GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Material
Publisher
Person/Organisation
Language
Years
FID
Subjects(RVK)
  • 1
    Online Resource
    Online Resource
    De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population
    Proceedings of the National Academy of Sciences ; 2020
    In:  Proceedings of the National Academy of Sciences Vol. 117, No. 5 ( 2020-02-04), p. 2560-2569
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 117, No. 5 ( 2020-02-04), p. 2560-2569
    Abstract: De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains 〈 1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability ( h 2 ), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1902766117
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2020
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations
    American Medical Association (AMA) ; 2023
    In:  JAMA Network Open Vol. 6, No. 2 ( 2023-02-21), p. e230191-
    Details
    In: JAMA Network Open, American Medical Association (AMA), Vol. 6, No. 2 ( 2023-02-21), p. e230191-
    Abstract: Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies. Objective To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations. Design, Setting, and Participants This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022. Main Outcomes and Measures Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants. Results Through training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022. Conclusions and Relevance In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.
    Type of Medium: Online Resource
    ISSN: 2574-3805
    URL: Article
    DOI: 10.1001/jamanetworkopen.2023.0191
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
    detail.hit.zdb_id: 2931249-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Landscapes of binding antibody and T-cell responses to pox-protein HIV vaccines in Thais and South Africans
    Public Library of Science (PLoS) ; 2020
    In:  PLOS ONE Vol. 15, No. 1 ( 2020-1-30), p. e0226803-
    Details
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 15, No. 1 ( 2020-1-30), p. e0226803-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.1371/journal.pone.0226803
    DOI: 10.1371/journal.pone.0226803.g001
    DOI: 10.1371/journal.pone.0226803.g002
    DOI: 10.1371/journal.pone.0226803.g003
    DOI: 10.1371/journal.pone.0226803.g004
    DOI: 10.1371/journal.pone.0226803.g005
    DOI: 10.1371/journal.pone.0226803.g006
    DOI: 10.1371/journal.pone.0226803.g007
    DOI: 10.1371/journal.pone.0226803.g008
    DOI: 10.1371/journal.pone.0226803.t001
    DOI: 10.1371/journal.pone.0226803.t002
    DOI: 10.1371/journal.pone.0226803.s001
    DOI: 10.1371/journal.pone.0226803.s002
    DOI: 10.1371/journal.pone.0226803.s003
    DOI: 10.1371/journal.pone.0226803.s004
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2020
    detail.hit.zdb_id: 2267670-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Next-Generation Sequencing Reveals That HLA-DRB3 , -DRB4 , and -DRB5 May Be Associated With Islet Autoantibodies and Risk for Childhood Type 1 Diabetes
    American Diabetes Association ; 2016
    In:  Diabetes Vol. 65, No. 3 ( 2016-03-01), p. 710-718
    Details
    In: Diabetes, American Diabetes Association, Vol. 65, No. 3 ( 2016-03-01), p. 710-718
    Abstract: The possible contribution of HLA-DRB3, -DRB4, and -DRB5 alleles to type 1 diabetes risk and to insulin autoantibody (IAA), GAD65 (GAD autoantibody [GADA]), IA-2 antigen (IA-2A), or ZnT8 against either of the three amino acid variants R, W, or Q at position 325 (ZnT8RA, ZnT8WA, and ZnT8QA, respectively) at clinical diagnosis is unclear. Next-generation sequencing (NGS) was used to determine all DRB alleles in consecutively diagnosed patients ages 1–18 years with islet autoantibody–positive type 1 diabetes (n = 970) and control subjects (n = 448). DRB3, DRB4, or DRB5 alleles were tested for an association with the risk of DRB1 for autoantibodies, type 1 diabetes, or both. The association between type 1 diabetes and DRB1*03:01:01 was affected by DRB3*01:01:02 and DRB3*02:02:01. These DRB3 alleles were associated positively with GADA but negatively with ZnT8WA, IA-2A, and IAA. The negative association between type 1 diabetes and DRB1*13:01:01 was affected by DRB3*01:01:02 to increase the risk and by DRB3*02:02:01 to maintain a negative association. DRB4*01:03:01 was strongly associated with type 1 diabetes (P = 10−36), yet its association was extensively affected by DRB1 alleles from protective (DRB1*04:03:01) to high (DRB1*04:01:01) risk, but its association with DRB1*04:05:01 decreased the risk. HLA-DRB3, -DRB4, and -DRB5 affect type 1 diabetes risk and islet autoantibodies. HLA typing with NGS should prove useful to select participants for prevention or intervention trials.
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    URL: Article
    DOI: 10.2337/db15-1115
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2016
    detail.hit.zdb_id: 1501252-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Tumor Evolution and Intratumor Heterogeneity of an Oropharyngeal Squamous Cell Carcinoma Revealed by Whole-Genome Sequencing
    Elsevier BV ; 2013
    In:  Neoplasia Vol. 15, No. 12 ( 2013-12), p. 1371-IN7
    Details
    In: Neoplasia, Elsevier BV, Vol. 15, No. 12 ( 2013-12), p. 1371-IN7
    Type of Medium: Online Resource
    ISSN: 1476-5586
    URL: Article
    DOI: 10.1593/neo.131400
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2013
    detail.hit.zdb_id: 2008231-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    An Object‐Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes
    Wiley ; 2016
    In:  Genetic Epidemiology Vol. 40, No. 4 ( 2016-05), p. 315-332
    Details
    In: Genetic Epidemiology, Wiley, Vol. 40, No. 4 ( 2016-05), p. 315-332
    Abstract: Recent genome‐wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA‐based disease predictive model. However, conventional model‐building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as “objects” or “exemplars,” one focuses on systemic associations of disease phenotype with “objects” via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease‐associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity‐based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease‐associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA‐DRB1, HLA‐DRB3, HLA‐DRB4, HLA‐DRB5, HLA‐DQA1, HLA‐DQB1, HLA‐DPA1, and HLA‐DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.
    Type of Medium: Online Resource
    ISSN: 0741-0395 , 1098-2272
    URL: Issue
    URL: Article
    DOI: 10.1002/gepi.2016.40.issue-4
    DOI: 10.1002/gepi.21968
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 1492643-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes
    Wiley ; 2017
    In:  Diabetes/Metabolism Research and Reviews Vol. 33, No. 8 ( 2017-11)
    Details
    In: Diabetes/Metabolism Research and Reviews, Wiley, Vol. 33, No. 8 ( 2017-11)
    Abstract: It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high‐risk subjects into longitudinal studies of effective prevention strategies. Methods Utilizing a case‐control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object‐oriented regression to build and validate a prediction model for T1D. Results In the training set, estimated risk scores were significantly different between patients and controls ( P  = 8.12 × 10 −92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a “biological validation” by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA‐2A (Z‐score = 3.628, P   〈  0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high‐risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Conclusion Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high‐risk subjects for prevention research in high‐risk populations.
    Type of Medium: Online Resource
    ISSN: 1520-7552 , 1520-7560
    URL: Issue
    URL: Article
    DOI: 10.1002/dmrr.v33.8
    DOI: 10.1002/dmrr.2921
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2001565-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    Estimating Regression Parameters in an Extended Proportional Odds Model
    Informa UK Limited ; 2012
    In:  Journal of the American Statistical Association Vol. 107, No. 497 ( 2012-03), p. 318-330
    Details
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 107, No. 497 ( 2012-03), p. 318-330
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    URL: Article
    DOI: 10.1080/01621459.2012.656021
    RVK:
    QA 10000
    RVK:
    SA 2455
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2012
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    Identifying Biomarkers for Acute GVHD.
    American Society of Hematology ; 2006
    In:  Blood Vol. 108, No. 11 ( 2006-11-16), p. 38-38
    Details
    In: Blood, American Society of Hematology, Vol. 108, No. 11 ( 2006-11-16), p. 38-38
    Abstract: Acute graft versus host disease (aGVHD) is a complication of allogeneic hematopoietic cell transplantation (HCT). Current prevention and treatment approaches are effective in many but not all cases. Acute GVHD after a myeloablative HCT has a median onset of ~21 days, with a few cases occurring as late as 3 months post-HCT. We hypothesized that global gene expression profiling of RNA from white blood cells (WBC) could identify biomarkers associated with onset of aGVHD and potentially provide insight into the mechanisms responsible for causing clinically significant aGVHD. Patients were enrolled prospectively and blood samples collected weekly until onset of aGVHD or day 90. RNA samples adequate for hybridization were collected between day 19 and 24 from 23 patients who developed aGVHD (GVHD+) and compared with RNA from 13 patients who remained free of aGVHD through day 90 (GVHD−). All blood samples from GVHD+ patients were obtained prior to initiation of steroid therapy. Biotin-labeled cRNA was hybridized on Affymetrix HG-U133A. Realizing quantitative complexities underlying gene expression assessment and different assumptions required by different algorithms, we used 5 different summary algorithms (Mas5, PLIER, RMA, gcRMA, Dchip) to compute gene expression levels. Subsequently, we carried out analyses and identified genes that were consistently identified in more than one analyses; replications imply robustness of discoveries, even though discovery efficiency is compromised to some degree. A total of 141 genes showing differential expression were discovered. Thirteen functional classes were identified for 101 genes. The remaining 40 genes had unknown function. Sixteen transcription factors were identified, 12 of them showing increased expression such as the early response genes EGR1, EGR2, FOS and JUN. Among 12 immune response genes identified increased expression was observed for CD83 and IL-18, associated with antigen presentation and adaptive immunity; and NRS2, MMP9 and CD157, associated with innate immune response. Decreased expression was observed for CD52 and PTPN11. Among 7cell cycle progression and proliferation genes, we observed increased expression for CSF2, VEGF, PTN and TGFa. These data are consistent with the hypothesis that the development of acute GVHD is associated with acute inflammation. Ongoing validation studies using Taqman have confirmed up regulation of CD83 and EGR2 and down regulation of CD52 in GVHD+ patients. To further understand the functional implications of the 141 genes identified, probe sets were also input into Ingenuity Pathway Analysis software to identify possible signaling and regulatory pathways. The Ingenuity defined pathways most densely populated by the 141 genes included pathways controlling cytokine production and inflammation. Network analysis showed upregulation of transcriptional pathways responsible for cell proliferation, growth and activation: IL18, CSF2, TGFa and the transcription factors EGR1, EGR2, FOS and JUN were linked to networks that appear to have important role in regulation of expression of VEGF suggesting that it may be involved in sustaining inflammatory response in GVHD. This study demonstrates that whole genome transcriptional analysis of WBC RNA can be informative for detecting biomarkers associated with aGVHD. Furthermore these findings suggest that these biomarkers can be used to better define cellular pathways associated with clinical onset and/or severity of acute GVHD and thereby provide potential targets for therapy.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood.V108.11.38.38
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2006
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 10
    Online Resource
    Online Resource
    Empirical evaluations of analytical issues arising from predicting HLA alleles using multiple SNPs
    Springer Science and Business Media LLC ; 2011
    In:  BMC Genetics Vol. 12, No. 1 ( 2011-12)
    Details
    In: BMC Genetics, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2011-12)
    Abstract: Numerous immune-mediated diseases have been associated with the class I and II HLA genes located within the major histocompatibility complex (MHC) consisting of highly polymorphic alleles encoded by the HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1 loci. Genotyping for HLA alleles is complex and relatively expensive. Recent studies have demonstrated the feasibility of predicting HLA alleles, using MHC SNPs inside and outside of HLA that are typically included in SNP arrays and are commonly available in genome-wide association studies (GWAS). We have recently described a novel method that is complementary to the previous methods, for accurately predicting HLA alleles using unphased flanking SNPs genotypes. In this manuscript, we address several practical issues relevant to the application of this methodology. Results Applying this new methodology to three large independent study cohorts, we have evaluated the performance of the predictive models in ethnically diverse populations. Specifically, we have found that utilizing imputed in addition to genotyped SNPs generally yields comparable if not better performance in prediction accuracies. Our evaluation also supports the idea that predictive models trained on one population are transferable to other populations of the same ethnicity. Further, when the training set includes multi-ethnic populations, the resulting models are reliable and perform well for the same subpopulations across all HLA genes. In contrast, the predictive models built from single ethnic populations have superior performance within the same ethnic population, but are not likely to perform well in other ethnic populations. Conclusions The empirical explorations reported here provide further evidence in support of the application of this approach for predicting HLA alleles with GWAS-derived SNP data. Utilizing all available samples, we have built "state of the art" predictive models for HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1. The HLA allele predictive models, along with the program used to carry out the prediction, are available on our website.
    Type of Medium: Online Resource
    ISSN: 1471-2156
    URL: Article
    DOI: 10.1186/1471-2156-12-39
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2011
    detail.hit.zdb_id: 2041497-3
    detail.hit.zdb_id: 3058779-7
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...