A Systems Biology approach to determine cell-specific gene regulatory effects of genetic associations in multiple sclerosis
Madireddy, Lohith; Kim, Kicheol; Baranzini, Sergio (2019), A Systems Biology approach to determine cell-specific gene regulatory effects of genetic associations in multiple sclerosis, v3, UC San Francisco, Dataset, https://doi.org/10.7272/Q6HQ3X3M
We have conducted a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS); which analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our approach makes extensive use of gene regulatory data generated by the ENCODE and REP projects to build data-driven models of the predicted regulatory effects (PRE) of each associated variant and their flanking correlated variation over a wide range of linkage disequilibrium thresholds.
The detailed mapping of regulatory information for each SNP suggests that if PRE are computed for a given cell type in a single individual based on the carriage of relevant risk alleles, these values should capture a non-negligible proportion of the variance in gene expression in that cell type. To test this hypothesis, we interrogated the expression of the entire transcriptome of FACS-sorted CD4+ T cells, and CD14+ monocytes from 25 MS patients by RNAseq and then assessed the correlation of their genotype dependent PRE and their actual gene expression in each cell type separately. Our results showed that the correlation observed was in all cases significantly higher than what would be expected by chance if these metrics were independent. Furthermore, the computed correlations were always higher for the matching cell type (CD4/CD8 expression with T cells PRE and CD14 expression with monocytes PRE). The average correlation between RNA expression and PRE within the same cell type was 0.331 (CD4 vs. T cells, p<10-300), 0.324 (CD8 vs. T cells, p<10-300), and 0.246 (CD14 vs. monocytes, p<10-300), representing a significantly higher than expected value for each cell type. Correlations between PRE and RNA expression of mismatched cell types were significantly lower. These results suggest that the computation of PRE can be applied to single patients and individual scores can be generated for each of them.
3’mRNA-Seq library for each cell subsets (CD4+, CD8+, and CD14+ cells) was prepared from 100 ng total RNA using QuantSeq kit (Lexogen) according to the manufacturer’s instructions and sequenced 50-bp single-end on the HiSeq 4000 (Illumina).