery rate 267-marker genetic risk score (FDR267) from markers that were significantly associated with CAD in the UK Biobank cohort meta-analysis. FDR267 was tested in the Atherosclerosis Risk in Communities cohort using logistic regression and Cox proportional hazards analyses in the European and African American groups.ResultsOur genetic risk score (FDR267) was associated with a 1.45 (95% confidence interval, 1.39-1.51) increase in odds ratio and a 1.32 (95% confidence interval, 1.26-1.38) increase in hazard ratio per standard deviation of the score. The score modestly improved the area under the curve (AUC) statistic when added to a clinical model (ΔAUC = 0.0112, P = 0.0002). FDR267 predicted incident CAD (C-index = 0.60), although it did not improve on clinical risk factors (ΔAUC = 0.0159, P = 0.0965). Individuals in the top quintile of FDR267 genetic risk were at approximately 2-fold increased risk compared with the bottom quintile, which is comparable to risk associated with self-reported family history. The performance of FDR267 was less robust in the African American sample.ConclusionsFDR267 is significantly associated with CAD in the European sample, with an effect size comparable to self-reported family history. FDR267 discriminated between individuals with and without CAD, but did not improve CAD risk prediction over clinical variables. FDR267 was less predictive of CAD risk in African Americans.
| S-EPMC7063618 | biostudies-literature
Project description:BackgroundAs genetic tests become cheaper, the possibility of their widespread availability must be considered. This study involves a risk score for lung cancer in smokers that is roughly 50% genetic (50% clinical criteria). The risk score has been shown to be effective as a smoking cessation motivator in hospital recruited subjects (not actively seeking cessation services).MethodsThis was an RCT set in a United Kingdom National Health Service (NHS) smoking cessation clinic. Smokers were identified from medical records. Subjects that wanted to participate were randomised to a test group that was administered a gene-based risk test and given a lung cancer risk score, or a control group where no risk score was performed. Each group had 8 weeks of weekly smoking cessation sessions involving group therapy and advice on smoking cessation pharmacotherapy and follow-up at 6 months. The primary endpoint was smoking cessation at 6 months. Secondary outcomes included ranking of the risk score and other motivators.Results67 subjects attended the smoking cessation clinic. The 6 months quit rates were 29.4%, (10/34; 95% CI 14.1-44.7%) for the test group and 42.9% (12/28; 95% CI 24.6-61.2%) for the controls. The difference is not significant. However, the quit rate for test group subjects with a "very high" risk score was 89% (8/9; 95% CI 68.4-100%) which was significant when compared with the control group (p = 0.023) and test group subjects with moderate risk scores had a 9.5% quit rate (2/21; 95% CI 2.7-28.9%) which was significantly lower than for above moderate risk score 61.5% (8/13; 95% CI 35.5-82.3; p = 0.03).ConclusionsOnly the sub-group with the highest risk score showed an increased quit rate. Controls and test group subjects with a moderate risk score were relatively unlikely to have achieved and maintained non-smoker status at 6 months. ClinicalTrials.gov ID NCT01176383 (date of registration: 3 August 2010).
| S-EPMC5653992 | biostudies-literature
Project description: Not available
| S-EPMC9579501 | biostudies-literature
Project description:BackgroundDupuytren's disease is a very common, highly heritable palmar fibromatosis. In a recent genome-wide association study, 26 single-nucleotide polymorphisms were found to be associated with development of Dupuytren's disease. The authors generated a weighted genetic risk score based on the genotype at these single-nucleotide polymorphisms. In two independent cohorts, they tested the association among high weighted genetic risk score, clinical features that predict a high risk of recurrence, and recurrence after surgery.MethodsClinical data were obtained from patient questionnaires and clinical records, with missing data accounted for by imputation. Genotyping was performed as part of the recent genome-wide association study. Logistic regression was performed to study the association among weighted genetic risk score, high-risk clinical features, and recurrence, with a weighted genetic risk score analyzed as a continuous variable, and also grouped into four categories.ResultsUsing univariable logistic regression, a high weighted genetic risk score was associated with the presence of all high-risk clinical features: early age of onset, bilateral disease, ectopic disease, and a positive family history (p ≤ 0.004). After multivariable logistic regression accounting for these factors, an increased weighted genetic risk score was still associated with the need for repeated Dupuytren's disease surgery (p = 0.004).ConclusionsThe authors' results suggest that a weighted genetic risk score is useful in predicting the risk of disease recurrence, and may be used by surgeons to personalize prognostication. In the future, a weighted genetic risk score may be useful for determining the most appropriate initial surgical procedure in patients with Dupuytren's disease.Clinical question/level of evidenceRisk, III.
| S-EPMC6358194 | biostudies-literature
Project description:It is important to identify the patients at highest risk of fractures. A recent large-scale meta-analysis identified 63 autosomal single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD), of which 16 were also associated with fracture risk. Based on these findings, two genetic risk scores (GRS63 and GRS16) were developed. Our aim was to determine the clinical usefulness of these GRSs for the prediction of BMD, BMD change, and fracture risk in elderly subjects. We studied two male (Osteoporotic Fractures in Men Study [MrOS] US, MrOS Sweden) and one female (Study of Osteoporotic Fractures [SOF]) large prospective cohorts of older subjects, looking at BMD, BMD change, and radiographically and/or medically confirmed incident fractures (8067 subjects, 2185 incident nonvertebral or vertebral fractures). GRS63 was associated with BMD (?3% of the variation explained) but not with BMD change. Both GRS63 and GRS16 were associated with fractures. After BMD adjustment, the effect sizes for these associations were substantially reduced. Similar results were found using an unweighted GRS63 and an unweighted GRS16 compared with those found using the corresponding weighted risk scores. Only minor improvements in C-statistics (AUC) for fractures were found when the GRSs were added to a base model (age, weight, and height), and no significant improvements in C-statistics were found when they were added to a model further adjusted for BMD. Net reclassification improvements with the addition of the GRSs to a base model were modest and substantially attenuated in BMD-adjusted models. GRS63 is associated with BMD, but not BMD change, suggesting that the genetic determinants of BMD differ from those of BMD change. When BMD is known, the clinical utility of the two GRSs for fracture prediction is limited in elderly subjects.
| S-EPMC4281709 | biostudies-literature