Abstract
BackgroundNext-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations.MethodsThe value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios.ResultsAccuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity.ConclusionsGBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.
Highlights
Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays
Current applications of genomic selection (GS) in livestock are typically based on single nucleotide polymorphism (SNP) genotypes called from SNP array data
Compared to genotypes obtained from SNP arrays, the quality of genotypes obtained with GBS tends to be lower since it depends on the genome-wide sequence read depth (x)
Summary
Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. Use of next-generation sequencing (NGS) techniques for genotyping could be a viable alternative to current low-cost SNP array strategies, with the potential to Gorjanc et al Genetics Selection Evolution (2015): increase the fraction of the genome captured in a costefficient manner [6,7,8]. Genotyping-by-sequencing (GBS) uses NGS technology to genotype large numbers of individuals and has the potential to drive the cost per sample below $10 through intensive multiplexing [9]. GBS and similar techniques such as RAD-Seq [6] are reduced representation approaches that use restriction enzymes to target the sequencing effort to a fraction of the genome This fraction of the genome can be readily adjusted according to the needs of the project and can potentially be much greater than the fraction captured by even the densest SNP arrays currently available in livestock. These drawbacks complicate the use of GBS data, but can be partially overcome by imputation and error correction methods [18,19,20]
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