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Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data.

Ariel W Chan1, Martha T Hamblin2, Jean-Luc Jannink1,3

  • 1Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States of America.

Plos One
|August 19, 2016
PubMed
Summary

This study evaluates Beagle v.4 for imputing missing genotypes in non-model species using Genotyping-By-Sequencing data. Beagle v.4 shows promise for accurate genotype imputation, outperforming existing methods in certain scenarios.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Plant Breeding

Background:

  • High-throughput sequencing (HTS) methods like Genotyping-By-Sequencing (GBS) are cost-effective for genomic studies in non-model species.
  • HTS data present challenges including sequencing errors, alignment issues, and missing data, complicating variant discovery and genotype calling.

Purpose of the Study:

  • To assess the efficacy of existing human-centric genotype imputation algorithms for non-human species HTS data.
  • To determine if methods optimized for ascertained variants are suitable for HTS-derived variants.

Main Methods:

  • Cross-validation experiments were conducted using GBS data from Manihot esculenta.
  • Beagle v.4 was tested against a benchmark LASSO-penalized linear regression method (glmnet).
  • Imputation accuracy was estimated by correlating observed and imputed genotype dosages, with computation time as a secondary metric.

Main Results:

  • Beagle v.4 demonstrated competitive imputation accuracy compared to glmnet.
  • Factors influencing imputation accuracy, including missing data levels, read depth, minor allele frequency (MAF), and reference panel composition, were analyzed.

Conclusions:

  • Human-developed imputation algorithms like Beagle v.4 can be effectively applied to HTS-derived genotype data in non-model species.
  • Further investigation into factors affecting imputation accuracy is crucial for optimizing genomic studies in under-resourced species.