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Identification of Genomic Regions Carrying a Causal Mutation in Unordered Genomes

Pilar Corredor-Moreno, Ed Chalstrey, Carlos A. Lugo, View ORCID ProfileDan MacLean
doi: https://doi.org/10.1101/026856
Pilar Corredor-Moreno
The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, NR4 7UH
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Ed Chalstrey
The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, NR4 7UH
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Carlos A. Lugo
The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, NR4 7UH
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Dan MacLean
The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, NR4 7UH
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  • ORCID record for Dan MacLean
  • For correspondence: dan.maclean@tsl.ac.uk
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Abstract

Whole genome sequencing using high-throughput sequencing (HTS) technologies offers powerful opportunities to study genetic variation. Mapping the mutations responsible for different phenotypes is generally an involved and time-consuming process so researchers have developed user-friendly tools for mapping-by-sequencing, yet they are not applicable to organisms with non-sequenced genomes. We introduce SDM (SNP Distribution Method), a reference independent method for rapid discovery of mutagen-induced mutations in typical forward genetic screens. SDM aims to order a disordered collection of HTS reads or contigs such that the fragment carrying the causative mutation can be identified. SDM uses typical distributions of homozygous SNPs that are linked to a phenotype-altering SNP in a non-recombinant region as a model to order the fragments. To implement and test SDM, we created model genomes with an idealised SNP density based on Arabidopsis thaliana chromosome 1 and analysed fragments with size distribution similar to reads or contigs assembled from HTS sequencing experiments. SDM groups the contigs by their normalised SNP density and arranges them to maximise the fit to the expected SNP distribution. We tested the procedure in existing datasets by examining SNP distributions in recent out-cross and back-cross experiments in Arabidopsis thaliana backgrounds. In all the examples we analysed, homozygous SNPs were normally distributed around the causal mutation. We used the real SNP densities obtained from these experiments to prove the efficiency and accuracy of SDM. The algorithm was able to successfully identify small sized (10-100 kb) genomic regions containing the causative mutation.

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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 4.0 International license.
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Posted September 15, 2015.
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Identification of Genomic Regions Carrying a Causal Mutation in Unordered Genomes
Pilar Corredor-Moreno, Ed Chalstrey, Carlos A. Lugo, Dan MacLean
bioRxiv 026856; doi: https://doi.org/10.1101/026856
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Identification of Genomic Regions Carrying a Causal Mutation in Unordered Genomes
Pilar Corredor-Moreno, Ed Chalstrey, Carlos A. Lugo, Dan MacLean
bioRxiv 026856; doi: https://doi.org/10.1101/026856

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