AN ALTERNATIVE MEASURE OF FERTILITY FOR GENETIC EVALUATION OF DAIRY CATTLE

SUMMARY We propose a method of genetic evaluation based on days open (as a measure of fertility), using information from survival status, culling reason, mating, pregnancy and calving data. Unlike calving interval (CI), days open (DO) can be recorded on cows that are pregnancy tested and later culled. Consequently, it better reflects the fertility of a bull's daughters than does CI. To overcome wide variation between herds in the distribution of DO or CI, a transformation is carried out based on the ranking of cows within a herd-year-season. The heritability of DO on the original scale (0.03) was the same as that on the transformed scale. Because many cows do not have a pregnancy diagnosis recorded, equations to predict DO from mating, culling and calving data were developed. These estimates can be used to predict transformed DO for all cows depending on the type of data they have and this can then be used for genetic evaluation for fertility. INTRODUCTION In the Australian economic index called Australian Profit Ranking fertility is one of the most important traits. Currently genetic evaluation of dairy cattle for fertility is largely based on calving interval (CI) because the amount of mating and pregnancy test (PD) data is still small. CI can only be determined for cows that have re-calved. Cows that do not re-calve, including those that are culled for poor fertility, are excluded. This means information on the least fertile group of cows is excluded and this may lead to inaccurately estimated breeding values for their sires. Another shortcoming of CI as a measure of fertility in Australia, where seasonal, split and year round calving systems are practiced, is the wide variation between herds in the distribution of CI. Seasonal calving herds may have a narrow range of CIs whereas other herds may have cows with a CI of over 2 years. In addition to CI there is also information for some cows on mating, PD, culling and culling reasons. Ideally, the genetic evaluation system would use all data and cows would not be excluded for missing data. In this paper we propose a method to achieve this by using all available information on each cow to predict her days open (DO). The predicted DO can then be used for genetic evaluation. The proposed method standardise the distribution of DO based on the rank of the cow within the heard-year season (HYS) and predicts DO when this information is missing. DO has been chosen because it can be recorded from PD even on cows that are later culled and so do not re-calve. Also non-pregnant cows could potentially calve after the pregnant cows within a HYS, so they can be assigned a meaningful rank for DO. The ranked DO are transformed so that the transformed DO follows an exponential distribution, as one would expect when a cow had a fixed probability to become pregnant in each time period (cycle of oestrous). In this report we compare estimates of heritability (h 2 ) of the transformed DO and the original trait (DO) to show the suitability of the transformed trait for genetic evaluation. Many cows have no PD data and all we know is their CI or the fact that they were culled. We use data where PD i

Evaluating the potential impact of selection for the A2 milk allele on inbreeding and performance in Australian Holstein cattle

Selection decisions are generally based on estimated breeding values (EBV) for a combination of traits that are polygenic (e.g. milk production). However, in some cases, there is additional intense selection for a single allele, or SNP, for a perceived benefit, such as selection for polled or A2 milk. Using a dataset where the A2 mutation was imputed using a reference population with whole genome sequence, we tested the hypothesis that intense selection in Australian Holstein cattle for the A2 allele in the β-casein gene may have resulted in increased inbreeding. We also estimated the average difference in performance between animals homozygous for the A1 or A2 allele for a range of traits. Using high-density genotypes we compared differences in genome-wide and regional inbreeding between Holstein cows homozygous for either the A1 or A2 β-casein alleles i.e. A1/A1 or A2/A2. This study shows that between the years 2000 to 2017, the frequency of the A2/A2 genotype increased by 20% in Holstein cows (from 32% to 52%). Our results suggest that selection for homozygosity at the β-casein A2 allele has increased inbreeding both across the genome and on chromosome 6 in A2/A2 Holstein cows. Animals that were A2/A2 were twice as likely to have a run of homozygosity of at least 1Mb long across the β-casein locus compared to animals that were A1/A1. Cows that are homozygous for the A2 allele had an average protein yield EBV advantage of 0.24 genetic standard deviations (SD) compared to A1/A1 homozygous cows. In contrast, A2/A2 homozygous animals were on average 0.2 genetic SD inferior on fertility EBV. As a result, the difference in the overall economic index (that includes traits contributing to profitability) there was only a small advantage of 0.05 SD for A2/A2 cows compared to A1/A1 cows. However, strong selection for the A2 allele has likely led to a higher level of regional and overall inbreeding which in the long term could harm genetic progress for some or all economic traits. Therefore, applying approaches that mitigate rapid inbreeding while selecting for preferred alleles and quantitative traits may be desirable

Traditional medicinal plant knowledge and use by local healers in Sekoru District, Jimma Zone, Southwestern Ethiopia

The knowledge and use of medicinal plant species by traditional healers was investigated in Sekoru District, Jimma Zone, Southwestern Ethiopia from December 2005 to November 2006. Traditional healers of the study area were selected randomly and interviewed with the help of translators to gather information on the knowledge and use of medicinal plants used as a remedy for human ailments in the study area. In the current study, it was reported that 27 plant species belonging to 27 genera and 18 families were commonly used to treat various human ailments. Most of these species (85.71%) were wild and harvested mainly for their leaves (64.52%). The most cited ethnomedicinal plant species was Alysicarpus quartinianus A. Rich., whose roots and leaves were reported by traditional healers to be crushed in fresh and applied as a lotion on the lesions of patients of Abiato (Shererit). No significant correlation was observed between the age of traditional healers and the number of species reported and the indigenous knowledge transfer was found to be similar. More than one medicinal plant species were used more frequently than the use of a single species for remedy preparations. Plant parts used for remedy preparations showed significant difference with medicinal plant species abundance in the study area

Accuracy of genomic breeding values in multi-breed dairy cattle populations

<p>Abstract</p> <p>Background</p> <p>Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.</p> <p>Methods</p> <p>Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.</p> <p>Results</p> <p>When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.</p> <p>Conclusion</p> <p>Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.</p

Tracking health system performance in times of crisis using routine health data: lessons learned from a multicountry consortium

COVID-19 has prompted the use of readily available administrative data to track health system performance in times of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health System Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People's Democratic Republic, Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health conditions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health information technologies; reduce delays and loosen restrictions for researchers to access the data; complement routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of service disruptions