Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues

Personal exome and genome sequencing provides access to loss-of-function and rare deleterious alleles whose interpretation is expected to provide insight into individual disease burden. However, for each allele, accurate interpretation of its effect will depend on both its penetrance and the trait's expressivity. In this regard, an important factor that can modify the effect of a pathogenic coding allele is its level of expression; a factor which itself characteristically changes across tissues. To better inform the degree to which pathogenic alleles can be modified by expression level across multiple tissues, we have conducted exome, RNA and deep, targeted allele-specific expression (ASE) sequencing in ten tissues obtained from a single individual. By combining such data, we report the impact of rare and common loss-of-function variants on allelic expression exposing stronger allelic bias for rare stop-gain variants and informing the extent to which rare deleterious coding alleles are consistently expressed across tissues. This study demonstrates the potential importance of transcriptome data to the interpretation of pathogenic protein-coding variants

Identifying RNA editing sites using RNA sequencing data alone

We show that RNA editing sites can be called with high confidence using RNA sequencing data from multiple samples across either individuals or species, without the need for matched genomic DNA sequence. We identified many previously unidentified editing sites in both humans and Drosophila; our results nearly double the known number of human protein recoding events. We also found that human genes harboring conserved editing sites within Alu repeats are enriched for neuronal functions

Accurate identification of human Alu and non-Alu RNA editing sites

We developed a computational framework to robustly identify RNA editing sites using transcriptome and genome deep-sequencing data from the same individual. As compared with previous methods, our approach identified a large number of Alu and non-Alu RNA editing sites with high specificity. We also found that editing of non-Alu sites appears to be dependent on nearby edited Alu sites, possibly through the locally formed double-stranded RNA structure

Correlation of gene expression and allelic ratios across ten somatic tissues.

<p>(<b>A</b>) Shared patterns of gene expression were detected for tissues with shared functional roles or embryonic origins. For example, the small intestine and colon are both digestive system organs derived from the endoderm and have a high degree of pairwise correlation (Spearman Correlation, <i>R</i> = 0.92). Likewise, the frontal lobe and cerebellum, which are both vital tissues nervous system derived from the ectoderm, have a high degree of shared expression (<i>R</i> = 0.91). The hierarchical clustering was generated using pairwise Spearman correlation coefficients of FPKM expression values for all genes. (<b>B</b>) Shared patterns of ASE were detected by mmPCR-Seq. The concordance of ASE between tissues does not as strongly reflect the relationships seen for shared gene expression or shared embryonic origin. The allelic ratio is calculated as the alternate allele reads divided by the total reads. Each data point represents a single heterozygous site tested for ASE with a total read depth greater than 200. The plots are colored by the degree of correlation of allelic bias between the pairwise tissues. These results indicate that relationships of allelic expression across tissues are much more complex than those of total expression level.</p