Data Supplement to "Clinical Evidence Supports a Protective Role for CXCL5 in Coronary Artery Disease"
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Schisler, Jonathan. Data Supplement to "clinical Evidence Supports a Protective Role for Cxcl5 In Coronary Artery Disease". 2017. https://doi.org/10.17615/C66P42APA
Schisler, J. (2017). Data Supplement to "Clinical Evidence Supports a Protective Role for CXCL5 in Coronary Artery Disease". https://doi.org/10.17615/C66P42Chicago
Schisler, Jonathan. 2017. Data Supplement to "clinical Evidence Supports a Protective Role for Cxcl5 In Coronary Artery Disease". https://doi.org/10.17615/C66P42- Creator
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Schisler, Jonathan
- ORCID: http://orcid.org/0000-0001-7382-2783
- Affiliation: School of Medicine, UNC McAllister Heart Institute
- Abstract
- This dataset consists of an excel file containing two data tables. Table S4: Correlation of microarray-derived gene expression levels to either circulating CXCL5 or CAD severity. The Agilent probe ID (Probeset ID) and gene information are provided as well as the correlation coefficient (rs), the absolute value of rs (|rs|), and the p value for both CXCL5 and CAD variables for 143 total study participants Table S9: cis-eQTL analysis on PMBMCs of SAMARA2 subjects. The top 10,000 cis-eQTLs are provided. The dbSNP identifier (dbSNP RS ID) is provided indicated for each SNP as well as the gene, the beta coefficient, t-stat, p, and false discovery rate (FDR) from the eQTL analysis. The Affymetrix SNP Array 6.0 probe identifier (Affy6 SNP), position, and strand identifier relative to dbSNP is indicated. The Agilent probe ID (Probeset ID) used for gene expression values and the indication of CXCL5 or CAD correlation with gene expression are included in addition to the location information of the cRNA probe.
- Methodology
- Gene chip analyses. RNA labeling, hybridization, and data extraction were performed in our laboratory. RNA was co-hybridized to Agilent G4112F Whole Human Genome 4x44K oligonucleotide arrays in the presence of Cyanine-3 labeled Universal Human Reference RNA (UHRR, Stratagene, LaJolla, CA) for array normalization, as described (Charles et al., 2008). Slides were hybridized and washed, then scanned on an Axon 4000b microarray scanner; data were processed using Agilent software. Probes were filtered by only including probes flagged as “detected” in at least 85% of all SAMARA phase 2 samples, resulting in 18,411 probes available for analysis. In addition, samples were filtered by only including RNA samples with at least 90% of the 18,411 probes flagged as “detected”. Missing data were imputed using the k-nearest neighbor algorithm (k=10). Datasets were deposited in the Gene Expression Omnibus of the National Center for Biotechnology Information (GEO Series accession number GSE90074) containing the 143 samples used in the biomarker analyses. The processing date (batch effect) and gender were controlled for using an analysis of variance (ANOVA) linear model using Partek Genomics Suite (v6.6, Partek Incorporated). Probes were updated using the annotations updated by Agilent on June 12, 2015. We used BioMart ID conversion for all significant probes that contained missing associated gene name annotations. Spearman rank correlation was utilized to generate a list of genes that correlated with the CAD score (0-4) or plasma levels of CXCL5 with a predefined cutoff of | rs | ≥0.25 using Partek Genomics Suite (v6.6) as described (Sinnaeve et al., 2009). SAMARA phase 1 microarray data was downloaded from GEO (accession number GSE12959), previously described (Schisler et al., 2009). DNA labeling, hybridization, and data extraction were performed by the DNA Array Core Facility at The Scripps Research Institute (Jupiter, FL). The Genome-Wide Human SNP Array 6.0 (Affymetrix®) was used for hybridizations. Of the 143 subjects with gene expression and biomarker analyses, 126 were analyzed via SNP array. Subsequently, 20 of the 126 arrays had “no call” rates > 20% and were dropped from further analysis, leaving 106 samples with 909,624 probes. Datasets were deposited in the Gene Expression Omnibus of the National Center for Biotechnology Information (GEO Series accession number GSE90073) containing 106 of the 143 samples used in the biomarker and RNA analyses. SNP probes were filtered to include only those with minor allele frequencies > 0.05 and Hardy-Weinberg equilibrium > 0.01 leaving 398,997 available for analysis. Principal components analysis was used to classify subjects as either Americans with African or European ancestry and missing genotypes were imputed using MaCH (v1.0.6) (Li et al., 2010) with estimated mismatch rates in the Markov model of 0.1256 and 0.00481 in each group, respectively. Expression quantitative trait loci analysis. Identification of local elements associated with expression (eQTLs) was performed with Matrix eQTL (v2.1.0) (Shabalin, 2012) in the software package R (v3.2.1). We used the linear additive model to identify cis-acting eQTLs (distance between SNP and expression probe < 1e6 base pairs) accounting for ancestry covariance and population structure. This resulted in testing 4,600,673 SNP-gene pairs in the 106 subjects with both DNA and RNA analyses. The DNA and RNA datasets are collated as a SuperSeries in the Gene Expression Omnibus of the National Center for Biotechnology Information (GEO Series accession number GSE90076). 1. Charles PC, Alder BD, Hilliard EG, Schisler JC, Lineberger RE, Parker JS, Mapara S, Wu SS, Portbury A, Patterson C, Stouffer GA: Tobacco use induces anti-apoptotic, proliferative patterns of gene expression in circulating leukocytes of Caucasian males. BMC Med Genomics 2008, 1:38. 2. Sinnaeve PR, Donahue MP, Grass P, Seo D, Vonderscher J, Chibout S-D, Kraus WE, Sketch M, Nelson C, Ginsburg GS, Goldschmidt-Clermont PJ, Granger CB: Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PloS One 2009, 4:e7037. 3. Schisler JC, Charles PC, Parker JS, Hilliard EG, Mapara S, Meredith D, Lineberger RE, Wu SS, Alder BD, Stouffer GA, Patterson C: Stable patterns of gene expression regulating carbohydrate metabolism determined by geographic ancestry. PloS One 2009, 4:e8183. 4. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes. Genet Epidemiol 2010, 34:816–834. 5. Shabalin AA: Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 2012, 28:1353–1358.
- Date of publication
- 2017
- Keyword
- DOI
- Kind of data
- Numeric
- Resource type
- Dataset
- License
- CC0 1.0 Universal
- Funder
- The University of North Carolina at Chapel Hill
- Le Fondation Leducq
- Language
- English
- Date uploaded
- June 14, 2017
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Table S4 and S9.xlsx | 2019-04-30 | Public | Download |