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For each sample, the number of the FAIRE-seq reads mapped to each region was counted and Selleck BEZ235 the read count was normalized as previously suggested [19,20] to obtain normalized chromatin accessibility, which was then further normalized to zero mean and unit variance across the 96 segregants. Trans-QTL analysis Sixty-three human samples were commonly present in the RNA-seq [5] and DNase-seq [8] data, and 96 yeast segregants in the microarray and FAIRE-seq data. Therefore, we used the common samples for our QTL mapping. Linear regression was carried out leading to 2,100,341 chromatin associations and 975,333 expression associations in human, and 110,802 chromatin linkages and 164,217 expression linkages in yeast at p 17-DMAG (Alvespimycin) HCl selleck chemicals human (Methods and Fig. 1). From the human chromatin accessibility data [8], we selected chromatin sites at the promoter or enhancer (as defined by Ernst et al. [21]). Yeast and human QTL mapping was different in the genetics setting (linkage vs. association) and technical platforms (DNA microarray vs. RNA-seq and FAIRE-seq vs. DNase-seq) (Fig. 1). Fig. 1 Data analysis scheme. Relationships between the genetic regulatory loci and the quantitative traits (chromatin accessibility or gene expression) were explored in yeast and human. Data from different experimental settings and technical platforms were integrated ... We selected significant trans-associations based on the p-value of linear regression and then examined the distribution of linkages between regulatory loci (genetic markers) and quantitative traits (gene expression levels or chromatin accessibility). First, there were particular regulatory loci that were associated with a large number of target chromatin traits but not with gene expression traits both in human and yeast (Fig. 2). Second, expression traits than chromatin traits were associated with a greater number of regulatory loci (Fig. 3). This trend was consistently found when varying p-values were used (Fig. 4).