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Potential of low-coverage genotyping-by-sequencing and imputation for cost-effective genomic selection in bi-parental segregating populations

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    Rights statement: Copyright © 2017. Copyright © by the Crop Science Society of America, Inc. This is an open access article distributed under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Original languageEnglish
Pages (from-to)1404-1420
JournalCrop science
Volume57
Issue number3
Early online date27 Mar 2017
DOIs
Publication statusPublished - May 2017

Abstract

Genotyping-by-sequencing is an alternative genotyping method to SNP arrays that has received considerable attention in the plant breeding community. In this study we use simulation to quantify the potential of low-coverage genotyping-by-sequencing (GBS) and imputation for cost-effective genomic selection in bi-parental segregating populations. The simulations comprised a range of scenarios where SNP array or GBS data were used to train the genomic selection model, to predict breeding values, or both. GBS data were generated with sequencing coverages (x) from 4x to 0.01x. The data were used either non-imputed or imputed by the AlphaImpute program. The size of the training and prediction sets was either held fixed or was increased by reducing sequencing coverage per individual. The results show that non-imputed 1x GBS data provided comparable prediction accuracy and bias, and for the used measurement of return on investment, outperformed the SNP array data. Imputation allowed for further reduction in sequencing coverage; to as low as 0.1x with 10K markers or 0.01x with 100K markers. The results suggest that using such data in bi-parental families gave up to 5.63 times higher return on investment than using the SNP array data. Reduction of sequencing coverage per individual and imputation can be leveraged to genotype larger training set to increase prediction accuracy and larger prediction sets to increase selection intensity, which both allow for higher response to selection and higher return on investment.

    Research areas

  • GBS, genotyping-by-sequencing, SNP, single-nucleotide polymorphism

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