Abstract
Genotyping‐by‐sequencing (GBS) is an alternative genotyping method to single‐nucleotide polymorphism (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 GBS and imputation for cost‐effective genomic selection in biparental 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. The GBS data were generated with sequencing coverages (x) from 4x to 0.01x. The data were used either nonimputed 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 nonimputed 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 10,000 markers or 0.01x with 100,000 markers. The results suggest that using such data in biparental 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 sets 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.
Highlights
This study quantifies the potential of low-coverage genotyping-by-sequencing (GBS) and imputation for cost-effective genomic selection in biparental segregating populations
The cost of genomic selection in early segregating populations is high because the training set for the genomic selection model must be large and because genomic predictions have to be obtained for a large number of individuals (Riedelsheimer and Melchinger, 2013; Hickey et al, 2014)
Most studies of genomic selection with GBS data have focused on settings with inbred individuals (Poland et al, 2012b; Crossa et al, 2013; Rutkoski et al, 2013) and have shown that the accuracy of genomic prediction using low-coverage GBS data was comparable with using single-nucleotide polymorphism (SNP) array or diversity arrays technology (DArT) data
Summary
This study quantifies the potential of low-coverage genotyping-by-sequencing (GBS) and imputation for cost-effective genomic selection in biparental segregating populations. Most studies of genomic selection with GBS data have focused on settings with inbred individuals (Poland et al, 2012b; Crossa et al, 2013; Rutkoski et al, 2013) and have shown that the accuracy of genomic prediction using low-coverage GBS data was comparable with using SNP array or diversity arrays technology (DArT) data These results may not hold for segregating populations, because capturing genetic variation with low-coverage sequencing in such a setting is challenging. Sequencing a heterozygous locus once (1x) reveals only one allele, and the genotype from such data would be wrongly called as a reference or alternative homozygote It is unknown if such low-coverage GBS data are useful for genomic selection in segregating populations. A simulation study in an outbred livestock population shows that low-coverage GBS data enable accurate and unbiased genomic predictions when a sufficient number of markers is available and coverage per individual is at least 1x (Gorjanc et al, 2015), which holds promise for segregating plant populations
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