Towards multi-breed genomic evaluations for female fertility of tropical beef cattle

Developing accurate genomic evaluations of fertility for tropical beef cattle must deal with at least two major challenges (i) recording cow fertility traits in extensive production systems on large numbers of cows and (ii) the genomic evaluations should work across the breeds, crossbreds, and composites used in tropical beef production. Here, we assess accuracy of genomic evaluations for a trait which can be collected on a large scale in extensive conditions, corpus luteum score (CLscore), which is 1 if ovarian scanning indicates a heifer has cycled by 600 d and 0 if not, in a multi-breed population. A total of 3,696 heifers, including 979 Brahmans, 914 Droughtmasters, and 1,803 Santa Gertrudis in seven herds across 3-yr cohorts with CLscores, were genotyped for 24,211 SNPs. Genotypes were imputed to 728,785 SNPs. GBLUP and BayesR were used to predict GEBV. Accuracy of GEBV was evaluated with two validation strategies. In the first strategy, the last year cohort of heifers from each herd was used for validation, such that every herd had heifers in both reference and validation populations. In the second validation strategy, each herd in turn was removed in its entirety from the reference population, and was used for validation. For both validation strategies, accuracy of GEBV for single breed and multi-breed reference populations was assessed. For the first validation strategy, accuracy of GEBV ranged from 0.2 for Brahmans to 0.4 for Droughtmasters. Increasing marker density from 24K SNPs to 728K SNPs resulted in a small increase in accuracy, and including multiple-breeds in the reference did not help improve accuracy. These results suggest that provided a herd has animals in the reference population, the accuracy of the GEBV is largely determined by within herd (linkage) information. The situation was very different when entire herds were predicted in the second validation. In this case accuracy of GEBV using only 24K SNPs and only a within breed reference was close to zero for all breeds. Accuracy increased substantially when 728K SNPs, BayesR, and a multi-breed reference were used, from 0.15 for Brahmans to 0.35 for Santa Gertrudis. Given the second validation strategy is more likely to reflect the situation for many herds in tropical beef production (no animals in the reference), genomic evaluations for fertility in tropical beef cattle should be based on high-density markers (728K SNPs) and should be multi-breed

Multivariate genomic predictions for age at puberty in tropically adapted beef heifers

Heifers that have an earlier age at puberty often have greater lifetime productivity. Age at puberty is moderately heritable so selection should effectively reduce the number of days to puberty, and improve heifer productivity and profitability as a result. However, recording age at puberty is intensive, requiring repeat ovarian scanning to determine age at first corpus luteum (AGECL). Genomic selection has been proposed as a strategy to select for earlier age at puberty; however, large reference populations of cows with AGECL records and genotypes would be required to generate accurate GEBV for this trait. Reproductive maturity score (RMS) is a proxy trait for age at puberty for implementation in northern Australia beef herds, where large scale recording of AGECL is not feasible. RMS assigns a score of 0 to 5 from a single ovarian scan to describe ovarian maturity at ~600 d. Here we use multivariate genomic prediction to evaluate the value of a large RMS data set to improve accuracy of GEBV for age at puberty (AGECL). There were 882 Brahman and 990 Tropical Composite heifers with AGECL phenotypes, and an independent set of 974 Brahman, 1,798 Santa Gertrudis, and 910 Droughtmaster heifers with RMS phenotypes. All animals had 728,785 real or imputed SNP genotypes. The correlation of AGECL and RMS (h2 = 0.23) was estimated as -0.83 using the genomic information. This result also demonstrates that using genomic information it is possible to estimate genetic correlations between traits collected on different animals in different herds, with minimal or unknown pedigree linkage between them. Inclusion of heifers with RMS in the multi-trait model improved the accuracy of genomic evaluations for AGECL. Accuracy of RMS GEBV generally did not improve by adding heifers with AGECL phenotypes into the reference population. These results suggest that RMS and AGECL may be used together in a multi-trait prediction model to increase the accuracy of prediction for age at puberty in tropically adapted beef cattle

Evaluation of IMproving Palliative care Education and Training Using Simulation in Dementia (IMPETUS-D) a staff simulation training intervention to improve palliative care of people with advanced dementia living in nursing homes: a cluster randomised controlled trial

Background: People with dementia have unique palliative and end-of-life needs. However, access to quality palliative and end-of-life care for people with dementia living in nursing homes is often suboptimal. There is a recognised need for nursing home staff training in dementia-specific palliative care to equip them with knowledge and skills to deliver high quality care. Objective: The primary aim was to evaluate the effectiveness of a simulation training intervention (IMPETUS-D) aimed at nursing home staff on reducing unplanned transfers to hospital and/or deaths in hospital among residents living with dementia. Design: Cluster randomised controlled trial of nursing homes with process evaluation conducted alongside. Subjects & setting: One thousand three hundred four people with dementia living in 24 nursing homes (12 intervention/12 control) in three Australian cities, their families and direct care staff. Methods: Randomisation was conducted at the level of the nursing home (cluster). The allocation sequence was generated by an independent statistician using a computer-generated allocation sequence. Staff from intervention nursing homes had access to the IMPETUS-D training intervention, and staff from control nursing homes had access to usual training opportunities. The predicted primary outcome measure was a 20% reduction in the proportion of people with dementia who had an unplanned transfer to hospital and/or death in hospital at 6-months follow-up in the intervention nursing homes compared to the control nursing homes. Results: At 6-months follow-up, 128 (21.1%) people with dementia from the intervention group had an unplanned transfer or death in hospital compared to 132 (19.0%) residents from the control group; odds ratio 1.14 (95% CI, 0.82-1.59). There were suboptimal levels of staff participation in the training intervention and several barriers to participation identified. Conclusion: This study of a dementia-specific palliative care staff training intervention found no difference in the proportion of residents with dementia who had an unplanned hospital transfer. Implementation of the intervention was challenging and likely did not achieve adequate staff coverage to improve staff practice or resident outcomes. Trial registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12618002012257. Registered 14 December 2018