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  • 1
    In: Journal of the Endocrine Society, The Endocrine Society, Vol. 5, No. Supplement_1 ( 2021-05-03), p. A656-A657
    Abstract: Background: Gestational diabetes (GDM) has profound effects on the intrauterine metabolic milieu, induces marked abnormalities in fetal glucose and insulin secretion and is linked to obesity and diabetes in the offspring, but the mechanisms remain largely unknown. Epigenetic modifications in stems cells may be one mechanism by which an in utero exposure can lead to the development of diabetes and obesity later in life. Objective: To identify novel pathways contributing to the development of diabetes and obesity in offspring exposed to GDM in utero by integrating data generated from transcriptome and methylome analysis from second trimester human amniocytes exposed to GDM in utero. Methods: We analyzed RNAseq and genome wide DNA methylation data (ERRBS) generated from second trimester amniocytes obtained from women with GDM (n=14). Amniocytes have stem cells-like characteristics and are derived from the fetus. Expression data of 22,271 genes were retrieved from RNAseq data. CpGs with significant changes in DNA methylation were mapped into 20,028 genes by collapsing methylation probes into promoter and gene regions. To better understand the associations among diverse gene sets or among gene sets and GDM,we first constructed two weighted co-expression networks from RNAseq and DNA methylation data, respectively. Then, two co-expression networks were integrated using a linear combination. With the integrated co-expression network, graph-based label propagation algorithm was employed to prioritize GDM-associated genes. Results: From the differential expression analysis between GDM and control, the top 20 query genes, including 11 genes and 9 methylated genes, were selected for label propagation. Finally, the top 100 genes were picked up for the pathway enrichment testusing an over-representation analysis approach. Significantly enriched pathways included: Interferon Signaling, N-glycan Antennae Elongation, Sphingolipid Pathway and Metabolism, Classical Complement Pathway, Complement and Coagulation Cascades, Tryptophan Metabolism, Peroxisomal Protein Import, Unsaturated Fatty Acid Metabolism, Complement Activation, Human Innate Immune Response, Ceramide Metabolism, Fertilization Pathway, Keratan Sulfate Biosynthesis Pathway, Transport to the Golgi and Modification Pathway (FDR q & lt;0.05 for all pathways). Conclusion: Using a novel bioinformatic approach to synthesize transcriptome and methylome data derived from human amniocytes exposed to GDM in utero, we identified potential pathways involved in programming of diabetes and obesity in offspring including pathways involving the immune response, complex lipid metabolism, the complement pathway, and protein transport and processing. Further investigation of these pathways may yield new mechanisms contributing to diabetes and obesity.
    Type of Medium: Online Resource
    ISSN: 2472-1972
    Language: English
    Publisher: The Endocrine Society
    Publication Date: 2021
    detail.hit.zdb_id: 2881023-5
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  • 2
    In: Alzheimer's & Dementia, Wiley, Vol. 19, No. S1 ( 2023-06)
    Abstract: Numerous GWAS studies of Alzheimer’s disease (AD) have identified over 70 AD risk variants using SNP‐based genotyping or sequencing data. Recently, a new whole‐genome tiling (WGT) representation of whole‐genome sequencing (WGS) data has been proposed to enable an innovative definition of an individual’s genome; this WGT representation can support supervised and unsupervised machine learning. In this study, we perform a new AD GWAS study on the WGT representation of the ADNI WGS data. Methods The detailed description, genome tiling pipeline, and a publicly available example of WGT data are available at: https://curii.co/su92l‐j7d0g‐swtofxa2rct8495 . In our analysis, we first performed quality control, imputation, and one‐hot encoding of tile variants (Fig. 1). Then, for each genome tile, we used the likelihood ratio test to compare two logistic regression models to get a single p‐value, where a full model used the tile variants and covariates to predict disease status, and a null model used only covariates including age, sex, education, APOE4, and first 20 PCs. Participants included 1,504 subjects (1,032 cases and 472 controls). In comparison, set‐based GWAS analysis was performed using PLINK 1.9 on ADNI SNP‐based WGS data. Results 8,560,743 tiles passed the QC process and were included in our analysis. The likelihood ratio test yielded 35,582 significant tiles with Bonferroni correction. A set‐based GWAS comparative study among all significant tiles using SNP‐based WGS data identified 1,535 sets with at least one significant SNP variant. Among 1,535 sets, 1,066 sets passed uncorrected p≤0.05; 115 sets passed p≤0.005; and 15 sets passed p≤0.0005 (Fig. 2). Conclusions Our initial investigation of the tiling data shows that the WGT representation has promising power for identifying significant tiles that cannot be detected using the SNP representation. Complementary to the genotype values examined in traditional SNP analysis, the WGT analysis focuses on examining the haplotype variants within each tile and can capture the interaction pattern among SNPs within the haplotype. This initial AD GWAS study on WGT data demonstrates the promise of the tile representation for revealing novel genetic risk and protective factors in AD.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2201940-6
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  • 3
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 30, No. 9 ( 2021-09-01), p. 1681-1688
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 30, No. 9 ( 2021-09-01), p. 1681-1688
    Abstract: Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer. Methods: We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case–control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings. Results: We identified 133 significant pathway–cancer pairs (85 replicated) and 90 significant gene–cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P & lt; 0.05). Conclusions: In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers. Impact: This work adds to the knowledge base and progress being made in precision medicine.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 2036781-8
    detail.hit.zdb_id: 1153420-5
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  • 4
    In: American Journal of Obstetrics and Gynecology, Elsevier BV, Vol. 229, No. 3 ( 2023-09), p. 298.e1-298.e19
    Type of Medium: Online Resource
    ISSN: 0002-9378
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2003357-6
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Journal of Personalized Medicine Vol. 11, No. 12 ( 2021-12-17), p. 1382-
    In: Journal of Personalized Medicine, MDPI AG, Vol. 11, No. 12 ( 2021-12-17), p. 1382-
    Abstract: Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications. Methods: We built a disease–disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge. Results: A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662248-8
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  • 6
    In: BMC Medical Genomics, Springer Science and Business Media LLC, Vol. 10, No. S1 ( 2017-5)
    Type of Medium: Online Resource
    ISSN: 1755-8794
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2411865-5
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  • 7
    In: BMC Medical Genomics, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2019-12)
    Type of Medium: Online Resource
    ISSN: 1755-8794
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2411865-5
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  International Journal of Obesity Vol. 46, No. 9 ( 2022-09), p. 1686-1693
    In: International Journal of Obesity, Springer Science and Business Media LLC, Vol. 46, No. 9 ( 2022-09), p. 1686-1693
    Type of Medium: Online Resource
    ISSN: 0307-0565 , 1476-5497
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2101927-7
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  • 9
    In: Cancers, MDPI AG, Vol. 13, No. 4 ( 2021-02-11), p. 751-
    Abstract: Background inflammatory status indicators have been reported as prognostic biomarkers of colorectal cancer (CRC). However, since inflammatory interactions with the colon involve various modes of action, the biological mechanism linking inflammation and CRC prognosis has not been fully elucidated. We comprehensively evaluated the predictive roles of the expression and methylation levels of inflammation-related genes for CRC prognosis and their pathophysiological associations. Method. An integrative analysis of 247 patients with stage I-III CRC from The Cancer Genome Atlas was conducted. Lasso-penalized Cox proportional hazards regression (Lasso-Cox) and statistical Cox proportional hazard regression (CPH) were used for the analysis. Results. Models to predict overall survival were designed with respective combinations of clinical variables, including age, sex, stage, gene expression, and methylation. An integrative model combining expression, methylation, and clinical features performed better (median C-index = 0.756) than the model with clinical features alone (median C-index = 0.726). Based on multivariate CPH with features from the best model, the methylation levels of CEP250, RAB21, and TNPO3 were significantly associated with overall survival. They did not share any biological process in functional networks. The 5-year survival rate was 29.8% in the low methylation group of CEP250 and 79.1% in the high methylation group (p 〈 0.001). Conclusion. Our study results implicate the importance of integrating expression and methylation information along with clinical information in the prediction of survival. CEP250, RAB21, and TNPO3 in the prediction model might have a crucial role in CRC prognosis and further improve our understanding of potential mechanisms linking inflammatory reactions and CRC progression.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 10
    In: Cancers, MDPI AG, Vol. 13, No. 8 ( 2021-04-14), p. 1864-
    Abstract: Urothelial carcinoma of the bladder (UC) is the fifth most common cancer in the United States. Germline variants, especially rare germline variants, may account for a portion of the disparity seen among patients in terms of UC incidence, presentation, and outcomes. The objectives of this study were to identify rare germline variant associations in UC incidence and to determine its association with clinical outcomes. Using exome sequencing data from the DiscovEHR UC cohort (n = 446), a European-ancestry, North American population, the complex influence of germline variants on known clinical phenotypes were analyzed using dispersion and burden metrics with regression tests. Outcomes measured were derived from the electronic health record (EHR) and included UC incidence, age at diagnosis, and overall survival (OS). Consequently, key rare variant association genes were implicated in MR1 and ADGRL2. The Kaplan–Meier survival analysis reveals that individuals with MR1 germline variants had significantly worse OS than those without any (log-rank p-value = 3.46 × 10−7). Those with ADGRL2 variants were found to be slightly more likely to have UC compared to a matched control cohort (FDR q-value = 0.116). These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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