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Dataset analysis was performed using AltAnalyze [ 21].
Dataset analysis was performed on the Yale High Performance Computing Cluster.
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Since GSEA does not allow the analysis of multiple datasets, analysis was performed pair wise comparing each treatment with the control.
Using the entire dataset, PCA analysis was performed using the R-package "prcomp".
For the dbGaP CAMP dataset, association analysis was performed using the transmission disequilibrium test (TDT) described by Spielman and Ewens [15].
For each dataset, the analysis was performed as follows (summarized in Figure S1): we have calculated a percentrank (pr) for each probe in each sample and each replicate.
After removal of patients with incomplete datasets, statistical analysis was performed.
To overcome the shortcomings of such partial datasets, bioinformatics analysis was performed to bridge the gap between differential gene expression and assumed changes in protein abundance, and presumed autoantigenicity.
In order to evaluate the value of adding cows to the reference dataset, an additional analysis was performed using a CH reference dataset from which 50% of the cows were deleted.
In order to investigate the low coverage of CpG sites in the WGS dataset, read depth analysis was performed on both the targeted control and WGS datasets.
According to the results of this whole dataset, the LD analysis was performed assuming the panel to be structured in two subgroups.
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