Interplay between heritability, genetic correlation and economic weighting in a selection index with and without genomic information

Abstract

The availability of genomic information demands proper evaluation on how the kind (phenotypic versus genomic) and the amount of information influences the interplay of heritability (h(2)), genetic correlation (r(GiGj)) and economic weighting of traits with regard to the standard deviation of the index (σI). As σI is directly proportional to response to selection, it was the chosen parameter for comparing the indices. Three selection indices incorporating conventional and genomic information for a two trait (i and j) breeding goal were compared. Information sources were chosen corresponding to pig breeding applications. Index I incorporating an own performance in trait j served as reference scenario. In index II, additional information in both traits was contributed by a varying number of full-sibs (2, 7, 50). In index III, the conventional own performance in trait j was combined with genomic information for both traits. The number of animals in the reference population (NP = 1000, 5000, 10,000) and thus the accuracy of GBVs were varied. With more information included in the index, σI became more independent of r(GiGj), h(j)(2) and relative economic weighting. This applied for index II (more full-sibs) and for index III (more accurate GBVs). Standard deviations of index II with seven full-sibs and index III with NP = 1000 were similar when both traits had the same heritability. If the heritability of trait j was reduced (h(j)(2) = 0.1), σI of index III with NP = 1000 was clearly higher than for index II with seven full-sibs. When enhancing the relative economic weight of trait j, the decrease in σI of the conventional full-sib index was much stronger than for index III. Our results imply that NP = 1000 can be considered a minimum size for a reference population in pig breeding. These conclusions also hold for comparing the accuracies of the indices.

Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN+

“…The same trend was observed in the present study ( Figure 1 ). Haberland et al (2013) also investigated the significance of the size of the reference population and concluded that N = 1,000 could be considered a minimum size for a reference population in pig breeding.…”

“…where, N is the size of the reference population, and r 2 is the reliability of the GEBV of the animals used in the reference population. In our calculations, we assumed N p = 1,000, which may be considered a minimum for GS in pigs and r 2 was assumed to be 0.49 for all traits ( Haberland et al, 2013 ). The proportion of genetic variance explained by markers ( q 2 ) was assumed to be 0.8 for all breeding goal traits as suggested by Erbe et al (2011) .…”

Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+

“…where, N is the size of the calibration set and r 2 TI is the reliability of the GEBV of the animals used in the calibration set. In our calculations, we assumed Np = 1000, which can be considered the minimum for GS in pigs (Haberland et al, 2013), while the proportion of genetic variance explained by markers (q2) was assumed to be 0.8 for all breeding goal traits as suggested by Erbe et al (2011). The reliability (r2) was assumed to be 0.5 for all traits.…”

Seasonal variation of Ca, P, Fe, Cu, Mn, Zn, and Se contents in Tibetan pigs

“…Furthermore, scenarios 4 and 5 showed higher accuracy than reference scenarios 1 and 2 when N=1000. Haberland et al (2013) also investigated the effective size of the reference population and found that N=1000 could be considered the minimum size for a reference population in pig breeding.…”

“…This approach requires that the correlation between the true value and the GEBV be defined for every genomic trait rGBV, which was accomplished using the approach developed by Erbe et al (2011) based on an equation derived by Daetwyler et al (2008): (1) where, N is the size of the calibration set and r2TI is the reliability of the GEBV of the animals used in the calibration set. In our calculations, we assumed Np = 1000, which can be considered the minimum for GS in pigs (Haberland et al, 2013), while the proportion of genetic variance explained by markers (q2) was assumed to be 0.8 for all breeding goal traits as suggested by Erbe et al (2011). The reliability (r2) was assumed to be 0.5 for all traits.…”