RefFree-Phaser: a Reference-Free Transformer-Based Framework for Genotype Phasing

Accurate phasing of genomic sequences is necessary for knowing genetic variation and its role in human genomics. Formal phasing methods, such as Mendel Impute, Eagle, and Beagle, require large reference panels, which curtail scalability and limit applicability to undersampled populations. To this, we propose RefFree-Phaser, a reference-free deep learning model for genotype phasing assembled on a BigBird transformer architecture. The BigBird model architecture, founded on sparse attention, is suitable for genomic sequencing as it is computationally less expensive than full-attention-based transformers while modeling long-range dependencies. The RefFree-Phaser’s framework operates with positional embeddings and tokenizes input sequences, processes them through a 12-layer BigBird transformer with sparse attention, and derives contextualized hidden states that are mapped into final hidden state, logits, confidence scores, and binary haplotype predictions aligned with unphased genotypes and true labels. We adopted a lexicographical haplotype-pairing strategy, in which all possible haplotypes were sorted and systematically tagged according to their input genotypes. We evaluated RefFree-Phaser across two cohorts, one from the 1000 Genomes Project subset and the other from Omni2.5 M common-variants dataset. Experiments across diverse populations showed strong performance for European (EUR) and other superpopulations, while accuracy was slightly lower for African (AFR) individuals, indicating higher phase-switch, genetic diversity, and limited representation in training data. RefFree-Phaser attains average accuracies of 92.60