Abstract #12
Section: Breeding and Genetics
Session: Breeding and Genetics Symposium: Joint Interbull/JAM Session: Milk spectral data—Cost-effective information to improve expensive and limited traits in dairy cattle breeding
Format: Oral
Day/Time: Sunday 10:00 AM–10:30 AM
Location: Sebastian I-1/2/3
Session: Breeding and Genetics Symposium: Joint Interbull/JAM Session: Milk spectral data—Cost-effective information to improve expensive and limited traits in dairy cattle breeding
Format: Oral
Day/Time: Sunday 10:00 AM–10:30 AM
Location: Sebastian I-1/2/3
# 12
Using milk spectroscopy phenotypes in genetic selection programs to improve the nutraceutical value of milk in dairy cows.
Henk Bovenhuis*1, 1Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands.
Key Words: infrared, prediction, milk fatty acids
Speaker Bio
Using milk spectroscopy phenotypes in genetic selection programs to improve the nutraceutical value of milk in dairy cows.
Henk Bovenhuis*1, 1Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands.
Milk is a unique product that is specifically suited to nourish the offspring and because initially milk is the young's only food it has to contain all essential ingredients. However, several studies have indicated that in modern human diets, a high intake of saturated fatty acids is a risk factor for cardiovascular diseases. Milk and dairy products are major sources of these fatty acids. Milk fat composition is strongly affected by genetic factors, which suggests that there are opportunities to change milk fat composition by means of selective breeding. A crucial factor in successful selection strategies is the availability of phenotypes. Milk fat composition can be accurately determined based on gas chromatography (GC); however, this analytical method is expensive and relatively time consuming. Alternatively, fatty acids can be predicted based on infrared spectra. Infrared prediction of unsaturated fatty acids might be based on indirect relations or on a direct relationship between infrared frequencies and the double bond within the fatty acid chain. Distinguishing direct and indirect relations is relevant as it might have consequences for the range of conditions under which prediction equations are valid. Bouwman (2014) compared genome-wide associations for GC-based fatty acids with infrared-predicted fatty acids and found considerable differences. A typical example was the region on BTA26 containing the SCD1 polymorphism. More recently, we studied the effect of the SCD1 polymorphism on all 1,060 individual infrared wavelengths (Wang et al., 2015). No wavelengths were significantly affected by the SCD1 polymorphism whereas the SCD1 polymorphism has been shown to have significant effects on the content of C10:0, C14:0, C18:0, C10:1, C12:1, C14:1, and C16:1 (Duchemin et al., 2012). This suggests that infrared spectra contain little direct information on the content of these fatty acids. This confirms results by Eskildsen et al. (2014), who concluded that prediction of individual fatty acids relies on correlations with fat content rather than on direct relations with specific Infrared frequencies.
Key Words: infrared, prediction, milk fatty acids
Speaker Bio
Henk Bovenhuis is associate professor at the Animal Breeding and Genomics Centre of Wageningen University. He obtained his PhD degree with honours at Wageningen University in 1992 which was titled “The relevance of milk protein polymorphisms for dairy cattle breeding”. He is involved in teaching courses in quantitative genetics, animal breeding and statistics. Henk is involved in quantitative genetic research in pigs poultry and cattle with emphasis on QTL detection and the genetic background of milk composition.