Components of the Accuracy of Genomic Prediction in a Multi-Breed Sheep Population

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r 5 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r 5 0.31) when G was used in crossbreds, but the worst (r 5 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

Section: Resultsmentioning

confidence: 99%

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

“…Second, the contribution of population and genome structure to genomic prediction accuracies of the SBF population was assessed by fitting chromosome-specific G matrices. Following the methodology of Daetwyler et al (2012a), 26 chromosomespecific G matrices were calculated, using only the SNPs on Riggio, Abdel-Aziz, Matika, Moreno, Carta and Bishop each chromosome. Each chromosome was then fitted instead of the overall G matrix.…”

Section: Methodsmentioning

confidence: 99%

“…Insight into the components contributing to the accuracy can be gained by regressing the difference in phenotypic variance explained by individually v. simultaneously fitted chromosomal G matrices on chromosome length (Yang et al, 2011;Daetwyler et al, 2012a). This was given by the following equation:…”

Section: Methodsmentioning

confidence: 99%

“…At present, the accuracy of GEBV has been evaluated in experiments involving several livestock species, such as dairy (Harris et al, 2009;Hayes et al, 2009b) and beef (Saatchi et al, 2011) cattle populations, chicken (González-Recio et al, 2009) and sheep (Daetwyler et al, 2010a(Daetwyler et al, , 2012a(Daetwyler et al, , 2012bDuchemin et al, 2012). Apart from the study by Kemper et al (2011), the use of high-density genomic information to select for nematode resistance in sheep has received less attention.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

Riggio

Abdel-Aziz

Matika

et al. 2014

Genomic prediction utilizes single nucleotide polymorphism (SNP) chip data to predict animal genetic merit. It has the advantage of potentially capturing the effects of the majority of loci that contribute to genetic variation in a trait, even when the effects of the individual loci are very small. To implement genomic prediction, marker effects are estimated with a training set, including individuals with marker genotypes and trait phenotypes; subsequently, genomic estimated breeding values (GEBV) for any genotyped individual in the population can be calculated using the estimated marker effects. In this study, we aimed to: (i) evaluate the potential of genomic prediction to predict GEBV for nematode resistance traits and BW in sheep, within and across populations; (ii) evaluate the accuracy of these predictions through within-population cross-validation; and (iii) explore the impact of population structure on the accuracy of prediction. Four data sets comprising 752 lambs from a Scottish Blackface population, 2371 from a Sarda × Lacaune backcross population, 1000 from a Martinik Black-Belly × Romane backcross population and 64 from a British Texel population were used in this study. Traits available for the analysis were faecal egg count for Nematodirus and Strongyles and BW at different ages or as average effect, depending on the population. Moreover, immunoglobulin A was also available for the Scottish Blackface population. Results show that GEBV had moderate to good within-population predictive accuracy, whereas across-population predictions had accuracies close to zero. This can be explained by our finding that in most cases the accuracy estimates were mostly because of additive genetic relatedness between animals, rather than linkage disequilibrium between SNP and quantitative trait loci. Therefore, our results suggest that genomic prediction for nematode resistance and BW may be of value in closely related animals, but that with the current SNP chip genomic predictions are unlikely to work across breeds.

“…The smaller the reference population, the larger the effect of family relationships compared with the effect of LD. It has been demonstrated repeatedly that the accuracy of genomic prediction strongly depends on the level of relationship between the reference population and the test population (Habier et al, 2010;Daetwyler et al, 2012;Pszczola et al, 2012;Wientjes et al, 2013). However, in larger reference populations, the effect of family relationships decreases (Clark et al, 2012) and similarities in allele frequencies, haplotypes and LD patterns become more important.…”

Section: Relevance Of Linkage Disequilibrium and Relationship Levelmentioning

confidence: 99%

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

Schöpke¹,

Swalve

2016

In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped.The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies.The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.