A Survey of Machine Learning Approaches for Genomic Variant Effect Prediction, with a Proposed Tri-Modal Framework for Variants of Uncertain Significance

Determining whether a genetic variant is harmful is one of the central open problems in clinical genomics. Sequencing throughput has grown far faster than the human expertise available to interpret its output, and the result is a growing backlog of variants that carry no actionable clinical label. Roughly two out of every five variants reported by clinical diagnostic tests in protein-coding regions are recorded as Variants of Uncertain Significance, and only a small fraction of these are ever reclassified. This survey reviews the machine learning methods developed for variant effect prediction over the past decade, organised into six families: alignment and conservation scores, feature-engineered ensemble classifiers, protein language models, DNA language models, graph neural network approaches, and structure- informed or phenotype-conditioned predictors. For each family we examine the representation used, the supervision signal, the treatment of class imbalance, the evaluation protocol, and the computational cost. Three recurring weaknesses emerge. First, most published systems are formulated as binary classifiers over confidently labelled variants and discard the uncertain class entirely, which removes the majority of clinically difficult cases from evaluation. Second, random row- level train and test splitting remains common, permitting gene-level memorisation and inflating reported accuracy. Third, the highest-performing recent systems depend on very large foundation models or on multi-source biomedical knowledge graphs whose compute requirements place them out of reach of ordinary laboratory hardware. We then present GenoHybrid-VEP, a proposed framework designed against these findings. It couples a one-dimensional convolutional encoder over a 101 base-pair genomic context window, a dense encoder over clinical and population biomarkers, and a graph convolutional encoder over variant co-occurrence relations, fusing the three into a single latent representation for three-way classification. Variants of Uncertain Significance are retained as a first-class label, training uses gene-grouped splitting with balanced class weighting, and epistemic uncertainty is quantified through Monte Carlo dropout so that low- confidence predictions can be flagged rather than silently reported. The framework is described here as an architectural proposal; empirical validation is reserved for subsequent work.