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type:Article, 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 ssGBLUP 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 ±

type:Article, Over half a million Holsteins are being genotyped annually in the United States. The computational cost of including all genotypes in single-step genomic (ssG)BLUP is high, although it is feasible to conduct large-scale genomic prediction using an efficient algorithm such as APY (algorithm for proven and young). An effective method to further reduce the computing cost could be the use of indirect genomic predictions (IGP) for genotyped animals when they have neither progeny nor phenotypes. These young genotyped animals have no effect on the other genotyped animals and could have their genomic prediction done indirectly. The main objective of this study was to calculate IGP for various groups of genotyped animals and investigate the reduction in computing time as well as bias and accuracy of the IGP. We compared IGP with genomic (G)EBV for 18 linear type traits in US Holsteins, including 2.3 million (M) genotyped animals. The full data set consisted of 10.9M records for 18 linear type traits up to 2018 calving, 13.6M animals in the pedigree, and 2.3M animals genotyped for 79K SNP. For IGP, ssGBLUP included all genotyped animals except those with neither progeny nor phenotypes by year from 2014 to 2018 (i.e., the target animals). The SNP marker effects were computed based on GEBV for genotyped animals that had progeny, or phenotypes, or both. Further, IGP were calculated for target genotyped animals in each year group. For all genotyped animal groups from 2014 to 2018, the coefficients of determination (R^2) of a linear regression of GEBV on IGP were 0.960 for males and 0.954 for females for 18 traits on average. To reduce computing costs, the SNP marker effects were calculated based on GEBV from randomly selected genotyped animals from 15K to 60K. By randomly selecting a small number of genotyped animals, the computing time was dramatically reduced. As more genotyped animals were randomly selected to calculate SNP effects, R^2 was higher (more accurate) and the regre