Breeding objectives for dairy cattle under low, medium and high production systems in the tropics.

A deterministic bio-economic model was developed to estimate economic weights for genetic improvement of lactation milk yield, fat yield, age at first calving, calving interval, mature weight and survival under low, medium and high production systems in the Tropics. Input parameters were obtained from dairy production systems in Kenya which has a tropical environment. The highest proportion of revenue is from the sale of milk followed by sale of heifers, cull cows and sale of male calves under all production systems. On the other hand, feed cost is the most important production cost followed by labour, marketing, reproduction and health costs, respectively. Economic values for the six traits were derived from a profit equation using revenue and production costs per cow per year. The economic values were then discounted using diffusion coefficients which account for differences between traits in the time when the improvement is expressed. Economic weights were robust to changes in input and output prices, changes in feeding strategies, and changes in milk and surplus heifer marketing strategies. Genetic standard deviations were multiplied by economic values to standardise the economic value of traits and to compare their potential for economic response. When expressed as proportion of their sum, these relative economic weights under the low, medium and high production systems for lactation milk yield were 51.36, 59.79 and 63.98%; for fat yield 4.50, 10.69 and 9.05%; for age at first calving 3.16, 2.66 and 0.55%; for calving interval 33.59, 19.88 and 20.05%; for mature weight 1.55, 1.34 and 1.19% and for survival rate 5.84, 5.64 and 5.18%, respectively. The predicted responses followed the same pattern as the relative economic weights. This shows that milk yield and calving interval were most important in all production systems but the value of response for traits differed between production systems with more emphasis on milk yield and less on calving interval in the high production systems. Moderate correlations were estimated between the breeding objective for the low, medium and high production systems. To maximise response in the overall breeding objective, different selection criteria are required for the three production systems.

Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya.

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiple-breed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium- (0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low- and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and -0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.

Genetic correlations between wool traits and meat quality traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and 25 meat quality and nutritional value traits were estimated for Merino sheep from an Information Nucleus (IN). Genetic correlations among the meat quality and nutritional value traits are also reported. The IN comprised 8 flocks linked genetically and managed across a range of sheep production environments in Australia. The wool traits included over 5,000 yearling and 3,700 adult records for fleece weight, fiber diameter, staple length, staple strength, fiber diameter variation, scoured wool color, and visual scores for breech and body wrinkle. The meat quality traits were measured on samples from the and included over 1,200 records from progeny of over 170 sires for intramuscular fat (IMF), shear force of meat aged for 5 d (SF5), 24 h postmortem pH (pHLL; also measured in the , pHST), fresh and retail meat color and meat nutritional value traits such as iron and zinc levels, and long-chain omega-3 and omega-6 polyunsaturated fatty acid levels. Estimated heritabilities for IMF, SF5, pHLL, pHST, retail meat color lightness (), myoglobin, iron, zinc and across the range of long-chain fatty acids were 0.58 ± 0.11, 0.10 ± 0.09, 0.15 ± 0.07, 0.20 ± 0.10, 0.59 ± 0.15, 0.31 ± 0.09, 0.20 ± 0.09, 0.11 ± 0.09, and range of 0.00 (eicosapentaenoic, docosapentaenoic, and arachidonic acids) to 0.14 ± 0.07 (linoleic acid), respectively. The genetic correlations between the wool production and meat quality traits were low to negligible and indicate that wool breeding programs will have little or no effect on meat quality. There were moderately favorable genetic correlations between important yearling wool production traits and the omega-3 fatty acids that were reduced for corresponding adult wool production traits, but these correlations are unlikely to be important in wool/meat breeding programs because they have high SE, and the omega-3 traits have little or no genetic variance. Significant genetic correlations among the meat quality traits included IMF with SF5 (-0.76 ± 0.24), fresh meat color * (0.50 ± 0.18), and zinc (0.41 ± 0.19). Selection to increase IMF will improve meat tenderness and color which may address some of the issues with Merino meat quality. These estimated parameters allow Merino breeders to combine wool and meat objectives without compromising meat quality.

Genetic parameters for wool traits, live weight, and ultrasound carcass traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and live weight and ultrasound fat depth (FAT) and eye muscle depth (EMD) traits were estimated from the Information Nucleus (IN). The IN comprised 8 genetically linked flocks managed across a range of Australian sheep production environments. The data were from a maximum of 9,135 progeny born over 5 yr from 184 Merino sires and 4,614 Merino dams. The wool traits included records for yearling and adult fleece weight, fiber diameter (FD), staple length (SL), fiber diameter CV (FDCV), scoured color, and visual scores for breech and body wrinkle. We found high heritability for the major yearling wool production traits and some wool quality traits, whereas other wool quality traits, wool color, and visual traits were moderately heritable. The estimates of heritability for live weight generally increased with age as maternal effects declined. Estimates of heritability for the ultrasound traits were also higher when measured at yearling age rather than at postweaning age. The genetic correlations for fleece weight with live weights were positive (favorable) and moderate (approximately 0.5 ± 0.1), whereas those with FD were approximately 0.3 (unfavorable). The other wool traits had lower genetic correlations with the live weights. The genetic correlations for FAT and EMD with FD and SL were positive and low, with FDCV low to moderate negative, but variable with wool weight and negligible for the other wool traits. The genetic correlations for FAT and EMD with postweaning weight were positive and high (0.61 ± 0.18 to 0.75 ± 0.14) but were generally moderate with weights at other ages. Selection for increased live weight will result in a moderate correlated increase in wool weight as well as favorable reductions in breech cover and wrinkle, along with some unfavorable increases in FD and wool yellowness but little impact on other wool traits. The ultrasound meat traits, FAT and EMD, were highly positively genetically correlated (0.8), and selection to increase them would result in a small unfavorable correlated increase in FD, moderately favorable reductions in breech cover and wrinkle, but equivocal or negligible changes in other wool traits. The estimated parameters provide the basis for calculation of more accurate Australian Sheep Breeding Values and selection indexes that combine wool and meat objectives in Merino breeding programs.

Genetic correlations between wool traits and carcass traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and 14 whole carcass measures and carcass component traits were estimated from the Information Nucleus of 8 flocks managed across a range of Australian sheep production environments and genetically linked. Wool data were from over 5,000 Merino progeny born over 5 yr, whereas carcass data were from over 1,200 wether progeny of over 176 sires, slaughtered at about 21 kg carcass weight, on average. Wool traits included yearling and adult records for wool weight, fiber diameter, fiber diameter variation, staple strength, scoured color, and visual scores for breech and body wrinkle. Whole carcass measures included HCW, dressing percentage (DP), and various measures of fat depth and eye muscle dimensions. Carcass components were obtained by dissection, and lean meat yield (LMY) was predicted. Heritability estimates for whole carcass measures ranged from 0.12 ± 0.08 to 0.35 ± 0.10 and ranged from 0.17 ± 0.10 to 0.46 ± 0.10 for carcass dissection traits, with no evidence of important genotype × environment interactions. Genetic correlations indicated that selection for increased clean wool weight will result in reduced carcass fat (-0.17 to -0.34) and DP (-0.48 ± 0.15), with little effect on carcass muscle. Selection for lower fiber diameter will reduce HCW (-0.48 ± 0.15) as well as carcass fat (0.14 to 0.27) and muscle (0.21 to 0.50). There were high genetic correlations between live animal measures of fat and muscle depth and the carcass traits (generally greater than 0.5 in size). Selection to increase HCW (and DP) will result in sheep with fewer wrinkles on the body (-0.57 ± 0.10) and barer breeches (-0.74 ± 0.12, favorable), with minor deterioration in scoured wool color (reduced brightness and increased yellowness). Selection for reduced fat will also result in sheep with fewer body wrinkles (-0.42 to -0.79). Increasing LMY in Merinos through selection would result in a large reduction in carcass fat and DP (-0.66 to -0.84), with a smaller increase in carcass muscle and some increase in wool weight and wrinkles. Although no major antagonisms are apparent between the wool and carcass traits, developing selection indexes for dual-purpose wool and meat breeding objectives will require accurate estimates of genetic parameters to ensure that unfavorable relationships are suitably considered. The findings will aid development of dual-purpose wool and meat breeding objectives.

Genomic prediction of reproduction traits for Merino sheep.

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

Prevalence of hearing loss and tinnitus in Iraq and Afghanistan Veterans: A Chronic Effects of Neurotrauma Consortium study.

The purpose of this study was to describe the prevalence of hearing loss and tinnitus in a cohort of Iraq and Afghanistan Veterans (IAV) with common post-deployment conditions, including traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), and other typical post-concussive conditions such as headaches and vertigo/dizziness. This retrospective observational study used data from the national Veterans Health Administration (VA) data repository from fiscal years 2001-2014. Veteran data was included if there were at least three years of VA care, with one or more years of care in 2007 or after. We identified comorbidities that may be associated with post-deployment hearing loss or tinnitus including TBI, PTSD, depression, and common post-concussive symptoms using International Classification of Diseases, 9th Revision, Clinical Modification codes. A multinomial logistic regression analysis was used to examine conditions associated with hearing loss or tinnitus. Among IAV, 570,332 were included in the final analysis. Of these, 7.78% of these were diagnosed with hearing loss alone, 6.54% with tinnitus alone, and 6.24% with both hearing loss and tinnitus. Comorbid TBI, PTSD, and depression were significantly associated with increased rates of hearing loss, tinnitus, or both conditions together. Older individuals, males, and those with TBI, PTSD, or vertigo/dizziness were significantly more likely to have hearing loss, tinnitus, or both. In order to provide more holistic post-deployment support, this myriad of conditions should be carefully considered in the planning of clinical care and beyond.

Hearing testing in the U.S. Department of Defense: Potential impact on Veterans Affairs hearing loss disability awards.

Hearing loss is the second most common disability awarded by the U.S. Department of Veterans Affairs (VA) to former members of the U.S. uniformed services. Hearing readiness and conservation practices differ among the four largest uniformed military services (Air Force, Army, Marine Corps, and Navy). Utilizing a data set consisting of all hearing loss claims submitted to the VA from fiscal years 2003-2013, we examined characteristics of veterans submitting claims within one year of separation from military service. Our results indicate that having a hearing loss disability claim granted was significantly more likely for men, individuals over the age of 26 years at the time of the claim, individuals most recently serving in the U.S. Army, and those with at least one hearing loss diagnosis. Importantly, individuals with at least one test record in the Defense Occupational and Environmental Health Readiness System-Hearing Conservation (DOEHRS-HC) system were significantly less likely to have a hearing loss disability claim granted by the VA. Within the DOEHRS-HC cohort, those with at least one threshold shift or clinical hearing loss diagnosis while on active duty were more than two and three times more likely to have a hearing loss disability claim granted, respectively. These findings indicate that an established history of reduced hearing ability while on active duty was associated with a significantly increased likelihood of an approved hearing loss disability claim relative to VA claims without such a history. Further, our results show a persistent decreased rate of hearing loss disability awards overall. These findings support increased inclusion of personnel in DoD hearing readiness and conservation programs to reduce VA hearing loss disability awards.

Accuracy of genomic selection for age at puberty in a multi-breed population of tropically adapted beef cattle.

Genomic selection is becoming a standard tool in livestock breeding programs, particularly for traits that are hard to measure. Accuracy of genomic selection can be improved by increasing the quantity and quality of data and potentially by improving analytical methods. Adding genotypes and phenotypes from additional breeds or crosses often improves the accuracy of genomic predictions but requires specific methodology. A model was developed to incorporate breed composition estimated from genotypes into genomic selection models. This method was applied to age at puberty data in female beef cattle (as estimated from age at first observation of a corpus luteum) from a mix of Brahman and Tropical Composite beef cattle. In this dataset, the new model incorporating breed composition did not increase the accuracy of genomic selection. However, the breeding values exhibited slightly less bias (as assessed by deviation of regression of phenotype on genomic breeding values from the expected value of 1). Adding additional Brahman animals to the Tropical Composite analysis increased the accuracy of genomic predictions and did not affect the accuracy of the Brahman predictions.

Estimation of accuracies and expected genetic change from selection for selection indexes that use multiple-trait predictions of breeding values.

Procedures are described for estimating selection index accuracies for individual animals and expected genetic change from selection for the general case where indexes of EBVs predict an aggregate breeding objective of traits that may or may not have been measured. Index accuracies for the breeding objective are shown to take an important general form, being able to be expressed as the product of the accuracy of the index function of true breeding values and the accuracy with which that function predicts the breeding objective. When the accuracies of the individual EBVs of the index are known, prediction error variances (PEVs) and covariances (PECs) for the EBVs within animal are able to be well approximated, and index accuracies and expected genetic change from selection estimated with high accuracy. The procedures are suited to routine use in estimating index accuracies in genetic evaluation, and for providing important information, without additional modelling, on the directions in which a population will move under selection.

Unknown-parent groups in single-step genomic evaluation.

In single-step genomic evaluation using best linear unbiased prediction (ssGBLUP), genomic predictions are calculated with a relationship matrix that combines pedigree and genomic information. For missing pedigrees, unknown selection processes, or inclusion of several populations, a BLUP model can include unknown-parent groups (UPG) in the animal effect. For ssGBLUP, UPG equations also involve contributions from genomic relationships. When those contributions are ignored, UPG solutions and genetic predictions can be biased. Options to eliminate or reduce such bias are presented. First, mixed model equations can be modified to include contributions to UPG elements from genomic relationships (greater software complexity). Second, UPG can be implemented as separate effects (higher cost of computing and data processing). Third, contributions can be ignored when they are relatively small, but they may be small only after refinements to UPG definitions. Fourth, contributions may approximately cancel out when genomic and pedigree relationships are constructed for compatibility; however, different construction steps are required for unknown parents from the same or different populations. Finally, an additional polygenic effect that also includes UPG can be added to the model.

Genetic correlations among and between wool, growth and reproduction traits in Merino sheep.

Data from seven research resource flocks across Australia were combined to provide accurate estimates of genetic correlations among production traits in Merino sheep. The flocks represented contemporary Australian Merino fine, medium and broad wool strains over the past 30 years. Over 110,000 records were available for analysis for each of the major wool traits, and 50,000 records for reproduction and growth traits with over 2700 sires and 25,000 dams. Individual models developed from the single trait analyses were extended to the various combinations of two-trait models to obtain genetic correlations among six wool traits [clean fleece weight (CFW), greasy fleece weight, fibre diameter (FD), yield, coefficient of variation of fibre diameter and standard deviation of fibre diameter], four growth traits [birth weight, weaning weight, yearling weight (YWT), and hogget weight] and four reproduction traits [fertility, litter size, lambs born per ewe joined, lambs weaned per ewe joined (LW/EJ)]. This study has provided for the first time a comprehensive matrix of genetic correlations among these 14 wool, growth and reproduction traits. The large size of the data set has also provided estimates with very low standard errors. A moderate positive genetic correlation was observed between CFW and FD (0.29 +/- 0.02). YWT was positively correlated with CFW (0.23 +/- 0.04), FD (0.17 +/- 0.04) and LWEJ (0.58 +/- 0.06), while LW/EJ was negatively correlated with CFW (-0.26 +/- 0.05) and positively correlated with FD (0.06 +/- 0.04) and LS (0.68 +/- 0.04). These genetic correlations, together with the estimates of heritability and other parameters provide the basis for more accurate prediction of outcomes in complex sheep-breeding programmes designed to improve several traits.

Duodenal osmoconcentration and food intake in pigs after ingestion of hypertonic nutrients.

Samples were taken from the duodenum of young pigs via indwelling catheters after ingestion of liquid meals. They had been deprived of feed and water overnight. Samples were taken every minute for 15 min and then at 20, 25, 30, 40, 50, and 60 min. After ingestion of hypertonic glucose solutions, duodenal osmolality rose rapidly to maximal levels within 10-15 min when osmoconcentrations were as follows: for 10% glucose, 480 mosmol/kg H2O; for 20%, 680; and for 40%, 1,200. After 5% glucose, osmolality was about 280 mosmol/kg H2O and did not change appreciably during the hour. Water ingestion caused a fall to 60 mosmol/kg H2O at 3 min and then a slow recovery. Ingestion of glucose-in-milk solutions gave similar but slightly higher values. When the glucose-in-water or -milk solutions were ingested just before a meal of feed pellets, meal size was depressed proportional to glucose concentration.