Transcript expression-aware annotation improves rare variant interpretation

Abstract

The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the ‘proportion expressed across transcripts’, which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

Mutations in normal tissues—some diagnostic and clinical implications

“…By comparing the expected and actual frequency of pLoF variants, the authors were able to classify the genes based on pLoF tolerance (from no effect to lethal). Another paper in the collection examined differences in tolerance of genes to pLoF, demonstrating that many of these variants appear in exons which minimize their effect [ 68 ]. The other papers discussed the potential utility of the gnomAD database in drug discovery [ 69 ] and conducted a large-scale analysis of structural variants in protein coding and non-coding regions [ 70 ].…”

VIP-HL: Semi-automated ACMG/AMP variant interpretation platform for genetic hearing loss

“…33 Recently, the transcript-level information from the GTEx project 32 was utilized and proved that incorporating exon expression data can improve interpretations of putative loss-of-function variants. 40 The second and third variants (NM_206933.3:c.949C>A and NM_022124.6:c.7362G>A) were synonymous variants, and their splicing impact should be curated from public literature if available. Nevertheless, these results indicated the importance of expression data in variant interpretation.…”

A molecular taxonomy of tumors independent of tissue-of-origin

“…For this study we assume a significant fraction of these mutations are generically disruptive to normal pathway activity since it is impossible to know the tumor promoting effects of all mutations, including rarely studied genes. Second, we know that low-expressed genes and non-expressed genes accumulate mutations at a higher rate due to transcription coupled repair (Kandoth et al 2013;Kim and Kim 2018;Aitken et al 2020;Cummings et al 2020). To address this issue, we identified genes with low expression in each type of cancer and eliminated them for that cancer type only (see methods for details).…”