Future directions for high‐throughput splicing assays in precision medicine

Abstract

Classification of variants of unknown significance is a challenging technical problem in clinical genetics. As up to one‐third of disease‐causing mutations are thought to affect pre‐mRNA splicing, it is important to accurately classify splicing mutations in patient sequencing data. Several consortia and healthcare systems have conducted large‐scale patient sequencing studies, which discover novel variants faster than they can be classified. Here, we compare the advantages and limitations of several high‐throughput splicing assays aimed at mitigating this bottleneck, and describe a data set of ~5,000 variants that we analyzed using our Massively Parallel Splicing Assay (MaPSy). The Critical Assessment of Genome Interpretation group (CAGI) organized a challenge, in which participants submitted machine learning models to predict the splicing effects of variants in this data set. We discuss the winning submission of the challenge (MMSplice) which outperformed existing software. Finally, we highlight methods to overcome the limitations of MaPSy and similar assays, such as tissue‐specific splicing, the effect of surrounding sequence context, classifying intronic variants, synthesizing large exons, and amplifying complex libraries of minigene species. Further development of these assays will greatly benefit the field of clinical genetics, which lack high‐throughput methods for variant interpretation.

RAD51D Aberrant Splicing in Breast Cancer: Identification of Splicing Regulatory Elements and Minigene-Based Evaluation of 53 DNA Variants

“…Despite the fact that assays using patient-derived RNA are considered the most suitable strategy to evaluate splicing outcomes, minigene assays have proved to be a useful, simple and robust approach to assess potential spliceogenic variants [ 12 , 40 ]. This method presents several significant advantages such as (1) analysis of single allele events without the meddling of the WT allele; (2) accurate quantification of isoforms by inhibiting the nonsense-mediated decay; (3) high versatility, allowing the study of different variants with a single minigene; (4) high reproducibility of physiological and pathological splicing patterns; (5) analysis in a cell type relevant for the disease.…”

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

“…CAGI5 included two full‐scale splicing challenges (Mount et al, ) and these have resulted in five papers from participants (Chen, Lu, Zhao, & Yang, ; Cheng, Çelik, Nguyen, Avsec, & Gagneur, ; Gotea, Margolin, & Elnitski, ; Naito, ; Wang, Wang, & Hu, ). The issue also contains an overview paper from one of the splicing data providers (Rhine et al, ). There had also been only one previous expression regulatory variant challenge, in CAGI4 (Kreimer et al, ).…”

Functional assessment of somatic STK11 variants identified in primary human non-small cell lung cancers

“…Splice-site variants are often assumed to disrupt mRNA production, but there are many reports demonstrating this is not necessarily true ( 29–32 ). Despite recent improvements to in silico splice-site predictive tools ( 35 , 36 ), reliably assessing the functional impact of splice-site variants still requires direct assessment of mRNA. We designed an approach to evaluate STK11 mRNA in tumors harboring clinically identified STK11 splice-site variants.…”