AUTHOR=Cecchinato Alessio , Toledo-Alvarado Hugo , Pegolo Sara , Rossoni Attilio , Santus Enrico , Maltecca Christian , Bittante Giovanni , Tiezzi Francesco 

TITLE=Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.563393

DOI=10.3389/fgene.2020.563393

ISSN=1664-8021

ABSTRACT=The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: i) estimating the genetic parameters for 30 measured milk traits and their infrared FTIR predictions and to investigate the additive genetic correlation between them, and ii) evaluating the effectiveness of FTIR-derived phenotyping mimicking candidate bull’s progeny testing or breeding value prediction at birth. Data were from 1,123 cows, phenotyped using the gold standard laboratory methodologies (LAB data). These records included phenotypes related to fine milk composition and milk technological characteristics, milk acidity and milk protein fractions. The FTIR dataset comprised the predictions from 729,202 test-day records of 51,059 Brown Swiss cows (FIELD data). A first approach consisted in estimating genetic parameters for LAB and FIELD data. A set of bivariate animal models were run and genetic correlations between LAB and FIELD were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations between LAB and FIELD (ra) had different strengths depending on the traits, but they were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator trait for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 LAB cows, 1,493 FIELD cows, 181 sires with both LAB and FIELD daughters and 540 sires with FIELD daughters only. Predictions were obtained using the single-step GBLUP method. A 4-fold cross-validation was used to assess the predictive ability of the different models. The correlation between observed and predicted LAB measures in validation was averaged over the 4 training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: i) LAB cows from the training set; ii) FIELD cows from the training set; and iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of the traits, which pushes towards the need of a robust collection of FIELD measures on daughters of proven bulls.