يعرض 1 - 20 نتائج من 93 نتيجة بحث عن '"genotype–phenotype associations"', وقت الاستعلام: 0.67s تنقيح النتائج
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    المساهمون: Universidad Complutense de Madrid, Gobierno de Aragón, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), CSIC - Instituto de Recursos Naturales y Agrobiología de Salamanca (IRNASA), Junta de Castilla y León, Gazol Burgos, Antonio, Camarero, Jesús Julio, González de Andrés, Ester, Colangelo, Michele, Valeriano, Cristina

    وصف الملف: application/pdf

    Relation: #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/AEI//RTI2018–096884-B-C31; Publisher's version; The underlying dataset has been published as supplementary material of the article in the publisher platform at https://doi.org/10.1016/j.scitotenv.2022.159778; https://doi.org/10.1016/j.scitotenv.2022.159778; Sí; Science of the Total Environment 858(Part 2): 159778 (2023); http://hdl.handle.net/10261/345131

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    المساهمون: Icahn School of Medicine at Mount Sinai New York (MSSM), Neuroscience Paris Seine (NPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Stanford University, Rush University Medical Center Chicago, University of Texas Southwestern Medical Center Dallas, Boston Children's Hospital, Harvard Medical School Boston (HMS), National Institute of Mental Health (NIMH), National Institutes of Health Bethesda, MD, USA (NIH), National Institute of Neurological Disorders and Stroke (U54 NS092090, R01NS105845) and the Intramural Research Program of the NIMH (1ZICMH002961)

    المصدر: ISSN: 0964-6906.

    Relation: info:eu-repo/semantics/altIdentifier/pmid/34559195; PUBMED: 34559195; PUBMEDCENTRAL: PMC8863417

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    وصف الملف: application/pdf

    Relation: Zigarelli, Angela M.; Venera, Hanna M.; Receveur, Brody A.; Wolf, Jack M.; Westra, Jason; Tintle, Nathan L. (2023). "Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks." Annals of Human Genetics 87(3): 125-136.; https://hdl.handle.net/2027.42/176253; Annals of Human Genetics; Tintle, N. L., Pottala, J. V., Lacey, S., Ramachandrane, V., Westra, J., Rogers, A., Clark, J., Olthoff, B., Larson, M., Harris, W., & Sheareri, G. C. ( 2015 ). A genome-wide association study of saturated, mono- and polyunsaturated red blood cell fatty acids in the framingham heart offspring study. Prostaglandins, Leukotrienes and Essential Fatty Acids, 94, 65 – 72.; Cichonska, A., Rousu, J., Marttinen, P., Kangas, A., Soininen, P., Lehtimäki, T., Raitakari, O. T., Järvelin, M. R., Salomaa, V., Ala-Korpela, M., Ripatti, S., & Pirinen, M. ( 2016 ). metacca: Sum- mary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, 32 ( 13 ), 1981 – 1989.; Dutta, D., Scott, L., Boehnke, M., & Lee, S. ( 2019 ). Multi-skat: General framework to test for rare-variant association with multiple phenotypes. Genetic Epidemiology, 43 ( 1 ), 4 – 23.; Gasdaska, A., Friend, D., Chen, R., Westra, J., Zawitowski, M., Lindsey, W., & Tintle, N. ( 2019 ). Leveraging summary statistics to make inferences about complex phenotypes in large biobanks. Pacific Symposium on Biocomputing, 24, 391 – 402.; Heatherly, R. ( 2016 ). Privacy and security within biobanking: The role of information technology. Journal of Law, Medicine Ethics, 44 ( 1 ), 156 – 160.; Huppertz, B., & Holzinger, A. ( 2014 ). Biobanks – a source of large biological data sets: Open problems and future challenges. In A. Holzinger, & I. Jurisica (Eds.), Interactive knowledge discovery and data mining in biomedical informatics. Springer.; Kalsbeek, A., Veenstra, J., Westra, J., Disselkoen, C., Koch, K., McKenzie, K. A., O’Bott, J., Vander Woude, J., Fischer, K., Shearer, G. C., Harris, W. S., & Tintle, N. L. ( 2018 ). A genome-wide asso- ciation study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham heart study offspring cohort. PLoS ONE, 13 ( 4 ), e0194882.; Kim, J., Bai, Y., & Pan, W. ( 2015 ). An adaptive association test for multiple phenotypes with gwas summary statistics. Genetic Epidemiology, 39 ( 8 ), 651 – 663.; Lee, S., Emond, M., Bamshad, M., Barnes, K., Rieder, M., & Nickerson, D. ( 2012 ). Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. American Journal of Human Genetics, 91 ( 2 ), 224 – 237.; Lee, S., Teslovich, T. M., Boehnke, M., & Lin, X. ( 2013 ). General framework for meta-analysis of rare variants in sequencing association studies. American Journal of Human Genetics, 93 ( 1 ), 42 – 53.; Li, B., & SM, L. ( 2008 ). Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data. American Journal of Human Genetics, 83 ( 3 ), 311 – 321.; Liu, Z., & Lin, X. ( 2018 ). Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics, 74 ( 1 ), 165 – 175.; Neale, B. M. ( 2018 ). Biobank gwas. Retrieved from http://www.nealelab.is/uk-biobank/; NLM. (n.d.). Dbgene. https://www.ncbi.nlm.nih.gov/gene; Pheweb. ( 2018 ). Retrieved from https://pheweb.sph.umich.edu/; Ray, D., & Boehnke, M. ( 2018 ). Methods for meta-analysis of multiple traits using gwas summary statistics. Genetic Epidemiology, 42 ( 2 ), 134 – 145.; Stephens, M. ( 2013 ). A unified framework for association analysis with multiple related phenotypes. PLoS ONE, 14 ( 3 ), e0213951.; Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. ( 2015 ). Uk biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12 ( 3 ), e1001779.; Svishcheva, G. R., Belonogova, N. M., Zorkoltseva, I. V., Kirichenko, A. V., & Axenovich, T. I ( 2019 ). Gene-based association tests using gwas summary statistics. Bioinformatics, 35 ( 19 ), 3701 – 3708.; van der Sluis, S., Posthuma, D., & Dolan, C. ( 2013 ). Tates: Efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLOS Genetics, 9, e1003235.; Veenstra, J., Kalsbeek, A., Westra, J., Disselkoen, C., Smith, C. E., & Tintle, N. ( 2017 ). Genome-wide interaction study of omega-3 pufas and other fatty acids on inflammatory biomarkers of cardiovascular health in the framingham heart study. Nutrients, 9 ( 8 ), 900.; Vuckovic, D., Gasparini, P., Soranzo, N., & Iotchkova, V. ( 2015 ). Multimeta: An r package for meta-analyzing multi-phenotype genome-wide association studies. Bioinformatics, 31 ( 16 ), 2754 – 2756.; Wolf, J., Barnard, M., Xia, X., Ryder, N., Westra, J., & Tintle, N. ( 2020 ). Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks. Pacific Symposium on Biocomputing, 25, 719 – 730.; Wolf, J., Westra, J., & Tintle, N. ( 2021 ). Using summary statistics to model multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates. Frontiers in Genetics, 12, https://doi.org/10.3389/fgene.2021.74590; Wu, M., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. ( 2011 ). Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics, 89 ( 1 ), 82 – 93.; Zhu, X., Feng, T., Tayo, B., Liang, J., Young, J., Franceschini, N., Smith, J. A., Yanek, L. R., Sun, Y. V., Edwards, T. L., Chen, W., Nalls, M., Fox, E., Sale, M., Bottinger, E., Rotimi, C., Liu, Y., McKnight, B., Liu, K., … Redline, S., COGENT BP Consortium. ( 2015 ). Meta-analysis of correlated traits via summary statistics from gwass with an application in hypertension. American Journal of Human Genetics, 96 ( 1 ), 21 – 36.; Canela-Xandri, O., Rawlik, K., & Tenesa, A. ( 2018 ). An atlas of genetic associations in UK biobank. Nature Genetics, 50, 1593 – 1599.

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