Pooled genotyping strategies for the rapid construction of genomic reference populations1

Abstract

The growing concern with the environment is making important for livestock producers to focus on selection for efficiency-related traits, which is a challenge for commercial cattle herds due to the lack of pedigree information. To explore a cost-effective opportunity for genomic evaluations of commercial herds, this study compared the accuracy of bulls' genomic estimated breeding values (GEBV) using different pooled genotype strategies. We used ten replicates of previously simulated genomic and phenotypic data for one low (t1) and one moderate (t2) heritability trait of 200 sires and 2,200 progeny. Sire's GEBV were calculated using a univariate mixed model, with a hybrid genomic relationship matrix (h-GRM) relating sires to: 1) 1,100 pools of 2 animals; 2) 440 pools of 5 animals; 3) 220 pools of 10 animals; 4) 110 pools of 20 animals; 5) 88 pools of 25 animals; 6) 44 pools of 50 animals; and 7) 22 pools of 100 animals. Pooling criteria were: at random, grouped sorting by t1, grouped sorting by t2, and grouped sorting by a combination of t1 and t2. The same criteria were used to select 110, 220, 440, and 1,100 individual genotypes for GEBV calculation to compare GEBV accuracy using the same number of individual genotypes and pools. Although the best accuracy was achieved for a given trait when pools were grouped based on that same trait (t1: 0.50-0.56, t2: 0.66-0.77), pooling by one trait impacted negatively on the accuracy of GEBV for the other trait (t1: 0.25-0.46, t2: 0.29-0.71). Therefore, the combined measure may be a feasible alternative to use the same pools to calculate GEBVs for both traits (t1: 0.45-0.57, t2: 0.62-0.76). Pools of 10 individuals were identified as representing a good compromise between loss of accuracy (~10%-15%) and cost savings (~90%) from genotype assays. In addition, we demonstrated that in more than 90% of the simulations, pools present higher sires' GEBV accuracy than individual genotypes when the number of genotype assays is limited (i.e., 110 or 220) and animals are assigned to pools based on phenotype. Pools assigned at random presented the poorest results (t1: 0.07-0.45, t2: 0.14-0.70). In conclusion, pooling by phenotype is the best approach to implementing genomic evaluation using commercial herd data, particularly when pools of 10 individuals are evaluated. While combining phenotypes seems a promising strategy to allow more flexibility to the estimates made using pools, more studies are necessary in this regard.

Keywords: DNA pooling; beef cattle; genomic selection; hybrid genomic relationship matrix.

Figure 1.

Experimental design. Three dots indicate analyses were performed as described immediately above.

Figure 2.

Heatmap of a hybrid relationship matrix of 200 individually genotyped sires and 220 pools of 10 animals each. Highlighted the difference between the three blocks relating only sires (individual genotypes), only progeny (pools) and sires to progeny (individual genotypes to pools). Black color represents values close to zero and intensity of red color increase as values increase.

Figure 3.

Mean accuracy of sires’ genomic estimated breeding value (GEBV) (A) and number of sires captured as top 100 GEBV that are also top 100 true breeding value (B) for a lowly (trait 1) and a moderately (trait 2) heritable trait, using pooled genotypes with different numbers of progeny chosen using four criteria: random (byRandom), grouped sorting by trait 1 (byT1), grouped sorting by trait 2 (byT2) or grouped sorting by a combination of traits (byCombo). Dots represent the average of 100 analyses within each pool size, pooling criteria and phenotype for each of the 10 replicates, except for pool size 1 that represents results when using individually genotyped progeny.

Figure 4.

Comparison between the percentage of genotype cost savings and the percentage of sires’ genomic estimated breeding value (GEBV) accuracy retained (AR) by each pool size (relative to the accuracy using individual genotypes) for trait 1, when pools are grouped sorting by itself (AR_T1byT1) or by combo (AR_T1byCombo), and for trait 2, when pools are grouped sorting by itself (AR_T2byT2) or by combo (AR_T2byCombo). The dashed line indicates the pool size which the percentage of genotyping cost savings meet the GEBV accuracy retained.

Figure 5.

Accuracy of sires’ genomic estimated breeding value (GEBV) and number of sires captured as top 100 GEBV that are also top 100 true breeding value (n = 200) for a lowly (trait 1—A and B, respectively) and a moderately (trait 2—C and D, respectively) heritable trait, using pooled genotypes with different numbers of randomly chosen progeny (n = 2,200; PS + number of animals in each pool). Distributions represent 100 analyses within each pool size and phenotype for one of the 10 replicates. Dashed lines indicate means, the continuous red line indicates results when using individually genotyped progeny and the continuous gray line indicate the limit around which results are completely random.

Figure 6.

Mean accuracy of sires’ genomic estimated breeding value (GEBV) (A) and number of sires captured as top 100 GEBV that are also top 100 true breeding value (B) for a lowly (trait 1) and a moderately (trait 2) heritable phenotype, using 110, 220, 440, or 1,100 individually genotyped progeny or all 2,200 progeny grouped in pools of 20, 10, 5, or 2 animals. Animals were assigned to pools using four criteria: random (byRandom), grouped sorting by trait 1 (byT1), grouped sorting by trait 2 (byT2) or grouped sorting by a combination of traits (byCombo). Individually genotyped animals were selected at random or as extremes of phenotypes. For pools, bars represent the average of 100 analysis within each trait, pooling criteria and phenotype for all 10 replicates. For individual animals, bars represent the average within replicates.