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 FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull’s progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes 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 (ra) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four 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 traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.
Highlights
In the omics- era, an emerging field of research is represented by phenomics, which is the study of phenotypes on a genome-wide scale (Bilder et al, 2009; Houle et al, 2010)
Heritability estimates were comparable between datasets for traits recorded earlier (e.g., RCT, kCF), whereas heritability estimates decreased for later measured LAB traits, but not for Fourier-transformed infrared spectroscopy (FTIR) predicted FIELDlact1 traits (a30, a45, CFP, CFmax)
The present study reported two approaches for assessing the contribution of FTIR calibration equations at the population level for dairy cattle breeding
Summary
In the omics- era, an emerging field of research is represented by phenomics, which is the study of phenotypes on a genome-wide scale (Bilder et al, 2009; Houle et al, 2010). The advance in high-throughput genomics has increased the need for simple, fast, accurate, and high-throughput phenotyping technologies. Fourier-transformed infrared spectroscopy (FTIR), including part of near- and mid-infrared (NIR and MIR) electromagnetic radiations, is a versatile and cost-effective analytical tool to collect individual data for monitoring traditional and novel milk traits in dairy cattle (Boichard and Brochard, 2012). Infrared technology has been proposed as an alternative method for the quantification of difficult- or expensive-to-measure milk phenotypes including protein fractions, fatty acids, and minerals as well as milk coagulation properties (MCP), cheese yield, and curd nutrient recoveries (Soyeurt et al, 2006a,b, 2011; Ferragina et al, 2013; Cecchinato et al, 2015; Sanchez et al, 2018). FTIR data has been shown to be a potentially valuable tool for predicting health and reproductive phenotypes (Belay et al, 2017; Toledo-Alvarado et al, 2018), as well as residual feed intake, dry matter intake (DMI), and methane emissions (Bittante and Cipolat-Gotet, 2018; Dórea et al, 2018)
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