Trio RNA sequencing in a cohort of medically complex children

Abstract

Genome sequencing (GS) is a powerful test for the diagnosis of rare genetic disorders. Although GS can enumerate most non-coding variation, determining which non-coding variants are disease-causing is challenging. RNA sequencing (RNA-seq) has emerged as an important tool to help address this issue, but its diagnostic utility remains understudied, and the added value of a trio design is unknown. We performed GS plus RNA-seq from blood using an automated clinical-grade high-throughput platform on 97 individuals from 39 families where the proband was a child with unexplained medical complexity. RNA-seq was an effective adjunct test when paired with GS. It enabled clarification of putative splice variants in three families, but it did not reveal variants not already identified by GS analysis. Trio RNA-seq decreased the number of candidates requiring manual review when filtering for de novo dominant disease-causing variants, allowing for the exclusion of 16% of gene-expression outliers and 27% of allele-specific-expression outliers. However, clear diagnostic benefit from the trio design was not observed. Blood-based RNA-seq can facilitate genome analysis in children with suspected undiagnosed genetic disease. In contrast to DNA sequencing, the clinical advantages of a trio RNA-seq design may be more limited.

Figure 1

Identification of gene-expression outliers and aberrant junctions in a cohort of medically complex children (A) Summary of gene-expression outliers using different cut-offs. Scatterplot showing number of over-expressed (gray) or under-expressed (purple) outlier genes, as compared to our in-house control cohort, at absolute Z score ≤ 2, 3, or 4 or adjusted p value (adjusting across all genes in all samples; adjusted p values [Padj]) < 0.05. Each dot represents one sample. Violin and boxplots summarize the distribution of values in each group. In all boxplots the middle line is the median, the box edges are the 25^th and 75^th percentiles, and the whiskers represent 1.5× the interquartile range. y axis, numbers of outlier genes (log10 scale). Left panel, all genes; right panel, only genes associated with relevant HPO terms. (B) Summary of aberrant junctions. Scatterplot showing number of reported genes (yellow), genes after additional filters (red), novel junctions (purple), reported outlier junctions (gray), and reported outlier junctions after additional filters (dark gray). Each dot represents one sample. Violin and boxplots summarize the distribution of values in each group. y axis, numbers of aberrant junctions or genes containing aberrant junctions. Left panel, all genes; right panel, only genes associated with relevant HPO terms. (C) Sashimi plot of representative aberrant junctions in CMC 46 revealing a predominance of transcripts that skipped exon 4 in ATP6AP2. The skipping of exon 4 is evident in the proband (red) compared to his mother and to 10 randomly selected controls from the cohort (blue). y axis, number of aligned reads. The number of reads supporting each junction is shown between exons. The minimum number of reads to be drawn was set to 5 for this plot, for better visualization. (D) Expression level of HDAC8 in CMC 6 (red dot) showing decreased expression compared to the rest of the samples in the cohort (gray dots). y axis, log2-normalized counts.

Figure 2

Trio vs. cohort analysis for expression outliers as well as aberrant junctions and allele-specific expression analysis in the cohort (A) Proportions of gene-expression outliers also detected in other samples showing that trio analysis is effective in filtering out statistically significant expression outliers, but not when using a more lenient cut-off (absolute Z score). Each dot represents a proband with family data available. y axis, proportion of gene-expression outlier defined by statistical significance (adjusted p value < 0.05) or Z scores (absolute Z score ≥ 3) that are also detected in family members (orange) or the rest of the cohort (purple). (B) Gene expression correlation is consistently higher between probands and their family members than between probands and the rest of the cohort. y axis, average Pearson correlation of gene expression. Each dot represents a proband. Lines connect the same proband in the two columns. Purple, duos; black, trios. (C) Proportions of total aberrant splicing events also detected in other samples. Each dot represents a proband with family data available. y axis, proportions of genes containing aberrant junctions detected in other family members (orange) or the rest of the cohort (purple). (D) Proportions of aberrant splicing events with at least one rare variant nearby also detected in other samples. Each dot represents a proband with family data available. y axis, proportions of genes containing aberrant junctions detected in other family members (orange) or the rest of the cohort (purple). (E) Bar plot of the number of reported SNVs and ASE SNVs for all the affected individuals. Red, total number of rare SNVs; blue, number of ASE events. (F) Violin plot of the distribution of ASE SNVs after parental filter and HPO term filter.