Protected designation of origin dry-cured hams are obtained from heavy pigs (slaughtered at about 160 kg of live weight). A specific breeding program designed to improve meat quality for this production has included as key traits the level of intermuscular fat between the leg muscles and ham weight loss during the seasoning period together with a balance between fat and lean cuts. In this study we carried out genome-wide association studies for seven traits used in the genetic merit of Italian Duroc heavy pigs, five related to meat and carcass quality traits (visible intermuscular fat, ham weight loss at first salting, backfat thickness, ham weight and lean cuts), and two related to performance and efficiency traits (average daily gain and feed : gain ratio). A total of 573 performance-tested pigs were genotyped with the Illumina PorcineSNP60 BeadChip and genome-wide association analyses were carried out using the Bayes B approach with the 1 Mb window option of GenSel and random residuals for each of the seven traits. Detected windows were supported by independent single nucleotide polymorphism analyses with a linear mixed model (LMM) approach on the same animals for the same traits. A total of 30 windows identifying different quantitative trait loci (QTL) were detected and among those, 27 were confirmed by LMM in one of these traits. Among the confirmed windows, three QTL were reported for visible intermuscular fat, seven for ham weight loss at first salting and five and four for backfat thickness and lean cut, respectively. A total of eight QTL were detected for the other production traits. No overlapping QTL were reported except for one window on porcine chromosome 10 between lean cuts and ham weight that contained the CACNB2 gene that has been already associated with loin marbling score in other Duroc pigs. Several regions contained genes that have been already associated with production traits in other pig breeds, including Duroc lines, related to fat deposition or muscle structure. This work reports, for the first time, genome-wide association study results for several traits in Italian Duroc heavy pigs. These results will be useful to dissect the genetic basis for dry-cured ham production traits that determine the total genetic merit index of Italian Duroc pigs.

In animals, new autosomal recessive genetic diseases (ARGD) arise all the time due to the regular, random mutations that occur during meiosis. In order to reduce the effect of any damaging new variant, it is necessary to find its cause. To evaluate the best way of doing this, 34 papers which found the exact location of a new genetic disease in livestock were reviewed and found to require at least two stages. In the initial stage the commonly used χ2 method, applied in a case-control association analysis with single nucleotide polymorphism (SNP)-chip data, was found to have limitations and was almost always used in conjunction with a second method to locate the target region on the genome containing the variant. The commonly used methods had their drawbacks; so a new method was devised based on long runs of homozygosity, a common feature of new ARGD. This ‘autozygosity by difference’ method was found to be as good as, or better than, all the reviewed methods tested based on its ability to unambiguously find the shortest known target region in an already analysed data set. Mean target region length was found to be 4.6 megabases in the published reports. Success did not depend on the size of commercial SNP-chip used, and studies with as few as three cases and four controls were large enough to find the target region. The final stage relied on either sequencing the candidate genes found in the target region or using whole genome sequencing (WGS) on a small number of cases. Sometimes this latter method was used in conjunction with WGS on a number of control animals or resources such as the 1000 bull genomes data. Calculations showed that, in cattle, less than 15 animals would be needed in order to locate the new variant when using WGS data. This could be any combination of cases plus parents or other unrelated animals in the breed. Using WGS data, it would be necessary to search the three billion bases of the cattle genome for base positions which were homozygous for the same allele in all cases and heterozygous for that allele in parents, or not containing that homozygote in unrelated controls. This site could be confirmed on other healthy animals using much cheaper methods, and then a genetic test could be devised for that variant in order to screen the whole population and to devise a breeding programme to eliminate the disorder from the population.

Improved management and use of estimated breeding values in breeding programmes, have resulted in rapid genetic progress for small ruminants (SR) in Europe and other developed countries. The development of single nucleotide polymorphisms chips opened opportunities for genomic selection (GS) in SR in these countries. Initially focused on production traits (growth and milk), GS has been extended to functional traits (reproductive performance, disease resistance and meat quality). The GS systems have been characterized by smaller reference populations compared with those of dairy cattle and consisting mostly of cross- or multi-breed populations. Molecular information has resulted in gains in accuracy of between 0.05 and 0.27 and proved useful in parentage verification and the identification of QTLs for economically important traits. Except for a few established breeds with some degree of infrastructure, the basic building blocks to support conventional breeding programmes in small holder systems are lacking in most developing countries. In these systems, molecular data could offer quick wins in undertaking parentage verification and genetic evaluations using G matrix, and determination of breed composition. The development of next-generation molecular tools has prompted investigations on genome-wide signatures of selection for mainly adaptive and reproduction traits in SR in developing countries. Here, the relevance of the developments and application of GS and other molecular tools in developed countries to developing countries context is examined. Worth noting is that in the latter, the application of GS in SR will not be a ‘one-size fits all’ scenario. For breeds with some degree of conventional genetic improvement, classical GS may be feasible. In small holder systems, where production is key, community-based breeding programmes can provide the framework to implement GS. However, in fragile growth systems, for example those found in marginal environments, innovative GS to maximize adaptive diversity will be required. A cost-benefit analysis should accompany any strategy of implementing GS in these systems.

The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

Genetic improvement in production efficiency traits can also drive reduction in greenhouse gas emissions. This study used international ‘best-practice’ methodology to quantify the improvements in system-wide CO2 equivalent emissions per unit of genetic progress in the Irish Maternal Replacement (MR) and Terminal (T) beef cattle indexes. Effects of each index trait on system gross emissions (GE) and system emissions intensity (EI) were modelled by estimating effects of trait changes on per-animal feed consumption and associated methane production, per-animal meat production and numbers of animals in the system. Trait responses to index selection were predicted from linear regression of individual bull estimated breeding values for each index trait on their MR or T index value, and the resulting regression coefficients were used to calculate trait-wise responses in GE and EI from index selection. Summed over all trait responses, the MR index was predicted to reduce system GE by 0.810 kg CO2e/breeding cow per year per € index and system EI by 0.009 kg CO2e/kg meat per breeding cow per year per € index. These reductions were mainly driven by improvements in cow survival, reduced mature cow maintenance feed requirements, shorter calving interval and reduced offspring mortality. The T index was predicted to reduce system EI by 0.021 kg CO2e/kg meat per breeding cow per year per € index, driven by increased meat production from improvements in carcass weight, conformation and fat. Implications for incorporating an EI reduction index to the current production indexes and long-term projections for national breeding programs are discussed.

Recent studies with Nile tilapia have shown divergent results regarding the possibility of selecting on morphometric measurements to promote indirect genetic gains in fillet yield (FY). The use of indirect selection for fillet traits is important as these traits are only measurable after harvesting. Random regression models are a powerful tool in association studies to identify the best time point to measure and select animals. Random regression models can also be applied in a multiple trait approach to analyze indirect response to selection, which would avoid the need to sacrifice candidate fish. Therefore, the aim of this study was to investigate the genetic relationships between several body measurements, weight and fillet traits throughout the growth period and to evaluate the possibility of indirect selection for fillet traits in Nile tilapia. Data were collected from 2042 fish and was divided into two subsets. The first subset was used to estimate genetic parameters, including the permanent environmental effect for BW and body measurements (8758 records for each body measurement, as each fish was individually weighed and measured a maximum of six times). The second subset (2042 records for each trait) was used to estimate genetic correlations and heritabilities, which enabled the calculation of correlated response efficiencies between body measurements and the fillet traits. Heritability estimates across ages ranged from 0.05 to 0.5 for height, 0.02 to 0.48 for corrected length (CL), 0.05 to 0.68 for width, 0.08 to 0.57 for fillet weight (FW) and 0.12 to 0.42 for FY. All genetic correlation estimates between body measurements and FW were positive and strong (0.64 to 0.98). The estimates of genetic correlation between body measurements and FY were positive (except for CL at some ages), but weak to moderate (−0.08 to 0.68). These estimates resulted in strong and favorable correlated response efficiencies for FW and positive, but moderate for FY. These results indicate the possibility of achieving indirect genetic gains for FW and by selecting for morphometric traits, but low efficiency for FY when compared with direct selection.

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys’ dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams’) genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam’s genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire’s mate (i.e. dam of the progeny).

Genomic selection has become increasingly important in the breeding of animals and plants. The response variable is an important factor, influencing the accuracy of genomic selection. The de-regressed proof (DRP) based on traditional estimated breeding value (EBV) is commonly used as response variable. In the current study, simulated data from 16th QTL-MAS Workshop and real data from Chinese Holstein cattle were used to compare accuracy and bias of genomic prediction with two methods of calculating DRP. Our results with simulated data showed that the correlation between genomic EBV and true breeding value achieved using the Jairath method (DRP_J) was superior to that achieved using the Garrick method (DRP_G) for simulated trait 1 but the reverse was true for simulated trait 3, and these two methods performed comparably for simulated trait 2. For all three simulated traits, DRP_J yielded larger bias of genomic prediction. However, DRP_J outperformed DRP_G in both accuracy and unbiasedness for four milk production traits in Chinese Holstein. In the estimation of genomic breeding value using genomic BLUP model, two methods for weighting diagonal elements of incidence matrix associated with residual error were also compared. With increasing the proportion of genetic variance unexplained by markers, the accuracy of genomic prediction was decreased and the bias was increased. Weighting by the reliability of DRP produced accuracy comparable to the evaluation where the proportion of genetic variance unexplained by markers was considered, but with smaller bias in general.

The ability of birds to modify dietary phosphorus utilisation when fed with low-phosphorus and calcium (Ca) diets was studied using different sequences of dietary phosphorus and Ca restriction (depletion) and recovery (repletion) during the grower and the finisher phases. A total of 3600 Ross 708 broilers were randomly divided into 10 replicate pens per treatment (60 per pen, six pens per block). Chicks were fed a common starter diet from days 0 to 10, then a grower control diet (C: 0.90% Ca, 0.39% non-phytate phosphorus, nPP), mid-level diet (M: 0.71% Ca, 0.35% nPP) or low Ca and nPP diet (L: 0.60% Ca, 0.30% nPP) from days 11 to 21, followed by a finisher diet C, M or L containing, respectively, 0.85%, 0.57% or 0.48% Ca and 0.35%, 0.29% or 0.24% nPP from days 22 to 37. Six treatment sequences were tested: CC, MM, LL, ML, LC and LM. Bone mineral content by dual-energy X-ray, tibia ash, toe ash weight and tibia breaking strength were measured on days 21 and 37. No significant effect was observed on growth performance throughout the experiment. Diet L reduced bone mineral content, breaking strength, tibia and toe ash by 9%, 13%, 11% and 10%, respectively, on day 21 (compared with diet C, for linear effect, P<0.05). On day 37, bone mineral content, breaking strength, tibia and toe ash remained lower compared with control values (CC v. MM v. LL, P<0.05 for linear and quadratic effects). Mineral depletion duration (ML v. LL) did not affect bone mineral status. Replenishing with the C diet during the finisher phase (LC) restored bone mineral content, tibia ash and toe ash weight better than the M diet did, but not to control levels (CC v. LC v. LM, for linear effect, P<0.05). These results confirm that dietary Ca and nPP may be reduced in the grower phase without affecting final growth performance or breaking strength as long as the finisher diet contains sufficient Ca and nPP. The practical applications of this strategy require further study in order to optimise the depletion and repletion steps.

Dietary fatty acid (FA) composition has an impact on human health. There is an increasing request from consumers for healthier food and pork industry must respond to it without worsening performance and the technological properties of pork products. The inclusion of genetic markers for carcass FA composition in pig selection schemes could be a useful tool to reach the right balance between unsaturated and saturated FAs to satisfy market demands. With the aim of finding genomic regions associated with porcine backfat FA composition, a genome-wide association study was performed on 798 Italian Large White pigs genotyped using Illumina PorcineSNP60 k. The strongest associations with backfat contents of palmitic, palmitoleic, oleic, medium-chain and long-chain FAs were found for the Sus scrofa chromosome (SSC) 8 region located at 119 to 122 Mb, where the gene ELOVL FA elongase 6 is mapped. Palmitic, palmitoleic, stearic and oleic acid contents were also found associated with SSC14, in particular with the genomic region at 121 to 124 Mb, where stearoyl-CoA desaturase Δ9 gene lies. On the other hand, the genomic regions associated with backfat contents of arachidic, arachidonic, n-6 and n-3 FAs showed to harbour mainly genes involved in dietary lipids and carbohydrates digestion, absorption and utilisation. To our knowledge, this is the first study performed in Large White pigs identifying markers and genomic regions associated with backfat FA composition. The results validate in Large White some associations previously detected in other pig breeds and indicate the involvement of distinct metabolic pathways in the deposition pattern of essential and non-essential FAs.

Some mutations (or ‘major genes’) have a desirable effect in heterozygous carriers but an undesirable effect in homozygous carriers. When these mutations affect a trait of significant economic importance, their eradication, depending on their effect and frequency, may be counterproductive. This is especially the case of major genes affecting the ovulation rate and thus the prolificacy in meat sheep populations. To manage such situations, a mating design based on the major genotypes of reproducers has to be optimized. Both the effect of the major gene and the cost of genotyping candidates at this locus influence the expected genetic progress and profitability of the breeding plan. The aim of this study was to determine the optimal combination of matings that maximizes profitability at the level of the whole population (nucleus + commercial flocks). A deterministic model was developed and, using sequential quadratic programming methodology, the optimal strategy (optimal combination of matings) that maximized the economic gain achieved by the population across a range of genotype effects and genotyping costs was determined. The optimal strategy was compared with simpler and more practical strategies based on a limited number of parental genotype mating types. Depending on the genotype effect and genotyping costs, the optimal strategy varied, such that either the heterozygous frequency and/or polygenic gain was maximized with a large number of animals genotyped, or when genotyping costs were higher, the optimization led to lower heterozygous frequency and/or polygenic gain with fewer animals genotyped. Comparisons showed that some simpler strategies were close to the optimal strategy. An overlapping model was then derived as an application of the real case of the French Lacaune meat sheep OVI-TEST breeding program. Results showed that a practical strategy based on mating non-carriers to heterozygous carriers was only slightly less effective than the optimal strategy, with a reduction in efficiency from 3% to 8%, depending on the genotyping costs. Based on only two different parental genotype mating types, this strategy would be easy to implement.

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.

The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, Fst, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products.

Up to the 18th century, the prevailing view of reproduction, or ‘generation’ as it was referred to, was that organisms develop from miniatures of themselves, termed preformation. The alternative theory, epigenesis, proposed that the structure of an animal emerges gradually from a relatively formless egg. The teachings of the Ancient Greeks who argued either that both sexes each contributed ‘semen’ to form the embryo, or held a more male-centred view that the female merely provided fertile ground for the male seed to grow, dominated thinking until the 17th century, when the combined work of numerous scholars led to the theory that all female organisms, including humans, produced eggs. The sequence of events leading to the commercial use of artificial insemination (AI) date back to the discovery of sperm in 1678, although it took almost 100 years to demonstrate that sperm were the agents of fertilisation and a further 100 years for the detailed events associated with fertilisation to be elucidated. The first successful AI, carried out in the dog, dates back to 1780 while it was not until the early to mid-1900s that practical methods for AI were described in Russia. Inspired by the Russian success, the first AI cooperative was established in Denmark in 1936 and later in the United States in 1938. The next major advances involved development of semen extenders, addition of antibiotics to semen, and the discovery in 1949 that glycerol protected sperm during cryopreservation. Almost four decades later, the flow cytometric separation of X- and Y-bearing sperm opened a new chapter in the application of AI for cattle breeding. As we look forward today, developments in imaging sperm and breakthroughs in gene editing and stem cell technology are opening up new possibilities to manipulate reproduction in a way never thought possible by the pioneers of the past. This review highlights some of the main milestones and individuals in the history of sperm biology and the development of technologies associated with AI in cattle.

A study was conducted to estimate the genetic relationship between weaning weight and milk yield in Nguni cattle. Milk yield data (n=125) were collected from 116 Nguni cows from Mara Research Station located in Limpopo Province and Loskop South Farm located in Mpumalanga Province using the weigh-suckle-weigh technique. Weaning weight data (n=19 065) were obtained from stud Nguni cattle from 146 herds distributed throughout South Africa. Estimates of (co)variance components for milk yield and weaning weight were calculated using PEST and VCE softwares. The average weaning weight, age of the calf at weaning and 24-h milk yield was 158.94 kg, 210 days and 5.25 kg/day, respectively. Heritability estimates for milk yield, direct and maternal weaning weight were 0.22±0.238, 0.47±0.039 and 0.25±0.029, respectively. Estimates of genetic correlations for milk yield and maternal weaning weight, milk yield and direct weaning weight, direct and maternal weaning weight were 0.97±0.063, −0.71±0.416 and −0.56±0.247, respectively. The results indicate that maternal weaning weight is genetically highly predictive of milk yield in Nguni cattle. Maternal breeding values for weaning weight could therefore be used as a selection criterion to improve milk yield in Nguni cattle.

We estimated the heritabilities (h2) and genetic and phenotypic correlations among individual and groups of fatty acids, as well as their correlations with six important carcass and meat-quality traits in Korean Hanwoo cattle. Meat samples were collected from the longissimus dorsi muscles of 1000 Hanwoo steers that were 30-month-old (progeny of 85 proven Hanwoo bulls) to determine intramuscular fatty acid profiles. Phenotypic data on carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), marbling score (MS), Warner–Bratzler shear force (WBSF) and intramuscular fat content (IMF) were also investigated using this half-sib population. Variance and covari.ance components were estimated using restricted maximum likelihood procedures under univariate and pairwise bivariate animal models. Oleic acid (C18:1n-9) was the most abundant fatty acid, accounting for 50.69% of all investigated fatty acids, followed by palmitic (C16:0; 27.33%) and stearic acid (C18:0; 10.96%). The contents of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) were 41.64%, 56.24% and 2.10%, respectively, and the MUFA/SFA ratio, PUFA/SFA ratio, desaturation index (DI) and elongation index (EI) were 1.36, 0.05, 0.59 and 0.66, respectively. The h2 estimates for individual fatty acids ranged from very low to high (0.03±0.14 to 0.63±0.14). The h2 estimates for SFAs, MUFAs, PUFAs, DI and EI were 0.53±0.14, 0.49±0.14, 0.23±0.10, 0.51±0.13 and 0.53±0.13, respectively. The genetic and phenotypic correlations among individual fatty acids and fatty acid classes varied widely (−0.99 to 0.99). Notably, C18:1n-9 had favourable (negative) genetic correlations with two detrimental fatty acids, C14:0 (−0.76) and C16:0 (−0.92). Genetic correlations of individual and group fatty acids with CWT, EMA, BFT, MS, WBSF and IMF ranged from low to moderate (both positive and negative) with the exception of low-concentration PUFAs. Low or near-zero phenotypic correlations reflected potential non-genetic contributions. This study provides insights on genetic variability and correlations among intramuscular fatty acids as well as correlations between fatty acids and carcass and meat-quality traits, which could be used in Hanwoo breeding programmes to improve fatty acid compositions in meat.