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In silico candidate variant and gene identification using inbred mouse strains

View ORCID ProfileMatthias Munz, Mohammad Khodaygani, View ORCID ProfileZouhair Aherrahrou, View ORCID ProfileHauke Busch, View ORCID ProfileInken Wohlers
doi: https://doi.org/10.1101/2020.09.04.282731
Matthias Munz
1Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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Mohammad Khodaygani
1Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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Zouhair Aherrahrou
2Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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Hauke Busch
1Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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  • For correspondence: hauke.busch@uni-luebeck.de inken.wohlers@uni-luebeck.de
Inken Wohlers
1Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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  • For correspondence: hauke.busch@uni-luebeck.de inken.wohlers@uni-luebeck.de
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ABSTRACT

Mice are the most widely used animal model to study genotype to phenotype relationships. Inbred mice are genetically identical, which eliminates genetic heterogeneity and makes them particularly useful for genetic studies. Many different strains have been bred over decades and a vast amount of phenotypic data has been generated. In addition, recently whole genome sequencing-based genome-wide genotype data for many widely used inbred strains has been released. Here, we present an approach for in silico fine-mapping that uses genotypic data of 37 inbred mouse strains together with phenotypic data provided by the user to propose candidate variants and genes for the phenotype under study. Public genome-wide genotype data covering more than 74 million variant sites is queried efficiently in real-time to provide those variants that are compatible with the observed phenotype differences between strains. Variants can be filtered by molecular consequences and by corresponding molecular impact. Candidate gene lists can be generated from variant lists on the fly. Fine-mapping together with annotation or filtering of results is provided in a Bioconductor package called MouseFM. In order to characterize candidate variant lists under various settings, MouseFM was applied to two expression data sets across 20 inbred mouse strains, one from neutrophils and one from CD4+ T cells. Fine-mapping was assessed for about 10,000 genes, respectively, and identified candidate variants and haplotypes for many expression quantitative trait loci (eQTLs) reported previously based on these data. For albinism, MouseFM reports only one variant allele of moderate or high molecular impact that only albino mice share: a missense variant in the Tyr gene, reported previously to be causal for this phenotype. Performing in silico fine-mapping for interfrontal bone formation in mice using four strains with and five strains without interfrontal bone results in 12 genes. Of these, three are related to skull shaping abnormality. Finally performing fine-mapping for dystrophic cardiac calcification by comparing 9 strains showing the phenotype with 8 strains lacking it, we identify only one moderate impact variant in the known causal gene Abcc6. In summary, this illustrates the benefit of using MouseFM for candidate variant and gene identification.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The revised version addresses valuable feedback and suggestions by several reviewers. We address their points in the significantly extended manuscript. Towards this, we generate and describe new results for fine-mapping of variants linked to expression changes and put this in context with previously identified eQTLs in inbred mice. In summary, the description and discussion of the scope, limitations, but importantly also of the potential of MouseFM has significantly improved.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 22, 2020.
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In silico candidate variant and gene identification using inbred mouse strains
Matthias Munz, Mohammad Khodaygani, Zouhair Aherrahrou, Hauke Busch, Inken Wohlers
bioRxiv 2020.09.04.282731; doi: https://doi.org/10.1101/2020.09.04.282731
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In silico candidate variant and gene identification using inbred mouse strains
Matthias Munz, Mohammad Khodaygani, Zouhair Aherrahrou, Hauke Busch, Inken Wohlers
bioRxiv 2020.09.04.282731; doi: https://doi.org/10.1101/2020.09.04.282731

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