DOI: 10.5281/zenodo.6960
title: "Interactive" Version Of Data Associated With The Elife Paper  "Integrative Genomic Analysis Of The Human Immune Response To Influenza Vaccination"

description: This archive contains an "interactive" version of data associated with the eLife paper

"Integrative genomic analysis of the human immune response to influenza vaccination"

by

Luis M Franco, Kristine L Bucasas, Janet M Wells, Diane Niño, Xueqing Wang, Gladys E Zapata, Nancy Arden, Alexander Renwick, Peng Yu, John M Quarles, Molly S Bray, Robert B Couch, John W Belmont, Chad A Shaw

http://dx.doi.org/10.7554/eLife.00299 (doi:10.7554/eLife.00299)

Installation:

(1) Download the rar file, link available from the paper.


(2) Unrar the file:

Mac OS X: Use UnRarX - http://www.unrarx.com
Linux : unrar command -  http://en.wikipedia.org/wiki/Unrar


(3) Open the file "vaxgenomics.htm" in your favorite browser use File&gt;Open :

Once opened in your browser, you should see a "Circos"-style circular plot of the data, and links to the tables and figures used in this application.

Each table includes a link-out from GeneID to the Entrez entry for that Gene as well as additional links to the data. The link to Entrez requires access to NCBI, so will only work if you have Internet availability for this to work.  All other links point to content contained within the application/archive.


(4) Figures:

High-resolution copies of the figures included in the paper.

Fig 1: eQTL profile of flu vaccination response.    Markers associated with 
cis gene expression identified in the discovery cohort were replicated in a 
vaidation cohort and -log10 p-values for both data sets are shown in the 
genome wide circularized graphic.

Fig 2:  Effect of the treatment on eQTL assocation.  We observed that the pattern of association between gene expression and SNP changed over time after vaccination.  Panel A of this figure shows this phenomena for a single gene NECAB2.  Panel B shows the aggregate character of this phenomena across large numbers of markers.  The change in R2 compared to the initial time point is depicted; this change in R squared appears to correspond to an increase the magnitude of the slope (additive association with genotype).

Fig 3: Pathways and processes identified as enriched in our candidates.  Both
Ingenuity IPA Analysis and GO and KEGG pathway databases were used.  Heavily 
implicated immunologic response classes are repsresented.

Fig 4: Human immune response as measured by our Antibody Response scores are
correlated with gene expression changes, and these patterns are recapituoated
in discovery and validation (male/female) cohorts.

Fig 5: Immune cellular context of the genes identifeies in our eQTL and immune
response analysis.     A striking number of our validated genes occuue in the
antigen processing and presentatio pnthway.

Fig 6: Q-Q plot depicting that the strength of association between genotype and phenotype (titer response) is stronger for markers that have a SNP association with expression and where expression is associated with titer response than would be expected for random SNPs.

Fig 7: Causal and Reactive Model Analyses.    Three-way association between
genotype, expression and trait.  Our data are more consistent with a causal relationship compared to reactive, but the results are not definitive.  We explored the sample size necessary to investigate this in the supplement.   

Fig 8: Diagram demonstrating the experimental design of this study.

Fig 9: Population structure analysis performed on our cohort using the genome 
wide SNP data confirms the European ancestry and ethnic homogeoneity of our
ty sample

Fig 10: Schematic of the eQTL analysis.    The time course of gene expression
change is integrated with a single model considering effects of Day, Genotype,
Day-Genotype interaction and random effects for each individual to account for
the longitudinal nature of the design.

email: cashaw@bcm.edu with questions or comments.