Genotyping by sequencing (GBS) is widely employed in aquaculture selective breeding programs to acquire SNP genotypes for various applications, including pedigree reconstruction, GWAS, and in the estimation of genetic parameters, genomic relationships and breeding values. Because sequencing libraries encompass all DNA present in fin-clip tissue, they also recover the non-host metagenomic fraction, including pathogens. We leveraged this property to survey for the presence of scale-drop disease virus (SDDV), responsible for 40-90 % mortality in farmed barramundi, while simultaneously genotyping the host. Raw reads from 4484 fish of four commercial cohorts (2239 moribund, 2245 asymptomatic) were aligned to the SDDV reference genome, and viral reads were normalised as reads per million (RPM) per individual. SDDV prevalence and load were tightly associated with clinical status: 88.9 % of moribund fish carried SDDV at 21.8 f 0.6 RPM, whereas only 0.2 % of healthy fish were positive with 0.002 f 0.001 RPM. Independent validation by quantitative PCR on fin and spleen from a nested subset of 172 fish (81 moribund, 91 healthy) yielded viral copy numbers strongly correlated with ddRADseq RPM (Spearman's rho = 0.84 for fin; rho = 0.76 for spleen; both P < 0.0001). Viral load was consistently higher in fin (mean 281 f 45 copies ng-1 DNA) than spleen (147 f 32 copies ng-1), corroborating the suitability of non-lethal fin tissue for surveillance. Prevalence and load distributions were homogeneous across cohorts, and no qPCRpositive individuals escaped detection by ddRADseq. These findings show that routine ddRADseq datasets in barramundi can also be repurposed into a sensitive epidemiological assay that unites SDDV monitoring with genomic improvement. Further, these findings suggest that breeding programs generating large ddRADseq GBS datasets may also serve pathogen surveillance purposes where the target pathogen infects the host genotyped tissue.

Radiata pine (Pinus radiata D. Don.) comprises 90% of New Zealand’s exotic plantation forests, backed by 70 years of breeding history. Maintaining large in-situ archives generated by decades of breeding is resource intensive and with fixed rotations makes germplasm management more complex than in traditional agricultural crops. Genomics tools offer a pathway to target resource management and reducing redundancy. In this study, we assembled 1,573 accessions from in-situ archives, spanning founders, landraces, and advanced lines and which are genotyped using 8,243 markers. Population structure analysis (STRUCTURE and PCA) identified an optimal cluster of K = 3, revealing low to moderate genetic differentiation ($$\:{F}_{ST}$$ = 0.021–0.045), and increasing admixture across generations. Two clusters correspond to known Californian mainland (Monterey) and Island provenances, the third cluster likely represents an under-characterized mainland lineage (Año Nuevo) within NZ landraces. While mainland ancestry remains dominant, signals from Guadalupe and Cedros Islands are evident in recent breeding series. Ancestry-weighted analysis revealed contrasting trait associations among clusters, with cluster 1 favouring growth traits, cluster 2 associated with improved Dothistroma resistance, and cluster 3 exhibiting superior wood quality traits but reduced growth performance. These results may offer the opportunity to incorporate ancestry via metafounders in single-step evaluations to reduce bias and support balanced genetic gain. Furthermore, Corehunter-derived subsets captured near complete allelic representation and high diversity with minimal redundancy. This study demonstrates the value of genomics-enabled core selection for streamlining germplasm conservation in both in-situ and ex-situ archives, ensuring a resilient foundation for ongoing radiata pine improvement.

Deer milk is a dairy product valued for its unique composition and market potential. Research into deer lactation dynamics remains limited. In this study, 4,386 test-day records from 170 red deer (Cervus elaphus L.) hinds in New Zealand were used to model the milk yield and composition curves using random regression models based on Legendre polynomials. Individual curves were modelled for milk, fat, protein and lactose yields and somatic cell score. Data collection began at the start of machine milking following the exclusive suckling period, so early-lactation peak yield and initial composition dynamics were not captured in these curves. Lactation persistency was defined as the ratio of the predicted accumulated milk yield in the second half of lactation to the predicted accumulated yield in the first half, expressed as a percentage. Milk yield averaged 68.5 kg per hind, lactose yield declined steeply, while fat yield and protein yields decreased gradually, resulting in greater milk solids as lactation advanced. Average persistency for milk yield ranged from 38.3 to 50.8% and was strongly correlated across traits. The effects of age group and birth herd were not significant. In contrast, deviation from the median fawning date was significant (p < 0.001), with later-fawning hinds showing lower yields and reduced persistency. These findings characterise milk yield and composition dynamics during the postweaning machine-milking period in a commercial New Zealand red deer herd during the 2024-2025 production season and provide a preliminary basis for future studies of management and breeding in deer milk production systems.

Conservation management of endangered species increasingly relies on genomic approaches to understand how long-term small population sizes affect the fitness of extant individuals. However, despite the growing investment in genomic resources by conservation programmes, the impact that sequencing methods have on the ability to detect inbreeding-related phenomena has been largely overlooked. Here, we compare the use of whole-genome and reduced-representation sequencing approaches in 148 individuals of the critically endangered parrot, the kākāpō ( Strigops habroptilus ), to assess inbreeding and its effects on female reproductive success. We explore how sequencing choice influences the identification of long stretches of homozygosity across the genome (runs of homozygosity, ROH), and compare the conservation implications of the results produced by each method. Both whole-genome and reduced-representation sequencing approaches provided comparable estimates of genome-wide inbreeding ( F ROH ) and revealed consistent effects on egg hatching success, suggesting that reduced-representation sequencing is capable of detecting inbreeding depression in wild populations under certain conditions. Whole-genome sequencing enabled chromosome-level inbreeding analyses, which revealed no strong evidence of chromosome-specific effects beyond the genome-wide signal. These results suggest that inbreeding depression in kākāpō reflects small effects across many chromosomes rather than strong effects on only a few, and that the primary benefit of whole-genome sequencing lies in improving the precision of genome-wide inbreeding estimates rather than identifying chromosome-specific effects. Our findings highlight the distinct benefits of each sequencing approach in conservation, particularly within the context of resource limitations.

Red clover (Trifolium pratense L.) is known for its large taproot, nitrogen fixation capabilities and production of forage high in protein and digestibility. It has the potential to strengthen temperate pastural systems against future adverse climatic events by providing higher biomass during periods of water deficit. Being outcrossing and self-incompatible, red clover is a highly heterozygous species. If evaluated and utilized correctly, this genetic diversity can be harnessed to develop productive, persistent cultivars. In this study, we selected 92 geographically diverse red clover novel germplasm populations for assessment in multi-location, multi-year field trials and for genetic diversity and genetic relationship characterization using pooled genotyping-by-sequencing (GBS). Through the development of integrated linear mixed models based on genomic, phenotypic, and environmental information we assessed variance components and genotype-by-environment (G x E) interactions for eight physiological and morphological traits. Key interactions between environmental variables and plant performance were also evaluated using a common garden site at Lincoln. We found that the genetic structure of the 92 populations was highly influenced by country of origin. The expected heterozygosity within populations ranged between 0.08 and 0.17 and varied with geographical origin. For the eight physiological and morphological traits measured there was high narrow-sense heritability (h2 > 0.70). The influence of environmental variables, such as mean precipitation, temperature and isothermality of the original collection locations, on plant and trait performance in the local field trials was also highlighted. Along with the identification of genes associated with these bioclimatic variables that could be used as genetic markers for selection in future breeding programs. Our study identifies the importance of diverse germplasm when adding genetic variation into breeding programs. It also identifies efficient evaluation methods and key climatic variables that should be considered when developing adaptive red clover cultivars.

BACKGROUND:Methane emissions from ruminant livestock pose a significant challenge to mitigating climate change. Genomic selection offers a promising approach to reduce methane emissions, but prediction accuracy remains low due to the high cost of measuring methane emissions. Integrating rumen microbiome composition (RMC) data may improve genomic prediction accuracy, yet the high dimensionality of RMC data presents computational challenges. This study aimed to (1) evaluate the effectiveness of principal component analysis (PCA) for reducing RMC data dimensionality while retaining essential information, and (2) assess whether incorporating PCA-reduced RMC data as intermediate traits in a Neural Network Genomic Best Linear Unbiased Prediction (NN-GBLUP) model improves genomic prediction accuracy for methane emissions and feed efficiency traits in sheep. RESULTS:For the first objective, Principal Components (PCs) explaining 100% of variation effectively captured RMC information, with microbiability estimates closely matching those from the full dataset. For the second objective, the NN-GBLUP model incorporating PCA-reduced RMC data improved prediction accuracy compared to standard GBLUP methods. Prediction accuracy for methane emissions increased from 0.09 to 0.30 in train-test validation and from 0.15 to 0.27 in five-fold cross-validation using PCA components explaining 25% of total RMC variation. For residual feed intake, accuracy improved from 0.25 to 0.37 in train-test validation and from 0.25 to 0.34 in cross-validation. Optimal PCA components varied by trait, with 25% and 50% components showing the best results. Prediction accuracy did not improve for carbon dioxide emissions, live weight, and mid-intake, indicating trait-dependent microbiome influence. CONCLUSIONS:Principal Component Analysis reduced the dimensionality of rumen microbiome data while preserving essential biological information. The integration of these PCA-reduced data with host genomic information through an NN-GBLUP model substantially improved genomic prediction accuracy for methane emissions and feed efficiency in sheep. Principal components explaining 25% and 50% of the variation yielded the highest accuracy, whereas higher components (75% and 95%) reduced accuracy for methane traits. This approach shows promise for implementing genomic selection strategies to reduce methane emissions and improve feed efficiency in ruminant livestock in a computationally efficient manner, thereby contributing to climate change mitigation efforts in agriculture.

Genomic selection is a powerful tool to reduce methane emissions in ruminants. However, it requires large-scale on-farm phenotypic measures of methane. Current technologies to measure methane emissions have several limitations and may not be suitable for lactating animals. Because enteric methane is closely linked to the fermentation process in the rumen, which in turn affects milk composition, breeding for low-methane ruminants may change the rumen microbial and milk composition. Consequently, these compositions may provide proxy measures of methane for use in selective breeding of low-methane ruminants. We investigated the effect on rumen and milk composition in sheep bred for divergent methane yield and the potential for generating proxy measures of methane emissions from rumen or milk samples in lactating ewes. Four hundred genotyped lactating ewes from a sheep research flock bred specifically for high and low-methane emissions had methane measured and rumen and milk samples collected approximately 6 wk post-lambing across 4 lactation years. Rumen samples were processed to generate VFA and metagenomic profiles of the rumen microbial community, and fatty acid profiles and mid-infrared spectra were generated for the milk samples. Although no differences in total fat, protein, and lactose percentages in the milk were found, the milk fatty acid profiles differed between methane selection lines, with higher PUFA and branched-chain fatty acids levels, and lower total SFA contents in ewes from the low-methane line. Lower proportions of acetate relative to propionate were found in the rumen samples from the low-methane ewes. Predictions of methane were obtained from the rumen VFA and metagenomic profiles and the fatty acid profiles and mid-infrared spectra from milk. These predictions formed the proxy methane measures and were heritable (between 0.12 to 0.36) and correlated (between 0.29 and 0.42) with the measured methane values. The genetic correlation between proxies and measured methane was between 0.52 and 0.71. The estimated efficiency of indirect selection for methane was higher for the milk sample proxies (49%-75%) than the rumen metagenomic profiles (45%-47%) and rumen VFA profiles (12%-38%). These results suggest that milk fatty acid, MIR spectroscopic, and rumen microbial composition phenotypes have the potential to be used as proxy measures of methane in lactating ruminants, with the milk-based proxies showing greater promise. Results show that the number of animals with methane proxy measures could be increased substantially and will enable access to breeding technology in countries with limited methane measurement infrastructure.

Global targets to reduce greenhouse gas emissions to meet international climate change commitments have driven the livestock industry to develop solutions to reduce methane emission in ruminants while maintaining production. Research has shown that selective breeding for low methane emitting ruminants using genomic selection is one viable solution to meet methane targets at a national level. However, this requires obtaining sufficient measures of methane on individual animals across the national herd. In sheep, one affordable method for measuring methane on-farm to rank animals on their methane emissions is portable accumulation chambers (PAC), although this method is not without its challenges. An alternative is to use a proxy trait that is genetically correlated with PAC methane measures. One such trait that has shown promise is rumen metagenome community (RMC) profiles. In this study, we investigate the potential of using RMC profiles as a proxy trait for methane emissions from PAC using a large sheep dataset consisting of 4585 mixed-sex lambs from several flocks and years across New Zealand. RMC profiles were generated from rumen samples collected on the animals immediately after being measured through PAC using restriction enzyme-reduced representation sequencing. We predicted methane (CH4) and carbon dioxide (CO2) emissions (grams per day), as well as the ratio CH4/(CO2 + CH4) (CH4Ratio), from the RMC profiles and SNP-array genotype data. Heritability and microbiability estimates were similar to values found in the literature for all traits. The correlation of PAC methane with predicted methane was 1.9- to 2.3-fold (CH4) and 1.2- to 1.5-fold (CH4Ratio) greater for RMC profiles compared to host genomics only. The genetic correlation between methane predicted from RMC profiles and PAC methane was 0.75 ± 0.12 for CH4 and 0.64 ± 0.11 for CH4Ratio when using a validation set consisting of the animals with the most recent year of birth in the dataset. RMC profiles are predictive of, and genetically correlated, with PAC methane measures. Therefore, RMC profiles are a suitable proxy trait for determining the genetic merit of an animal’s methane emissions and could be incorporated into existing breeding programs to facilitate selective breeding for low methane emitting sheep.

BACKGROUND: Genotyping-by-sequencing (GBS) offers a cost-effective solution to access genomic information on many samples. The advent of GBS has promoted large scale genomic analyses, including linkage analysis, heritability estimation, inference of genetic relatedness and genome-wide prediction and association studies, in many non-model species. Low-depth GBS can be used as a cost-effective method to obtain genotypes; however, not all alleles will be captured during the sequencing process, which can lead to high levels of genotype uncertainty. New methods that take genotype uncertainty into account are needed, but these methods can be difficult to develop and benchmark on real data. Simulations provide a convenient way to benchmark different approaches, and therefore, are essential for assessing existing methods and guiding future method development. RESULTS: SimGBS is a method for simulating large-scale GBS data from any real (e.g., reference) or simulated diploid genome. It is implemented in Julia but can also be accessed from R through the JuliaCall interface. Users can define populations with distinct demographic histories by modifying population size over a specified number of generations or providing a pedigree to generate structured populations. Most GBS parameters are highly customisable, including the choice of restriction enzyme and sequencing depth. SimGBS outputs both true genotype calls and DNA sequences in FASTQ format. The method is computationally efficient, capable of generating datasets for thousands of individuals; for example, simulating 172 samples required less than two hours while successfully reproducing the complexities observed in empirical GBS data. CONCLUSION: SimGBS is a valuable tool to assist with designing GBS experiments or evaluating bioinformatic pipelines and statistical methods for GBS data analysis in large scale population genetics studies.

The deer industry in New Zealand has made notable genetic progress in recent decades. Initially based around live weight and velvet antler, deer are now selected on several traits including carcass composition, reproduction, and disease resistance within DEERSelect, the industry performance recording system in New Zealand. Due to its low cost and manageable logistics for deer, the genotyping-by-sequencing technology has replaced DNA microsatellites for parentage assignment. Since 2015 more than 60,000 animals in the national herd have been genotyped using this technology. Genomic information, however, is not yet fully exploited as evaluations currently only use pedigree information to estimate genetic merit. To assess the benefits of using genomic information, we compare pedigree, genomic and single-step genomic BLUP approaches for weaning weight, yearling weight and velvet weight at 2 years of age, in red deer from New Zealand. Using forward validation, we estimate the prediction accuracy for 56,178 animals (red and red x wapiti crossbreds) born between 1995 and 2022. We show that incorporating genomic information explicitly improves prediction accuracy by at least 15% in three key industry traits, allowing better ranking of animals across birth years. We recommend the incorporation of genomic information in the industry evaluations performed by DEERSelect.

Sequencing-based methods are increasingly being used for genetic studies. Genotypes derived from the sequence data are sometimes assigned incorrectly, due to having no reads for one of the alleles. This is particularly the case when the data has low sequencing depth. Here, we develop a correction that can be applied to both a measure and a test for the population differentiation metric F ST when considering populations as a fixed effect, to account for this incomplete genotyping. It is shown that the measure of differentiation is not influenced much by this correction in reasonably sized studies but that significance testing is too liberal without the correction. This correction will allow appropriate inference in population studies.

Changes in wool prices, labour costs and animal welfare have sparked interest in several new sheep traits. Shedding, the ability of moulting or seasonal wool loss in sheep, is one of them. Here, we aimed to develop breeding values for shedding using existing data from New Zealand flocks. We used breeder-collected shedding scores, 0 = 'no shedding on any part of the body' to 10 = 'wool shed fully from all the body', for Wiltshire and Wiltshire-composite lambs born between 2003 and 2022. After QC, 20,653 phenotypic records from 25,066 animals with pedigree information (393 sires and 7439 dams from 12 flocks) were included in the analyses. Genetic parameters and breeding values were obtained using a genetic additive model with pedigree data (BLUP model), assuming shedding scores were quantitative. The genetic model incorporated the following variables: date of birth, sex, birth and rearing rank, age of dam and contemporary group. We found a high heritability for lamb shedding, 0.54 (0.02), and estimated a low-to-moderate prediction accuracy of breeding values (ranging from 0.09 to 0.36) using forward-validation. Our results confirmed that shedding can be subject to selection and that pedigree-based breeding values for this trait can be a useful tool for breeders.

Key message An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. Abstract High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Abstract Background A genotype-by-environment (G × E) interaction is defined as genotypes responding differently to different environments. In salmonids, G × E interactions can occur in different rearing conditions, including changes in salinity or temperature. However, water flow, an important variable that can influence metabolism, has yet to be considered for potential G × E interactions, although water flows differ across production stages. The salmonid industry is now manipulating flow in tanks to improve welfare and production performance, and expanding sea pen farming offshore, where flow dynamics are substantially greater. Therefore, there is a need to test whether G × E interactions occur under low and higher flow regimes to determine if industry should consider modifying their performance evaluation and selection criteria to account for different flow environments. Here, we used genotype-by-sequencing to create a genomic-relationship matrix of 37 Chinook salmon, Oncorhynchus tshawytscha, families to assess possible G × E interactions for production performance under two flow environments: a low flow regime (0.3 body lengths per second; bl s−1) and a moderate flow regime (0.8 bl s−1). Results Genetic correlations for the same production performance trait between flow regimes suggest there is minimal evidence of a G × E interaction between the low and moderate flow regimes tested in this study, for Chinook salmon reared from 82.9 ± 16.8 g ($${\overline{\text{x}}}$$ x ¯ ± s.d.) to 583.2 ± 117.1 g ($${\overline{\text{x}}}$$ x ¯ ± s.d.). Estimates of genetic and phenotypic correlations between traits did not reveal any unfavorable trait correlations for size- (weight and condition factor) and growth-related traits, regardless of the flow regime, but did suggest measuring feed intake would be the preferred approach to improve feed efficiency because of the strong correlations between feed intake and feed efficiency, consistent with previous studies. Conclusion This new information suggests that Chinook salmon families do not need to be selected separately for performance across different flow regimes. However, further studies are needed to confirm this across a wider range of fish sizes and flows. This information is key for breeding programs to determine if separate evaluation groups are required for different flow regimes that are used for production (e.g., hatchery, post smolt recirculating aquaculture system, or offshore).

Increasing the efficiency of rumen fermentation is one of the main ways to maximize the production of ruminants. It is therefore important to understand the ruminal microbiome, as well as environmental influences on that community. However, there are no studies that describe the ruminal microbiota in buffaloes in the Amazon. The objective of this study was to characterize the rumen microbiome of the water buffalo (Bubalus bubalis) in the eastern Amazon in the dry and rainy seasons in three grazing ecosystems: Baixo Amazonas (BA), Continente do Pará (CP), Ilha do Marajó (IM), and in a confinement system: Tomé-Açu (TA). Seventy-one crossbred male buffaloes (Murrah × Mediterranean) were used, aged between 24 and 36 months, with an average weight of 432 kg in the rainy season and 409 kg in the dry season, and fed on native or cultivated pastures. In the confinement system, the feed consisted of sorghum silage, soybean meal, wet sorghum premix, and commercial feed. Samples of the diet from each ecosystem were collected for bromatological analysis. The collections of ruminal content were carried out in slaughterhouses, with the rumen completely emptied and homogenized, the solid and liquid fractions separated, and the ruminal pH measured. DNA was extracted from the rumen samples, then sequenced using Restriction Enzyme Reduced Representation Sequencing. The taxonomic composition was largely similar between ecosystems. All 61 genera in the reference database were recognized, including members of the domains Bacteria and Archaea. The abundance of 23 bacterial genera differed significantly (p < 0.01) between the Tomé-Açu confinement and other ecosystems. Bacillus, Ruminococcus, and Bacteroides had lower abundance in samples from the Tomé-Açu system. Among the Archaea, the genus Methanomicrobium was less abundant in Tomé-Açu, while Methanosarcina was more abundant. There was a difference caused by all evaluated factors, but the diet (available or offered) was what most influenced the ruminal microbiota.

BACKGROUND:Producing animal protein while reducing the animal's impact on the environment, e.g., through improved feed efficiency and lowered methane emissions, has gained interest in recent years. Genetic selection is one possible path to reduce the environmental impact of livestock production, but these traits are difficult and expensive to measure on many animals. The rumen microbiome may serve as a proxy for these traits due to its role in feed digestion. Restriction enzyme-reduced representation sequencing (RE-RRS) is a high-throughput and cost-effective approach to rumen metagenome profiling, but the systematic (e.g., sequencing) and biological factors influencing the resulting reference based (RB) and reference free (RF) profiles need to be explored before widespread industry adoption is possible.RESULTS:Metagenome profiles were generated by RE-RRS of 4,479 rumen samples collected from 1,708 sheep, and assigned to eight groups based on diet, age, time off feed, and country (New Zealand or Australia) at the time of sample collection. Systematic effects were found to have minimal influence on metagenome profiles. Diet was a major driver of differences between samples, followed by time off feed, then age of the sheep. The RF approach resulted in more reads being assigned per sample and afforded greater resolution when distinguishing between groups than the RB approach. Normalizing relative abundances within the sampling Cohort abolished structures related to age, diet, and time off feed, allowing a clear signal based on methane emissions to be elucidated. Genus-level abundances of rumen microbes showed low-to-moderate heritability and repeatability and were consistent between diets.CONCLUSIONS:Variation in rumen metagenomic profiles was influenced by diet, age, time off feed and genetics. Not accounting for environmental factors may limit the ability to associate the profile with traits of interest. However, these differences can be accounted for by adjusting for Cohort effects, revealing robust biological signals. The abundances of some genera were consistently heritable and repeatable across different environments, suggesting that metagenomic profiles could be used to predict an individual's future performance, or performance of its offspring, in a range of environments. These results highlight the potential of using rumen metagenomic profiles for selection purposes in a practical, agricultural setting.

In developing countries, the use of simple and cost-efficient molecular technology is crucial for genetic characterization of local animal resources and better development of conservation strategies. The genotyping by sequencing (GBS) technique, also called restriction enzyme- reduced representational sequencing, is an efficient, cost-effective method for simultaneous discovery and genotyping of many markers. In the present study, we applied a two-enzyme GBS protocol (PstI/MspI) to discover and genotype SNP markers among 197 Tunisian sheep samples. A total of 100 333 bi-allelic SNPs were discovered and genotyped with an SNP call rate of 0.69 and mean sample depth 3.33. The genomic relatedness between 183 samples grouped the samples perfectly to their populations and pointed out a high genetic relatedness of inbred subpopulation reflecting the current adopted reproductive strategies. The genome-wide association study contrasting fat vs. thin-tailed breeds detected 41 significant variants including a peak positioned on OAR20. We identified FOXC1, GMDS, VEGFA, OXCT1, VRTN and BMP2 as the most promising for sheep tail-type trait. The GBS data have been useful to assess the population structure and improve our understanding of the genomic architecture of distinctive characteristics shaped by selection pressure in local sheep breeds. This study successfully investigates a cost-efficient method to discover genotypes, assign populations and understand insights into sheep adaptation to arid area. GBS could be of potential utility in livestock species in developing/emerging countries.

Chinook salmon (Oncorhynchus tshawytscha) is an important aquaculture species in New Zealand and selective breeding since the 1990s has resulted in significant gains in growth related traits. However, feed conversion ratio (FCR) remains highly variable in this species and higher than in other farmed salmonids. Some farmed Chinook salmon are also susceptible to late onset spinal curvature, which further reduces production efficiency. Therefore, the objective of this study was to determine the potential for selecting for improved FCR and reduced spinal curvature while also improving growth. For the evaluation, 82 pedigree families were obtained from two commercial breeding programmes and genotyped using genotyping-by-sequencing to generate a genomic relatedness matrix. Genotyped and tagged juveniles were reared together for 12 months in seawater trial tanks up to similar to 2.0 Kg. Multiple assessments of weight, condition factor (CF), feed intake (FI), FCR and skeletal deformity were completed and analysed to estimate genetic parameters (heritability, phenotypic and genetic correlations). High heritabilities were estimated for weight and CF and moderate for spinal curvature. Growth, feed intake and FCR traits had low to moderate heritabilities and demonstrated potential for improvement through selection. Genetic correlations suggested some progress in FCR would be made through selection for growth, but larger gains will be achieved by quantifying FI. An unfavourable genetic correlation was uncovered between CF and spinal curvature and this should be considered when developing breeding goals for commercial broodstock selection.

Physical traits that improve welfare and disease outcomes for sheep are becoming increasingly important due to both increased climate challenges and societal expectations. Such traits include tail length, the amount of skin (vs. wool) on the underside of the tail, and the area of no-wool (hair) on the belly and breech areas (surrounding the anus) of the animal. An industry dataset consisting of records from individual stud breeders and industry progeny tests was available to estimate the genetic parameters associated with these traits and to investigate the potential for within-breed genetic selection. The heritability estimate for tail length was 0.68 ± 0.01 when breed was not fitted, and 0.63 ± 0.01 when breed was fitted. Similar trends were observed for breech and belly bareness which had heritability estimates around 0.50 (± 0.01). The estimates for these bareness traits are both higher than previous reports from animals of the same age. There was, however, between breed variation in the starting point for these traits, with some breeds having significantly longer tails and a wooly breech and belly, and limited variability. Overall, the results of this study show that flocks exhibiting some variation will be able to make rapid genetic progress in selecting for bareness and tail length traits, and therefore have the potential to make progress towards a sheep that is easier to look after and suffers fewer welfare insults. For those breeds that showed limited within-breed variation, outcrossing may be required to introduce genotypes that exhibit shorter tail length and bareness of belly and breech to increase the rate of genetic gain. Whatever approach is taken by the industry, these results support that genetic improvement can be used to breed "ethically improved sheep".