Many rare genetic variants have unrecognized large-effect disruptions to exon recognition

Abstract

Any individual’s genome contains ∼4-5 million genetic variants that differ from reference, and understanding how these variants give rise to trait diversity and disease susceptibility is a central goal of human genetics1. A vast majority (96-99%) of an individual’s variants are common, though at a population level the overwhelming majority of variants are rare2–5. Because of their scarcity in an individual’s genome, rare variants that play important roles in complex traits are likely to have large functional effects6,7. Mutations that cause an exon to be skipped can have severe functional consequences on gene function, and many known disease-causing mutations reduce or eliminate exon recognition8. Here we explore the extent to which rare genetic variation in humans results in near complete loss of exon recognition. We developed a Multiplexed Functional Assay of Splicing using Sort-seq (MFASS) that allows us to measure exon inclusion in thousands of human exons and surrounding intronic sequence simultaneously. We assayed 27,733 extant variants in the Exome Aggregation Consortium (ExAC)9 within or adjacent to 2,339 human exons, and found that 3.8% (1,050) of the variants, almost all of which were extremely rare, led to large-effect defects in exon recognition. Importantly, we find that 83% of these splice-disrupting variants (SDVs) are located outside of canonical splice sites, are distributed evenly across distinct exonic and intronic regions, and are difficult to predict a priori. Our results indicate that loss of exon recognition is an important and underappreciated means by which rare variants exert large functional effects, and that MFASS enables their empirical assessment for splicing defects at scale.

Using secondary structure to predict the effects of genetic variants on alternative splicing

“…We next compared the performance of PEPSI and PEPSI‐noSS in classifying SDVs among SNPs assayed in the MFASS experiment (Cheung et al, ) using several state‐of‐the‐art tools, including HAL (Rosenberg et al, ), SPANR (Xiong et al, ), and MutPredSplice (Mort et al, ). We first compared the sensitivity and precision of PEPSI and PEPSI‐noSS to SPANR in classifying SDVs from all 2,094 SNPs in the curated test set given that SPANR is capable of scoring both exonic and intronic SNPs.…”

“…To carry out this benchmark analysis, we used splicing functional assay data from the Multiplexed Functional Assay of Splicing using Sort‐seq (MFASS) experiment (Cheung et al, ). The MFASS experiment assayed a total of 27,733 ExAC SNPs within or adjacent to 2,339 exons.…”

“…We next compared the performance of PEPSI and PEPSI-noSS in classifying SDVs among SNPs assayed in the MFASS experiment (Cheung et al, 2019) using several state-of-the-art tools, including HAL (Rosenberg et al, 2015), SPANR (Xiong et al, 2014), and…”

Modular modeling improves the predictions of genetic variant effects on splicing

“…We compared MMSplice with three stateof-the-art splicing variant scoring models: SPANR [17], HAL [18] and MaxEntScan [7] on the held-out Vex-seq test data. The methods HAL [18] and SPANR [17] have been reported to be the two best performed existing methods on a recent large-scale perturbation assay probing 27,733 rare variants [29], while Max-EntScan [7] was considered as a baseline reference model. SPANR scores exonic and intronic SNVs up to 300 nt around splice junctions.…”

“…To further compare models on predicting exon skipping level with independent datasets that no model has been trained on, we used the splicing functional assay from Cheung et al [29]. Cheung et al found 1,050 splicedisrupting variants (SDVs), the majority are extremely rare, after examining 27,733 ExAC single-nucleotide variants (SNV) with Multiplexed Functional Assay of Splicing using Sort-seq (MFASS) ( Fig.…”