Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major conc...

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Bibliographic Details
Published in:PloS one 2019-01, Vol.14 (1), p.e0208614-e0208614
Main Authors: Alipour, Hadi, Bai, Guihua, Zhang, Guorong, Bihamta, Mohammad Reza, Mohammadi, Valiollah, Peyghambari, Seyed Ali
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Agronomy
Algorithms
Barley
Biology and Life Sciences
Bread
Breeding
Chromosomes, Plant - genetics
Corn
Data points
Deoxyribonucleic acid
DNA
DNA sequencing
Engineering and Technology
Forecasts and trends
Gene frequency
Gene Frequency - genetics
Gene sequencing
Genetic aspects
Genetic diversity
Genome, Plant
Genomes
Genotyping
Genotyping Techniques - methods
Germplasm
Haplotypes
High-Throughput Nucleotide Sequencing - methods
Hordeum - genetics
Library collections
Missing data
Nucleotide sequence
Plant breeding
Polymorphism, Single Nucleotide - genetics
Population
Reference Standards
Research and analysis methods
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Studies
Supply and demand
Triticum - genetics
Wheat
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Summary:Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0208614