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  • 1
    In: Cancer Communications, Wiley, Vol. 41, No. 12 ( 2021-12), p. 1387-1397
    Abstract: DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method. Methods Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single‐nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred‐funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls. Results Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history. Conclusions We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.
    Type of Medium: Online Resource
    ISSN: 2523-3548 , 2523-3548
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2922913-3
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2021
    In:  Alzheimer's & Dementia: Translational Research & Clinical Interventions Vol. 7, No. 1 ( 2021-01)
    In: Alzheimer's & Dementia: Translational Research & Clinical Interventions, Wiley, Vol. 7, No. 1 ( 2021-01)
    Abstract: Several Mendelian randomization studies have been conducted that identified multiple risk factors for Alzheimer's disease (AD). However, they typically focus on a few pre‐selected risk factors. Methods A two‐sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1037 risk factors/medical conditions and 31 drugs with the risk of late‐onset AD. To correct for multiple comparisons, the false discovery rate was set at  〈  0.05. Results There was strong evidence of a causal association between glioma risk, reduced trunk fat‐free mass, lower education levels, lower intelligence and a higher risk of AD. For 31 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations. Discussion MR found robust evidence of causal associations between glioma, trunk fat‐free, and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.
    Type of Medium: Online Resource
    ISSN: 2352-8737 , 2352-8737
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2832891-7
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  • 3
    Online Resource
    Online Resource
    Mary Ann Liebert Inc ; 2023
    In:  OMICS: A Journal of Integrative Biology Vol. 27, No. 8 ( 2023-08-01), p. 372-380
    In: OMICS: A Journal of Integrative Biology, Mary Ann Liebert Inc, Vol. 27, No. 8 ( 2023-08-01), p. 372-380
    Type of Medium: Online Resource
    ISSN: 1557-8100
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2023
    detail.hit.zdb_id: 2030312-9
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  BMC Medicine Vol. 20, No. 1 ( 2022-11-07)
    In: BMC Medicine, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2022-11-07)
    Abstract: The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations.  Methods An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case–control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. Results In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634–0.646), 0.718 (95% CI, 0.713–0.723), and 0.753 (95% CI, 0.748–0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and − 0.023 (95% CI, − 0.025 to − 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category. Conclusions Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.
    Type of Medium: Online Resource
    ISSN: 1741-7015
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2131669-7
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  • 5
    In: Carcinogenesis, Oxford University Press (OUP), ( 2023-09-28)
    Abstract: A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO, and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4, and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate (FDR) ≤ 0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.
    Type of Medium: Online Resource
    ISSN: 0143-3334 , 1460-2180
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1474206-8
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  • 6
    In: Human Molecular Genetics, Oxford University Press (OUP), ( 2023-08-25)
    Abstract: Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 14 ( 2021-08-04), p. 1933-1940
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 14 ( 2021-08-04), p. 1933-1940
    Abstract: Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer’s disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e. DNA methylation) and functional regulatory information (i.e. enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD. Results We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer–target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71 880 cases and 383 378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods. Availabilityand implementation The data used in this work were obtained from the following publicly available datasets: IGAP1, GWAX, UK Biobank, a 2019 meta-analyzed AD GWAS results and a imaging-derived phenotype GWAS results. The data resources are summarized in Supplementary Table S7. We used the publicly available software and tools for competing methods. All codes used to generate results that are reported in this manuscript and software for our newly proposed method CMO are available at https://github.com/ChongWuLab/CMO. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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