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Simultaneous testing of rule- and model-based approaches for runs of homozygosity detection opens up a window into genomic footprints of selection in pigs

MPG-Autoren
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Berghöfer,  Jan
Veterinary Functional Genomics Group (Julia Metzger), Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Khaveh,  Nadia
Veterinary Functional Genomics Group (Julia Metzger), Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Mundlos,  Stefan
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Metzger,  Julia
Veterinary Functional Genomics Group (Julia Metzger), Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Berghöfer, J., Khaveh, N., Mundlos, S., & Metzger, J. (2022). Simultaneous testing of rule- and model-based approaches for runs of homozygosity detection opens up a window into genomic footprints of selection in pigs. BMC Genomics, 23: 564. doi:10.1186/s12864-022-08801-4.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-EB12-9
Zusammenfassung
Background: Past selection events left footprints in the genome of domestic animals, which can be traced back
by stretches of homozygous genotypes, designated as runs of homozygosity (ROHs). The analysis of common ROH
regions within groups or populations displaying potential signatures of selection requires high-quality SNP data
as well as carefully adjusted ROH-defining parameters. In this study, we used a simultaneous testing of rule- and
model-based approaches to perform strategic ROH calling in genomic data from different pig populations to detect
genomic regions under selection for specific phenotypes.
Results: Our ROH analysis using a rule-based approach offered by PLINK, as well as a model-based approach run
by RZooRoH demonstrated a high efficiency of both methods. It underlined the importance of providing a high-
quality SNP set as input as well as adjusting parameters based on dataset and population for ROH calling. Particularly,
ROHs ≤ 20 kb were called in a high frequency by both tools, but to some extent covered different gene sets in sub-
sequent analysis of ROH regions common for investigated pig groups. Phenotype associated ROH analysis resulted in
regions under potential selection characterizing heritage pig breeds, known to harbour a long-established breeding
history. In particular, the selection focus on fitness-related traits was underlined by various ROHs harbouring disease
resistance or tolerance-associated genes. Moreover, we identified potential selection signatures associated with ear
morphology, which confirmed known candidate genes as well as uncovered a missense mutation in the ABCA6 gene
potentially supporting ear cartilage formation.
Conclusions: The results of this study highlight the strengths and unique features of rule- and model-based
approaches as well as demonstrate their potential for ROH analysis in animal populations. We provide a workflow
for ROH detection, evaluating the major steps from filtering for high-quality SNP sets to intersecting ROH regions.
Formula-based estimations defining ROHs for rule-based method show its limits, particularly for efficient detection of
smaller ROHs. Moreover, we emphasize the role of ROH detection for the identification of potential footprints of selec-
tion in pigs, displaying their breed-specific characteristics or favourable phenotypes.