Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Molecular biology reports
Update of Mexican population databases for complex kinship interpretation with twelve and seven X-STRs
BACKGROUND: X-chromosome short tandem repeats (X-STRs) are valuable for resolving complex kinship cases. Most population databases are based on the ArgusX12 kit (Qiagen), using PCR and capillary electrophoresis (CE) to identify length-based (LB) alleles, and are formatted for FamlinkX software. However, haplotype diversity is often underrepresented by population studies, compelling database updates ( https://famlink.se/fx_databases.html ). The ForenSeq DNA Signature Prep kit (Verogen) uses massive parallel sequencing (MPS) to analyze 7 X-STRs and report sequence-based (SB) alleles, for which population databases are currently unavailable. Because these 7 markers are a subset of the ArgusX12 panel, existing LB-based databases can support kinship interpretation of MPS-derived data. METHODS AND RESULTS: We therefore updated a Mexican population database for 12 X-STRs and generated a corresponding 7-marker subset with FamlinkX-compatible files. Using 500 haplotypes from the Investigator Argus X-12 kit QS (Qiagen), we estimated forensic parameters and evaluated the impact of the updated database by calculating likelihood ratios (LRs) in four representative kinship scenarios. The updated database (n = 1433) increased LRs by an average of 55% compared to the previous version (n = 933). CONCLUSIONS: These results support improved forensic interpretation in Mexican and Latin American populations lacking comprehensive national X-STR databases based on ArgusX12 and ForenSeq DNA Signature kits.
29 June 2026
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Coalescence and translation: A language model for population genetics.
Probabilistic models such as the sequentially Markovian coalescent have long provided a powerful framework for population genetic inference, enabling reconstruction of demographic history and ancestral relationships from genomic data. However, these methods are inherently specialized, relying on predefined assumptions and/or limited scalability. Recent advances in simulation and deep learning provide an alternative approach: learning directly to generalize from synthetic genetic data to infer hidden evolutionary processes. Here we reframe the inference of coalescence times as a problem of translation between two biological languages: the sparse, observable patterns of mutation along the genome and the unobservable ancestral recombination graph that gave rise to them. Inspired by large language models, we develop cxt, a decoder-only transformer that autoregressively predicts coalescent events conditioned on local mutational context. We show that cxt performs competitively with state-of-the-art Markov Chain Monte Carlo-based likelihood models across a broad range of demographic scenarios, matching their accuracy in-distribution and approaching it in out-of-distribution, with the potential for improvement via fine-tuning. Trained on simulations spanning the stdpopsim catalog [J. R. Adrion et al., eLife 9, e54967 (2020); M. E. Lauterbur et al., eLife 12, e84874 (2023); G. Gower et al., Accessible, realistic genome simulation with selection using stdpopsim. bioRxiv [Preprint] (2025). https://doi.org/10.1101/2025.03.23.644823.], the model generalizes robustly and enables efficient inference at scale, producing over a million coalescence predictions in minutes. cxt also produces well-calibrated approximate posteriors, enabling principled uncertainty quantification. We apply cxt to population genomic data from both humans and mosquitoes, highlighting the model's ability to deal with the complexities of empirical data.
17 Apr 2026
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