Systematic identification of disease-causing promoter and untranslated region variants in 8,040 undiagnosed individuals with rare disease

Abstract

Background: Both promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown.

Methods: We present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8,040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7,862 unrelated probands against matched unaffected controls.

Results: We prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individual's phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations.

Conclusions: Overall, we demonstrate the value of screening promoters and UTRs to uncover additional diagnoses for previously undiagnosed individuals with rare disease and provide a framework for doing so without dramatically increasing interpretation burden.

Keywords: Untranslated regions; non-coding; promoters; rare disease; regulatory regions; splicing.

Figure 1:. Prioritised de novo variants split by region and variant annotations.

DNVs were identified from the Genomics England de novo dataset in the following regions: Promoter (mustard), UTR exons (raspberry), UTR/Promoter overlapping region (mustard and raspberry stripes), and UTR introns (teal). The gene names corresponding to identified DNVs are written above the corresponding bar. Those in black represent likely diagnoses (nine probands), with those in grey not being a good phenotypic match (two probands). Novel potential diagnoses are marked by an asterisk. Vertical bars in the top panel denote the number of variants identified with specific region and variant annotations that are represented by the bar colour (region annotations), and in the upset plot below (variant annotations). The total number of DNVs with each variant annotation is shown by the horizontal bars to the left of the upset.

Figure 2:. Candidate diagnostic de novo variants.

A. Gene diagram showing the creation of an out of frame overlapping ORF (oORF; in red) in the SLC2A1 gene in the proband. B. Illustration of the AG exclusion zone in the NIPBL gene. The T>A variant at the −17 position is marked in red, the most strongly predicted branch point (Branchpointer(62) 0.48), directly upstream of the AG exclusion zone is shown in blue. C. Multidimensional scaling plot showing differential methylation in SETD5. The position of both variants found in this gene are shown as red dotted lines. D. Sashimi plot showing aberrant splicing in the MANE Plus clinical transcript ENST00000371085. The proband shows some retention of the intron containing the variant (which is marked by a red dotted line) and increased skipping of the following exon compared to the controls (6.06X% vs 0.65X% and 1X%).

Figure 3:. Burden testing results.

Counts of variants and odd ratios (log10) testing for an enrichment of variants in cases compared to matched control participants (Fisher’s test), collectively by A. region annotation, and B. variant annotation. Annotation groups with fewer than 10 participants are omitted. Error bars represent 95% confidence intervals. Variants in 5’UTRs (P=0.032) and variants with SpliceAI ≥0.5 (P=0.008) are enriched in cases over matched controls, but neither is significant after correcting for multiple testing (Bonferroni threshold adjusting for 16 tests =0.0031). Full results are in Supplementary Table 5.