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The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre‐ and post‐weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS. [ABSTRACT FROM AUTHOR]

Simple Summary: Meat tenderness is crucial for eating quality, particularly in Australia, where it is a top palatability trait, with 50% consumer acceptance when shear force is below 42.6 N. Consumers are willing to pay a premium for guaranteed meat quality, making tenderness a priority for industry producers. Research has focused on the genetic and environmental factors affecting meat's biological, structural and physiological characteristics. Objective evaluation methods like shear force are commonly used but can be influenced by pH–temperature interactions. Our findings indicate that controlling pH–temperature decline during processing enhances tenderness. Additionally, genetics can impact the risk of cold shortening. Shear force is commonly used to evaluate tenderness, one of the most crucial eating quality aspects of sheep meat. The effect size of various factors on tenderness is still unknown. Studies have suggested that both genetic and environmental factors contribute to the variation in meat tenderness, and there are possible interactions between these factors. An extensive data set (n = 23,696) was analyzed to examine genetic and non-genetic influences on the shear force at 5 days postmortem (SF5). SF5 was measured on lamb loins (Longissimus lumborum) taken from lambs reared over 12 years at eight sites across Australia. The results showed that all carcass traits had a significant (p < 0.001) impact on SF5, with the largest effect on SF5 associated with intramuscular fat (IMF %) (f = 1035). There was also a significant effect of sex, cold shortening at 18 °C, sire type and cohort on SF5 (p < 0.001), with a large variation observed between the minimum cohort at 15.9 ± 1.5 N and maximum at 51.2 ± 2.1 N. In conclusion, a complex matrix of production, processing and genetic factors impact lamb tenderness as measured by shear force. This experiment helps identify the size of the contribution of these factors towards lamb tenderness, enabling the sheep industry to enhance consumers' satisfaction. [ABSTRACT FROM AUTHOR]

Maintaining genetic diversity and variation in livestock populations is critical for natural and artificial selection promoting genetic improvement while avoiding problems due to inbreeding. In Laos, there are concerns that there has been a decline in genetic diversity and a rise in inbreeding among native goats in their village‐based smallholder system. In this study, we investigated the genetic diversity of Lao native goats in Phin, Songkhone and Sepon districts in Central Laos for the first time using Illumina's Goat SNP50 BeadChip. We also explored the genetic relationships between Lao goats with 163 global goat populations from 36 countries. Our results revealled a close genetic relationship between Lao native goats and Chinese, Mongolian and Pakistani goats, sharing ancestries with Guangfen, Jining Grey and Luoping Yellow breeds (China) and Teddi goats (Pakistan). The observed (Ho) and expected (He) heterozygosity were 0.292 and 0.303 (Laos), 0.288 and 0.288 (Sepon), 0.299 and 0.308 (Phin) and 0.289 and 0.305 (Songkhone), respectively. There was low to moderate genetic differentiation (FST: 0.011–0.043) and negligible inbreeding coefficients (FIS: −0.001 to 0.052) between goat districts. The runs of homozygosity (ROH) had an average length of 5.92–6.85 Mb, with short ROH segments (1–5 Mb length) being the most prevalent (66.34%). Longer ROH segments (20–40 and >40 Mb length categories) were less common, comprising only 4.81% and 1.01%, respectively. Lao goats exhibit moderate genetic diversity, low‐inbreeding levels and adequate effective population size. Some genetic distinctions between Lao goats may be explained by geographic and cultural features. [ABSTRACT FROM AUTHOR]

Well designed and targeted animal breeding programs allow a sustainable genetic improvement of livestock with increases in animal productivity of 1-2% per annum. Over time, massive improvements have been achieved, e.g. in dairy and pig and poultry production systems, now resulting in higher production that requires much fewer input of resources. Although reproductive and genomic technologies contribute nowadays to increases in rates of genetic improvement, the key to successful breeding programs lies in a strong focus on simple and well-defined breeding objectives, effective investment in trait measurement, a clear understanding of the structure of the breeding program, and efficient systems for genetic evaluation, selection and mating of elite animals. The dissemination of the genetics of selected animals to the wider population also needs consideration, requiring commercial farmers or smallholders to have the means to have access to improved genetics as well as an understanding of the value of using improved bulls. Definition of the breeding objectives as well as evaluation of genetic merit needs to be based on the local environment. Compared with other interventions to improve productivity and stability, genetic improvement is a critical and cost-effective approach as genetic gains are permanent and cumulative.

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single‐step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single‐step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single‐trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR‐method' by a cross‐validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut‐off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut‐off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62–0.67) for FD and +44% (0.36–0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12–0.14) for TWW and +117% (0.18–0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex‐limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks. [ABSTRACT FROM AUTHOR]