Equivalence of variance components between standard and recursive genetic models using LDL' transformations.

BACKGROUND: Recursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL' or block-LDL' transformations. RESULTS: The procedure was employed on a dataset comprising five traits (birth weight-BW, weight at 90 days-W90, weight at 210 days-W210, cold carcass weight-CCW and conformation-CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models. CONCLUSIONS: The LDL' or block-LDL' transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.

The heritable landscape of near-infrared and Raman spectroscopic measurements to improve lipid content in Atlantic salmon fillets.

BACKGROUND: Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. RESULTS: Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27-0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ([Formula: see text] = 0.91-0.98) obtained with the reference method using Lin's concordance correlation coefficient (CCC = 0.63-0.90), and were significantly heritable ([Formula: see text] = 0.52-0.67). CONCLUSIONS: Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.

Development of sustainable performance index based on production, reproduction, health and longevity for genetic improvement of Sahiwal cattle.

An attempt was made for the first time in India to develop the performance indices for quantifying the relative emphasis to be given to production, reproduction, health and longevity traits for selection and genetic improvement of Sahiwal cattle in a sustainable manner. The study was conducted using information related to various production, reproduction, health and longevity traits spread over a period of 29 years in Sahiwal cattle. Using income and expenditure method, the relative economic values for 305 days milk yield (305DMY), average daily milk yield (ADMY), calving to first insemination interval (CFI), days open (DO) and longevity (LNG) were estimated as 1, 1, -6.62, -6.62 and 5.96 in Sahiwal cattle. A total of three performance indices were constructed using three-trait combination, that is production, reproduction and longevity before and after excluding the days suffered by the animals. Correlation between the aggregate genotype and index (RIH ) was computed to determine the accuracy of each performance index, and based on highest RIH value, best performance index was identified for selection of Sahiwal cattle. Developed indices were further assessed for robustness by increasing the relative economic values of the traits by 25% and 50%. The performance index (305DMY, DO and LNG) was found to be the best index before as well as after excluding days suffered by the animals, but in later case emphasis to be given to different traits was found to be more balanced. The best index indicated that about 47, 42 and 11% relative emphasis to be assigned to 305 days milk yield, days open and longevity for selection of Sahiwal animals for sustainable genetic improvement.

Recommended housing densities for research mice: filling the gap in data-driven alternatives.

Space recommendations for mice made in the Guide for Care and Use of Laboratory Animals have not changed since 1972, despite important improvements in husbandry and caging practices. The 1996 version of the Guide put forth a challenge to investigators to produce new data evaluating the effects of space allocation on the well-being of mice. In this review, we summarize many studies published in response to this challenge. We distinguish between studies using ventilated or nonventilated caging systems and those evaluating reproductive performance or general well-being of adult mice. We discuss how these studies might affect current housing density considerations in both production and research settings and consider gaps in mouse housing density research. Additionally, we discuss reliable methods used to monitor and quantify general well-being of research mice. Collectively, this large body of new data suggests that husbandry practices dictating optimal breeding schemes and space allocation per mouse can be reconsidered. Specifically, these data demonstrate that prewean culling of litters has no benefit, trio breeding is an effective production strategy without adversely affecting pup survival and well-being, and housing of adult mice at densities of up to twice current Guide recommendations does not compromise well-being for most strains.-Svenson, K. L., Paigen, B. Recommended housing densities for research mice: filling the gap in data-driven alternatives.

Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.

BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.

Use of gene expression and whole-genome sequence information to improve the accuracy of genomic prediction for carcass traits in Hanwoo cattle.

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.

How does stage of lactation and breeding worth affect milk solids production and production efficiency of grazing dairy cows?

The study investigated the effect of stage of lactation and Breeding Worth (BW) index on estimated dry matter intake (DMI), milk solids (MS) production, energy use efficiency (EUE) and feed conversion efficiency (FCE) of grazing cows. Two hundred crossbred cows with similar calving date (14 August ± 9.97 days), live weight (471.5 ± 44.02) and age (7.5 ± 1.25 years) were separated into five groups (n = 40) based on New Zealand BW index: Low BW (BW = 63.1); Medium Low BW (BW = 88.2); Medium BW (BW = 19.1); Medium High BW (BW = 128.9); and High BW (BW = 146.9). Milk samples were collected in early, mid and late lactation and herbage samples were taken the day before milk sampling. The DMI was estimated by back-calculation based on metabolizable energy requirement for maintenance and production. The MS production, herbage DMI, EUE and FCE declined from early to late lactation. The overall results suggest regardless of the stage of lactation, cows with higher BW had a higher DMI, MS production and FCE.

Genetic parameters estimation in an Italian horse native breed to support the conversion from agricultural uses to riding purposes.

Horses are nowadays mainly used for sport and leisure purposes, and several local breeds, traditionally used in agriculture, are exposed to the risk of extinction. The long-term survival of local horse breeds depends on strategies to both monitor their genetic diversity and to find their sustainable role in the equine market. Thus, several local horse breeds need to adapt their breeding objective to allow a modernization process. The Bardigiano is an example of such horse breeds; we, therefore, studied the existing evaluation protocol from a genetic standpoint to assess the protocol's suitability to convert the Bardigiano from an agricultural to a riding horse. To this end, we estimated genetic parameters for four conformation measurements, ten grading traits and 23 linear traits. For conformation measurements, the heritabilities ranged from 0.31 for cannon bone circumference to 0.63 for height at withers. For conformation and attitude grading traits, the highest heritability (0.34) was estimated for development and the lowest (0.09) for gaits. The heritabilities for linear traits ranged from 0.05 for the leg straightness to 0.32 for the coat colour. Genetic correlations between linear traits and corresponding grading traits varied considerably, ranging from -0.42 to 0.98. This study showed that the current evaluation protocol in the Bardigiano horse is appropriate for genetic evaluation. Genetic parameters estimation can, in turn, be used to develop novel breeding values to help this conversion. Our study paves the way to optimize the Bardigiano horse breeding programme, and it may help several other local horse breeds experiencing similar issues.

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review.

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.

Development of an animal adaptability index: Application for dairy cows.

The objective of this study was designed to evaluate the association between thermoregulatory responses, hormonal, hematological and serum biochemical values features aiming to propose an adaptability index (AI) for dairy cows under tropical conditions. Thirty adult dairy cows were used, including 15 Sindi and 15 Girolando breeds with mean weight of 338.52 ± 42.79 kg and 487.54 ± 60.67 kg, respectively. These data included rectal temperature (RT) and respiratory rate (RR). For the serum of blood sample, the levels of glucose (GLU), cholesterol (CHO), triglycerides (TRI), total protein (TP), creatinine (CRE), albumin (ALB), urea (URE), aspartate aminotrasferase (AST) and alanine aminotrasferase (ALT) and hormonal concentrations (triiodothyronine - T3 and thyroxine - T4). For the total blood samples were analyzed for hematology variables: red blood cell count (RBC); hemoglobin concentration (HC); packet cell volume (PCV); mean corpuscular volume (MCV); white blood cell (WBC). The AI was development using multivariate analysis to "weigh" the influence of each variable in the animal responses. The variables more important for adaptive aspects of Sindi and Girolando cows were respectively: GLU; MCV; AST; PCV; RBC; HE; TP; ALB; ALT; RR; T4; RT; T3; URE; CHO; TRI; CRE; WBC and ALB; GLU; TP; AST; RR; ALT; CRE; CHO; RT; RBC; T4; HE; PCV; MCV; URE; TRI; WBC; T3. The difference between breeds was just over 8% for AI. We developed a preliminary AI, able to measure the level of adaptability of dairy cows under tropical region. The methodological framework has potential to inform decision-makers on the adaptability animal conditions and high inference of the blood values from adaptability of cows.

Elusive Diagnostic Markers for Russian Wheat Aphid Resistance in Bread Wheat: Deliberating and Reviewing the Status Quo.

Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a severe pest of wheat, Triticum aestivum L., throughout the world. Resistant cultivars are viewed as the most economical and environmentally viable control available. Studies to identify molecular markers to facilitate resistance breeding started in the 1990s, and still continue. This paper reviews and discusses the literature pertaining to the D. noxia R-genes on chromosome 7D, and markers reported to be associated with them. Individual plants with known phenotypes from a panel of South African wheat accessions are used as examples. Despite significant inputs from various research groups over many years, diagnostic markers for resistance to D. noxia remain elusive. Factors that may have impeded critical investigation, thus blurring the accumulation of a coherent body of information applicable to Dn resistance, are discussed. This review calls for a more fastidious approach to the interpretation of results, especially considering the growing evidence pointing to the complex regulation of aphid resistance response pathways in plants. Appropriate reflection on prior studies, together with emerging knowledge regarding the complexity and specificity of the D. noxia-wheat resistance interaction, should enable scientists to address the challenges of protecting wheat against this pest in future.

Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs.

BACKGROUND: Phenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships. RESULTS: Using genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes. CONCLUSIONS: The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

Update on advanced semen-processing technologies and their application for in vitro embryo production in horses.

The increased commercialisation of intracytoplasmic sperm injection (ICSI) in horses creates more opportunities to incorporate advanced reproductive technologies, such as sex-sorted, refrozen and lyophilised spermatozoa, into a breeding program. This paper reviews the status of these semen-handling technologies in light of their use in equine ICSI programs. Pregnancies have been achieved from each of these advanced technologies when combined with ICSI in horses, but refinements in the semen-handling processes underpinning these technologies are currently being explored to produce more reliable and practical improvements in the results from equine ICSI.

Domestication and genetic improvement: balancing improved production against increased disease risks from inbreeding.

Successful selective breeding programmes have been under way in aquacultural species for many decades. Gains in growth rate as high as 900% have been reported. Programmes selecting for resistance and/or tolerance of specific pathogens have had similar success. However, no more than 10-20% of global aquaculture production is sourced from well-documented breeding programmes. Direct selection for resistance and/or tolerance in biosecure breeding programmes is difficult when classical breeding methods are used. Genomic selection is widely expected to become the most effective mode of selection against pathogens in fish and shellfish. In this paper, the authors explore the possible negative effects of genetic improvement programmes, especially those that stem from interactions between genetics and other components of the aquaculture production system, particularly disease. The main focus is the interaction between selective breeding and biosecurity. They suggest that a self-amplifying feedback loop can be created when biosecurity regulation causes a progressive reduction in genetic diversity and an increase in inbreeding, especially in smallholder hatcheries and farms in developing countries. The resulting inbreeding depression causes increased susceptibility to disease, which in turn increases the frequency and severity of epizootics. Greater losses due to disease again increase regulatory pressures and the cycle begins once more. This 'biosecurity-agro-economic-genetic' feedback loop is analogous to an 'extinction vortex' in wild populations. The authors believe that the loop can be broken by biosecure, aquacultural, genetic exchange networks, modelled on existing breed associations for terrestrial domestic animals. Such networks would constitute a global aquacultural gene pool, with enhanced environmental resilience, long-term capacity for adaptation and minimal inbreeding depression. However, such networks will also require innovative new pathogen management procedures, documentation and regulations to facilitate the exchange of broodstock and seed while still maintaining effective biosecurity.

Des programmes d'élevage sélectif d'espèces aquacoles sont mis en œuvre avec succès depuis plusieurs décennies. Des gains de croissance atteignant 900 % ont ainsi été enregistrés. Les programmes de sélection pour la résistance et/ou la tolérance vis-à-vis de certains agents pathogènes ont également été couronnés de succès. Néanmoins, la production issue des programmes de sélection connus et documentés ne représente guère plus de 10 à 20 % de la production aquacole mondiale. Il est difficile de procéder à une sélection directe des traits de résistance et/ou de tolérance tout en garantissant un bon niveau de biosécurité dès lors que les méthodes utilisées relèvent de l'élevage classique. La sélection génomique présente un immense potentiel et deviendra sans aucun doute la méthode de sélection la plus efficace contre les agents pathogènes affectant les poissons, les mollusques et les crustacés. Les auteurs analysent les effets négatifs potentiels associés aux programmes d'amélioration génétique, en particulier ceux résultant d'interactions entre les composantes génétiques et d'autres aspects, notamment sanitaires, des systèmes de production aquacole. Leur analyse est principalement axée sur les interactions entre l'élevage sélectif et la biosécurité. Ils évoquent le risque de création d'une boucle de réaction auto-amplificatrice dans les contextes où l'application de la réglementation en matière de biosécurité entraîne un déclin progressif de la diversité génétique et une hausse de la consanguinité, en particulier dans les écloseries et fermes de petite dimension des pays en développement. Le phénomène de dépression de consanguinité qui en résulte entraîne une sensibilité accrue aux maladies, ce qui à son tour accroît la fréquence et la gravité des épizooties. Le regain des pertes dues à ces maladies se traduit par de nouvelles pressions exercées au plan réglementaire, de sorte que le cycle est relancé une nouvelle fois. Cette boucle de réaction biosécurité-agroéconomie-génétique est similaire à la « spirale de l'extinction ¼ qui affecte les populations sauvages. Les auteurs considèrent que cette boucle peut être rompue en mettant en place des réseaux d'échange de ressources génétiques biosécurisées pour l'aquaculture, sur le modèle des associations d'éleveurs opérant dans le domaine des espèces domestiques terrestres. Ces réseaux permettraient de constituer un patrimoine génétique mondial pour l'aquaculture doté d'une résilience environnementale améliorée, d'une capacité d'adaptation sur le long terme et d'un niveau minime de dépression de consanguinité. Néanmoins, de tels réseaux nécessiteront des procédures nouvelles et innovantes de gestion des agents pathogènes, ainsi qu'une documentation et des réglementations spécifiques afin de faciliter les échanges de stocks de géniteurs et de semence tout en garantissant un niveau approprié de biosécurité.

Hace ya muchos decenios que se vienen aplicando con éxito programas de reproducción selectiva de especies acuícolas, con aumentos de las tasas de crecimiento que llegan a veces hasta un 900%. Los programas de selección de rasgos de resistencia y/o de tolerancia a determinados patógenos han cosechado un éxito similar. Sin embargo, no más de un 10% a un 20% de la producción acuícola mundial tiene su origen en programas de reproducción bien documentados. La selección directa de rasgos de resistencia y/o tolerancia mediante programas reproductivos que ofrezcan garantías de seguridad biológica resulta difícil cuando se aplican métodos clásicos de cría. Ahora cunde la expectativa de que la selección genómica llegue a ser el modo de selección más eficaz contra la presencia de patógenos en el pescado y el marisco. Los autores examinan los posibles efectos negativos de los programas de mejora genética, sobre todo los que resultan de la interacción entre la genética y otros componentes del sistema de producción acuícola, en particular las enfermedades, prestando especial atención a la interacción entre cría selectiva y seguridad biológica y postulando que se puede engendrar un ciclo de retroalimentación que se va autoamplificando cuando los reglamentos de seguridad biológica inducen una progresiva reducción de la diversidad genética y un incremento de la consanguinidad (o endogamia), especialmente en los pequeños viveros y granjas de países en desarrollo. La consiguiente depresión endogámica genera una mayor sensibilidad a las enfermedades, lo que a su vez incrementa la frecuencia y gravedad de las epizootias. El aumento de las pérdidas por enfermedades se traduce en una mayor presión reglamentaria, y así da comienzo de nuevo el ciclo. Este ciclo de retroalimentación entre seguridad biológica, agroeconomía y genética es análogo a un «vórtice de extinción¼ en las poblaciones silvestres. Los autores piensan que es posible quebrar esta dinámica recurriendo a redes de intercambio de material genético biológicamente seguro en el ámbito de la acuicultura, a imagen de las asociaciones que existen entre los criadores de animales domésticos terrestres. Estas redes constituirían una reserva mundial de genes de acuicultura que ofrecerían mayor resiliencia a las condiciones ambientales y una duradera capacidad de adaptación, así como niveles mínimos de depresión endogámica. Semejantes redes, sin embargo, también exigirán nuevos y novedosos procedimientos de lucha contra patógenos, sistemas de documentación y reglamentos que faciliten el intercambio de genitores y alevines manteniendo a la vez niveles elevados de seguridad biológica.

Survivability, immunity, growth and production traits in indigenous and White Leghorn breeds of chicken.

1. The survivability, innate and adaptive immunity, growth and production traits up to 72 weeks of age were determined in Ghagus, Nicobari (unimproved indigenous) and White Leghorn (WLH) breeds and the study investigated links between innate and adaptive immunity and survivability and production traits.2. At 20 and 40 weeks of age, there was a significant effect of breed on innate immunity assessed by measuring titres of natural antibody (NAb) binding to rabbit red blood cells (RRBC) and adaptive immunity assessed by measuring specific antibody titre (SpAb) to Newcastle disease virus.3. Highest survivability was in WLH (91.6%) followed by Nicobari (87.1%) and Ghagus (82.9%) breeds. Growth traits at different ages were higher (P< 0.001) in Ghagus followed by WLH and Nicobari breeds. Egg production up to 72 weeks was higher (P < 0.001) in WLH followed by Nicobari and Ghagus breeds, whereas egg weight at different ages was higher (P < 0.001) in WLH than Ghagus and Nicobari breeds.4. NAb titres measured at 20 weeks were significantly (P = 0.002) associated with the survivability of hens during 20 to 72 weeks of age. Breed-wise analysis showed a significant (P = 0.019) association between NAb titres at 20 weeks and survivability in the Ghagus breed. Furthermore, NAb titres at 20 weeks were higher in hens which survived to 72 weeks compared with those that died (P = 0.002).5. Measuring NAb titres to RRBC is quick, economical and simple. This method has potential to be used in a breeding programme to increase survivability of laying hens.

Identifying breeding goals to develop selection index for buffalo in Egypt using preference survey.

The aim of the study was to identify the breeding goals, derive the relative goal values (RGV), and develop selection index (SI) for buffalo in Egypt. A structured questionnaire survey of milk-producing buffalo holders in 10 Egyptian governorates was carried out to collect data. The total number of surveyed holders was 1797. Buffalo holders were asked to identify the breeding goal groups (BGG). Breeding goal groups were ranked, according to relative importance to farmers, in descending order. The breeding goal traits (BGT) within each BGG were chosen and ranked in descending order. The survey results reflected the importance of milk production traits as the main breeding goal. Traits of milk yield and fat yield were given the greatest attention. In order to estimate the relative goal values (RGV), the subjective determination procedure was used. A principal component analysis (PCA) followed by cluster analysis (CA) of the principal components were performed. The CA led to the identification of three farmer clusters. Six selection indices were proposed from the PCA. The indices yielded different precision with a maximum of 93% of the holder's preferences variability. The current study revealed that subjective method could be used to identify the RGV. Further studies are needed particularly those related to the production system. Also, measurements of market data and consumers requests and their influence on the selected traits are badly needed.

Benefits of testing in both bio-secure and production environments in genomic selection breeding programs for commercial broiler chicken.

BACKGROUND: A breeding program for commercial broiler chicken that is carried out under strict biosecure conditions can show reduced genetic gain due to genotype by environment interactions (G × E) between bio-secure (B) and commercial production (C) environments. Accuracy of phenotype-based best linear unbiased prediction of breeding values of selection candidates using sib-testing in C is low. Genomic prediction based on dense genetic markers may improve accuracy of selection. Stochastic simulation was used to explore the benefits of genomic selection in breeding schemes for broiler chicken that include birds in both B and C for assessment of phenotype. RESULTS: When genetic correlations ([Formula: see text]) between traits measured in B and C were equal to 0.5 and 0.7, breeding schemes with 15, 30 and 45% of birds assessed in C resulted in higher genetic gain for performance in C compared to those without birds in C. The optimal proportion of birds phenotyped in C for genetic gain was 30%. When the proportion of birds in C was optimal and genotyping effort was limited, allocating 30% of the genotyping effort to birds in C was also the optimal genotyping strategy for genetic gain. When [Formula: see text] was equal to 0.9, genetic gain for performance in C was not improved with birds in C compared to schemes without birds in C. Increasing the heritability of traits assessed in C increased genetic gain significantly. Rates of inbreeding decreased when the proportion of birds in C increased because of a lower selection intensity among birds retained in B and a reduction in the probability of co-selecting close relatives. CONCLUSIONS: If G × E interactions ([Formula: see text] of 0.5 and 0.7) are strong, a genomic selection scheme in which 30% of the birds hatched are phenotyped in C has larger genetic gain for performance in C compared to phenotyping all birds in B. Rates of inbreeding decreased as the proportion of birds moved to C increased from 15 to 45%.

Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers.

BACKGROUND: Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP model (MBMG) that weighs pre-selected markers separately, uses the remaining markers to explain the remaining genetic variance that can be explained by markers, and weighs information of breeds in the reference population by their genetic correlation with the validation breed. METHODS: Genotype and phenotype data were used on 595 Jersey bulls from New Zealand and 5503 Holstein bulls from the Netherlands, all with deregressed proofs for stature. Different sets of markers were used, containing either pre-selected markers from a meta-genome-wide association analysis on stature, remaining markers or both. We implemented a multi-breed bivariate GREML model in which we fitted either a single multi-breed GRM (MBSG), or two distinct multi-breed GRM (MBMG), one made with pre-selected markers and the other with remaining markers. Accuracies of predicting stature for Jersey individuals using the multi-breed models (Holstein and Jersey combined reference population) was compared to those obtained using either the Jersey (within-breed) or Holstein (across-breed) reference population. All the models were subsequently fitted in the analysis of simulated phenotypes, with a simulated genetic correlation between breeds of 1, 0.5, and 0.25. RESULTS: The MBMG model always gave better prediction accuracies for stature compared to MBSG, within-, and across-breed GP models. For example, with MBSG, accuracies obtained by fitting 48,912 unselected markers (0.43), 357 pre-selected markers (0.38) or a combination of both (0.43), were lower than accuracies obtained by fitting pre-selected and unselected markers in separate GRM in MBMG (0.49). This improvement was further confirmed by results from a simulation study, with MBMG performing on average 23% better than MBSG with all markers fitted. CONCLUSIONS: With the MBMG model, it is possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between breeds.

Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions.

BACKGROUND: Genetic connectedness is classically used as an indication of the risk associated with breeding value comparisons across management units because genetic evaluations based on best linear unbiased prediction rely for their success on sufficient linkage among different units. In the whole-genome prediction era, the concept of genetic connectedness can be extended to measure a connectedness level between reference and validation sets. However, little is known regarding (1) the impact of non-additive gene action on genomic connectedness measures and (2) the relationship between the estimated level of connectedness and prediction accuracy in the presence of non-additive genetic variation. RESULTS: We evaluated the extent to which non-additive kernel relationship matrices increase measures of connectedness and investigated its relationship with prediction accuracy in the cross-validation framework using best linear unbiased prediction and coefficients of determination. Simulated data assuming additive, dominance, and epistatic gene action scenarios and real swine data were analyzed. We found that the joint use of additive and non-additive genomic kernel relationship matrices or non-parametric relationship matrices led to increased capturing of connectedness, up to 25%, and improved prediction accuracies compared to those of baseline additive relationship counterparts in the presence of non-additive gene action. CONCLUSIONS: Our findings showed that connectedness metrics can be extended to incorporate non-additive genetic variation of complex traits. Use of kernel relationship matrices designed to capture non-additive gene action increased measures of connectedness and improved whole-genome prediction accuracy, further broadening the scope of genomic connectedness studies.

Assessment of the performance of hidden Markov models for imputation in animal breeding.

BACKGROUND: In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations, particularly in populations where pedigree information is not readily available. METHODS: To test the performance of hidden Markov model-based imputation, we evaluated the accuracy and computational cost of several methods in a series of simulated populations and a real animal population without using a pedigree. First, we tested single-step (diploid) imputation, which performs both phasing and imputation. Second, we tested pre-phasing followed by haploid imputation. Overall, we used four available diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and Minimac3). RESULTS: We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, among all the methods tested, pre-phasing with Eagle2 or HAPI-UR and imputing with Minimac3 or IMPUTE2 gave the highest accuracies with both simulated and real data. CONCLUSIONS: The results of this study suggest that hidden Markov model-based imputation algorithms are an accurate and computationally feasible approach for performing imputation without a pedigree when pre-phasing and haploid imputation are used. Of the algorithms tested, the combination of Eagle2 and Minimac3 gave the highest accuracy across the simulated and real datasets.