National single-step genomic method that integrates multi-national genomic information

Abstract

The aim of this paper was to develop a national singlestep genomic BLUP that integrates multi-national genomic estimated breeding values (EBV) and associated reliabilities without double counting dependent data contributions from the different evaluations. Simultaneous use of all data, including phenotypes, pedigree, and genotypes, is a condition to obtain unbiased EBV. However, this condition is not always fully met, mainly due to unavailability of foreign raw data for imported animals. In dairy cattle genetic evaluations, this issue is traditionally tackled through the multiple across-country evaluation (MACE) of sires, performed by Interbull Centre (Uppsala, Sweden). Multiple across-country evaluation regresses all the available national information onto a joint pedigree to obtain country-specific rankings of all sires without sharing the raw data. In the context of genomic selection, the issue is handled by exchanging sire genotypes and by using MACE information (i.e., MACE EBV and reliabilities), as a valuable source of "phenotypic" data. Although all the available data are considered, these "multi-national" genomic evaluations use multi-step methods assuming independence of various sources of information, which is not met in all situations. We developed a method that handles this by single-step genomic evaluation that jointly (1) uses national phenotypic, genomic, and pedigree data; (2) uses multi-national genomic information; and (3) avoids double counting dependent data contributions from an animal's own records and relatives' records. The method was demonstrated by integrating multi-national genomic EBV and reliabilities of Brown Swiss sires, included in the InterGenomics consortium at Interbull Centre, into the national evaluation in Slovenia. The results showed that the method could (1) increase reliability of a national (genomic) evaluation; (2) provide consistent ranking of all animals: bulls, cows, and young animals; and (3) increase the size of a genomic training population. These features provide more efficient and transparent selection throughout a breeding program.

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle

“…The calculation of an animal's breeding value aggregates information from the animal's own records (i.e., contributions due to records) and from records of its relatives (i.e., contributions due to relationships) (Misztal and Wiggans, 1988). When a national evaluation is based on integration of EBV from external sources (i.e., MACE EBV), avoiding double-counting of information becomes of utmost importance and a failure to do so could lead to biased predictions and inflated reliabilities (Vandenplas et al, 2017). In the present study, a total of 1,729 and 1,788 bulls with Interbull evaluations were available for the analysis of MT and MSP, respectively.…”

“…On the other hand, de-regression is expected to have a significant effect on EBV with substantially lower reliabilities (i.e., those with more contributions from relatives; Calus et al, 2016). If the model does not properly account for differences in reliabilities and contributions, double-counting of information, and overestimation of reliabilities can occur (Calus et al, 2016;Vandenplas et al, 2017).…”

“…Gengler and Vanderick (2008) also proposed a feasible alternative to include external evaluations into a national evaluation system through a Bayesian approach, but indicated the need to further improve the method due to its assumptions. These previous studies led to a further extension to ssGBLUP to integrate MACE data into a domestic single-step genomic evaluation, as proposed by Vandenplas et al (2014) and implemented by Andonov et al (2017), Vandenplas et al (2017), andColinet et al (2018).…”

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

“…Further, 346 some phenotyping resources could be diverted to genotyping to maximize return on investment. A 347 comparable level of accuracy can be also achieved with international reference populations (Jorjani, 348 2012;Špehar et al, 2013) or a combination of national and international reference populations 349 (Vandenplas and Gengler, 2015;Vandenplas et al, 2017;Vandenplas et al, 2018). When this level 350 of accuracy is combined with a reduced generation interval, small populations can achieve 351 substantially larger genetic gains than with progeny testing.…”

“…It has 46 doubled the rate of genetic gain through decreased generation interval, increased selection accuracy 47 for young animals, increased selection intensity, and identification and management of recessive 48 lethal alleles (Cole, 2015;Garcia-Ruiz et al, 2016;Wiggans et al, 2017). The prerequisite for these 49 gains is a large number of genotyped animals, which is an issue for small populations (Thomasen et 50 al., 2014;Jenko et al, 2017;Ducrocq et al, 2018), though this problem can be addressed with 51 international training populations (Jorjani, 2012;Liu, 2013;Vandenplas et al, 2017). An effective 52 implementation also requires an optimal use of genomic selection for different groups of animals 53 (Thomasen et al, 2014).…”