Developing flexible models for genetic evaluations in smallholder crossbred dairy farms.

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Predictors of breast self-examination performance among Jordanian university female students.

Breast cancer is considered one of the main types of cancer among female worldwide and in Jordan also. Early detection of it will improve the prognosis and decrease the mortality rate also. Thus, this study was conducted to assess the predictors of breast self-examination performance among Jordanian university female students. Across-sectional design was utilised in this study. A sample of 100 participants was completed the study survey (The Champion's Health Belief Model Scale). The main results or regression analysis showed that confidence (ß = .71, p < .0001) and perceived barriers (ß = -.061, p = .0004) were significant predictors of breast self-examination performance. In summary, other variables of Health belief model were found not be significant indicators of BSE performance in this study. However, the HBM is considered a valid framework to assess the predictors of breast self-examination knowledge, attitude, beliefs and barriers among Jordanian college female students.