Polygenic risk score for alcohol drinking behavior improves prediction of inflammatory bowel disease risk.
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ABSTRACT:
SUBMITTER: Di Narzo AF
PROVIDER: S-EPMC8599895 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundForty distinct primary sclerosing cholangitis (PSC) genomic loci have been identified through multiancestry meta-analyses. The polygenic risk score (PRS) could serve as a promising tool to discover unique disease behaviour, like PSC, underlying inflammatory bowel disease (IBD).AimTo test whether PRS indicates PSC risk in patients with IBD.Materials and methodsMayo Clinic and Washington University at St Louis IBD cohorts were used to test our hypothesis. PRS was modelled through the published PSC loci and weighted with their corresponding effect size. Logistic regression was applied to predict the PSC risk.ResultsIn total, 63 (5.6%) among 1130 patients with IBD of European ancestry had PSC. Among 381 ulcerative colitis (UC), 12% had PSC; in contrast to 1.4% in 761 Crohn disease (CD). Compared with IBD alone, IBD-PSC had significantly higher PRS (PSC risk: 3.0% at the lowest PRS quartile vs 7.2% at the highest PRS quartile, Ptrend =.03). In IBD subphenotypes subgroup analysis, multivariate analysis shows that UC-PSC is associated with more extensive UC disease (OR, 5.60; p=0.002) and younger age at diagnosis (p=0.02). In CD, multivariate analysis suggests that CD-PSC is associated with colorectal cancer (OR, 50; p=0.005).ConclusionsWe found evidence that patients with IBD with PSC presented with a clinical course difference from that of patients with IBD alone. PRS can influence PSC risk in patients with IBD. Once validated in an independent cohort, this may help identify patients with the highest likelihood of developing PSC.
| S-EPMC10583098 | biostudies-literature
Project description:More than 1 million prostate biopsies are conducted yearly in the United States. The low specificity of prostate-specific antigen (PSA) results in diagnostic biopsies in men without prostate cancer (PCa). Additional information, such as genetic markers, could be used to avoid unnecessary biopsies.To determine whether single nucleotide polymorphisms (SNPs) associated with PCa can be used to determine whether biopsy of the prostate is necessary.The Stockholm-1 cohort (n = 5241) consisted of men who underwent a prostate biopsy during 2005 to 2007. PSA levels were retrieved from databases and family histories were obtained using a questionnaire. Thirty-five validated SNPs were analysed and converted into a genetic risk score that was implemented in a risk-prediction model.When comparing the nongenetic model (based on age, PSA, free-to-total PSA, and family history) with the genetic model and using a fixed number of detected PCa cases, it was found that the genetic model required significantly fewer biopsies than the nongenetic model, with 480 biopsies (22.7%) avoided, at a cost of missing a PCa diagnosis in 3% of patients characterised as having an aggressive disease. However, the overall genetic model does not discriminate between aggressive and nonaggressive cases.Although the genetic model reduced the number of biopsies more than the nongenetic model, the clinical significance of this finding requires further evaluation.
| S-EPMC4417350 | biostudies-literature
Project description:Hereditary factors have a strong influence on prostate cancer (PC) risk and poorer outcomes, thus stratification by genetic factors addresses a critical need for targeted PC screening and risk-adapted follow-up. In this Finnish population-based retrospective study 2283 clinically diagnosed and 455 screen-detected patients from the Finnish Randomised Study of Screening for Prostate Cancer (FinRSPC), 2400 healthy individuals have been involved. Individual genetic risk through establishment of a polygenic risk score based on 55 PC risk SNPs identified through the Finnish subset of the Collaborative Oncological Gene-Environment Study was assessed. Men with PC had significantly higher median polygenic risk score compared to the controls (6.59 vs. 3.83, P < 0.0001). The polygenic risk score above the control median was a significant predictor of PC (OR 2.13, 95% CI 1.90-2.39). The polygenic risk score predicted the risk of PC with an AUC of 0.618 (95% CI 0.60-0.63). Men in the highest polygenic risk score quartile were 2.8-fold (95% CI 2.4-3.30) more likely to develop PC compared with men in the lowest quartile. In the FinRSPC cohort, a significantly higher percentage of men had a PSA level of ≥ 4 ng/mL in polygenic risk score quartile four compared to quartile one (18.7% vs 8.3%, P < 0.00001). Adding the PRS to a PSA-only model contributed additional information in predicting PC in the FinRSPC model. Results strongly suggest that use of the polygenic risk score would facilitate the identification of men at increased risk for PC.
| S-EPMC7553910 | biostudies-literature
Project description:BackgroundWhile postoperative myocardial injury remains a major driver of morbidity and mortality, the ability to accurately identify patients at risk remains limited despite decades of clinical research. The role of genetic information in predicting myocardial injury after noncardiac surgery (MINS) remains unknown and requires large scale electronic health record and genomic data sets.MethodsIn this retrospective observational study of adult patients undergoing noncardiac surgery, we defined MINS as new troponin elevation within 30 days following surgery. To determine the incremental value of polygenic risk score (PRS) for coronary artery disease, we added the score to 3 models of MINS risk: revised cardiac risk index, a model comprised entirely of preoperative variables, and a model with combined preoperative plus intraoperative variables. We assessed performance without and with PRSs via area under the receiver operating characteristic curve and net reclassification index.ResultsAmong 90 053 procedures across 40 498 genotyped individuals, we observed 429 cases with MINS (0.5%). PRS for coronary artery disease was independently associated with MINS for each multivariable model created (odds ratio=1.12 [95% CI, 1.02-1.24], P=0.023 in the revised cardiac risk index-based model; odds ratio, 1.19 [95% CI, 1.07-1.31], P=0.001 in the preoperative model; and odds ratio, 1.17 [95% CI, 1.06-1.30], P=0.003 in the preoperative plus intraoperative model). The addition of clinical risk factors improved model discrimination. When PRS was included with preoperative and preoperative plus intraoperative models, up to 3.6% of procedures were shifted into a new outcome classification.ConclusionsThe addition of a PRS does not significantly improve discrimination but remains independently associated with MINS and improves goodness of fit. As genetic analysis becomes more common, clinicians will have an opportunity to use polygenic risk to predict perioperative complications. Further studies are necessary to determine if PRSs can inform MINS surveillance.
| S-EPMC7442662 | biostudies-literature
Project description:PurposeElevated intraocular pressure (IOP) is an important risk factor for glaucoma. We constructed polygenic risk scores (PRSs) for IOP using the UK Biobank (UKB) data set to test whether the PRSs are associated with IOP and whether using them improves glaucoma prediction.MethodsWe conducted this study using 435,678 European participants from the UKB. We constructed weighted and unweighted PRSs using single nucleotide polymorphisms (SNPs) derived from the UKB data and previously reported IOP SNPs. We examined the associations of the PRSs with IOP and primary open-angle glaucoma (POAG) using linear and logistic regression, respectively. To quantify the discriminatory ability of the PRSs on POAG, we used the area under the receiver operating characteristic curve (AUC).ResultsThe weighted PRS was significantly associated with IOP (P ? 10-200), after adjusting for age and sex. The PRS explained an additional 4% of variance in IOP. The weighted PRS was also significantly associated with POAG (P = 1.8 × 10-77). Subjects in the top quintile of the IOP PRS were 6.34 (95% confidence interval [CI]: 4.82-8.33; P = 2.1 × 10-57) times more likely to have POAG, compared to those in the bottom category. The weighted PRS improved the discriminatory power for POAG (AUC increased by 5%, P = 6.2 × 10-22) when added to the other covariates. The unweighted PRS exhibited similar results.ConclusionsWe determined that IOP PRSs are significantly associated with IOP and improve the prediction of POAG.Translational relevancePRSs help reduce the burden of glaucoma by early detection of genetically susceptible individuals.
| S-EPMC6450641 | biostudies-literature
Project description:BackgroundPredicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn's disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach.MethodsWe performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation.ResultsOn average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis.ConclusionsOur analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.
| S-EPMC5576242 | biostudies-literature
Project description:BackgroundEvidence is lacking on the impact of alcohol consumption on colorectal cancer (CRC) risk (overall and by age at diagnosis) by polygenic risk score (PRS) levels, and it is unclear how the magnitude of CRC risk associated with alcohol consumption compares to the magnitude of genetically determined risk.MethodsMultiple logistic regression was used to assess the association between alcohol consumption and colorectal cancer (CRC) across PRS levels based on 140 CRC-related loci among 5104 CRC cases and 4131 controls from a large population-based case-control study. We compared the effects for alcohol consumption and PRS on CRC risk using the "Genetic Risk Equivalent (GRE)" for effective risk communication. Specific analyses were conducted for early-onset CRC (EOCRC, <55 years) and late-onset CRC (LOCRC, ≥55 years).FindingsHigh alcohol consumption, and to a lower extent, also alcohol abstinence were associated with increased CRC risk. Compared to low alcohol consumption (0·1-<25 g/d), lifetime average alcohol consumption ≥25 g/d was more strongly associated with EOCRC [odds ratio (OR) 1·8, 95% confidence interval (CI) 1·2-2·8] than with LOCRC risk (OR 1·3, 95% CI 1·1-1·4) (P-value for interaction with age =0·011). Interactions between alcohol consumption and PRS did not reach statistical significance for either EOCRC or LOCRC risk. The estimated impact of high lifetime alcohol consumption on EOCRC was equivalent to the effect of having 47 percentiles higher PRS (GRE 47, 95% CI 12-82), stronger than the impact on LOCRC (GRE 18, 95% CI 8-29).InterpretationExcessive alcohol use was strongly associated with EOCRC risk, independent of PRS levels. Abstaining from heavy drinking could reduce risk for CRC, in particular for EOCRC to an extent that would be equivalent to having a much lower genetically determined risk.FundingThe first author (X.C.) was supported by the Guangzhou Elite Project (GEP). The DACHS study was supported by grants from the German Research Council (BR 1704/6-1, BR1704/6-3, BR 1704/6-4, BR 1704/6-6, CH 117/1-1, BR 1704/17-1, HO 5117/2-1) and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01GL1712).
| S-EPMC9126769 | biostudies-literature
Project description:BackgroundAccurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction.MethodsWe examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors.ResultsA standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072.ConclusionsWe generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
| S-EPMC7860212 | biostudies-literature
Project description:Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ?3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.
| S-EPMC5033764 | biostudies-literature
Project description:Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N = 54 799) and its performance was tested using a cohort testing dataset ( N = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.
| S-EPMC10309112 | biostudies-literature