BiTUGA: scalable prevalence-based unitig association testing for binary traits.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	BiTUGA: scalable prevalence-based unitig association testing for binary traits.
Συγγραφείς:	Mittelbach J; Center for Bioinformatics, MIN-Faculty, University of Hamburg, 22761 Hamburg, Hamburg, Germany., Kersten B; Genome Research, Thünen Institute of Forest Genetics, 22927 Großhansdorf, Schleswig-Holstein, Germany., Kurtz S; Center for Bioinformatics, MIN-Faculty, University of Hamburg, 22761 Hamburg, Hamburg, Germany.
Πηγή:	G3 (Bethesda, Md.) [G3 (Bethesda)] 2026 Aug 05; Vol. 16 (8).
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Print Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE
Imprint Name(s):	Publication: 2021- : [Oxford] : Oxford University Press Original Publication: Bethesda, MD : Genetics Society of America, 2011-
Ιατρικοί όροι (MeSH):	Software* , Genetic Association Studies* , Quantitative Trait, Heritable* , Genome-Wide Association Study*, Populus/genetics ; Ginkgo biloba/genetics ; Genomics/methods ; Genome, Plant ; Phenotype ; Sex Determination Processes
Περίληψη:	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. (© The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America.)
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Grant Information:	University of Hamburg
Contributed Indexing:	Keywords: k-mers; alignment-free; genome-wide association studies; plant genomics; sex determination
Entry Date(s):	Date Created: 20260615 Date Completed: 20260805 Latest Revision: 20260813
Update Code:	20260813
PubMed Central ID:	PMC13439920
DOI:	10.1093/g3journal/jkag149
PMID:	42296179
Βάση Δεδομένων:	MEDLINE

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ISSN:	2160-1836
DOI:	10.1093/g3journal/jkag149