Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Division of Pediatric Gastroenterology, Hepatology, and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
HGG Adv. 2024 Oct 10;5(4):100351. doi: 10.1016/j.xhgg.2024.100351. Epub 2024 Sep 6.
Haploinsufficiency of JAG1 is the primary cause of Alagille syndrome (ALGS), a rare, multisystem disorder. The identification of JAG1 intronic variants outside of the canonical splice region as well as missense variants, both of which lead to uncertain associations with disease, confuses diagnostics. Strategies to determine whether these variants affect splicing include the study of patient RNA or minigene constructs, which are not always available or can be laborious to design, as well as the utilization of computational splice prediction tools. These tools, including SpliceAI and Pangolin, use algorithms to calculate the probability that a variant results in a splice alteration, expressed as a Δ score, with higher Δ scores (>0.2 on a 0-1 scale) positively correlated with aberrant splicing. We studied the consequence of 10 putative splice variants in ALGS patient samples through RNA analysis and compared this to SpliceAI and Pangolin predictions. We identified eight variants with aberrant splicing, seven of which had not been previously validated. Combining these data with non-canonical and missense splice variants reported in the literature, we identified a predictive threshold for SpliceAI and Pangolin with high sensitivity (Δ score >0.6). Moreover, we showed reduced specificity for variants with low Δ scores (<0.2), highlighting a limitation of these tools that results in the misidentification of true splice variants. These results improve genomic diagnostics for ALGS by confirming splice effects for seven variants and suggest that the integration of splice prediction tools with RNA analysis is important to ensure accurate clinical variant classifications.
JAG1 杂合性缺失是 Alagille 综合征（ALGS）的主要原因，ALGS 是一种罕见的多系统疾病。除了经典剪接区域之外，JAG1 内含子变异体和错义变异体也被认为与疾病有不确定的关联，这使得诊断变得复杂。确定这些变异体是否影响剪接的策略包括研究患者的 RNA 或小基因构建体，但这些方法并不总是可用，或者设计起来可能很繁琐，同时也可以利用计算剪接预测工具。这些工具，包括 SpliceAI 和 Pangolin，使用算法计算变异体导致剪接改变的概率，用 Δ 分数表示，较高的 Δ 分数（0-1 尺度上的>0.2）与异常剪接呈正相关。我们通过 RNA 分析研究了 10 种 ALGS 患者样本中的假定剪接变异体的后果，并将其与 SpliceAI 和 Pangolin 的预测结果进行了比较。我们确定了 8 种具有异常剪接的变异体，其中 7 种以前没有经过验证。将这些数据与文献中报道的非经典和错义剪接变异体结合起来，我们确定了 SpliceAI 和 Pangolin 的高灵敏度预测阈值（Δ 分数>0.6）。此外，我们发现低 Δ 分数（<0.2）的变异体特异性降低，这突出了这些工具的一个局限性，导致真正的剪接变异体被错误识别。这些结果通过确认 7 种变异体的剪接效应，改善了 ALGS 的基因组诊断，并表明将剪接预测工具与 RNA 分析相结合对于确保准确的临床变异分类很重要。
