March 7, 2022 Dataset Open Access

Moreno, Victor; Diez-Obrero, Virginia; Diaz-Villanueva, Anna; Sanz-Pamplona, Rebeca

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  <identifier identifierType="DOI">10.5281/zenodo.6334768</identifier>
  <creators>
    <creator>
      <creatorName>Moreno, Victor</creatorName>
      <givenName>Victor</givenName>
      <familyName>Moreno</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2818-5487</nameIdentifier>
      <affiliation>Catalan Institute of Oncology, IDIBELL, UB, CIBERESP</affiliation>
    </creator>
    <creator>
      <creatorName>Diez-Obrero, Virginia</creatorName>
      <givenName>Virginia</givenName>
      <familyName>Diez-Obrero</familyName>
      <affiliation>IDIBELL, CIBERESP</affiliation>
    </creator>
    <creator>
      <creatorName>Diaz-Villanueva, Anna</creatorName>
      <givenName>Anna</givenName>
      <familyName>Diaz-Villanueva</familyName>
      <affiliation>IDIBELL, CIBERESP</affiliation>
    </creator>
    <creator>
      <creatorName>Sanz-Pamplona, Rebeca</creatorName>
      <givenName>Rebeca</givenName>
      <familyName>Sanz-Pamplona</familyName>
      <affiliation>Catalan Institute of Oncology, IDIBELL, UB, CIBERESP</affiliation>
    </creator>
  </creators>
  <titles>
    <title>COLONOMICS - predictive models for normal colon gene expression and DNA methylation for TWAS and MWAS</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>predictdb, TWAS, MWAS, gene expression, DNA methylation, microRNAs, SNP prediction models</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-03-07</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6334768</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDerivedFrom" resourceTypeGeneral="Dataset">10.34810/data169</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsDescribedBy" resourceTypeGeneral="Dataset">https://www.colonomics.org</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6334767</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;We provide&amp;nbsp;significant SNP prediction models derived from the COLONOMICS data (&lt;a href="https://www.colonomics.org"&gt;https://www.colonomics.org&lt;/a&gt;). Genotypes were obtained by Affymetrix 6.0 array, imputed to TopMed panel. Gene expression was obtained from Affymetrix U219 array, DNA methylation was obtained with Illuminan 450K array and miRNA expression was obtained by NGS.&amp;nbsp;We provide SNP prediction models for 1,758 genes, 30,530 CpG probes and 38 miRNAs obtained from colon normal biopsy samples. These features can be predicted from SNPs located within &amp;plusmn;1Mb, which we assumed they act through cis mechanisms. We include&amp;nbsp;the model&amp;rsquo;s summary statistics and corresponding SNP weights in SQLite objects. Models were trained using the elastic net procedure employed in the PredictDB pipeline (&lt;a href="https://predictdb.org/"&gt;https://predictdb.org&lt;/a&gt;), according to which only models with a predictive performance p-value &amp;lt; 0.05 and R&lt;sup&gt;2&lt;/sup&gt; &amp;gt; 0.1 are considered significant. We adjusted the models by basic covariates, i.e., sex, age, tissue type and colon anatomic location where biopsies were collected (left and right colon). Genome coordinates refer to GRCh37/hg19.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100011102</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/FP7/223378/">223378</awardNumber>
      <awardTitle>Development of High Performance Diagnostic Array Replication Technology</awardTitle>
    </fundingReference>
  </fundingReferences>
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