Dataset Open Access

The MWAS and mQTL data

Xin Sheng1,2, Chengxiang Qiu1,2, Hongbo Liu1,2, Caroline Gluck1,2, Jesse Y. Hsu3,4, Jiang He5, Chi-yuan Hsu6, Daohang Sha4, Matthew R. Weir7, Tamara Isakova8,9, Dominic Raj10, Hernan Rincon-Choles11, Harold I. Feldman1,3,4, Raymond Townsend1, Hongzhe Li3,4, Katalin Susztak1,2


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    "description": "<p>This dataset contains the MWAS and mQTL data of paper entitled &quot;<strong>Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease&quot; (</strong>www.pnas.org/cgi/doi/10.1073/pnas.2005905117<strong>)</strong></p>\n\n<p>&quot;<a href=\"https://zenodo.org/api/files/084761c9-5726-4af1-b7f0-5c1852e094c8/FinalA1cSum.txt\">FinalA1cSum.txt</a>&quot; contains&nbsp;the MWAS results for CpG sites that associated with&nbsp;HgbA1c</p>\n\n<p>&quot;<a href=\"https://zenodo.org/api/files/084761c9-5726-4af1-b7f0-5c1852e094c8/FinalAlbuSum.txt\">FinalAlbuSum.txt</a>&quot; contains&nbsp;the MWAS results for CpG sites that associated with&nbsp;albuminuria</p>\n\n<p>&quot;<a href=\"https://zenodo.org/api/files/084761c9-5726-4af1-b7f0-5c1852e094c8/FinaleGFRSum.txt\">FinaleGFRSum.txt</a>&quot;&nbsp; contains&nbsp;the MWAS results for CpG sites that associated with eGFR (kidney function)</p>\n\n<p>&quot;<a href=\"https://zenodo.org/api/files/084761c9-5726-4af1-b7f0-5c1852e094c8/FinalSlpSum.txt\">FinalSlpSum.txt</a>&quot;&nbsp;contains&nbsp;the MWAS results for CpG sites that associated with eGFR slope (kidney function decline)</p>\n\n<p>&quot;<a href=\"https://zenodo.org/api/files/084761c9-5726-4af1-b7f0-5c1852e094c8/mqtlFinalSum.txt.gz\">mqtlFinalSum.txt.gz</a>&quot; contains mQTL data in 473 blood samples of CRIC cohort.</p>", 
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    "title": "The MWAS and mQTL data", 
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    "version": "version 1.0", 
    "keywords": [
      "Methylation quantitative trait loci (mQTL), multi-trait co-localization analysis (moloc), Mendelian Randomization, multi-omics integration analysis, chronic kidney disease, causal genes, inflammation, epigenetics, genetics"
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    "publication_date": "2020-10-28", 
    "creators": [
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        "orcid": "0000-0002-3620-3577", 
        "affiliation": "1Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA 2Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA 3Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 4Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 5Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Tulane University Translational Science Institute, New Orleans, LA, USA 6Division of Nephrology, Department of Medicine, University of California, San Francisco, CA, USA 7Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA 8Division of Nephrology and Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 9Center for Translational Metabolism and Health, Institute of Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 10Division of Kidney Disease and Hypertension, George Washington University School of Medicine, Washington DC, USA 11Department of Nephrology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA", 
        "name": "Xin Sheng1,2, Chengxiang Qiu1,2, Hongbo Liu1,2, Caroline Gluck1,2, Jesse Y. Hsu3,4, Jiang He5, Chi-yuan Hsu6, Daohang Sha4, Matthew R. Weir7, Tamara Isakova8,9, Dominic Raj10, Hernan Rincon-Choles11, Harold I. Feldman1,3,4, Raymond Townsend1, Hongzhe Li3,4, Katalin Susztak1,2"
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