Clinical variant classification of pathogenic versus benign genetic variants
remains a challenge in clinical genetics. Recently, the proposition of genomic
foundation models has improved the generic variant effect prediction (VEP)
accuracy via weakly-supervised or unsupervised training. However, these VEPs
are not disease-specific, limiting their adaptation at the point of care. To
address this problem, we propose DYNA: Disease-specificity fine-tuning via a
Siamese neural network broadly applicable to all genomic foundation models for
more effective variant effect predictions in disease-specific contexts. We
evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus
on various cardiovascular diseases, where gene-disease relationships of
loss-of-function vs. gain-of-function dictate disease-specific VEP. For
non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory
axis of RNA splicing, the most common non-coding pathogenic mechanism in
established clinical VEP guidelines. In both cases, DYNA fine-tunes various
pre-trained genomic foundation models on small, rare variant sets. The DYNA
fine-tuned models show superior performance in the held-out rare variant
testing set and are further replicated in large, clinically-relevant variant
annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant
effect prediction method, excelling in intra-gene generalization and
generalization to unseen genetic variants, making it particularly valuable for
disease associations and clinical applicability.

通过提出 DYNA 方法，通过 Siamese 神经网络针对所有基因组学基础模型进行疾病特异性微调，从而提高了变异效应预测的准确性，这对疾病关联和临床适用性具有重要价值。

DYNA：用于变异致病性的疾病特定语言模型

DYNA: Disease-Specific Language Model for Variant Pathogenicity

Clinical variant classification of pathogenic versus benign genetic variants
remains a pivotal challenge in clinical genetics. Recently, the proposition of
protein language models has improved the generic variant effect prediction
(VEP) accuracy via weakly-supervised or unsupervised training. However, these
VEPs are not disease-specific, limiting their adaptation at point-of-care. To
address this problem, we propose a disease-specific \textsc{pro}tein language
model for variant \textsc{path}ogenicity, termed ProPath, to capture the
pseudo-log-likelihood ratio in rare missense variants through a siamese
network. We evaluate the performance of ProPath against pre-trained language
models, using clinical variant sets in inherited cardiomyopathies and
arrhythmias that were not seen during training. Our results demonstrate that
ProPath surpasses the pre-trained ESM1b with an over $5\%$ improvement in AUC
across both datasets. Furthermore, our model achieved the highest performances
across all baselines for both datasets. Thus, our ProPath offers a potent
disease-specific variant effect prediction, particularly valuable for disease
associations and clinical applicability.

我们提出了一种疾病特异性的蛋白质语言模型 ProPath，通过孪生网络捕捉罕见错义变异的伪对数似然比，从而解决了临床基因变异的分类问题，并在家族性心肌病和心律失常的临床变异集中取得了最好的性能表现。