Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation

Abstract

BackgroundA condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For example, in dairy cattle, internal (i.e. local) populations lead to evaluations based only on internal records while widely used foreign sires have been selected using internally unavailable external records. In such cases, internal genetic evaluations may be less accurate and biased. Because external records are unavailable, methods were developed to combine external information that summarizes these records, i.e. external estimated breeding values and associated reliabilities, with internal records to improve accuracy of internal genetic evaluations. Two issues of these methods concern double-counting of contributions due to relationships and due to records. These issues could be worse if external information came from several evaluations, at least partially based on the same records, and combined into a single internal evaluation. Based on a Bayesian approach, the aim of this research was to develop a unified method to integrate and blend simultaneously several sources of information into an internal genetic evaluation by avoiding double-counting of contributions due to relationships and due to records.ResultsThis research resulted in equations that integrate and blend simultaneously several sources of information and avoid double-counting of contributions due to relationships and due to records. The performance of the developed equations was evaluated using simulated and real datasets. The results showed that the developed equations integrated and blended several sources of information well into a genetic evaluation. The developed equations also avoided double-counting of contributions due to relationships and due to records. Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated information and, therefore, more reliable estimated breeding values were obtained.ConclusionsThe proposed unified method integrated and blended several sources of information well into a genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. The unified method can also be extended to other types of situations such as single-step genomic or multi-trait evaluations, combining information across different traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0059-3) contains supplementary material, which is available to authorized users.

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

“…To our knowledge, this is the first time that the results of a multi-national genomic-based evaluation have been integrated into a national genomic evaluation. This work builds on previous theoretical and applied work (Quaas and Zhang, 2006;Legarra et al, 2007;Vandenplas and Gengler, 2012;Vandenplas et al, 2013), in particular on the previous work of integrating multi-national pedigree-based (MACE) information into a national pedigree-based evaluation (Vandenplas et al, 2014) or 473 Figure 2. Reliabilities from the national genomic evaluation with integration against reliabilities from the multi-national genomic evaluation for milk yield in bulls with phenotyped daughters in the national data set.…”

“…However, the developed equations can be applied to more general cases, such as genotyped and nongenotyped foreign animals associated with foreign information. Following Vandenplas et al (2014) …”

“…Such a method would have to consider that the multi-national information might be partially based on national information, which needs to be accounted for to avoid double counting. Specifically, double counting can occur due to double use of data that pertains to an individual animal, as well as to double use of data that is correlated among relatives (Vandenplas and Gengler, 2012;Vandenplas et al, 2014). We will refer to these 2 as double counting contributions from records and double counting contributions from relatives, respectively.…”

“…is a diagonal variance matrix with the ith element equal to ERC i e * σ −2 and ERC i * being the effective number of records (Misztal and Wiggans, 1988; also called "effective record contributions") that contributed to the multi-national ssGBLUP for the ith animal (Vandenplas et al, 2014). Because contributions from relationships are already accounted through the matrix H −1 , the effective number of records must be free of contributions from relatives (Vandenplas and Gengler, 2012).…”

“…Multi-national genomic information, dependent of national information, can be integrated into the national ssGBLUP by assuming the following prior (see Vandenplas et al (2014) for details):…”