Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score

Abstract

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.

Genome-wide association mapping including phenotypes from relatives without genotypes

“…Such information can be combined with genomic data for greater detection power and estimation precision through a properly scaled and augmented relationship matrix Misztal et al, 2009). The ssGBLUP method has been shown to provide more consistent solutions and better accuracy than the multiple-step approach (Aguilar et al, 2010;Chen et al, 2011;Forni et al, 2011).…”

Meta‐analysis of genome‐wide association from genomic prediction models

“…2009; Aguilar et al . 2010), which has been recently adapted to perform GWA (Wang et al . 2012, 2014; Gualdrón Duarte et al .…”

A simple method for genomic selection of moderately sized dairy cattle populations

“…Method II has the advantage over the method of Aguilar et al (2010) in that computation of Method II GEBVs for new bulls with genotypes requires only calculation of a relatively simple regression equation, whereas the method of Aguilar et al (2010) requires recalculation of the entire set of modified mixed model equations. An advantage of the method of Aguilar et al (2010) is that the number of equations is independent of the number of markers. Thus, this method could also use all data from the high density SNP-chips with 777 000 markers.…”

“…If the regression is less than unity, then the evaluations of the bulls with the highest GEBVs will be inflated relative to the true genetic values of these bulls. Aguilar et al (2010) found that regressions of EBVs based on progeny tests on GEBVs, derived by the method of VanRaden (2008), were less than unity for the final score. Liu et al (2009) developed a method for correcting for bias generated by the selection of animals for genotyping.…”

“…This parameter is the population-genetic equivalent of variance components in traditional mixed model analyses. With 'optimal' scaling, the regression of GEBVs on EBVs based on daughter records using the method of Aguilar et al (2010) was 0.90, as opposed to 0.83 by the multi-step method of VanRaden (2008), although both methods had nearly equal coefficient of determination (R 2 ) values. Similar results were obtained on five additional conformation traits .…”