Predictive potential of genotypes within the prolactin, growth hormone and insulin-like growth factor-I pathways in genetic evaluation of 305 days milk yield in Holstein cows in Sonora, Mexico
Date
2015
Authors
Hernandez Cordero, Ana Isabel, author
Thomas, Milton G., advisor
Enns, Richard M., advisor
Speidel, Scott, committee member
McConnel, Craig, committee member
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Abstract
The objective of this study was to calculate a molecular breeding value (MBV) using single nucleotide polymorphims (SNP) within genes of the prolactin (PRL) and growth hormone and insulin-like growth factor (GH-IGF1) pathways associated with milk production traits and evaluate their effectiveness in genetic prediction in Holstein cows in Sonora, Mexico. We hypothesized that MBV constructed using DNA markers within the PRL and GH-IGF1 pathways have the potential to predict milk production traits in heat-stressed lactating Holstein cows. The data contained observations of 659 Holstein dairy cows collected during 2012 from the city of Obregón, Sonora, Mexico. Milk yield observations were recorded monthly and 305 d milk yield was calculated. Cows were genotyped for 179 tag SNP within 43 genes in the PRL and GH-IGF1 pathways. Eight SNP within 5 genes were associated with 305d milk yield (P ≤ 0.05). No previous research reported these associations. Their effects were used to estimate a MBV. The linear correlation of the MBV and 305 d milk yield was 0.21 and the adjusted R² was 4.5%. Genetic parameters were estimated in ASREML for 305 d milk yield (h² = 0.39 ± 0.11). A training and predicting exercise, was performed using SAS 9.4 with the same data set. The SNP effects and association were estimated and used to calculate an MBV. The MBV was estimated and evaluated by comparing estimates from a 5-fold strategy of random clustering. This procedure was repeated five times, resulting in five MBV. To evaluate the effectiveness of these MBV, correlations and adjusted R² were estimated between MBV and 305 d milk yield. One MBV (MBV5) was correlated (-0.27) and had an adjusted R² of 6.37%. The MBV estimated from SNP within the PRL and GH-IGF1 pathways genes was positive but weakly associated with 305 d milk yield. In the training-predicting exercise, only 1 of the 5 MBV explained a portion of the variation in 305 d milk yield. The small amount of phenotypic variation may be due to the small numbers of SNP used to calculate the MBV and the polygenic nature of the trait under heat stress conditions. The quality of the data, could also affect the results. We accept our hypothesis, the MBV was capable of predicting a portion of the phenotypic variation in 305 d milk yield in lactating Holstein cows in Sonora, MX. Nevertheless, the accuracy and amount of variability explained was not enough to be feasible for use in genetic selection procedures.
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Subject
IGF1
prolactin
GH
SNP
MBV