Estimated Breeding Values Research Articles

Analysis of residual feed intake in Nellore bulls of different ages, rib eye area, and backfat thickness

The aim of the present study was to use different models that include body composition phenotypes for the evaluation of residual feed intake (RFI) in Nellore bulls of different ages. Phenotypic and genotypic data of bulls that had participated in feed efficiency tests of a commercial (COM) and an experimental (EXP) herd between 2007 and 2019 were used. The mean entry age in the two herds was 645 and 279days, respectively. The phenotypes were evaluated: rib eye area (REA), backfat thickness (BFT), residual feed intake (RFIKOCH), RFI adjusted for REA (RFIREA), RFI adjusted for BFT (RFIBFT), and RFI adjusted for REA and BFT (RFIREA BFT). The (co)variance components and prediction of genomic estimated breeding values (GEBV) were obtained by REML using ssGBLUP in single and two-trait analyses. Spearman's correlations were calculated based on the GEBV for RFIKOCH. The RFI phenotypes exhibited moderate heritability estimates in both herds (0.17 ± 0.03 to 0.27 ± 0.04). The genetic correlation between phenotypes was positive and high (0.99) in the two herds, a fact that permitted the creation of a single database (SDB). The heritability estimates of the SDB were also of moderate magnitude for the different definitions of RFI (0.19 ± 0.04 to 0.21 ± 0.04). The genetic correlations were positive and high between RFI traits 0.97 ± 0.01 to 0.99 ± 0.01), and positive and low/moderate between REA and BFT (0.01 ± 0.10 to 0.31 ± 0.12). The selection of animals based on the GEBV for RFIKOCH did not alter the ranking of individuals selected for RFIREA, RFIBFT, and RFIREA BFT. The results of the present study suggest that records of Nellore bulls of different ages and with different body compositions can be combined in a SDB for RFI calculation. Therefore, young animals can be evaluated in feed efficiency tests in order to reduce costs and the generation interval and possibly to obtain a higher response to selection.

G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction.

Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method.

Accuracy of Genomic Prediction of Yield and Sugar Traits in Saccharum spp. Hybrids

Genomic selection (GS) has been demonstrated to enhance the selection process in breeding programs. The objectives of this study were to experimentally evaluate different GS methods in sugarcane hybrids and to determine the prospect of GS in future breeding approaches. Using sugar and yield-related trait data from 432 sugarcane clones and 10,435 single nucleotide polymorphisms (SNPs), a study was conducted using seven different GS models. While fivefold cross-validated prediction accuracy differed by trait and by crop cycle, there were only small differences in prediction accuracy among the different models. Prediction accuracy was on average 0.20 across all traits and crop cycles for all tested models. Utilizing a trait-assisted GS model, we could effectively predict the fivefold cross-validated genomic estimated breeding value of ratoon crops using both SNPs and trait values from the plant cane crop. We found that the plateau of prediction accuracy could be achieved with 4000 to 5000 SNPs. Prediction accuracy did not decline with decreasing size of the training population until it was reduced below 60% (259) to 80% (346) of the original number of clones. Our findings suggest that GS is possibly a new direction for improving sugar and yield-related traits in sugarcane.

Estimating genetic parameters of resistance against Edwardsiella tarda in turbot (Scophthalmus maximus)

Ascites disease, caused by Edwardsiella tarda, is one of the most destructive diseases in turbot (Scophthalmus maximus). Genetic improvement of host germplasm aiming at resistance to E. tarda is the fundamental way to control this disease. However, genetic parameters for this trait in turbot are so far completely unknown, which are necessary for designing breeding protocols. In the current study, genetic parameters of this trait were estimated based on phenotype data collected using infection artificially. Challenge test survival was defined using three different trait definitions: Survival days after infection (SD), survival status at the half lethal time (SS50), test-day survival (TDS), and each trait definition was analyzed using appropriate models. In the challenge test, all the fish died from 6 to 14 days post infection. The heritability for SD and SS50 based on animal model were 0.43 ± 0.11 and 0.24 ± 0.07. When sire-dam model was used, all the heritability estimates were consistent and high, ranged from 0.72 to 0.76. Genetic correlation between resistance to E. tarda and harvest body weight, approximated by Pearson correlations between their average family estimated breeding values (EBVs), ranged from 0.14 to 0.45. These results indicated that selective breeding for resistance to E. tarda can be successfully applied in the breeding population.

Neural network and Bayesian-based prediction of breeding values in Beetal goat.

The estimation of breeding values is prime concern for animal breeders in order to achieve desired genetic progress of farm animals. However, current methods for estimating BV involve simultaneous selection of animal model which are computationally intensive and time-consuming. The present attempt was made to predict breeding values of weaning trait under artificial neural networks (ANN), Bayesian technique (BT), and multiple regression (MR) methods. The data records comprising year of birth, sex, type of birth, dam's weight at kidding, birth weight, weaning weight, and estimated breeding values (BV) for weaning weight (under animal model) pertaining to 849 kids born to 37 sires and 237 dams between 2004 and 2019 were used in this study. All three methods, viz., ANN under multilayer perceptron (2 hidden layers), BT under Markov chain Monte Carlo (MCMC) approach, and MR under full model, were used for 75% training dataset initially and prediction model developed was applied on 25% test dataset. The initial analysis showed positive and significant (P < 0.01) relationship of BV with other variables which hinted that BV may be predicted with accuracy. Then, it was revealed from the results indicated that ANN, BT, and MR methods have similar accuracy (r = 0.86 to 0.87) for prediction of BV. However, ANN showed slightly higher but negligible model adequacy than BT and MR method. The prediction error under three methods was almost equal. The results indicated that these methods could be used as potential alternative for recurrent prediction of BV based on phenotypic data in order to optimize selection plans at young age in resourced population of Beetal goat.

Progress in Genomic Mating in Domestic Animals.

Simple SummarySince animal domestication, breeders have been selecting candidates for breeding based on phenotypic performance. Estimating breeding values through the best linear unbiased prediction method represents a revolutionary shift in animal breeding. On this basis, selection and mating are utilized to improve the production level of animals. The application of genomic selection has once again revolutionized animal breeding methods. However, although this kind of truncated selection based on breeding values can significantly improve genetic gain, the genetic relationship between individuals with a high breeding value is usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the rate of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic gain and inbreeding is required. Genomic mating is the use of candidate individuals’ genomic information to implement optimized breeding and mating, which can effectively control the rate of inbreeding in the population and achieve long-term and sustainable genetic gain. It is more suitable for modern animal breeding, especially for conservation and genetic improvement of local domestic animal breeds.Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry.

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency

BackgroundSharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows.ResultsGEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase.ConclusionsThe accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.

Associations between polymorphisms of LAP3 and SIRT1 genes with clinical mastitis and milk production traits in Sahiwal and Karan Fries dairy cattle.

Bovine mastitis continues to remain as the most challenging disease in dairy cattle, as a result improvement of selection methods has great economic relevance while a deeper understanding of the genetic mechanisms regulating milk production traits and mastitis is of general scientific interest. This study aimed to evaluate the association of SNPs of the LAP3 and SIRT1 genes with estimated breeding values (EBVs) of milk production traits and clinical mastitis in dairy cattle of Indian origin. DNA samples from 263 animals (Sahiwal and Karan Fries cattle) were genotyped by PCR-RFLP to assess their pattern of genetic variation. EBVs of milk production traits and phenotypic records of incidences of clinical mastitis were used for association analysis. A total of 9 SNPs were identified, including three (rs110932626: A>G, rs716493845: C>T and rs43702363: C>T) in intron 12, four (g.24904G>C, rs110839532: G>T, rs43702361: T>C and rs41255599: C>T) in exon 13 and within 3'UTR of LAP3 gene and two (rs110250233: G>A and rs42140046: C>G) in the promoter region of SIRT1 gene. Eight of these identified SNPs were chosen for subsequent genotyping and association analyses. Association analysis revealed that SNP rs41255599: C>T was significantly associated with lactation milk yield, 305-day milk yield, 305-day fat yield, 305-day solid not fat yield, lactation length and incidence of clinical mastitis (p <0.05) in Sahiwal cattle. For Karan Fries cattle, two SNPs including rs110932626: A>G and rs43702363: C>T showed significant association with 305-day milk yield. Overall, these findings provide evidence for association of the LAP3 gene with milk production traits and clinical mastitis in dairy cattle, indicating the potential role of LAP3 variants in these traits.

International single-step SNPBLUP beef cattle evaluations for Limousin weaning weight

BackgroundCompared to national evaluations, international collaboration projects further improve accuracies of estimated breeding values (EBV) by building larger reference populations or performing a joint evaluation using data (or proxy of them) from different countries. Genomic selection is increasingly adopted in beef cattle, but, to date, the benefits of including genomic information in international evaluations have not been explored. Our objective was to develop an international beef cattle single-step genomic evaluation and investigate its impact on the accuracy and bias of genomic evaluations compared to current pedigree-based evaluations.MethodsWeaning weight records were available for 331,593 animals from seven European countries. The pedigree included 519,740 animals. After imputation and quality control, 17,607 genotypes at a density of 57,899 single nucleotide polymorphisms (SNPs) from four countries were available. We implemented two international scenarios where countries were modelled as different correlated traits: an international genomic single-step SNP best linear unbiased prediction (SNPBLUP) evaluation (ssSNPBLUPINT) and an international pedigree-based BLUP evaluation (PBLUPINT). Two national scenarios were implemented for pedigree and genomic evaluations using only nationally submitted phenotypes and genotypes. Accuracies, level and dispersion bias of EBV of animals born from 2014 onwards, and increases in population accuracies were estimated using the linear regression method.ResultsOn average across countries, 39 and 17% of sires and maternal-grand-sires with recorded (grand-)offspring across two countries were genotyped. ssSNPBLUPINT showed the highest accuracies of EBV and, compared to PBLUPINT, led to increases in population accuracy of 13.7% for direct EBV, and 25.8% for maternal EBV, on average across countries. Increases in population accuracies when moving from national scenarios to ssSNPBLUPINT were observed for all countries. Overall, ssSNPBLUPINT level and dispersion bias remained similar or slightly reduced compared to PBLUPINT and national scenarios.ConclusionsInternational single-step SNPBLUP evaluations are feasible and lead to higher population accuracies for both large and small countries compared to current international pedigree-based evaluations and national evaluations. These results are likely related to the larger multi-country reference population and the inclusion of phenotypes from relatives recorded in other countries via single-step international evaluations. The proposed international single-step approach can be applied to other traits and breeds.

Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls.

The purposes of this study were (1) to explore the relationship between 3 milk mid-infrared predicted features including nitrogen intake (NINT), milk true protein N (MTPN), and milk urea-N yield (MUNY); (2) to integrate these 3 features into an N efficiency index (NEI) and analyses approximate genetic correlations between the NEI and 37 traits (indices) of interest; and (3) to assess the potential effect of including the NEI into breeding programs of bulls. The edited data were 1,043,171 test-day records on 342,847 cows in 1,931 herds and 143,595 test-day records on 53,660 cows in 766 herds used for estimating breeding values (EBV) and variance components, respectively. The used records were within 5 to 50 d in milk. The records were grouped into primiparous and multiparous. The genetic parameters for the included mid-infrared features and EBV of the animals included in the pedigree were estimated using a multiple-trait repeatability animal model. Then, the EBV of the NINT, MTPN, MUNY were integrated into the NEI using a selection index assuming weights based on the N partitioning. The approximate genetic correlations between the NEI and 37 traits of interest were estimated using the EBV of the selected bulls. The bulls born from 2011 to 2014 with NEI were selected and the NEI distribution of these bulls having EBV for the 8 selected traits (indices) was checked. The heritability and repeatability estimates for NINT, MTPN, and MUNY ranged from 0.09 to 0.13, and 0.37 to 0.65, respectively. The genetic and phenotypic correlations between NINT, MTPN, and MUNY ranged from -0.31 to 0.87, and -0.02 to 0.42, respectively. The NEI ranged from -13.13 to 12.55 kg/d. In total, 736 bulls with reliability ≥0.50 for all included traits (NEI and 37 traits) and at least 10 daughters distributed in at least 10 herds were selected to investigate genetic aspects of the NEI. The NEI had positive genetic correlations with production yield traits (0.08-0.46), and negative genetic correlations with the investigated functional traits and indices (-0.71 to -0.07), except for the production economic index and functional type economic index. The daughters of bulls with higher NEI had lower NINT and MUNY, and higher MTPN. Furthermore, 26% of the bulls (n = 50) with NEI born between 2011 to 2014 had higher NEI and global economic index than the average in the selected bulls. Finally, the developed NEI has the advantage of large-scale prediction and therefore has the potential for routine application in dairy cattle breeding in the future.

Comparison of the estimated breeding value and accuracy by imputation reference Beadchip platform and scaling factor of the genomic relationship matrix in Hanwoo cattle

Comparison of the estimated breeding value and accuracy by imputation reference Beadchip platform and scaling factor of the genomic relationship matrix in Hanwoo cattle

Estimation of breeding value of blackcurrant genotypes for growth vigour, plant habit and their susceptibility to fungal diseases.

The study was conducted in 2012–2014 at the Experimental Orchard of the National Institute of Horticultural Research in Skierniewice, central Poland. The aim of the research was to assess the breeding value, based on the general combining abilities (GCA), specific combining abilities (SCA) and reciprocal (RE) effects, of six dessert parental forms of blackcurrant for plant growth vigour, plant habit and resistance to fungal diseases: Podosphaera mors-uvae (causal agent of powdery mildew of currant), Drepanopeziza ribis (leaf anthracnose) and Cronartium ribicola (white pine blister rust). The plant material consisted of seedlings of F1 generation obtained by crossing of six blackcurrant genotypes: ‘Bona’, ‘Ceres’, clone D13B/11, ‘Sofiivska’, ‘Vernisazh’ and ‘Big Ben’ in a diallel design according to Griffing’s Method III.It was shown that the cultivars ‘Big Ben’, ‘Sofiivska’ and ‘Vernisazh’ had significantly positive GCA effects for growth vigour and resistance of plants to powdery mildew, whereas ‘Ceres’ – for plant habit, which indicated their high breeding value for these traits. The significantly positive SCA values were estimated for the hybrid family: ‘Big Ben’ × ‘Ceres’ for low susceptibility of plants to powdery mildew and anthracnose. Statistically significant effects for reciprocal crosses (RE) were obtained only for few hybrid families.

Quantitative trait loci identification and breeding value estimation of grain weight-related traits based on a new wheat 50K single nucleotide polymorphism array-derived genetic map.

Mining novel and less utilized thousand grain weight (TGW) related genes are useful for improving wheat yield. In this study, a recombinant inbred line population from a cross between Zhongkemai 138 (ZKM138, high TGW) and Chuanmai 44 (CM44, low TGW) was used to construct a new Wheat 50K SNP array-derived genetic map that spanned 1,936.59 cM and contained 4, 139 markers. Based on this map, ninety-one quantitative trait loci (QTL) were detected for eight grain-related traits in six environments. Among 58 QTLs, whose superior alleles were contributed by ZKM138, QTgw.cib-6A was a noticeable major stable QTL and was also highlighted by bulked segregant analysis with RNA sequencing (BSR-Seq). It had a pyramiding effect on TGW enhancement but no significant trade-off effect on grain number per spike or tiller number, with two other QTLs (QTgw.cib-2A.2 and QTgw.cib-6D), possibly explaining the excellent grain performance of ZKM138. After comparison with known loci, QTgw.cib-6A was deduced to be a novel locus that differed from nearby TaGW2 and TaBT1. Seven simple sequence repeat (SSR) and thirty-nine kompetitive allele-specific PCR (KASP) markers were finally developed to narrow the candidate interval of QTgw.cib-6A to 4.1 Mb. Only six genes in this interval were regarded as the most likely candidate genes. QTgw.cib-6A was further validated in different genetic backgrounds and presented 88.6% transmissibility of the ZKM138-genotype and a 16.4% increase of TGW in ZKM138 derivatives. And the geographic pattern of this locus revealed that its superior allele is present in only 6.47% of 433 Chinese modern wheat varieties, indicating its potential contribution to further high-yield breeding.

Evaluation of Models for Utilization in Genomic Prediction of Agronomic Traits in the Louisiana Sugarcane Breeding Program

Sugarcane (Saccharum spp.) is an important perennial grass crop for both sugar and biofuel industries. The Louisiana sugarcane breeding program is focused on improving sugar yield by incrementally increasing genetic gain. With the advancement in genotyping and (highthroughput) phenotyping techniques, genomic selection is a promising marker-assisted breeding tool. In this study, we assessed ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program. Datasets (individual and pooled in various combinations) were constructed based on soil type (light—Commerce silty loam, heavy—Sharkey clay) and crop (plant cane, ratoon). A total of 3906 SNPs were used to predict the genomic estimated breeding values (GEBVs) of the clones for sucrose content and cane and sugar yield. Prediction accuracy was estimated by both Spearman’s rank correlation and Pearson’s correlation between phenotypic breeding values and GEBVs. All traits showed significant variation with moderate (42% for sucrose content) to high (85% for cane and sugar yield) heritability. Prediction accuracy based on rank correlation was high (0.47–0.80 for sucrose content; 0.61–0.69 for cane yield, and 0.56–0.72 for sugar yield) in all cross-effect prediction models where soil and crop types were considered as fixed effects. In general, Bayesian models demonstrated a higher correlation than rrBLUP. The Pearson’s correlation without soil and crop type as fixed effects was lower with no clear pattern among the models. The results demonstrate the potential implementation of genomic prediction in the Louisiana sugarcane variety development program.

Genome-Wide Association Study of Parasite Resistance to Gastrointestinal Nematodes in Corriedale Sheep.

Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

Non genetic factors affecting udder type traits in Sahiwal cattle

The present study was planned to find out the magnitude of environmental factors affecting udder type traits in Sahiwal cattle. Fourteen type of udder traits were measured for each of randomly selected 100 lactating cattle maintained at Livestock Research farm, NDRI, Karnal, India. The data were analysed by least squares methods using the general linear model procedures including parity, stage of lactation and season of calving at classification as fixed effects. Traits viz. Udder balance, Rear Udder Height, Teat Diameter, Teat Length, Central Ligament, Udder Circumference, Udder Width and Teat Circumference were significantly (p&lt;0.05) affected by parity. Stage of lactation was significant (P&lt;0.05) source of variation for Shortest distance from teat ends to floor, Rear Udder Height, Central ligament and Udder Circumference while season of calving significantly (p&lt;0.05) affected Rear Udder Height, Teat Length and Rear Udder Width. It is, therefore, suggested that in breeding value estimation for udder conformation, these environmental factors should be considered which would justify focusing genetic improvement in Sahiwal cattle.

Additive genetic variation in resistance of Nile tilapia (Oreochromis niloticus) to Francisella orientalis and its genetic (co)variation to both harvest weight and resistance to Streptococcus agalactiae Ib

An understanding of the genetic relationship between traits of economic importance for a widely cultured species such as Nile tilapia (Oreochromis niloticus) is important, in particular the relationship between disease resistance and growth. Francisellosis results in economic losses for the industry, and we confirmed that Francisellosis resistance is under additive genetic control in our population. Analysing four generations, significant additive genetic variation was found for survival to F. orientalis challenge (P < 0.001; Log-likelihood-ratio test) with an estimated heritability of h2 = 0.31 ± 0.05 across all generations. The random effect common to full-sibs was also significant, but of lower magnitude (c2 = 0.075 ± 0.02) (P < 0.001; Log-likelihood-ratio test). Results of assortative mating groups confirmed that genetic gain could be obtained for survival to F. orientalis challenge since offspring of fish selected with high estimated breeding values exhibited higher survival to F. orientalis challenge. The genetic correlation between harvest weight and survival to F. orientalis challenge was very low (rg = −0.02 ± 0.13) and not significantly different from zero (Log-likelihood-ratio test p > 0.05). The genetic correlation between survival to F. orientalis challenge and survival to S. agalactiae Ib challenge was positive but not significantly different from zero (rg = 0.14 ± 0.11; Log-likelihood-ratio test p > 0.05). The genetic correlation between harvest weight and survival to S. agalactiae challenge was negative but also not significantly different (rg = − 0.19 ± 0.10; Log-likelihood-ratio test p > 0.05). Lack of significant favourable genetic relationships between economically important traits favours multi-trait selection since selection for growth alone does not improve survival to F. orientalis challenge or survival to S. agalactiae challenge.

Genome-Wide Association Study Reveals Additive and Non-Additive Effects on Growth Traits in Duroc Pigs.

Growth rate plays a critical role in the pig industry and is related to quantitative traits controlled by many genes. Here, we aimed to identify causative mutations and candidate genes responsible for pig growth traits. In this study, 2360 Duroc pigs were used to detect significant additive, dominance, and epistatic effects associated with growth traits. As a result, a total number of 32 significant SNPs for additive or dominance effects were found to be associated with various factors, including adjusted age at a specified weight (AGE), average daily gain (ADG), backfat thickness (BF), and loin muscle depth (LMD). In addition, the detected additive significant SNPs explained 2.49%, 3.02%, 3.18%, and 1.96% of the deregressed estimated breeding value (DEBV) variance for AGE, ADG, BF, and LMD, respectively, while significant dominance SNPs could explain 2.24%, 13.26%, and 4.08% of AGE, BF, and LMD, respectively. Meanwhile, a total of 805 significant epistatic effects SNPs were associated with one of ADG, AGE, and LMD, from which 11 sub-networks were constructed. In total, 46 potential genes involved in muscle development, fat deposition, and regulation of cell growth were considered as candidates for growth traits, including CD55 and NRIP1 for AGE and ADG, TRIP11 and MIS2 for BF, and VRTN and ZEB2 for LMD, respectively. Generally, in this study, we detected both new and reported variants and potential candidate genes for growth traits of Duroc pigs, which might to be taken into account in future molecular breeding programs to improve the growth performance of pigs.

Breeding Dairy Cattle for Female Fertility and Production in the Age of Genomics.

Phenotypic and genetic changes for female fertility and production traits in the Israeli Holstein population over the last three decades were studied in order to determine if long term selection has resulted in reduced heritability and negative genetic correlations. Annual means for conception status, defined as the inverse of the number of inseminations to conception in percent, decreased from 55.6 for cows born in 1983 to 46.5 for cows born in 2018. Mean estimated breeding values increased by 1.8% for cow born in 1981 to cows born in 2018. Phenotypic records from 1988 to 2016 for the nine Israeli breeding index traits were divided into three time periods for multi-trait REML analysis by the individual animal model. For all traits, heritabilities increased or stayed the same for the later time periods. Heritability for conception status was 0.05. The first parity genetic correlation between conception status and protein yield was -0.38. Heritabilities decreased with the increase in parity for protein but remained the same for conception status. Realized genetic trends were greater than expected for cows born from 2008 through 2016 for somatic cell score, conception status and herd-life, and lower than expected for the production traits.

Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP.

Single-step genomic BLUP (ssGBLUP) relies on the combination of the genomic ( ) and pedigree relationship matrices for all ( ) and genotyped ( ) animals. The procedure ensures and are compatible so that both matrices refer to the same genetic base ('tuning'). Then is combined with a proportion of ('blending') to avoid singularity problems and to account for the polygenic component not accounted for by markers. This computational procedure has been implemented in the reverse order (blending before tuning) following the sequential research developments. However, blending before tuning may result in less optimal tuning because the blended matrix already contains a proportion of . In this study, the impact of 'tuning before blending' was compared with 'blending before tuning' on genomic estimated breeding values (GEBV), single nucleotide polymorphism (SNP) effects and indirect predictions (IP) from ssGBLUP using American Angus Association and Holstein Association USA, Inc. data. Two slightly different tuning methods were used; one that adjusts the mean diagonals and off-diagonals of to be similar to those in and another one that adjusts based on the average difference between all elements of and . Over 6million Angus growth records and 5.9million Holstein udder depth records were available. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Average realized relationship estimates among groups of animals were similar across scenarios. Scatterplots show that GEBV, SNP effects and IP did not noticeably change for all animals in the evaluation regardless of the order of computations and when using blending parameter of 0.05. Formulas were derived to determine the blending parameter that maximizes changes in the genomic relationship matrix and GEBV when changing the order of blending and tuning. Algebraically, the change is maximized when the blending parameter is equal to 0.5. Overall, tuning before blending, regardless of blending parameter used, had a negligible impact on genomic predictions and SNP effects in this study.