title: The unified approach to the use of genomic and pedigree information in genomic evaluations revisited.
abstract: Previous proposals for a unified approach for amalgamating information from animals with or without genotypes have combined the numerator relationship matrix A with the genomic relationship G estimated from the markers. These approaches have resulted in biased genomic EBV (GEBV), and methodology was developed to overcome these problems. Firstly, a relationship matrix, G(FG) , based on linkage analysis was derived using the same base population as A, which (i) utilizes the genomic information on the same scale as the pedigree information and (ii) permits the regression coefficients used to propagate the genomic data from the genotyped to ungenotyped individuals to be calculated in the light of the genomic information, rather than ignoring it. Secondly, the elements of G were regressed back towards their expected values in the A matrix to allow for their estimation errors. These developments were combined in a methodology LDLAb and tested on simulated populations where either parents were phenotyped and offspring genotyped or vice versa. The LDLAb method was demonstrated to be a unified approach that maximized accuracy of GEBV compared to previous methodologies and removed the bias in the GEBV. Although LDLAb is computationally much more demanding than MLAC, it demonstrates how to make best use the marker information and also shows the computational problems that need to be solved in the future to make best use of the marker data.
authors: Institute of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Norway. theo.meuwissen@umb.no
authors: T H E
authors: TH
authors: Meuwissen
authors: T
authors: T
authors: Luan
authors: J A
authors: JA
authors: Woolliams
mesh_terms: D000818:Animals; D005260:Female; D023281:Genomics / Q000379:methods*; D005838:Genotype; D008297:Male; D015233:Models, Statistical; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012044:Regression Analysis