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Breeding for improved protein fractions and free amino acids concentration in bovine milk.

Giulio VisentinDonagh P BerryAngela CostaAudrey McDermottMassimo De MarchiSinead McParland
Published in: Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie (2022)
Considerable resources are required to routinely measure detailed milk compositional traits. Hence, an insufficient volume of phenotypic data can hinder genetic progress in these traits within dairy cow breeding programmes. The objective of the present study was to quantify the opportunities for breeding for improved milk protein and free amino acid (FAA) composition by exploiting mid-infrared spectroscopy (MIRS) predictions routinely recorded from milk samples. Genetic parameters for protein fractions and FAA composition were estimated using 134,546 test-day records from 16,166 lactations on 9,572 cows using linear mixed models. Heritability of MIRS-predicted protein fractions ranged from 0.19 (α-lactalbumin) to 0.55 (β-lactoglobulin A), while heritability of MIRS-predicted FAA ranged from 0.08 for glycine to 0.29 for glutamic acid. Genetic correlations among the MIRS-predicted FAA were moderate to strong ranging from -0.44 (aspartic acid and lysine) to 0.97 (glutamic acid and total FAA). Adjustment of the genetic correlations for the genetic merit of 24-h milk yield did not greatly affect the correlations. Results from the current study highlight the presence of exploitable genetic variation for both protein fractions and FAA in dairy cow milk. Besides, the direction of genetic correlations reveals that breeding programmes directly selecting for greater milk protein concentration carry with them favourable improvement in casein and whey fractions.
Keyphrases
  • amino acid
  • genome wide
  • protein protein
  • binding protein
  • gene expression
  • data analysis
  • big data
  • neural network