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Genomic analyses for predicted milk fatty acid composition throughout lactation in North American Holstein cattle
P H F Freitas1, H R Oliveira2, F F Silva3
1Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Department of Animal Sciences, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
Abstract:
Milk fat composition has important implications in the nutritional and processing properties of milk. Additionally, milk fat composition is associated with cow physiological and health status. The main objectives of this study were (1) to estimate genetic parameters for 5 milk fatty acid (FA) groups (i.e., short-chain, medium-chain, long-chain, saturated, and unsaturated) predicted from milk infrared spectra using a large data set; (2) to predict genomic breeding values using a longitudinal single-step genomic BLUP approach; and (3) to conduct a single-step GWAS aiming to identify genomic regions, candidate genes, and metabolic pathways associated with milk FA, and consequently, to understand the underlying biology of these traits. We used 629,769 test-day records of 201,465 first-parity Holstein cows from 6,105 herds. A total of 8,865 genotyped (Illumina BovineSNP50K BeadChip, Illumina, San Diego, CA) animals were considered for the genomic analyses. The average daily heritability ranged from 0.24 (unsaturated FA) to 0.47 (medium-chain and saturated FA). The reliability of the genomic breeding values ranged from 0.56 (long-chain fatty acid) to 0.74 (medium-chain fatty acid) when using the default τ and ω scaling parameters, whereas it ranged from 0.58 (long-chain fatty acid) to 0.73 (short-chain fatty acid) when using the optimal τ and ω values (i.e., τ = 1.5 and ω = 0.6), as defined in a previous study in the same population. Relevant chromosomal regions were identified in Bos taurus autosomes 5 and 14. The proportion of the variance explained by 20 adjacent single nucleotide polymorphisms ranged from 0.71% (saturated FA) to 15.12% (long-chain FA). Important candidate genes and pathways were also identified. In summary, our results contribute to a better understanding of the genetic architecture of predicted milk FA in dairy cattle and reinforce the relevance of using genomic information for genetic analyses of these traits.
