Genetic Analysis of Groups of Mid-infrared Predicted Fatty Acids in Milk

This thesis investigated the phenotypic and genetic variation of five groups of mid-infrared (MIR) predicted fatty acids; short-chain (4 to 10 carbons), medium-chain (12 to 16 carbons), long-chain (17 to 22 carbons), saturated, and unsaturated fatty acids. The predicted fatty acid values (g/dL of milk) were log transformed in order to improve normality. The data set included 49,127 test-day records from 10,029 first lactation Holstein cows in 810 herds. The total number of animals in the pedigree was 76,074. The random regression animal test-day model included: days in milk, herd-test date, and season-age of calving (polynomial regression) as fixed effects, and herd-year of calving, animal and permanent environment effects as random polynomial regressions, and random residual effect. Third and fourth degree of Legendre polynomial were estimated for fixed regression of season-age of calving effect and for the three random regressions, respectively. The average daily heritability estimates were 0.24, 0.32, 0.23, 0.33, and 0.21 for short-chain, medium-chain, long-chain, saturated, and unsaturated fatty acids, respectively. Estimated average daily genetic correlations were positive among all fatty acid groups and ranged from moderate to high (0.63-0.96). This study demonstrates the existence of genetic variation in milk fatty acid profile predicted by MIR spectra. Therefore, milk fatty acid composition can be improved through genetic selection. Keywords: milk fatty acid, mid-infrared, random regression model, heritability