分子生物学
IVD分子诊断
细胞培养与分析
蛋白研究
细胞因子
重组蛋白
抗体
高通量测序建库
病原检测UCF系列
生物医药
工具酶
抑制剂激活剂与常用试剂
仪器
耗材

When splicing is not all or none: GT>GC 5′ splice-site variants as a model for intermediate effects and challenges in variant classification

Jin-Huan Lin, Hao Wu, Xin-Ying Tang, Emmanuelle Masson, Peter D. Stenson, Andrew D. Phillips, David N. Cooper, Claude Férec, Zhuan Liao, Wen-Bin Zou, Jian-Min Chen

Journal:Human Genetics and Genomics Advances

IF:3.1

DOI:10.1016/j.xhgg.2026.100602

PMID:41928472

Published:2026-04-01

research field:分子生物学生物信息学RNA剪接遗传学基因组医学

Abstract

Variants with intermediate functional effects—neither fully disruptive nor functionally neutral—represent an underrecognized source of genetic complexity and define a functional grey zone that complicates variant classification. Here, we address this issue using GT>GC (+2T>C) 5′ splice-site variants as a tractable model, as approximately 15–18% of such substitutions retain variable levels of residual wild-type (WT) transcript. Using residual WT transcript as a quantitative functional readout, we first revisited disease-associated GT>GC variants previously shown to retain substantial WT transcript, including SPINK1 c.194+2T>C, HBB c.315+2T>C, and BRCA2 c.8331+2T>C, illustrating how intermediate splicing effects complicate clinical interpretation across distinct genes and disease contexts. We then performed a locus-wide assessment of all 26 theoretically possible GT>GC substitutions in CFTR , integrating SpliceAI delta donor-loss scores with classifications from expert-curated databases. Minigene splicing analyses of four selected CFTR variants, together with full-length and minigene analyses of a BAP1 GT>GC variant with conflicting clinical interpretations, revealed heterogeneous and context-dependent splicing outcomes, underscoring both inter-assay variability and the inherent limitations of commonly used splicing assay systems. Collectively, our findings indicate that GT>GC variants capable of generating appreciable residual WT transcript exemplify a broader class of intermediate-effect alleles that expose the limitations of both computational prediction and experimental assessment. These observations highlight the need for classification frameworks that incorporate quantitative functional data and better capture the continuum of variant effects.

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