Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A-C Covariance.
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SUBMITTER: Dolan CV
PROVIDER: S-EPMC8093156 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis figure based on UK Biobank data for asthma shows that the proposed method is applicable to GWAS.
| S-EPMC9318573 | 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: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
Project description:Genome-wide association studies (GWASs) have identified at least 10 single-nucleotide polymorphisms (SNPs) associated with papillary thyroid cancer (PTC) risk. Most of these SNPs are common variants with small to moderate effect sizes. Here we assessed the combined genetic effects of these variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models in three PTC study groups from Ohio (1,544 patients and 1,593 controls), Iceland (723 patients and 129,556 controls), and the United Kingdom (534 patients and 407,945 controls). A PRS based on the 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors model, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P ? 1.0 × 10-9). Adding an extended PRS based on 592,475 common variants did not significantly improve the prediction power compared with the 10-SNP model, suggesting that most of the remaining undiscovered genetic risk in thyroid cancer is due to rare, moderate- to high-penetrance variants rather than to common low-penetrance variants. Based on the 10-SNP PRS, individuals in the top decile group of PRSs have a close to sevenfold greater risk (95% CI, 5.4-8.8) compared with the bottom decile group. In conclusion, PRSs based on a small number of common germline variants emphasize the importance of heritable low-penetrance markers in PTC.
| S-EPMC7084156 | biostudies-literature
Project description:BACKGROUND:Obesity susceptibility genes are highly expressed in the brain suggesting that they might exert their influence on body weight through eating-related behaviors. OBJECTIVES:To examine whether the genetic susceptibility to obesity is mediated by eating behavior patterns. METHODS:Participants were 3977 twins (33% monozygotic, 56% females), aged 31-37 y, from wave 5 of the FinnTwin16 study. They self-reported their height and weight, eating behaviors (15 items), diet quality, and self-measured their waist circumference (WC). For 1055 twins with genome-wide data, we constructed a polygenic risk score for BMI (PRSBMI) using almost 1 million single nucleotide polymorphisms. We used principal component analyses to identify eating behavior patterns, twin modeling to decompose correlations into genetic and environmental components, and structural equation modeling to test mediation models between the PRSBMI, eating behavior patterns, and obesity measures. RESULTS:We identified 4 moderately heritable (h2 = 36-48%) eating behavior patterns labeled "snacking," "infrequent and unhealthy eating," "avoidant eating," and "emotional and external eating." The highest phenotypic correlation with obesity measures was found for the snacking behavior pattern (r = 0.35 for BMI and r = 0.32 for WC; P < 0.001 for both), largely due to genetic factors in common (bivariate h2 > 70%). The snacking behavior pattern partially mediated the association between the PRSBMI and obesity measures (?indirect = 0.06; 95% CI: 0.02, 0.09; P = 0.002 for BMI; and ?indirect = 0.05; 95% CI: 0.02, 0.08; P = 0.003 for WC). CONCLUSIONS:Eating behavior patterns share a common genetic liability with obesity measures and are moderately heritable. Genetic susceptibility to obesity can be partly mediated by an eating pattern characterized by frequent snacking. Obesity prevention efforts might therefore benefit from focusing on eating behavior change, particularly in genetically susceptible individuals.
| S-EPMC7528566 | biostudies-literature
Project description:The etiology of major depressive disorder (MDD) is likely to be heterogeneous, but postpartum depression (PPD) is hypothesized to represent a more homogenous subset of MDD. We use genome-wide SNP data to explore this hypothesis. We assembled a total cohort of 1,420 self-report cases of PPD and 9,473 controls with genome-wide genotypes from Australia, The Netherlands, Sweden and the UK. We estimated the total variance attributable to genotyped variants. We used association results from the Psychiatric Genomics Consortia (PGC) of bipolar disorder (BPD) and MDD to create polygenic scores in PPD and related MDD data sets to estimate the genetic overlap between the disorders. We estimated that the percentage of variance on the liability scale explained by common genetic variants to be 0.22 with a standard error of 0.12, p?=?0.02. The proportion of variance (R (2)) from a logistic regression of PPD case/control status in all four cohorts on a SNP profile score weighted by PGC-BPD association results was small (0.1 %) but significant (p?=?0.004) indicating a genetic overlap between BPD and PPD. The results were highly significant in the Australian and Dutch cohorts (R (2)?>?1.1 %, p?<?0.008), where the majority of cases met criteria for MDD. The genetic overlap between BPD and MDD was not significant in larger Australian and Dutch MDD case/control cohorts after excluding PPD cases (R (2)?=?0.06 %, p?=?0.08), despite the larger MDD group affording more power. Our results suggest an empirical genetic evidence for a more important shared genetic etiology between BPD and PPD than between BPD and MDD.
| S-EPMC4341990 | biostudies-literature
Project description:Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.
| S-EPMC8445431 | biostudies-literature
Project description:Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p = 0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p = 0.0099; PROCESSING SPEED, p = 0.0006; WORKING MEMORY, p = 0.0023; and REASONING, p = 0.0015). Class II had modest reduction of positive symptoms (p = 0.0492) and better PROCESSING SPEED (p = 0.0071). Class IV had a specific reduction of negative symptoms (p = 0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.
| S-EPMC7396092 | biostudies-literature
Project description:ObjectiveTo investigate the association between a genetic risk score (GRS) and familial late-onset Alzheimer disease (LOAD) and its predictive value in families multiply affected by the disease.MethodsUsing data from the National Institute on Aging Genetics Initiative for Late-Onset Alzheimer Disease (National Institute on Aging-Late-Onset Alzheimer's Disease Family Study), mixed regression models tested the association of familial LOAD with a GRS based on single nucleotide polymorphisms (SNPs) previously associated with LOAD. We modeled associations using unweighted and weighted scores with estimates derived from the literature. In secondary models, we adjusted subsequent models for presence of the APOE ε4 allele and further tested the interaction between APOE ε4 and the GRS. We constructed a similar GRS in a cohort of Caribbean Hispanic families multiply affected by LOAD by selecting the SNP with the strongest p value within the same regions.ResultsIn the NIA-LOAD families, the GRS was significantly associated with LOAD (odds ratio [OR] 1.29; 95% confidence interval 1.21-1.37). The results did not change after adjusting for APOE ε4. In Caribbean Hispanic families, the GRS also significantly predicted LOAD (OR 1.73; 1.57-1.93). Higher scores were associated with lower age at onset in both cohorts.ConclusionsHigh GRS increases the risk of familial LOAD and lowers the age at onset, regardless of ethnic group.
| S-EPMC5373783 | biostudies-literature
Project description:The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.
| S-EPMC7431089 | biostudies-literature