ArticleG3 (Bethesda, Md.)2026
BiTUGA: scalable prevalence-based unitig association testing for binary traits.
Article in G3 (Bethesda, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sequence-based association studies can be challenging in large and highly repetitive genomes. While reference-free k-mer approaches enable direct analysis of sequence variation from sequencing reads, current tools often rely on constructing full k-mer-by-sample matrices, creating computational bottlenecks. Here, we present BiTUGA, a pipeline to test associations for discrete binary traits designed to overcome these limitations and optimized for large genomes. Shifting the statistical focus from raw abundance to group-level prevalence, BiTUGA assembles filtered k-mers into unitigs and tests unitig presence for association across groups. We validated BiTUGA on the binary trait sex, targeting structurally complex Sex-Determining Regions (SDRs) that remain largely unresolved in many plant species. BiTUGA identified sex-associated unitigs, detecting the known SDRs in Populus tremula and Ginkgo biloba, processing up to 750 Gbp in 14-25 h within 70 GB RAM. BiTUGA is available at https://github.com/JMittelbach/BiTUGA.git.
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