The potential correlation between rs12442183 genotype and RGMA expression was tested with publicly available microarray data. First, R package ESLiMc (Exon Splicing by Linear Modeling Analysis core) (14) was used to analyze alternative expression of RGMA transcripts among 11 different healthy human tissues, including breast, cerebellum, heart, kidney, liver, muscle, pancreas, prostate, spleen, testes, and thyroid, data from which were obtained from www.affymetrix.com and included in ESLiMc analysis by its author (14). The rationale for using ESLiMc was that if there are no alternative splicing events, an increase in the global expression of a gene should correlate with a higher expression of all of its exons. Linear regression was used by ESLiMc to establish the above relationship between each exon and its corresponding gene. The difference (referred to as gene-exon residual regression score) generated by ESLiMc between the observed exon expression and the predicted exon expression was interpreted as being due to an alternative splicing (14). Next, we tested the correlation between rs12442183 genotype and the expression of RGMA transcript variants. We downloaded rs12442183 genotype information from BRAINEAC (15) and
