Effect of Genetic Architecture on Accuracy of Multi Breed Genomic Prediction

Objectives were to investigate effect of genetic architecture and including random across and within breed effects in GBLUP on accuracy of multi breed genomic prediction. High-density genotypes and imputed synonymous, missense and premature stop codon mutations using sequence data were available for 3000 Holstein Friesians and 3000 Jerseys. Phenotypes of traits with different genetic architectures, regarding allele frequency spectra and number of breed specific QTL, were simulated by sampling 100 QTL from a mutation class. Accuracies of genomic breeding values were estimated using GBLUP including random across and within breed effects. Increase in accuracy by adding individuals of another breed to the reference population and accuracy of across breed genomic prediction was low. Genetic architecture influenced accuracies; accuracies reduced when QTL allele frequencies were lower and QTL were more breed specific. Including a random within breed effect did not affect accuracies

(A)cross-breed Genomic Prediction

Genomic prediction holds the promise to use information of other populations to improve prediction accuracy. Thus far, empirical evaluations showed limited benefit of multi-breed compared to single reed genomic prediction. We compared prediction accuracy of different models based on two losely related and one unrelated line of layer chickens. Multi-breed genomic prediction may be successful when lines are closely related, and when the number of training animals of the additional line is large compared to the line itself. Multi-breed genomic prediction requires models that are lexible enough to use beneficial and ignore detrimental sources of information in the training data. Combining linear and non-linear models may lead to small increases in accuracy of multibreed genomic prediction. Multitrait models, modelling a separate trait for each breed, appear especially beneficial when elationships between breeds are very low, or when the genetic correlation between breeds is negative

Multi-population genomic prediction

Cum laude graduatio

Board invited review: The purebred-crossbred correlation in pigs: A review of theory, estimates, and implications

Pig and poultry production relies on crossbreeding of purebred populations to produce production animals. In those breeding schemes, selection takes place within the purebred population to improve crossbred performance (CB performance). The genetic correlation between purebred performance (PB performance) and CB performance () is, however, lower than unity for many traits. When is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of , and to review existing literature that report estimates in pigs. The has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for

The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction

Although the concept of genomic selection relies on linkage disequilibrium (LD) between quantitative trait loci and markers, reliability of genomic predictions is strongly influenced by family relationships. In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures. Five groups of selection candidates were simulated by taking different information sources from the reference population into account: (1) allele frequencies, (2) LD pattern, (3) haplotypes, (4) haploid chromosomes, and (5) individuals from the reference population, thereby having real family relationships with reference individuals. Reliabilities were predicted using genomic relationships among 529 reference individuals and their relationships with selection candidates and with a deterministic formula where the number of effective chromosome segments (Me) was estimated based on genomic and additive relationship matrices for each scenario. At a heritability of 0.6, reliabilities based on genomic relationships were 0.002 ± 0.0001 (allele frequencies), 0.022 ± 0.001 (LD pattern), 0.018 ± 0.001 (haplotypes), 0.100 ± 0.008 (haploid chromosomes), and 0.318 ± 0.077 (family relationships). At a heritability of 0.1, relative differences among groups were similar. For all scenarios, reliabilities were similar to predictions with a deterministic formula using estimated Me. So, reliabilities can be predicted accurately using empirically estimated Me and level of relationship with reference individuals has a much higher effect on the reliability than linkage disequilibrium per se. Furthermore, accumulated length of shared haplotypes is more important in determining the reliability of genomic prediction than the individual shared haplotype lengt

Increasing genetic gain by selecting for higher Mendelian sampling variance

Because of linkage and variation in heterozygosity, individuals differ in the Mendelian sampling variance on their gametes. Thus some parents produce genetically more variable offspring than others. With genomic EBV and phased genotypes, these differences can be quantified and potentially used to increase genetic gain. Here we show that genetic gain and the probability of breeding a top-ranking individual can be increased by selecting individuals on an index of their GEBV and the standard deviation on the GEBV of their gametes (SDGEBV). The optimum index was , where is the standardized truncation point belonging to the selected proportion p. Compared to selection on ordinary GEBV, in dairy cattle the probability of breeding a top-ranking individual can be increased by 36%, and response to selection by 3.6% when selection is strong (p = 0.001). Preselection on GEBV facilitates implementation with little loss of gain