  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.
