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LinkImputeR: user-guided genotype calling and imputation for non-model organisms.

Daniel Money1, Zoë Migicovsky2, Kyle Gardner2

  • 1Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada. daniel.money@dal.ca.

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Summary

LinkImputeR software improves genotype imputation by utilizing all DNA sequence data, enhancing accuracy and data quantity for genomic studies, especially in non-model organisms.

Keywords:
GBSImputationRead countSNP

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic studies require genome-wide genotype data, often incomplete due to technology limitations.
  • Existing genotype imputation methods discard valuable read information below certain thresholds.
  • Current methods are often unsuitable for non-model organisms due to reliance on ordered markers.

Purpose of the Study:

  • Introduce LinkImputeR, a novel software for genotype calling and imputation.
  • Exploit ignored read count information from next-generation sequencing (NGS) data.
  • Develop a method suitable for non-model organisms without requiring ordered markers or reference panels.

Main Methods:

  • Developed LinkImputeR software to utilize all available DNA sequence information, including read counts.
  • Applied LinkImputeR to NGS data from apple, cannabis, and grape.
  • Quantified the impact of varying read count and missingness thresholds on genotype data quantity and quality.

Main Results:

  • LinkImputeR increased genotype calls by over an order of magnitude.
  • Genotyping accuracy improved by several percent.
  • Demonstrated that filter effects vary significantly across datasets, necessitating per-study investigation.

Conclusions:

  • LinkImputeR significantly enhances the quantity and quality of genotype data from NGS.
  • The software allows users to examine and select optimal thresholds for their specific data.
  • LinkImputeR substantially increases the utility of NGS data, particularly for non-model organisms.