RESUMEN
Long-term genomic selection (GS) requires strategies that balance genetic gain with population diversity, to sustain progress for traits under selection, and to keep diversity for future breeding. In a simulation model for a recurrent selection scheme, we provide the first head-to-head comparison of two such existing strategies: genomic optimal contributions selection (GOCS), which limits realized genomic relationship among selection candidates, and weighted genomic selection (WGS), which upscales rare allele effects in GS. Compared to GS, both methods provide the same higher long-term genetic gain and a similar lower inbreeding rate, despite some inherent limitations. GOCS does not control the inbreeding rate component linked to trait selection, and, therefore, does not strike the optimal balance between genetic gain and inbreeding. This makes it less effective throughout the breeding scheme, and particularly so at the beginning, where genetic gain and diversity may not be competing. For WGS, truncation selection proved suboptimal to manage rare allele frequencies among the selection candidates. To overcome these limitations, we introduce two new set selection methods that maximize a weighted index balancing genetic gain with controlling expected heterozygosity (IND-HE) or maintaining rare alleles (IND-RA), and show that these outperform GOCS and WGS in a nearly identical way. While requiring further testing, we believe that the inherent benefits of the IND-HE and IND-RA methods will transfer from our simulation framework to many practical breeding settings, and are therefore a major step forward toward efficient long-term genomic selection.
Asunto(s)
Genoma/genética , Genómica , Selección Genética , Frecuencia de los Genes/genética , Endogamia , Modelos Genéticos , FenotipoRESUMEN
Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65-0.68). Using genotypes imputed from a large reference panel (accuracy: R(2) = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R(2) = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.
Asunto(s)
Cruzamiento , Genoma , Porcinos/genética , Animales , Genómica , Genotipo , Haplotipos , Modelos Genéticos , Fenotipo , Polimorfismo de Nucleótido SimpleRESUMEN
BACKGROUND: Genotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation. RESULTS: TagSNP density of at least 1 tagSNP per 340kb (~7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end). CONCLUSION: The new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.
Asunto(s)
Polimorfismo de Nucleótido Simple , Porcinos/genética , Animales , Genotipo , HaplotiposRESUMEN
BACKGROUND: The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance. RESULTS: Average r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire. CONCLUSIONS: Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.