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Audald Lloret-Villas @audald.bsky.social · 04/10/2025

We explored that in this new study in Molecular Ecology Resources 🔎 onlinelibrary.wiley.com/doi/10.1111/... @vbtram.bsky.social @huizhentan.bsky.social @katarinastuart.bsky.social @annasanture.bsky.social

onlinelibrary.wiley.com

Assessing Genotype Imputation Methods for Low‐Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels

Low-coverage sequencing (LCS) followed by genotype imputation has become a cost-efficient approach for obtaining whole-genome SNPs. Several imputation methods for LCS data have been developed over th....

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Audald Lloret-Villas @audald.bsky.social · 04/10/2025

👩🏻‍💻 We tested 5️⃣ imputation methods (GLIMPSE2, GeneImp, QUILT2, STITCH, Beagle5.4), simulated realistic populations with different genetic structures, and applied the methods to data from 283 wild hihi (stitchbird) 🧬.

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Audald Lloret-Villas @audald.bsky.social · 04/10/2025

🔑 Choosing your imputation tool: - All tools worked well in highly related populations. - Accuracy varied in populations with low relatedness, but GLIMPSE2 & QUILT2 generally outperformed the other tools. - STITCH is useful when no reference panel exists but results in some missing data.

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