Browsing by Browse by FOR 2020 "300305 Animal reproduction and breeding"

For multivariate, single-step genomic best linear unbiased prediction analyses fitting a breeding value model, it is often assumed that the proportions of total genetic variance accounted for by genomic markers and residual polygenic effects are the same for all traits. Different covariance matrices for the two types of genetic effects are readily taken into account by fitting them separately. However, this can lead to slow convergence rates in iterative solution schemes. We propose an alternative computing strategy which – exploiting a canonical transformation – allows for trait-specific covariances whilst directly fitting total genetic effects only. Its effects on convergence rates and gains in accuracy and bias of genomic evaluation compared to analyses assuming proportionality of covariance matrices are examined using a small simulation study. Results show comparatively little improvement in accuracies but worthwhile reductions in overdispersion of predicted genetic merits for genotyped individuals without phenotypes.

Context. Genomic prediction is the use of genomic data in the estimation of genomic breeding values (GEBV) in animal breeding. In beef cattle breeding programs, genomic prediction increases the rates of genetic gain by increasing the accuracy of selection at earlier ages. Aims. The objectives of the study were to examine the effect of single-nucleotide polymorphism (SNP) density and to evaluate the effect of using SNPs preselected from imputed whole-genome sequence for genomic prediction. Methods. Genomic and phenotypic data from 2110 Hanwoo steers were used to predict GEBV for marbling score (MS), meat texture (MT), and meat colour (MC) traits. Three types of SNP densities including 50k, high-density (HD), and whole-genome sequence data and preselected SNPs from genome-wide association study (GWAS) were used for genomic prediction analyses. Two scenarios (independent and dependent discovery populations) were used to select top significant SNPs. The accuracy of GEBV was assessed using random cross-validation. Genomic best linear unbiased prediction (GBLUP) was used to predict the breeding values for each trait. Key results. Our result showed that very similar prediction accuracies were observed across all SNP densities used in the study. The prediction accuracy among traits ranged from 0.29 ± 0.05 for MC to 0.46 ± 0.04 for MS. Depending on the studied traits, up to 5% of prediction accuracy improvement was obtained when the preselected SNPs from GWAS analysis were included in the prediction analysis. Conclusions. High SNP density such as HD and the whole-genome sequence data yielded a similar prediction accuracy in Hanwoo beef cattle. Therefore, the 50K SNP chip panel is sufficient to capture the relationships in a breed with a small effective population size such as the Hanwoo cattle population. Preselected variants improved prediction accuracy when they were included in the genomic prediction model. Implications. The estimated genomic prediction accuracies are moderately accurate in Hanwoo cattle and for searching for SNPs that are more productive could increase the accuracy of estimated breeding values for the studied traits.

Rumen microbial profiles have been shown to be accurate predictors of methane emissions in a variety of species, however, it can be very costly and slow to generate a dataset with a sufficient number of individuals measured for methane who also have had rumen samples collected and processed into rumen microbial profiles for these benefits to be applied in industry. We evaluated the potential of combining datasets from New Zealand and Australian sheep to improve our ability to accurately predict methane emissions in Australian sheep. Prediction of Australian sheep methane emissions using rumen microbial profiles and phenotypes from New Zealand was possible, however, it was important to closely match the diets the sheep were fed to have confidence in the predictions. Prediction accuracies of Australian sheep methane emissions were higher when training on data collected on Australian sheep than training on New Zealand sheep; however augmentation of New Zealand data collected on a similar diet enabled more complex models to be run and an improvement in prediction accuracy.

This collection reviews the latest research on dairy cattle genetics and advanced methods of genetic evaluation and selection.

After an overview of genetic improvements achieved so far, Part 1 assesses the problem of inbreeding and genetic diversity in modern dairy cattle as well as opportunities for crossbreeding. Part 2 then goes onto review research on targeting non-production traits such as fertility, feed conversion efficiency and methane emissions as well as resistance to disease and resilience to heat stress.

Part 3 then surveys the latest techniques and advances in genomic selection (GS) in such areas as functional annotation and use of sequence variants to improve genomic prediction, as well as developments in genetic evaluation (GE). The final part of the book reviews developments in embryo technologies, gene editing and the way new techniques are being integrated in practice into dairy breeding programmes.

571 females from six beef breeds (Angus, Brahman, Charolais, Hereford, Shorthorn and Wagyu) from the first cohort of the Southern MultiBreed project were recorded for fertility traits at different physiological stages up until their second mating. Traits included age at puberty, days to calving and days to return to oestrus following first calving. Sire least-square means for these traits were used to examine relationships between traits. There was a strong positive relationship between age at puberty and days to calving, indicating that sires whose progeny reached puberty at a later age also conceived and calved later. There was a weaker positive relationship between age at puberty and return to oestrus indicating that sires whose progeny reached puberty at a later age also took longer to return to oestrus after the birth of their first calf. A weak negative relationship between days to calving and return to oestrus indicates that sires whose progeny calved later in the calving season exhibited a quicker return to oestrus. The nature of the relationship between these two traits was unexpected given previous studies, and further analyses once data from other years/cohorts is available will be required to gain confidence in the nature of the relationships between these three traits.

This thesis explores methods for estimating genotype by environment (G×E) interactions in livestock and plants. Genotype by environment interactions occur when the genetic architecture of a trait changes depending on the environment it exists in. They are particularly interesting as a source of genetic variation that could be utilised in breeding programs to select genotypes who have genetic merit that is more robust to environmental variation. This thesis aims to estimate genotype by environment interactions in livestock and plant populations using different methods and improve our understanding of how these interactions could be used in breeding programs to increase the robustness of agricultural populations to environmental variation.

The first experiment of this thesis investigated genotype by environment interactions in the bodyweight of Australian sheep using reaction norm and multi-trait models in combination with genomic data. It found significant variation in the slope of the reaction norm model that could be used to increase robustness of sheep, and that this variation was highly polygenic. It highlighted that both heterogenous genetic variance (scale-type G×E) and heterogenous genetic correlations (rank-type G×E) contributed to the variation in the reaction norm slope and found it could be important to separate these sources to better understand the genetic variation in robustness.

The second experiment of this thesis utilised a multi-environment trial of a Barley population to examine the effectiveness of two methods to partition the different types of G×E interactions when estimating the robustness of genotypes in reaction norm models. It found that genetic regression, which made breeding values for the slope independent of the intercept, was very effective in removing the impact of G×E interactions due to scale and isolating the variability across environments due to heterogenous genetic correlations. This enables the change in genetic architecture of traits across environments to be studied more clearly in reaction norm models. We also showed that factor analytic models, which are an alternative to reaction norms, are better equipped to capture complex G×E interactions because of their flexibility.

The third experiment of this thesis examined the use of factor analytic models to capture genotype by environment interactions in a multi-environment trial of sheep. The factor analytic models were able to approximate the unstructured genetic co(variance) matrix between 31 discrete environments using 85% fewer parameters than what would have been required with a multi-trait model. The model enabled the flock-years to be clustered by their similarity and showed that G×E interactions were large both between flocks and across years within flocks. It was unclear whether factor analytic models were preferrable to reaction norms based on the goodness-of-fit tests, and the estimates of heritability, genetic variance and genetic correlations between environments were inconsistent between the models.

The final experimental chapter assessed the capacity of reaction norm models to predict the robustness of a sire’s progeny performance across different growth environments. Using data collected in a research flock, the reaction norm models were predictive of the ability of a sire’s progeny to reliably gain weight across different growth environments at an accuracy that was consistent with the power of the data. It then found that these breeding values for robustness were consistent with breeding values estimated using data from the wider industry population recorded by commercial stud breeders. Selection based on reaction norm breeding values could be used to increase the robustness of body weight gain in Australian sheep to variation in growth environments.

Context. A thorough analysis of the reasons for culling was made to understand the phenotypic trend in herd life. In addition, identification of culling reasons could enable to develop a strategy for further evaluation of longevity in Australian dairy cows.
Aims. The aim of this study was to investigate the main causes of culling in Australian dairy herds and thereby to assess the trend of reason-specific culling over time.

Methods. Culling reasons in Australian dairy cattle were studied based on culling records from 1995 through 2016. A total of 2 452 124 individual cow culling observations were obtained from Datagene, Australia, of which 2 140 337 were Holstein and 311 787 were from Jersey cows. A binary logistic regression model was used to estimate effects of breed and age and the trend of a particular culling reason over time.

Key results. The most important culling reasons identified over the 21-year period were infertility (17.0%), mastitis (12.9%), low production (9.3%), sold for dairy purpose (6.4%) and old age (6.2%), whereas 37.4% were 'other reasons not reported'. The average age at culling was nearly the same for Holstein (6.75 years) and Jersey (6.73 years) cows. The estimated age at culling was slightly increased for Holstein cows (by 3.7 days) and somewhat decreased for Jersey cows (by 11 days) over the last two decades. The probability of culling cows for infertility and low production was high in early parities and consistently declined as age advanced, and culling due to mastitis was higher in older cows. The trend of main culling reasons over time was evaluated, indicating that the probability of culling due to infertility has progressively increased over the years in both breeds, and culling for mastitis in Jersey cows has also increased. Culling of cows due to low production sharply decreased from 2.5 to -8% for Holstein and from 73 to 60% for Jersey cows over the 21-year period.

Conclusions. Culling age has changed only little in both breeds whereas culling reasons have changed over the last two decades, with low production becoming a less important reason for culling and infertility becoming more important for Holstein and Jersey breeds.

Implications. Due to changes of culling reasons, there could be a change in the meaning of survival over time as well. As a result, genetic correlation with survival and other traits might be changed and accuracy and bias of genetic evaluations could be affected.

Sheep are an important part of the global agricultural economy. Growth and meat production traits are significant economic traits in sheep. The Texel breed is the most popular terminal sire breed in the UK, mainly selected for muscle growth and lean carcasses. This is a study based on a genome-wide association approach that investigates the links between some economically important traits, including computed tomography (CT) measurements, and molecular polymorphisms in UK Texel sheep. Our main aim was to identify single nucleotide polymorphisms (SNP) associated with growth, carcass, health and welfare traits of the Texel sheep breed. This study used data from 384 Texel rams. Data comprised ten traits, including two CT measured traits. The phenotypic data were placed in four categories: growth traits, carcass traits, health traits and welfare traits. De-regressed estimated breeding values (EBV) for these traits together with sire genotypes derived with the Ovine 50 K SNP array of Illumina were jointly analysed in a genome wide association analysis. Eight novel chromosome-wise significant associations were found for carcass, growth, health and welfare traits. Three significant markers were intronic variants and the remainder intergenic variants. This study is a first step to search for genomic regions controlling CT-based productivity traits related to body and carcass composition in a terminal sire sheep breed using a 50 K SNP genome-wide array. Results are important for the further development of strategies to identify causal variants associated with CT measures and other commercial traits in sheep. Independent studies are needed to confirm these results and identify candidate genes for the studied traits.

Including genomics in genetic evaluations can effectively increase selection response, especially for hard to measure, sex limited, and late in life traits. Modelling the increase in accuracy is useful when designing reference data projects and when breeders choose animals to genotype. Theoretical equations exist to predict the EBV accuracy of un-phenotyped animals. However, there are anecdotal reports that the accuracy obtained in practice was often lower than theoretical predictions. This paper validated an empirical approach to predicting accuracy in Australian Brahman data for nine traits. The empirical approach required the accuracy of reference and target animals from a standard pedigree BLUP genetic evaluation and the accuracy of reference animals from a GBLUP genetic evaluation. Using this information, a series of equations were applied to obtain the predicted GBLUP accuracy for target animals. Forward cross-validation showed that the empirical predicted GBLUP was comparable to the actual GBLUP accuracy observed for target animals (accuracy differed between 0.9% and 3.6%). In contrast, theoretical predictions differed from the observed GBLUP accuracy between 5.2% and 21.8%. For smaller (<4,000) reference populations, the theoretical accuracy was closer to the observed GBLUP accuracy, with differences ranging from 5.2% to 11.6%. The theoretical accuracy was overestimated by between 20.7% and 21.8% for larger reference populations. Empirical estimates of the effective number of chromosome segments (M_e) were between 2.0 and 3.9 times that of theoretical M_e, with the greatest difference being for the traits with larger reference sizes. This suggests that the theoretical M_e is the reason for overestimated theoretical accuracy predictions.

In developing countries, the livestock sector plays an important role in achieving food security and alleviating poverty for the rapidly growing human population. Cattle populations are an important part of these resources and have significant socio-economic and cultural importance to the livelihood of farmers in Africa. The advances in genotyping technology, such as high-density marker arrays or even whole-genome sequencing, provide unique opportunities for in-depth assessments of genetic structure and genetic diversity of indigenous cattle populations. Understanding population sub-structures plays an important role in several allied fields, including conservation genetics, association studies, and quantitative genetics. Genomic markers also allow breeders to trace relationships between animals accurately and can thus be useful for facilitating genetic improvement and breeding decisions.

This thesis focuses on providing important insights into the genetic diversity and structure of African cattle populations and presents methods and tools for genetic improvement in smallholder crossbred dairy systems in Africa. A brief description of the research chapters of the thesis follows:

Chapter 1 presents a general introduction to cattle domestication and a description of African cattle breeds, followed by a review of smallholder dairy farming based on crossbred cattle in Africa. The chapter also discusses linkage disequilibrium and several statistical approaches that are used for genetic diversity estimation, selection of informative SNP panels for breed proportion and parentage assignment, and methods for genotype imputation.

In Chapter 2, the genetic diversity and structure of the African indigenous and dairy breed proportions of crossbred cattle populations were studied in relation to Bos indicus, European Bos taurus, and African Bos taurus reference populations using medium-density SNP data. I found that all African indigenous cattle populations are hybrids between Bos indicus and African Bos taurus except some West African pure Bos taurus breeds, with West African and South African populations showing a lower Bos indicus content than East African populations. Estimates of effective population sizes declined for all African cattle breeds from a large population at 2,000 generations ago. The largest European dairy proportions found in Kenyan and Tanzanian crossbreds were Holstein/Friesian and Ayrshire with some influence also from Jersey and Guernsey. In Uganda and Ethiopia, the dairy ancestry was mostly from Holstein/Friesian and in Senegal, the dairy proportion were mostly from Monbeliarde and Holstein.

Chapter 3 focused on the selection of informative SNP markers for estimation of total dairy breed proportion and parentage assignment in African crossbred dairy cattle. For estimation of dairy breed proportion, small SNP panels performed better when the highest proportion of markers was selected to differentiate African Bos taurus from European Bos taurus ancestral populations, compared to markers distinguishing Bos indicus from Bos taurus. In all African crossbred populations, unambiguous parentage assignment was possible with ≥300 SNPs for the majority of the panels when parents were sought among all animals with known genotypes, and ≥200 SNPs when parents were sought only among animals known to be a parent of at least one progeny.

Chapter 4 investigated the inference of local and global ancestry and estimated the heterozygosity proportions in West African crossbreds, and genotype imputation accuracies in African indigenous and crossbred cattle populations. Two approaches were tested to estimate the admixture of crossbred cattle, assuming either two or three ancestral populations. Estimates from both the LAMP-LD and ADMIXTURE approaches were highly correlated (r ≥0.981) and showed an average European Bos taurus content of ~49.7% with SD of ±19.8%. The observed heterozygosity proportions in putative F1 crosses were much higher than for other crossbred and pure breed individuals. The imputation accuracy was generally higher when the reference data came from the same geographical region as the target population, and when crossbred reference data was used to impute crossbred genotypes. The lowest imputation accuracies were observed for indigenous breeds.

Finally, Chapter 5 summarises the overall conclusion of all the research chapters and a general perspective on the findings and their relevance to the field, and highlighting the main findings of the thesis.