Comparison of methods for deriving phenotypes from incomplete observation data with an application to age at puberty in dairy cattle

• Published in: Journal of animal science and biotechnology Vol. 14; no. 1; p. 119
• Main Authors: Stephen, Melissa A., Burke, Chris R., Pryce, Jennie E., Steele, Nicole M., Amer, Peter R., Meier, Susanne, Phyn, Claire V. C., Garrick, Dorian J.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.09.2023; AL Rae Centre for Genetics and Breeding-Massey University,Ruakura,Hamilton 3214,New Zealand%DairyNZ Ltd,605 Ruakura Road,Hamilton 3240,New Zealand%Agriculture Victoria Research,AgriBio,Centre for AgriBioscience,Bundoora,Victoria 3083,Australia; School of Applied Systems Biology,La Trobe University,Bundoora,Victoria 3083,Australia%AbacusBio,Dunedin,New Zealand%AL Rae Centre for Genetics and Breeding-Massey University,Ruakura,Hamilton 3214,New Zealand; DairyNZ Ltd,605 Ruakura Road,Hamilton 3240,New Zealand; BMC
• Subjects: Age; Agriculture; Animal breeding; Animal Genetics and Genomics; Animal Physiology; Animals; Biomedical and Life Sciences; Biotechnology; Blood plasma; Cattle; Continuity (mathematics); crossbreds; Dairy cattle; Data augmentation; Data reduction; Food Science; Genetic crosses; Genotype & phenotype; Gibbs sampler; Heritability; Holstein; Intervals; Jersey; Life Sciences; Life span; longevity; Lower bounds; Markov-chain Monte Carlo (MCMC); Methods; New Zealand; Offspring; Parameter estimation; Pasture; phenotype; Phenotypes; Phenotyping; Plasma; progeny; Progesterone; Puberty; Reproductive status; sires; Statistical analysis; Time dependence; Upper bounds; variance; New Zealand; Gibbs sampler; Puberty; Cattle; Markov-chain Monte Carlo (MCMC); Markov-chain Monte Carlo(MCMC)
• ISSN: 2049-1891; 1674-9782; 2049-1891
• DOI: 10.1186/s40104-023-00921-5

Background Many phenotypes in animal breeding are derived from incomplete measures, especially if they are challenging or expensive to measure precisely. Examples include time-dependent traits such as reproductive status, or lifespan. Incomplete measures for these traits result in phenotypes that are subject to left-, interval- and right-censoring, where phenotypes are only known to fall below an upper bound, between a lower and upper bound, or above a lower bound respectively. Here we compare three methods for deriving phenotypes from incomplete data using age at first elevation (> 1 ng/mL) in blood plasma progesterone (AGEP4), which generally coincides with onset of puberty, as an example trait. Methods We produced AGEP4 phenotypes from three blood samples collected at about 30-day intervals from approximately 5,000 Holstein–Friesian or Holstein–Friesian × Jersey cross-bred dairy heifers managed in 54 seasonal-calving, pasture-based herds in New Zealand. We used these actual data to simulate 7 different visit scenarios, increasing the extent of censoring by disregarding data from one or two of the three visits. Three methods for deriving phenotypes from these data were explored: 1) ordinal categorical variables which were analysed using categorical threshold analysis; 2) continuous variables, with a penalty of 31 d assigned to right-censored phenotypes; and 3) continuous variables, sampled from within a lower and upper bound using a data augmentation approach. Results Credibility intervals for heritability estimations overlapped across all methods and visit scenarios, but estimated heritabilities tended to be higher when left censoring was reduced. For sires with at least 5 daughters, the correlations between estimated breeding values (EBVs) from our three-visit scenario and each reduced data scenario varied by method, ranging from 0.65 to 0.95. The estimated breed effects also varied by method, but breed differences were smaller as phenotype censoring increased. Conclusion Our results indicate that using some methods, phenotypes derived from one observation per offspring for a time-dependent trait such as AGEP4 may provide comparable sire rankings to three observations per offspring. This has implications for the design of large-scale phenotyping initiatives where animal breeders aim to estimate variance parameters and estimated breeding values (EBVs) for phenotypes that are challenging to measure or prohibitively expensive.