Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data.
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ABSTRACT:
SUBMITTER: Yan S
PROVIDER: S-EPMC9318573 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA.MethodsThe mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. $\alpha $ was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component.ResultsParameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates $\alpha $, as compared with the RL estimates. The RL $\alpha $ estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean.ConclusionsBOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model.Impact: The methods described facilitate comprehensive breast cancer risk assessment.
| S-EPMC9986688 | biostudies-literature
Project description:Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.
| S-EPMC8127405 | biostudies-literature
Project description:Polygenic risk scores (PRSs) derived from genotype data and family history (FH) of disease provide valuable information for predicting disease risk, but PRSs perform poorly when applied to diverse populations. Here, we explore methods for combining both types of information (PRS-FH) in UK Biobank data. PRSs were trained using all British individuals (n = 409,000), and target samples consisted of unrelated non-British Europeans (n = 42,000), South Asians (n = 7,000), or Africans (n = 7,000). We evaluated PRS, FH, and PRS-FH using liability-scale R 2, primarily focusing on 3 well-powered diseases (type 2 diabetes, hypertension, and depression). PRS attained average prediction R 2s of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R 2s of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans. In conclusion, including family history improves the accuracy of polygenic risk scores, particularly in diverse populations.
| S-EPMC9351615 | biostudies-literature
Project description:The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)-common environmental (C) covariance (σAC) identified. We study the statistical power to reject σAC = 0 in the ACE model and present the results of simulations.
| S-EPMC8093156 | biostudies-literature
Project description:BackgroundPolygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry.ResultsWe introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data.ConclusionsWe show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.
| S-EPMC8759285 | biostudies-literature
Project description:BackgroundTo develop a breast cancer prediction model for Korean women using published polygenic risk scores (PRS) combined with nongenetic risk factors (NGRF).MethodsThirteen PRS models generated from single or multiple combinations of the Asian and European PRSs were evaluated among 20,434 Korean women. The AUC and increase in OR per SD were compared for each PRS. The PRSs with the highest predictive power were combined with NGRFs; then, an integrated prediction model was established using the Individualized Coherent Absolute Risk Estimation (iCARE) tool. The absolute breast cancer risk was stratified for 18,142 women with available follow-up data.ResultsPRS38_ASN+PRS190_EB, a combination of Asian and European PRSs, had the highest AUC (0.621) among PRSs, with an OR per SD increase of 1.45 (95% confidence interval: 1.31-1.61). Compared with the average risk group (35%-65%), women in the top 5% had a 2.5-fold higher risk of breast cancer. Incorporating NGRFs yielded a modest increase in the AUC of women ages >50 years. For PRS38_ASN+PRS190_EB+NGRF, the average absolute risk was 5.06%. The lifetime absolute risk at age 80 years for women in the top 5% was 9.93%, whereas that of women in the lowest 5% was 2.22%. Women at higher risks were more sensitive to NGRF incorporation.ConclusionsCombined Asian and European PRSs were predictive of breast cancer in Korean women. Our findings support the use of these models for personalized screening and prevention of breast cancer.ImpactOur study provides insights into genetic susceptibility and NGRFs for predicting breast cancer in Korean women.
| S-EPMC10472098 | biostudies-literature
Project description:Neuroticism as a personality trait represents a heritable risk for psychiatric disorders. The polygenic risk score for neuroticism (N-PRS) is used to study genetic vulnerability to neuroticism. The current data present the association of the genetic risk for neuroticism to neural reward-punishment processing using functional magnetic resonance imaging. N-PRS was computed based on the individual's genotype information and a genome-wide association study on the UK Biobank data. While individuals performed a monetary incentive delay task, their neural activations for upcoming incentives (reward: gain, punishment: loss) were measured in blood oxygen level dependent (BOLD) signals during the delay phase. Multivariate ANCOVAs were used to analyze BOLD signals for finding the association between N-PRS and reward-punishment processing by the incentive valence (Related research article: H. Park, K.L. Forthman, R. Kuplicki, T.A. Victor, Tulsa 1000 Investigators, H.W. Yeh, W.K. Thompson, M.P. Paulus, Polygenic risk for neuroticism modulates response to gains and losses in the amygdala and caudate: evidence from a clinical cohort. J. Affect. Disord. 293 (2021) 124-132. https://doi.org/10.1016/j.jad.2021.06.016). These data can be used as reference data for future studies examining the role of the genetic propensity for personality traits in the context of psychiatric disorders.
| S-EPMC8924281 | biostudies-literature
Project description:BackgroundThis study explores the association between chronotypes and adolescent health risk behaviors (HRBs) by testing how genetic background moderates these associations and clarifies the influence of chronotypes and polygenic risk score (PRS) on adolescent HRBs.MethodsUsing VOS-viewer software to select the corresponding data, this study used knowledge domain mapping to identify and develop the research direction with respect to adolescent risk factor type. Next, DNA samples from 264 students were collected for low-depth whole-genome sequencing. The sequencing detected HRB risk loci, 49 single nucleotide polymorphisms based to significant SNP. Subsequently, PRSs were assessed and divided into low, moderate, and high genetic risk according to the tertiles and chronotypes and interaction models were constructed to evaluate the association of interaction effect and clustering of adolescent HRBs. The chronotypes and the association between CLOCK-PRS and HRBs were examined to explore the association between chronotypes and mental health and circadian CLOCK-PRS and HRBs.ResultsFour prominent areas were displayed by clustering information fields in network and density visualization modes in VOS-viewer. The total score of evening chronotypes correlated with high-level clustering of HRBs in adolescents, co-occurrence, and mental health, and the difference was statistically significant. After controlling covariates, the results remained consistent. Three-way interactions between chronotype, age, and mental health were observed, and the differences were statistically significant. CLOCK-PRS was constructed to identify genetic susceptibility to the clustering of HRBs. The interaction of evening chronotypes and high genetic risk CLOCK-PRS was positively correlated with high-level clustering of HRBs and HRB co-occurrence in adolescents, and the difference was statistically significant. The interaction between the sub-dimensions of evening chronotypes and the high genetic CLOCK-PRS risk correlated with the outcome of the clustering of HRBs and HRB co-occurrence.ConclusionsThe interaction of PRS and chronotype and the HRBs in adolescents appear to have an association, and the three-way interaction between the CLOCK-PRS, chronotype, and mental health plays important roles for HRBs in adolescents.
| S-EPMC10731716 | biostudies-literature
Project description:Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.
| S-EPMC7722089 | biostudies-literature
Project description:PurposePolygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples.MethodsWe calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in self-reported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC; N = 759-11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966; N = 3263-4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC.ResultsThe PRS accounted from 0.07% to 1.44% of the variation (R) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%-28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001).ConclusionsPA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS.
| S-EPMC7292502 | biostudies-literature