Genomic predictions for yield traits in US Holsteins with unknown parent groups

Abstract

The objective of this study was to assess the reliability and bias of estimated breeding values (EBV) from traditional BLUP with unknown parent groups (UPG), genomic EBV (GEBV) from single-step genomic BLUP (ssGBLUP) with UPG for the pedigree relationship matrix (A) only (SS_UPG), and GEBV from ssGB-LUP with UPG for both A and the relationship matrix among genotyped animals (A 22 ; SS_UPG2) using 6 large phenotype-pedigree truncated Holstein data sets. The complete data included 80 million records for milk, fat, and protein yields from 31 million cows recorded since 1980. Phenotype-pedigree truncation scenarios included truncation of phenotypes for cows recorded before 1990 and 2000 combined with truncation of pedigree information after 2 or 3 ancestral generations. A total of 861,525 genotyped bulls with progeny and cows with phenotypic records were used in the analyses. Reliability and bias (inflation/deflation) of GEBV were obtained for 2,710 bulls based on deregressed proofs, and on 381,779 cows born after 2014 based on predictivity (adjusted cow phenotypes). The BLUP reliabilities for young bulls varied from 0.29 to 0.30 across traits and were unaffected by data truncation and number of generations in the pedigree. Reliabilities ranged from 0.54 to 0.69 for SS_UPG and were slightly affected by phenotype-pedigree truncation. Reliabilities ranged from 0.69 to 0.73 for SS_UPG2 and were unaffected by phenotype-pedigree truncation. The regression coefficient of bull deregressed proofs on (G)EBV (i.e., GEBV and EBV) ranged from 0.86 to 0.90 for BLUP, from 0.77 to 0.94 for SS_UPG, and was 1.00 ± 0.03 for SS_UPG2. Cow predictivity ranged from 0.22 to 0.28 for BLUP, 0.48 to 0.51 for SS_UPG, and 0.51 to 0.54 for SS_UPG2. The highest cow predictivities for BLUP were obtained with the most extreme truncation, whereas for SS_UPG2, cow predictivities were also unaffected by phenotype-pedigree truncations. The regression coefficient of cow predictivities on (G)EBV was 1.02 ± 0.02 for SS_UPG2 with the most extreme truncation, which indicated the least biased predictions. Computations with the complete data set took 17 h with BLUP, 58 h with SS_UPG, and 23 h with SS_UPG2. The same computations with the most extreme phenotype-pedigree truncation took 7, 36, and 15 h, respectively. The SS_UPG2 converged in fewer rounds than BLUP, whereas SS_UPG took up to twice as many rounds. Thus, the ssGBLUP with UPG assigned to both A and A 22 provided accurate and unbiased evaluations, regardless of phenotype-pedigree truncation scenario. Old phenotypes (before 2000 in this data set) did not affect the reliability of predictions for young selection candidates, especially in SS_UPG2.

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

“…This was already suggested by Lourenco et al (2014). Cesarani et al (2021) showed approximately unbiased evaluations, regardless of data truncation, when the most correct model (an SSGBLUP method) was used. However, for seemingly incorrect models (another SSGB-LUP method or BLUP), Cesarani et al (2021) observed a positive effect (reduction of overdispersion) when truncating old data; the effect was marked in cows and rather small in bulls.…”

“…Cesarani et al (2021) showed approximately unbiased evaluations, regardless of data truncation, when the most correct model (an SSGBLUP method) was used. However, for seemingly incorrect models (another SSGB-LUP method or BLUP), Cesarani et al (2021) observed a positive effect (reduction of overdispersion) when truncating old data; the effect was marked in cows and rather small in bulls. The reason why we observed a more marked effect is probably because AI rams did not have as many daughters as AI bulls (hundreds for AI rams compared with thousands or more for AI bulls).…”

“…For SSGBLUP, we used (fixed) unknown parent groups using the QP transformation (Misztal et al, 2013). Note that another, more recent, option uses the QP transformation only for the A −1 relationship matrix but not for the G −1 , which seems to avoid double counting and results in better predictions (Cesarani et al, 2021); we have not considered it. Alternatively, to using unknown parent groups, we used the theory of metafounders, which explicitly includes relationships across genetic groups (Legarra et al, 2015).…”

Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP

“…Few researchers have examined AltQP-M. and Masuda et al (2019a) observed a reduction in inflation of GEBV with AltQP-M relative to QP-M for type and production traits in US Holstein. Using more recent data for production traits in the same population, AltQP-M showed greater predictive ability and generally less inflation in GEBV for young bulls and heifers than QP-M (Cesarani et al, 2021). These authors also found the predictive ability of AltQP-M to be more stable than that of QP-M at various pedigree depths.…”

Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor

“…Despite past limits due to problems with unknown parent groups (UPG) and computational cost, recent studies demonstrated the validity of this method for evaluating breeding values in several different livestock species including dairy (Himmelbauer et al, 2021;Liu and Alkhoder, 2021;Pimentel et al, 2021) and beef cattle (Lourenco et al, 2015), buffalo (Aspilcueta-Borquis et al, 2015;Cesarani et al, 2021a), goats (Teissier et al, 2018), and sheep (Cesarani et al, 2019;Macedo et al, 2020). Recently, better prediction features of ssGBLUP compared with BLUP in US Holstein were reported (Cesarani et al, 2021b). Those authors used about 860k genotyped animals and demonstrated that old phenotypic records (i.e., before 2000) can be removed without reducing prediction accuracy of young selection candidates.…”