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Key message The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. Abstract The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24–0.26 to 0.80–0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055–0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.

Economic values for annual milk yield (MY, kg), annual fat yield (FY, kg), annual protein yield (PY, kg), age at first calving (AFC, days), number of services per conception (NSC), calving interval (CI, days) and mastitis episodes (MS) were derived for temperate dairy cattle breeds in tropical Sri Lanka using a bio-economic model. Economic values were calculated on a per cow per year basis. Derived economic values in rupees (LKR) for MY, FY and PY were 107, -162 and -15, while for AFC, NSC, CI and MS, economic values were -59, -270, -84 and -8,303. Economic values for FY and PY further decreased with higher feed prices, and a less negative economic value for FY was obtained with increased price for fat. Negative economic values for FY and PY show that genetic improvement for these traits is not economical due to the high feed costs and/or the insufficient payment for fat and protein. Therefore, revision of milk fat and protein payments is recommended. Furthermore, the breeding objective developed in this study was dominated by milk production and fertility traits. Adaptability and functional traits that are important in a temperate dairy cattle breeding programme in tropical Sri Lanka, such as longevity, feed efficiency, disease resistance and heat tolerance should be recorded to incorporate them in the breeding objective. Continued trait recording of all traits is recommended to ensure dairy cows can be selected more effectively in a tropical environment based on a breeding objective that also includes adaptability and functional traits.

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

The aim of this study was to find significant genomic regions associated with carcass traits in Hanwoo cattle and to compare the benefit of using additional information from non-genotyped animals. Imputed whole-genome sequence data were used along with phenotypic data on 13 715 genotyped animals as well as phenotypes of 440 284 non-genotyped animals that were offspring of 454 genotyped sires. For carcass weight, 15 083 SNPs in 33 QTL regions and 313 candidate genes were identified. We found 410 SNPs in 17 QTL regions containing 122 candidate genes for back fat thickness. In total, 656 SNPs in 19 QTLs with 137 candidate genes for eye muscle area and 79 SNPs in 12 QTL regions with 77 candidate genes were identified for marbling score. The most important candidate genes included ZFAT, TG, PLAG1, CHCHD7, and TOX for carcass weight and eye muscle area, NOG for back fat thickness, and EVOVL5 for marbling score. This study showed that the use of phenotypic records on non-genotyped progeny along with imputed whole-genome sequence data increased the power of detecting new significant genomic regions.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter < 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP.Author SummaryDespite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor (α), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.

Context A thorough analysis of the reasons for culling was made to understand the phenotypic trend in herd life. In addition, identification of culling reasons could enable to develop a strategy for further evaluation of longevity in Australian dairy cows. Aims The aim of this study was to investigate the main causes of culling in Australian dairy herds and thereby to assess the trend of reason-specific culling over time. Methods Culling reasons in Australian dairy cattle were studied based on culling records from 1995 through 2016. A total of 2 452 124 individual cow culling observations were obtained from Datagene, Australia, of which 2 140 337 were Holstein and 311 787 were from Jersey cows. A binary logistic regression model was used to estimate effects of breed and age and the trend of a particular culling reason over time. Key results The most important culling reasons identified over the 21-year period were infertility (17.0%), mastitis (12.9%), low production (9.3%), sold for dairy purpose (6.4%) and old age (6.2%), whereas 37.4% were ‘other reasons not reported’. The average age at culling was nearly the same for Holstein (6.75 years) and Jersey (6.73 years) cows. The estimated age at culling was slightly increased for Holstein cows (by 3.7 days) and somewhat decreased for Jersey cows (by 11 days) over the last two decades. The probability of culling cows for infertility and low production was high in early parities and consistently declined as age advanced, and culling due to mastitis was higher in older cows. The trend of main culling reasons over time was evaluated, indicating that the probability of culling due to infertility has progressively increased over the years in both breeds, and culling for mastitis in Jersey cows has also increased. Culling of cows due to low production sharply decreased from 2.5 to –8% for Holstein and from 73 to 60% for Jersey cows over the 21-year period. Conclusions Culling age has changed only little in both breeds whereas culling reasons have changed over the last two decades, with low production becoming a less important reason for culling and infertility becoming more important for Holstein and Jersey breeds. Implications Due to changes of culling reasons, there could be a change in the meaning of survival over time as well. As a result, genetic correlation with survival and other traits might be changed and accuracy and bias of genetic evaluations could be affected.

Objective: This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka.Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model.Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and –0.35 in Jersey-Friesian cows.Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention., Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability.

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.

Genome-Wide Association Studies are an important tool for identifying genetic markers associated with a trait, but it has been plagued by the multiple testing problem, which necessitates a multiple testing correction method. While many multiple testing methods have been suggested, e.g. Bonferroni and Benjamini-Hochberg’s False Discovery Rate, the quality of the adjusted threshold based on these methods is not as well investigated. The aim of this study was to evaluate the balance between power and false positive rate of a Genome-Wide Association Studies experiment with Bonferroni and Benjamini-Hochberg’s False Discovery Rate multiple testing correction methods and to test the effects of various experimental design and genetic architecture parameters on this balance. Our results suggest that when the markers are independent the threshold from Benjamini-Hochberg’s False Discovery Rate provides a better balance between power and false positive rate in an experiment. However, with correlations between markers the threshold of Benjamini-Hochberg’s False Discovery Rate becomes too lenient with an excessive number of false positives. Experimental design parameters such as sample size and number of markers used, as well as genetic architecture of a trait affect the balance between power and false positive rate. This experiment provided guidance in selecting an appropriate experimental design and multiple testing correction method when conducting an experiment.

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.

Objective Estimate genetic parameters, the rate of inbreeding, and the effect of inbreeding on growth and egg production traits of a Thai native chicken breed Lueng Hang Kao Kabinburi housed under intensive management under a tropical climate. Methods Genetic parameters were estimated for weight measured at four weekly intervals from body weight at day 1 (BW1D) to body weight at 24 weeks (BW24) of age, as well as weight at first egg, age at first egg (AFE), egg weight at first egg, and total number of eggs (EN) produced during the first 17 weeks of lay using restricted maximum likelihood. Inbreeding depression was estimated using a linear regression of individual phenotype on inbreeding coefficient. Results Direct additive genetic effect was significant for all traits. Maternal genetic effect and permanent environmental hen effects were significant for all early growth traits, expect for BW24. For BW24, maternal genetic effect was also significant. Permanent environmental hen effect was significant for AFE. Direct heritabilities ranged from 0.10 to 0.47 for growth traits and ranged from 0.15 to 0.16 for egg production traits. Early growth traits had high genetic correlations between them. The EN was lowly negatively correlated with other traits. The average rate of inbreeding for the population was 0.09% per year. Overall, the inbreeding had no effect on body weight traits, except for BW1D. An increase in inbreeding coefficient by 1% reduced BWID by 0.09 g (0.29% of the mean). Conclusion Improvement in body weight gain can be achieved by selecting for early growth traits. Selection for higher body weight traits is expected to increase the weight of first egg. Due to low but unfavorable correlations with body weight traits, selection on EN needs to be combined with other traits via multi-trait index selection to improve body weight and EN simultaneously.

Benefits of genomic selection (GS) in livestock breeding operations are well known particularly where traits are sex-limited, hard to measure, have a low heritability and/or measured later in life. Sheep and beef breeders have a higher cost:benefit ratio for GS compared to dairy. Therefore, strategies for genotyping selection candidates should be explored to maximize the economic benefit of GS. The aim of the paper was to investigate, via simulation, the additional genetic gain achieved by selecting proportions of male selection candidates to be genotyped via truncation selection. A two-trait selection index was used that contained an easy and early-in-life measurement (such as post-weaning weight) as well as a hard-to-measure trait (such as intra-muscular fat). We also evaluated the optimal proportion of female selection candidates to be genotyped in breeding programmes using natural mating and/or artificial insemination (NatAI), multiple ovulation and embryo transfer (MOET) or juvenile in vitro fertilization and embryo transfer (JIVET). The final aim of the project was to investigate the total dollars spent to increase the genetic merit by one genetic standard deviation (SD) using GS and/or reproductive technologies. For NatAI and MOET breeding programmes, females were selected to have progeny by 2 years of age, while 1-month-old females were required for JIVET. Genomic testing the top 20% of male selection candidates achieved 80% of the maximum benefit from GS when selection of male candidates prior to genomic testing had an accuracy of 0.36, while 54% needed to be tested to get the same benefit when the prior selection accuracy was 0.11. To achieve 80% of the maximum benefit in female, selection required 66%, 47% and 56% of female selection candidates to be genotyped in NatAI, MOET and JIVET breeding programmes, respectively. While JIVET and MOET breeding programmes achieved the highest annual genetic gain, genotyping male selection candidates provides the most economical way to increase rates of genetic gain facilitated by genomic testing.

De novo mutations (DNM) create new genetic variance and are an important driver for long-term selection response. We hypothesized that genomic selection exploits mutational variance less than traditional selection methods such as mass selection or selection on pedigree-based breeding values, because DNM in selection candidates are not captured when the selection candidates’ own phenotype is not used in genomic selection, DNM are not on SNP chips and DNM are not in linkage disequilibrium with the SNP on the chip. We tested this hypothesis with Monte Carlo simulation. From whole-genome sequence data, a subset of ∼300,000 variants was used that served as putative markers, quantitative trait loci or DNM. We simulated 20 generations with truncation selection based on breeding values from genomic best linear unbiased prediction without (GBLUP_no_OP) or with own phenotype (GBLUP_OP), pedigree-based BLUP without (BLUP_no_OP) or with own phenotype (BLUP_OP), or directly on phenotype. GBLUP_OP was the best strategy in exploiting mutational variance, while GBLUP_no_OP and BLUP_no_OP were the worst in exploiting mutational variance. The crucial element is that GBLUP_no_OP and BLUP_no_OP puts no selection pressure on DNM in selection candidates. Genetic variance decreased faster with GBLUP_no_OP and GBLUP_OP than with BLUP_no_OP, BLUP_OP or mass selection. The distribution of mutational effects, mutational variance, number of DNM per individual and nonadditivity had a large impact on mutational selection response and mutational genetic variance, but not on ranking of selection strategies. We advocate that more sustainable genomic selection strategies are required to optimize long-term selection response and to maintain genetic diversity.

The objective of this study was to investigate the accuracy of genomic prediction of body weight and eating quality traits in a numerically small sheep population (Dorper sheep). Prediction was based on a large multi-breed/admixed reference population and using (a) 50k or 500k single nucleotide polymorphism (SNP) genotypes, (b) imputed whole-genome sequencing data (~31 million), (c) selected SNPs from whole genome sequence data and (d) 50k SNP genotypes plus selected SNPs from whole-genome sequence data. Furthermore, the impact of using a breed-adjusted genomic relationship matrix on accuracy of genomic breeding value was assessed. The selection of genetic variants was based on an association study performed on imputed whole-genome sequence data in an independent population, which was chosen either randomly from the base population or according to higher genetic proximity to the target population. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of genomic prediction was assessed according to the correlation between genomic breeding value and corrected phenotypes divided by the square root of trait heritability. The accuracy of genomic prediction was between 0.20 and 0.30 across different traits based on common 50k SNP genotypes, which improved on average by 0.06 (absolute value) on average based on using prioritized genetic markers from whole-genome sequence data. Using prioritized genetic markers from a genetically more related GWAS population resulted in slightly higher prediction accuracy (0.02 absolute value) compared to genetic markers derived from a random GWAS population. Using high-density SNP genotypes or imputed whole-genome sequence data in GBLUP showed almost no improvement in genomic prediction accuracy however, accounting for different marker allele frequencies in reference population according to a breed-adjusted GRM resulted to on average 0.024 (absolute value) increase in accuracy of genomic prediction.

Background In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals. Results Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy. Conclusions Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.

The Korean Hanwoo breed possesses a high capacity to accumulate intramuscular fat, which is measured as a marbling score in the beef industry. Unfortunately, the development of marbling is not completely understood and the identification of differentially expressed genes at an early age is required to better understand this trait. In this study, we took muscle samples from 12 Hanwoo steers at the age of 18 and 30 months. From the contrast between age and marbling score, we identified in total 1883 differentially expressed genes (FDR &lt, 0.05 and logarithm fold change &ge, 1.5) with 782 genes up-regulated and 1101 down-regulated. Differences in gene expression were higher between the ages x marbling groups rather than between high and low marbling groups. At 18 months of age, the genes SLC38A4, ABCA10, APOL6, and two novel genes (ENSBTAG00000015330 and ENSBTAG00000046041) were up-regulated in the high marbling group. From the protein&ndash, protein interaction network analysis, we identified unique networks when comparing marbling scores between different ages. Nineteen genes (AGT, SERPINE1, ADORA1, FOS, LEP, FOXO1, FOXO3, ADIPOQ, ITGA1, SDC1, SDC4, ITGB3, ITGB4, CXCL10, ACTG2, MX1, EDN1, ACTA2, and ESPL1) were identified to have an important role in marbling development. Further analyses are needed to better understand the role of these genes.

Background Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. Methods and Results We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Conclusions Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

The objective of this study was to explore the sensitivity of breeding values for growth rate and worm egg count (WEC, cube root transformed) to environmental worm burden, measured as the average WEC for each contemporary group (CGWEC). Growth rate and WEC were measured on 7,818 naturally infected Merino lambs in eight flocks across Australia, linked through common use of AI sires. Through bivariate analysis, genetic correlations of 0.55 ± 0.23 and 0.30 ± 0.16 were found for growth rate and WEC between low and high CGWEC, respectively. In a second analysis, breeding values for growth rate and WEC were regressed on CGWEC with a random regression model. The heritability for growth rate varied from 0.23 to 0.16 from low to high CGWEC, and the heritability for WEC varied from 0.25 to 0.36. Results suggest that breeding values for both growth rate and WEC are sensitive to environmental worm burden. Animals expressed less genetic variation for growth rate and more genetic variation for WEC in high CGWEC than in low CGWEC. This form of genotype-by-environment interaction should therefore be considered in genetic evaluation of both growth rate and WEC, to increase the efficiency of selection for animals that are more parasite resistant and more resilient to environmental worm challenge.

Genetic correlations between 16 meat quality and nutritional value traits and live weight at various ages, live ultrasound fat and muscle depth, carcass measures, and carcass dissection traits were estimated for Merino sheep in the Information Nucleus (IN). Genetic correlations between live weight at various ages and the carcass traits are also reported. The IN comprised 8 genetically linked flocks managed across a range of Australian sheep environments. Meat quality traits included between 1,200 and 1,300 records for progeny from over 170 sires for intramuscular fat (IMF), lean meat yield (LMY), shear force (SF5), pH, meat color, and meat nutritional value traits including iron and zinc levels and long-chain omega-3 and omega-6 polyunsaturated fatty acid levels. The genetic correlations indicated that selection of Merino sheep to either reduce fat or increase muscle using ultrasound assessments will result in little change in IMF and SF5. Myoglobin levels would tend to be reduced following selection for reduced ultrasound fat depth (0.35 ± 0.21, 0.43 ± 0.14), whereas increases in myoglobin levels would occur due to selection for increased ultrasound muscle depth (0.25 ± 0.24, 0.38 ± 0.15). Selection for increased live weight will result in favorable correlated responses in hot carcass weight (0.76 to 0.97), dressing percentage (0.13 to 0.47), and carcass muscle (0.37 to 0.95), but unfavorable responses of increases in carcass fatness (0.13 to 0.65) and possible small reductions in muscle oxidative activity (-0.13 ± 0.14 to -0.73 ± 0.33) and iron content (-0.14 ± 0.15 to -0.38 ± 0.16), and a possible deterioration of shear force from selection at later ages (0.15 ± 0.26, 0.27 ± 0.24). Negligible changes are generally expected for LMY and meat color traits following selection for increased live weight (most genetic correlations less than 0.20 in size). Selection for increased LMY would tend to result in unfavorable changes in several aspects of meat quality, including reduced IMF (-0.27 ± 0.18), meat tenderness (0.53 ± 0.26), and meat redness (-0.69 ± 0.40), as well as reduced iron levels (-0.25 ± 0.22). These genetic correlations are a first step in assisting the development of breeding values for new traits to be incorporated into genetic evaluation programs to improve meat production from Merino sheep and other dual-purpose sheep breeds.

Background In horned sheep breeds, breeding for polledness has been of interest for decades. The objective of this study was to improve prediction of the horned and polled phenotypes using horn scores classified as polled, scurs, knobs or horns. Derived phenotypes polled/non-polled (P/NP) and horned/non-horned (H/NH) were used to test four different strategies for prediction in 4001 purebred Merino sheep. These strategies include the use of single ‘single nucleotide polymorphism’ (SNP) genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide genomic best linear unbiased prediction and information from imputed sequence variants from the region including the RXFP2 gene. Low-density genotypes of these animals were imputed to the Illumina Ovine high-density (600k) chip and the 1.78-kb insertion polymorphism in RXFP2 was included in the imputation process to whole-genome sequence. We evaluated the mode of inheritance and validated models by a fivefold cross-validation and across- and between-family prediction. Results The most significant SNPs for prediction of P/NP and H/NH were OAR10_29546872.1 and OAR10_29458450, respectively, located on chromosome 10 close to the 1.78-kb insertion at 29.5 Mb. The mode of inheritance included an additive effect and a sex-dependent effect for dominance for P/NP and a sex-dependent additive and dominance effect for H/NH. Models with the highest prediction accuracies for H/NH used either single SNPs or 3-SNP haplotypes and included a polygenic effect estimated based on traditional pedigree relationships. Prediction accuracies for H/NH were 0.323 for females and 0.725 for males. For predicting P/NP, the best models were the same as for H/NH but included a genomic relationship matrix with accuracies of 0.713 for females and 0.620 for males. Conclusions Our results show that prediction accuracy is high using a single SNP, but does not reach 1 since the causative mutation is not genotyped. Incomplete penetrance or allelic heterogeneity, which can influence expression of the phenotype, may explain why prediction accuracy did not approach 1 with any of the genetic models tested here. Nevertheless, a breeding program to eradicate horns from Merino sheep can be effective by selecting genotypes GG of SNP OAR10_29458450 or TT of SNP OAR10_29546872.1 since all sheep with these genotypes will be non-horned. Electronic supplementary material The online version of this article (10.1186/s12711-018-0398-6) contains supplementary material, which is available to authorized users.

International audience; AbstractBackgroundThis study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.ResultsThe accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).ConclusionsOur results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.

The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.

The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using Illumina Bovine 50k SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.

Both genetic and non-genetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on—hence their predispositions to—cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,896-7,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure; and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and non-genetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified individuals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for individualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and non-genetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.

The objectives of this study were to compare different models for analysing body weight (BW) and average daily feed intake (ADFI) data collected during a 70-day feedlot test period and to explore whether genetic parameters change over time to evaluate the implications of selection response. (Co)variance components were estimated using repeatability and random regression models in 2,071 Angus steers. Models included fixed effects of contemporary group, defined as herd-year-observation_date-age, with additive genetic and permanent environmental components as random effects. Models were assessed based on the log likelihood, Akaike's information criterion and the Bayesian information criterion. For both traits, random regression models (RRMs) presented a better fit, indicating that genetic parameters change over the test period. Using a two-trait RRM, the heritability from day 1 up to day 70 for BW increased from 0.40 to 0.50, while for ADFI, it decreased from 0.44 to 0.33. The genetic correlation increased from 0.53 at day 1 up to 0.79 at day 70. Selection based on an index assuming no change in genetic parameters would yield a 2.78%-3.13% lower selection response compared to an index using parameters estimated with RRMs and assuming these genetic parameters are correct. Results imply that it may be beneficial to implement RRMs to account for the change of parameters across the feedlot period in feed efficiency traits.

Reference populations for genomic selection usually involve selected individuals,which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is,Best Linear Unbiased Prediction of breeding values using pedigree‐based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and aSingle‐Step approach (SSGBLUP) using both. For a scenario with no‐selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices.Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single‐Step approach to obtain accurate and unbiased prediction of GEBV. Refereed/Peer-reviewed