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.

Background The effects of environmental disturbances on livestock are often observed indirectly through the variability patterns of repeated performance records over time. Sheep are frequently exposed to diverse extensive environments but currently lack appropriate measures of resilience (or sensitivity) towards environmental disturbance. In this study, random regression models were used to analyse repeated records of the fbre diameter of wool taken along the wool staple (bundle of wool fbres) to investigate how the genetic and environmental variance of fbre diameter changes with different growing environments.

Results A model containing a fifth, fourth and second-order Legendre polynomial applied to the fixed, additive and permanent environmental effects, respectively, was optimal for modeling fbre diameter along the wool staple. The additive genetic and permanent environmental variance both showed variability across the staple length trajectory. The ranking of sire estimated breeding values (EBV) for fbre diameter was shown to change along the staple and the genetic correlations decreased as the distance between measurements along the staple increased. This result suggests that some genotypes were potentially more resilient towards the changes in the growing environment compared to others. In addition, the eigenfunctions of the random regression model implied the ability to change the fbre diameter trajectory to reduce its variability along the wool staple.

Conclusions These Results show that genetic variation in fbre diameter measured along the wool staple exists and this could be used to provide greater insight into the ability to select for resilience in extensively raised sheep populations.

The hypothesis tested by this study was that sheep with divergent estimated breeding values (EBV) for fleece weight differ in gut metabolism and anatomy" regardless of the level of intake. Adult Merino wethers with contrasting EBVs for fleece weight were fed at two levels of intake in two 7-week periods in a crossover design, where wool growth, gut metabolism and anatomy of the sheep were evaluated. Regardless of the level of intake, wool genotype affected wool growth (P<0.05); however, rumen metabolism and gut anatomy did not differ between wool genotypes (P<0.05). Increases in the level of intake increased the supply of nutrients to the animal and the measured end-products of the process (wool production, live weight, methane) independent of wool genotype. The results obtained in this study indicate that differences in gut fermentation and anatomy are not a major cause of differences in wool production among sheep of different estimated genetic merit for fleece weight when fed restricted intakes.

BreedObject is the software used to formalise breeding objectives and create the selection indexes produced by BREEDPLAN. The BreedObject breeding objectives and selection indexes allow cattle producers to identify the most profitable cattle genetics for the beef production system modelled by each selection index. Since the release of the latest version (6.2) of the BreedObject software, eight Australian beef cattle breed organisations have implemented 29 new or revised selection indexes. This paper discusses the process by which the selection indexes were developed in conjunction with the relevant breed societies, summarises the EBV emphases applied in these new selection indexes, and discusses the breeder feedback and implications of the selection indexes in the greater industry.

The Australian sheep industry has used genomically enhanced breeding values since 2012. The ability to incorporate genomic information was only made possible by industry investment in a national genomic reference. Initiated in 2007, the Sheep CRC Information Nucleus Flock and its later incarnation the MLA Resource Flock has provided growth, carcase, and wool phenotypes on ~40k genotyped individuals across Merino, Terminal and Maternal breed types. Australian Wool Innovation's Merino Lifetime Productivity project and the Australian Merino Sire Evaluation sites also provided valuable data for the Merino breed. By maintaining a successful genomic reference that directly contributes to the national genetic evaluation producers were shown the direct value of genotyping. Currently, across the core national Sheep Genetic analyses, there are over 500k genotyped animals from industry and research contributing to the evaluation. As a result, the genomic reference is expanding beyond the research population and the role of industry levies to fund the reference population is declining. Future funding will depend on co-investment by breeders beyond levy contributions, with investment dependent on breeders perceiving value in a genomic reference. New and hard to measure traits continue to require recording in research flocks. Consequently, the next iteration of the resource flock will focus on underrepresented and future traits of importance. Providing the core population for the recording of methane and feed intake as well as resilience and maternal behaviour traits. The role of the reference population can be further enhanced by creating stronger linkages between different maternal and Merino ram sources, needed for prediction of breeding values across breed types and of crossbred animals. This would achieve more reliable genomic prediction reliability for key traits such as reproduction and meat eating quality across the commercial breeding flocks.

This thesis aimed to define a suitable definition of ewe longevity for the Australian sheep industry and determine the merit of incorporating the trait into the Australian sheep breeding objectives. Therefore, the fundamental requirements for incorporating ewe longevity in the Australian sheep breeding objectives, the genetic parameters and economic values were estimated for ewe longevity and stayability traits followed by an estimation of response to selection.

The first part of this thesis deals with the data exploration of the MERINOSELECT and LAMBPLAN maternal databases within the Sheep Genetics and the estimation of genetic parameters for the ewe longevity and stay ability traits in Merino and maternal breeds. The majority of the flocks submitting data to the MERINOSELECT and LAMBPLAN maternal databases do not have sufficient recording patterns to derive longevity. However, the contemporary groups with regular recording patterns were selected. The contemporary groups were defined as the site × flock × year of birth. These contemporary groups of ewes with regular recording patterns within the MERINOSELECT database were; 1) born since the year 2000, 2) had spent a minimum of 3 years in the flock, 3) had their own annual weight (weaning, postweaning or yearling) or wool record and reproduction record (from 2 yrs) up to 6 years of age, 4) contained at least 30 ewes, and 5) at least 70% of the animals were assigned a sire (chapter 3).The ewe longevity or time in flock (TIF) was defined as the period between birth and the last available production record. The stay ability traits were defined as the presence of a ewe in flock up to certain periods of time. The heritability estimates of the ewe's longevity and stayability traits were moderate if not corrected for the ewes’ production and reproductive performance. However, after correcting for these traits, the ewe's longevity and stayability traits were lowly heritable. The correlation between the ewes’ longevity and stayability traits was strong.

The correlation between ewe longevity and production and reproduction traits was estimated via a series of bivariate analyses. The analysed production traits were weaning weight (wwt), post-weaning weight (pwt), post-weaning C-site fat (pcf), post-weaning eye muscle depth (pemd), post-weaning faecal egg count (pfec), yearling weight (ywt), yearling Csite fat (ycf), yearling eye muscle depth (yemd), yearling faecal egg count (yfec), yearling greasy fleece weight (ygfw), yearling fibre diameter (yfd), adult greasy fleece weight (agfw) and adult fibre diameter (afd). The reproductive traits analysed were fertility (fert), litter size (ls), number of lambs born (nlb), ewe rearing ability (era) and number of lambs weaned (nlw). The ewes’ TIF was lowly heritable and correlated to the production and reproduction traits. Therefore, a breeding objective was to be formulated that considers longevity as an objective trait, which requires calculating the economic value of the ewes’ TIF trait.

The second part of this thesis deals with the estimation of economic value and response to selection. The economic value of the ewes’ TIF was large across the fine wool Merino, dual purpose Merino and maternal production systems. Ewe longevity has a positive correlation with the current breeding objectives suggesting that selection on the current breeding objectives will improve ewe longevity across the three production systems within the Australian sheep industry. However, including longevity trait in the breeding objectives will further increase the overall genetic gain, and particularly improve genetic gain for longevity. In the maternal production system, the genetic gain of the growth and carcase traits slows down a little after including the longevity trait in the breeding scenarios. The results showed a 2 to 3% increase in the total dollar response across the three production systems after incorporating the ewes’ TIF trait into the breeding objectives. However, incorporating genomic information of TIF into breeding objectives increased the overall response by 13 to 16% across three production systems. The results suggest that selection based on the current breeding objectives will improve ewe longevity within the Merino and maternal production systems but noticeably higher genetic gain can be attained if the genomic information of ewes’ TIF is incorporated into the breeding objectives.

The final chapter discusses the research findings and concludes with recommendations for future research areas. These recommendations include important encouragements like improving the data quality, the importance of recording culling reasons, estimating accurate genetic parameters of ewe longevity and the potential of incorporating the ewe longevity as a trait in the Australian sheep breeding objectives to achieve higher genetic gain. This thesis contributes significantly to define ewe longevity and using the genetic parameters in the Australian sheep breeding objectives.

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, market-ing, 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.

In 2016 an Australian project, the Advanced Livestock Measurement Technologies project (ALMTech), was initiated to accelerate the development and implementation of technologies that measure lean meat yield and eating quality. This led to the commercial testing, and implementation of a range of new technologies in the lamb, beef, and pork industries. For measuring lean meat yield %, these technologies included dual energy X-ray absorptiometry, hand-held microwave systems, and 3-D imaging systems. For measuring beef rib-eye traits and intramuscular fat %, both pre- and post-chilling technologies were developed. Post-chilling, a range of camera systems and near infrared spectrophotometers were developed. While pre-chilling, technologies included insertable needle probes, nuclear magnetic resonance, and X-ray systems. Initially these technologies were trained to predict the pre-existing traits already traded upon within industry. However, this approach was limiting because the technologies could measure attributes that were either non-existent in the trading language, were superior as calibrating standards, or more accurately reflected value than the pre-existing trait. Therefore, we introduced IMF% into the trading language for both beef and sheep meat, and carcase lean%, fat%, and bone % for sheep meat. These new technologies and the traits that they predict have delivered multiple benefits. Technology provider-companies are instilled with the confidence to commercialise due to the provision of achievable accreditation standards. Processors have the confidence to invest in these technologies and establish payment grids based upon their measurements. And lastly, it has enhanced data flow into genetic databases, industry data systems (MSA), and as feedback to producers.

In the last decades, multiple methods using molecular genetic data have been developed and their application in breeding programs have been evaluated. Particularly, due to the development and cost of dense genotyping arrays (SNP chips) and sequencing technologies (whole genome and RNA sequencing), these techniques have become standard methods to study the association between genetic variants and phenotypic variation in important traits in livestock (‘genomics’) as well as levels of expression for contrasting groups in age of development or phenotype (‘transcriptomics’). This thesis explores the use of transcriptomics and genomics applied to better understand economically important complex traits in beef cattle (marbling and residual feed intake) as well as the utility of using information generated by these technologies in the estimation of breeding values.

In this thesis, in chapter 3, we describe the characterization of muscle development and the progress of fat deposition at the transcriptomic level in Hanwoo cattle. This Korean breed has the genetic potential to accumulate intramuscular fat to reach very high levels of marbling and high prices in the market. To examine the key genes and pathways that regulate the differentiation process in satellite cells, we extracted these cells from Longissimus dorsi (LD) and semimembranosus (SM) of three newborn calves, promote cell differentiation in culture cells and evaluated the differences between muscles in a time-series RNA-seq experiment. The histological (differentiation index) results indicated that LD muscle differentiated faster from myoblast into multinucleated myotubes than SM. These results agreed with the gene expression of the myogenic regulatory factors (MRF) which tend to be significantly up-regulated at the end of the differentiation in LD, specifically the genes MYOD, MYF6 and MYOG. The number of genes differentially expressed was larger across time than across muscles. In total, thirteen genes (HOXB2, HOXB4, HOXB9, HOXC8, FOXD1, IGFN1, ZIC2, ZIC4, HOXA11, HOXC11, PITX1, SIM2 and TBX4) were differentially expressed (DE) between muscles, which seem to be involved in modulating the muscle lineage development during myogenesis. In addition, our results indicated, in agreement with previous studies in other species, that some of the DE genes modulated the expression of myogenic regulatory factors (MYOD and MYF5) during the differentiation process.

The use of RNA-seq on the marbling development of Hanwoo helped to better understand variation in gene expression related to high or low marbling phenotypes. In chapter 4, we describe an experiment where muscle samples from Longissimus dorsi were studied at the age of 18 (by biopsy) and 30 months. Twelve animals were grouped according to their marbling score in Low (average 2.4, range from 1-5), and High (average 6.28, range from 6-9). In total, 1,883 differentially expressed genes were identified from multiple contrasts, among them 782 genes were up-regulated and 1,101 were down-regulated. Differences in transcriptome were higher between ages rather than between marbling groups. The genes SLC38A4, ABCA10, APOL6, and two novel genes (ENSBTAG00000015330, ENSBTAG00000046041) were up-regulated in the High marbling group at 18 months of age. These genes are likely to have important roles in energy transport and utilization during growth of steers. Potential markers for marbling development (LEP, MEDAG, FOXO1, ADIG, ADIPOQ, CMKLR1, and FABP4) were identified from the functional analysis as involved in regulation of fat cell differentiation or brown fat cell differentiation. These results imply a potential use of gene expression technologies to identify younger steers that will develop high marbling. Further functional studies would need to be conducted to better understand the role of these genes on marbling.

The combination of multiple omic technologies opens up the possibility to improve the interpretation of a trait from many approaches. The combination of expression studies with mapping quantitative trait loci was applied in chapter 5 for studying the genetic architecture of residual feed intake (RFI) in 2190 Angus steers. First, the imputation from low density to medium density and later to high-density genotyping arrays was performed for 2,190 animals using a larger population of Angus with genotypes as reference. Additionally, the RNA sequences from 126 Angus cattle divergently selected for RFI were analyzed in a multi-tissue experiment (from liver, blood and muscle). The estimated heritability for RFI was 0.3 and we identified 78 SNPs associated with RFI on six QTL located on BTA1, BTA6, BTA14, BTA17, BTA20 and BTA26. The most significant SNP was on chromosome BTA20 (rs42662073) for which STC2 was the closest gene. The genes OAS2, SHOX, XKR4, and SGMS1 were the closest to the significant QTL on BTA17, BTA1, BTA14, and BTA26, respectively. In the 2 Mb windows around the six significant QTL, we identified fifteen genes whose expression was significantly associated with RFI selection line: NEURL1B, CPEB4, RITA1, CCDC42B, OAS2, RPL6, ERP29, ATP6V1H, MRPL15, MFSD1, RARRES1, A1CF, SGMS1, PAPSS2, and PTEN. The results imply that the integration of GWAS and gene expression analysis may help to contribute with knowledge and help to understand better genetic variation in complex traits like RFI.

The ultimate goal in livestock breeding programs is always to find the most accurate way to select breeding animals according to a defined breeding objective and one way to achieve this could be to apply genomic selection. The final study in this thesis (chapter 6) attempts to investigate the utility of integrating information from GWAS or gene expression studies into increasing the accuracy of genomic prediction of RFI. We used the results from chapter 5 and evaluated the accuracy of genomic prediction using data on 2,190 Angus steers with two cross-validation designs; one where the GWAS was performed on the same data used for training the genomic prediction named four cross-validation (4CV) and a design where GWAS and training sets were separate data denoted as four by four cross validation (4x4CV). The accuracy of prediction of RFI did not improve when using a 770k SNP panel compared with 50k SNP panel. There were 1.2% and 2.7% point increase in accuracy when top SNPs from GWAS were added to the 50k or 770k panels in the 4x4CV design. The 4CV design showed lower accuracy when using top SNPs and the predictions were much more biased. Genomic prediction accuracy can be slightly improved when using selected SNPs from GWAS and GSA. Further analysis would need to be conducted using a larger population to confirm these results.

Finally, the thesis ends with some thoughts on the application of “omics” in livestock breeding programs, the impact on accuracy of prediction and the improvement of experimental design. This chapter features a final summary and concluding remarks about the research involved in producing this thesis as well as notes on the interpretation of complex trait research and reflections future research directions regarding the use of gene expression and gene association to improve livestock production systems.

Stochastic simulation was used to test the hypothesis that optimum-contribution selection with genomic relationships using marker loci with low minor allele frequency (MAF) below a predefined threshold (referred as TGOCS) to control inbreeding maintained more genetic variation than pedigree relationships (POCS) at the same rate of true genetic gain (∆G_true). Criteria to measure genetic variation were the number of segregating QTL loci (quantitative trait loci) and the average number of founder alleles per locus. Marker alleles having a MAF below 0.025 were used in forming the genomic relationships in TGOCS strategy. For centering in establishing genomic relationships, when the allele frequency of marker loci with low MAF set to 0.5 the TGOCS strategy maintained 66% fewer founder alleles than POCS and there were 30% fewer QTL segregating. This TGOCS strategy maintained 61% fewer founder alleles than GOCS and 28% fewer segregating QTL loci. When the allele frequency of marker loci with low MAF was set to observed allele frequency these figures were 8%, 2%, 5% and 2%, respectively. Using marker loci with low MAF in the TGOCS strategy was inferior to both GOCS and POCS. Both TGOCS and GOCS were affected by the same constraint that is LD (linkage disequilibrium) between markers and QTL. Therefore, POCS is a more efficient method to maintain genetic variation in the population until a better way to use genomic information in optimum-contribution selection is identified.

Background:

Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to "fly strike" (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle.

Results:

A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed.

Conclusions:

The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.

The capacity to measure and select livestock that are more resilient to environmental fluctuation is of increasing importance amidst climate change, labour shortages and increasing production demand. Currently, however, there is no consensus on how to quantify resilience, particularly in extensive sheep populations. In this study, we explored the ability to derive resilience indicator traits from fibre diameter variation measured longitudinally (5 mm increments) along the wool staple. Fibre diameter varies in relation to the supply of nutrients to the wool follicles and thereby provides a stable archive of the animal's physiological status across the preceding wool growth period. From this fibre diameter variation, ways to detect and characterise an animal's ability to withstand or be minimally affected by its environment were explored. The heritability estimates of these traits were shown to be low to moderate (0.10 to 0.31), indicating that genetic variation exists for fibre diameter variation measured along the wool staple which may be interpreted as a measure of resilience. The inclusion of such measures in sheep breeding programs has the potential to improve the resilience of sheep to environmental challenges, which may have positive implications for sheep enterprise profitability, health and welfare.

The 50K genotypes of 2,935 animals from the 5 most common temperate beef breeds in Australia were used to identify genomic footprints of selection based on fixation index (FST). A principal component analysis on genomic relationships between all individuals showed that Angus, Hereford and Wagyu are the most genetically differentiated breeds. Therefore, 3 pairwise FST comparisons were implemented between Angus vs. Wagyu, Angus vs. Hereford and Hereford vs. Wagyu. Genome-wide comparison of patterns of the FST values revealed 14 candidate regions under selection on chromosomes 2:6, 8, 12, 13, 20, and 24. Several of the identified candidate regions in this study have been previously reported for different economically important traits in beef cattle. In addition, our identified candidate regions for signatures of selection harboured genes in several enriched annotation clusters. If validated, the results from this study can be incorporated in genomic selection of the Australian beef cattle population.

This thesis examines the methods to improve the productivity of a dual-purpose native chicken breed in Thailand. Five generations of body weights and egg production data on Lueng Hang Kao Kabinburi (LHKK) chickens were used for estimation of genetic parameters in chapters 3 and 4. Traits considered were body weights, measured at four-weekly intervals from hatch to 24 weeks of age (BW1D, BW4, BW8, BW12, BW16, BW20, and BW24), body weight at first egg (BWFE), age at first egg (AFE), egg weight at first egg (EWFE), and egg number (EN).There were 11,588 chickens from 486 cocks and 1,461 hens that had records for growth and egg production traits. Relationships between growth rate and egg production traits were also explored. The level of inbreeding and its effect on growth and egg production were explored. In chapter 5, hatchability (HAT), rate of lay (RL), average daily gain (ADG), and survival rate (SUR) were identified as economically important traits and were used in a selection index to optimize the genetic response to selection. Finally, in chapter 6, population sizes and mating ratios were explored to optimize the genetic response in HAT, RL, ADG and SUR while minimizing the rate of inbreeding in the nucleus flock of LHKK chickens by simulating 20 generations of chickens for recurrent selection.

Univariate and bivariate analysis were used to estimate genetic parameters in body weight and egg production traits. Fixed effects of year and hatch within year were significant for all traits and sex was significant for all body weight traits, except for BWFE. The direct additive genetic effect was significant for all traits and the maternal genetic effect was significant only for growth traits, except BWFE. The maternal permanent environmental effect was significant for all growth traits, except for BW24 and BWFE. The estimates of heritability for direct additive genetic effect ranged from 0.10 to 0.47 for body weight traits and ranged from 0.15 to 0.16 for egg production traits. High positive genetic correlations were estimated between all traits, except for negative genetic correlations between EN and other traits. Inbreeding effect on growth and egg production traits was not significant, except for BW1D, where BW1D reduced by 0.09 g when the rate of inbreeding increased by 1% per generation.

Univariate random regression model was used to estimate genetic parameters for body weight traits along the growth trajectory from BW1D to BW24. A quadratic Legendre polynomial was identified as the best model to estimate variance structure for all random effects fitting heterogeneous residual variances based on six growth periods. Heritability estimates ranged from 0.34 to 0.54 and 0.04 to 0.06 for direct additive and maternal genetic effects, respectively. Estimated variance ratios ranged from 0.19 to 0.48 and 0.10 to 0.12 for direct and maternal permanent environmental effects, respectively. All genetic correlations between body weight traits were high and positive.

Genetic relationships between growth rates, measured at four-weekly intervals, and AFE and EWFE were estimated using bivariate analysis. Estimated heritabilities for growth rates ranged from 0.06 to 0.28. Estimated heritabilities for AFE and EWFW were 0.24 and 0.16, respectively. Genetic correlations between growth rates and AFE ranged from -0.22 to 0.02, and between growth rate and EWFE ranged from -0.05 to 0.40. The result suggested that selecting chicken with a high growth rate at an early growth period (at 28 days of age) would improve the body weight and egg weight at sexual maturity while reducing the age at sexual maturity.

Breeding strategies to improve the meat and egg production of the LHKK chickens under intensive (IPS) and extensive production system (EPS) were explored. A bioeconomic model was developed to calculate economic weights for HAT, RL, ADG and SUR. Estimated economic weights and the response to selection showed that LHKK chicken production was economically viable under the IPS and the EPS in Thailand. Annual economic return per hen in the EPS (621THB) was higher than the annual economic return per hen in the IPS (132THB) due to the lower cost of production under the EPS than the IPS. Calculated combined economic weights from the IPS and the EPS were 21.01THB for HAT, 56.52THB for RL, 106.52THB for ADG, and 15.76THB for SUR. The estimated relative economic weights showed that RL was the most important economic trait in LHKK chicken production. Decreased feed price and increased fattening chicken price is expected to increase the monetary return from the native chicken farms. Using the multi-trait selection index resulted in predicted responses of 0.97% in HAT, 2.41% in RL, 1.38g in ADG and 0.73% in SUR. Thus, implementing a single breeding objective strategy in the nucleus flock of the LHKK chickens will improve the productivity under both IPS and EPS.

A stochastic simulation using an optimal contribution selection (OCS) approach with a target of 25º for mate selection was used to minimize the level of inbreeding and maximize genetic gain in LHKK nucleus flock. The level of inbreeding and genetic gain for a range of population sizes and mating ratios were compared after 20 generations of recurrent selection. The predicted level of inbreeding indicated that increasing the population size by 30% from the current population size would reduce the level of inbreeding in LHKK flock by around 1% per generation. Reducing the nucleus size by 30%, in comparison to the current nucleus size, will increase the level of inbreeding by 1.55% per generation. The highest level of inbreeding was found in the higher mating ratios of one cock to floating hens (one to 10 hens) and the lowest level of inbreeding was found in the lowest mating ratio of one cock to three hens. The predicted genetic responses obtained across the four mating ratios were not significantly different to each other.

In summary, this study found that the growth and egg production traits in LHKK chicken are heritable and also have high genetic correlations between them. Therefore, both meat and egg production of LHKK chickens can be improved by implementing a multiple traits selection strategy. Furthermore, implementing a common selection strategy in the nucleus flock of LHKK chickens is expected to increase the monetary return under an intensive and extensive production system in Thailand. However, the effects of using a system specific selection strategy for IPS and EPS on the profitability under each production system need to be explored. Increasing the current nucleus flock size and reducing the mating ratio will fulfil the main objective of the current LHKK breeding program by further improving the meat and egg production, while maintaining the breed characteristics of LHKK chickens.

Common bean (Phaseolus vulgaris L.) is a major component of agricultural systems and diets of the urban and rural populations of East and Central Africa, providing Fe and Zn essential to the health and well-being of African women and children, and protein essential for the entire household. However, bean consumption is limited by constraints such as long cooking time (CT). Cooking demands large amounts of water, fuel and time. It has negative effects on the environment, livelihoods, security and health. Genetic variability in cooking time is documented. Recent development of new breeding methods based on pedigree and genomic selection together with optimal contribution selection (OCS) offers an opportunity to accelerate breeding for rapid CT and higher Fe and Zn grain content. Genotypic and phenotypic data of an African diversity pool, representing key bean market classes, were used to generate genomic estimated breeding values (GEBVs) for grain yield, CT, Fe and Zn. GEBV's were weighted to maximise the desired outcome in an economic index. From 161 candidate bean genotypes with GEBVs, 67 were chosen for 80 matings within six major grain market classes. An additional 22 breeder nominated matings were included. The predicted outcomes in the first cycle showed a major improvement in population mean for index (+286.77 US$/ha), 6.2% increase in GY and 7.3% reduction in CT, with an achieved increase in population co-ancestry of 0.0753. A 30% reduction in the mean population CT and improved Fe (15%) and Zn (10%), is expected after 5 cycles of annual recurrent selection.

It is well known that male and female broilers differ in their growth performance and that many physiological factors contribute to this difference. The aim of this experiment is to investigate if there are differences between males and female broilers in cecal microbiota and nutrient transporter gene expression and if these differences play a role in the growth performance of broilers. The possible effect of protein level and its interaction with sex on microbiota and expression of the nutrient transporters were also investigated. Samples were collected from male and female birds fed either standard crude protein (SCP) or reduced crude protein diets (RCP) at the age of d 35. The experiment was designed as a 2 × 2 factorial arrangement of treatments consisting of 448 Cobb 500 broilers assigned to 32 floor pens with 4 treatments, 8 replicates, and 14 birds per pen for performance measurements. The factors were sex (male or female) and dietary crude protein (CP) level (standard or reduced). Body weight gain (BWG), feed intake and feed conversion ratio were recorded for each pen. Sex had a significant effect on BWG and FCR (P < 0.001) where males had a significantly higher BWG and better FCR compared to females. There was a significant interaction between sex and protein level on feed intake (FI) (P < 0.05), where male birds had a higher FI compared to female birds only when the birds were fed SCP but not RCP diets. There was a significant interaction between CP level and sex on the expression of CAT2 (P = 0.02) and PEPT2 (P = 0.026) where the genes were significantly upregulated in females but only when the RCP diet was fed. The RCP diet upregulated the expression of B^oAT (P = 0.03) as a main effect. Female birds had significantly higher expression of the PepT-2 gene compared to the males. The alpha diversity of cecal microbiota showed differences among the treatments. The Shannon diversity index was statistically higher (P = 0.036) for males fed the SCP diet and the Chao1 index for evenness was statistically higher (P = 0.027) in females fed the SCP diet. There was also a difference in the relative abundance of the 15 most common genera found in the cecal content of the broilers in this experiment and lastly, the differential composition of microbiota between the different treatments was also significantly different.

This study suggests that chickens are able to compensate for a reduction in AA substrates when fed a low CP diet through the upregulation of certain AA transporters, females may adapt to low CP diets better by such upregulation compared to males, and lastly, sex has an effect on the cecal microbial population and these differences contribute towards the performance differences between male and female broilers.

Genotype-by-environment interaction is caused by variation in genetic environmental sensitivity (GES), which can be subdivided into macro- and micro-GES. Macro-GES is genetic sensitivity to macro-environments (definable environments often shared by groups of animals), while micro-GES is genetic sensitivity to micro-environments (individual environments). A combined reaction norm and double hierarchical generalised linear model (RN-DHGLM) allows for simultaneous estimation of base genetic, macro- and micro-GES effects. The accuracy of variance components estimated using a RN-DHGLM has been explicitly studied for balanced data and recommendation of a data size with a minimum of 100 sires with at least 100 offspring each have been made. In the current study, the data size (numbers of sires and progeny) and structure requirements of the RN-DHGLM were investigated for two types of unbalanced datasets. Both datasets had a variable number of offspring per sire, but one dataset also had a variable number of offspring within macro-environments. The accuracy and bias of the estimated macro- and micro-GES effects and the estimated breeding values (EBVs) obtained using the RN-DHGLM depended on the data size. Reasonably accurate and unbiased estimates were obtained with data containing 500 sires with 20 offspring or 100 sires with 50 offspring, regardless of the data structure. Variable progeny group sizes, alone or in combination with an unequal number of offspring within macro-environments, had little impact on the dispersion of the EBVs or the bias and accuracy of variance component estimation, but resulted in lower accuracies of the EBVs. Compared to genetic correlations of zero, a genetic correlation of 0.5 between base genetic, macro- and micro-GES components resulted in a slight decrease in the percentage of replicates that converged out of 100 replicates, but had no effect on the dispersion and accuracy of variance component estimation or the dispersion of the EBVs. The results show that it is possible to apply the RN-DHGLM to unbalanced datasets to obtain estimates of variance due to macro- and micro-GES. Furthermore, the levels of accuracy and bias of variance estimates when analysing macroand micro-GES simultaneously are determined by average family size, with limited impact from variability in family size and/or cohort size. This creates opportunities for the use of field data from populations with unbalanced data structures when estimating macro- and micro-GES.

A SNP array of 50k SNP markers was used in single-step GBLUP (SS-GBLUP) models to estimate breeding values in the Australian sheep genetic evaluation system. In 2019, Neogen launched a new GeneSeek Genomic Profiler Ovine 50k chip, which included ~5000 SNPs that were identified based on Sheep CRC research as highly predictive for growth, carcass and eating quality traits. The objective of this work was to apply a five-fold cross-validation approach to compare different models for the use of predictive SNPs for post-weaning weight (PWT), carcass eye muscle depth (CEMD), carcass fat at C site (CCFAT), intramuscular fat (IMF) and shear force (SF5) based on the LAMBPLAN terminal sire genetic evaluation. Correlation and regression coefficients between adjusted phenotypes and SS-GBLUP EBVs for validation animals from the different models were calculated. The results indicated that adding predictive SNPs slightly improved the correlation and regression coefficient of EBVs, but there was no advantage in giving them more weight via a separate term in the model, confirming that the current industry evaluation model using a single genomic relationship matrix is the best of the tested models for these traits.