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Dataset . 2018
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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
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Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"

Authors: Pablo Cordero; Victoria Parikh; MIchael Morley; Thomas Cappola; Euan Ashley; Kenneth Margulies;

Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"

Abstract

These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively. Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors. Four files are provided, two for each cohort (cases and controls): - peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001 - peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test. The column names are those from QTLtools, in order: 1. The phenotype ID 2. The chromosome ID of the phenotype 3. The start position of the phenotype 4. The end position of the phenotype 5. The strand orientation of the phenotype 6. The total number of variants tested in cis 7. The distance between the phenotype and the tested variant (accounting for strand orientation) 8. The ID of the tested variant ( in Affy 6.0 SNP ids) 9. The chromosome ID of the variant 10. The start position of the variant 11. The end position of the variant 12. The nominal P-value of association between the variant and the phenotype 13. The corresponding regression slope 14. A binary flag equal to 1 is the variant is the top variant in cis

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Keywords

eqtl, heart failure

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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