Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP.

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

Single-step genome-wide association study for susceptibility to Teratosphaeria nubilosa and precocity of vegetative phase change in Eucalyptus globulus.

Introduction: Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement. Methods: We investigated the genetic basis of MLD resistance in a breeding population of E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS). Results: The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants. Discussion: Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.

Prediction ability of an alternative multi-trait genomic evaluation for residual feed intake.

Selection for feed efficiency is the goal for many genetic breeding programs in beef cattle. Residual feed intake has been included in genetic evaluations to reduce feed intake without compromising performance traits as liveweight, body gain or carcass traits. However, measuring feed intake is expensive, and only a small percentage of selection candidates are phenotyped. Genomic selection has become a very important tool to achieve effective genetic progress in these traits. Another effective strategy has been the implementation of multi-trait prediction using easily recordable predictor traits on both reference animals and candidates without phenotypes, and this could be another inexpensive way to increase accuracy. The objective of this work was to analyse and compare the prediction ability of two alternative different approaches to predict GEBVs for RFI. The population of inference was Hereford bulls in Uruguay that were genotyped candidates for to selection. The first model was the conventional univariate model for RFI and the second model was a multi-trait model which included a predictor trait (weaning weight, WW), in addition to the traits used in the first one (dry matter intake, metabolic mid test weight, average daily gain and ultrasound back fat) (DMI, MWT, ADG, UBF, respectively). GEBVs from the multi-trait model were combined using selection index theory to derive RFI values. All analyses were performed using ssGBLUP procedure. The prediction ability of both models was tested using two validation strategies (30 different replicates of random groups of animals and validation across 9 different feed intake tests). The prediction quality was assessed by the following parameters: bias, dispersion, ratio of accuracies and the relative increase in accuracy by adding phenotypic information. All parameters showed that the univariate model outperforms the multi-trait model, regardless of the validation strategy considered. These results indicate that including WW as a proxy trait in a multi-trait analysis does not improve the prediction ability when all animals to be predicted are genotyped.

The Epidemiological Profile of Multiple Casualty Incidents in Northern Spain: 2014-2020.

OBJECTIVE: To describe the epidemiological profile of multiple casualty incidents (MCI) and contribute to the better understanding of their impacts in Northern Spain. METHOD: Retrospective, population-based observational study of MCI between 2014 and 2020 in 5 autonomous communities (Aragón, Castilla y León, Galicia, the Basque Country and Principado de Asturias) that participated in the MCI Database of Northern Spain. Inclusion criteria was any incident with 4 or more patients needing ambulance mobilization. A total of 54 variables were collected. This study presents the most relevant results. RESULTS: There were 253 MCI. Of these, 79.8% were road traffic accidents, 12.3% fires or explosions, 2.0% poisonings and 5.9% defined as others. Monthly average was 2.9 (SD = 0.35; EEM = 15.90), average of victims by MCI was 6.8 (CI95% 6.16 - 7.60). There were significantly (P < 0.05) more victims in 3 types of MCI (fires, poisonings, and others). We saw 37.7% of MCI involved 4 victims, 18.8% 5 victims, and 37.9% more than 5. Mean response time was 30.8 minutes (95% CI 28.6 - 33.1), longer in maritime incidents. A total of 67% (95% CI 64.5 - 69.5) of victims were mild. CONCLUSIONS: Road traffic accidents are the most frequent MCI and minor injuries predominate. More than 50% of the MCI have 5 or fewer patients. Fires had significantly more mild patients and significantly more resources deployed. Maritime incidents had a significantly longer response time.

Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle.

The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity-related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel (~29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100%), and unknown maternal grandsires (0, 25, 50, 75, and 100%). The prediction accuracies and abilities were calculated. The estimated breeding value accuracies decreased as the proportion of unknown sires and maternal grandsires increased. The genomic estimated breeding value accuracy using the ssGBLUP was higher in scenarios with a lower proportion of known pedigree when compared to the BLUP methodology. The results obtained with the ssGBLUP showed that it is possible to obtain reliable direct and indirect predictions for young animals from commercial herds without pedigree structure.

Lifting COVID-19 mitigation measures in Spain (May-June 2020).

Introduction: The state of alarm was declared in Spain due to the COVID-19 epidemic on March 14, 2020, and established population confinement measures. The objective is to describe the process of lifting these mitigation measures. Methods: The Plan for the Transition to a New Normality, approved on April 28, contained four sequential phases with progressive increase in socio-economic activities and population mobility. In parallel, a new strategy for early diagnosis, surveillance and control was implemented. A bilateral decision mechanism was established between the Spanish Government and the autonomous communities (AC), guided by a set of qualitative and quantitative indicators capturing the epidemiological situation and core capacities. The territorial units were established ad-hoc and could be from Basic Health Zones to entire AC. Results: The process run from May 4 to June 21, 2020. AC implemented plans for reinforcement of core capacities. Incidence decreased from a median (50% of territories) of 7.4 per 100,000 in 7 days at the beginning to 2.5 at the end. Median PCR testing increased from 53% to 89% of suspected cases and PCR total capacity from 4.5 to 9.8 per 1000 inhabitants weekly; positivity rate decreased from 3.5% to 1.8%. Median proportion of cases with traced contacts increased from 82% to 100%. Conclusion: Systematic data collection, analysis, and interterritorial dialogue allowed adequate process control. The epidemiological situation improved but, mostly, the process entailed a great reinforcement of core response capacities nation-wide, under common criteria. Maintaining and further reinforcing capacities remained crucial for responding to future waves.

Introducción: El 14 de marzo de 2020 España declaró el estado de alarma por la pandemia por COVID-19 incluyendo medidas de confinamiento. El objetivo es describir el proceso de desescalada de estas medidas. Métodos: Un plan de transición hacia una nueva normalidad, del 28 de abril, incluía 4 fases secuenciales incrementando progresivamente las actividades socioeconómicas y la movilidad. Concomitantemente, se implementó una nueva estrategia de diagnóstico precoz, vigilancia y control. Se estableció un mecanismo de decisión bilateral entre Gobierno central y comunidades autónomas (CCAA), guiado por un panel de indicadores cualitativos y cuantitativos de la situación epidemiológica y las capacidades básicas. Las unidades territoriales evaluadas comprendían desde zonas básicas de salud hasta CCAA. Resultados: El proceso se extendió del 4 de mayo al 21 de junio y se asoció a planes de refuerzo de las capacidades en las CCAA. La incidencia disminuyó de una mediana inicial de 7,4 por 100.000 en 7 días a 2,5 al final del proceso. La mediana de pruebas PCR aumentó del 53% al 89% de los casos sospechosos, y la capacidad total de 4,5 a 9,8 pruebas semanales por 1.000 habitantes; la positividad disminuyó del 3,5% al 1,8%. La mediana de casos con contactos trazados aumentó del 82% al 100%. Conclusión: La recogida y análisis sistemático de información y el diálogo interterritorial logaron un adecuado control del proceso. La situación epidemiológica mejoró, pero sobre todo, se aumentaron las capacidades, en todo el país y con criterios comunes, cuyo mantenimiento y refuerzo fue clave en olas sucesivas.

Lifting COVID-19 mitigation measures in Spain (May-June 2020).

INTRODUCTION: The state of alarm was declared in Spain due to the COVID-19 epidemic on March 14, 2020, and established population confinement measures. The objective is to describe the process of lifting these mitigation measures. METHODS: The Plan for the Transition to a New Normality, approved on April 28, contained four sequential phases with progressive increase in socio-economic activities and population mobility. In parallel, a new strategy for early diagnosis, surveillance and control was implemented. A bilateral decision mechanism was established between the Spanish Government and the autonomous communities (AC), guided by a set of qualitative and quantitative indicators capturing the epidemiological situation and core capacities. The territorial units were established ad-hoc and could be from Basic Health Zones to entire AC. RESULTS: The process run from May 4 to June 21, 2020. AC implemented plans for reinforcement of core capacities. Incidence decreased from a median (50% of territories) of 7.4 per 100,000 in 7 days at the beginning to 2.5 at the end. Median PCR testing increased from 53% to 89% of suspected cases and PCR total capacity from 4.5 to 9.8 per 1000 inhabitants weekly; positivity rate decreased from 3.5% to 1.8%. Median proportion of cases with traced contacts increased from 82% to 100%. CONCLUSION: Systematic data collection, analysis, and interterritorial dialogue allowed adequate process control. The epidemiological situation improved but, mostly, the process entailed a great reinforcement of core response capacities nation-wide, under common criteria. Maintaining and further reinforcing capacities remained crucial for responding to future waves.

Reliabilities of estimated breeding values in models with metafounders.

BACKGROUND: Reliabilities of best linear unbiased predictions (BLUP) of breeding values are defined as the squared correlation between true and estimated breeding values and are helpful in assessing risk and genetic gain. Reliabilities can be computed from the prediction error variances for models with a single base population but are undefined for models that include several base populations and when unknown parent groups are modeled as fixed effects. In such a case, the use of metafounders in principle enables reliabilities to be derived. METHODS: We propose to compute the reliability of the contrast of an individual's estimated breeding value with that of a metafounder based on the prediction error variances of the individual and the metafounder, their prediction error covariance, and their genetic relationship. Computation of the required terms demands only little extra work once the sparse inverse of the mixed model equations is obtained, or they can be approximated. This also allows the reliabilities of the metafounders to be obtained. We studied the reliabilities for both BLUP and single-step genomic BLUP (ssGBLUP), using several definitions of reliability in a large dataset with 1,961,687 dairy sheep and rams, most of which had phenotypes and among which 27,000 rams were genotyped with a 50K single nucleotide polymorphism (SNP) chip. There were 23 metafounders with progeny sizes between 100,000 and 2000 individuals. RESULTS: In models with metafounders, directly using the prediction error variance instead of the contrast with a metafounder leads to artificially low reliabilities because they refer to a population with maximum heterozygosity. When only one metafounder is fitted in the model, the reliability of the contrast is shown to be equivalent to the reliability of the individual in a model without metafounders. When there are several metafounders in the model, using a contrast with the oldest metafounder yields reliabilities that are on a meaningful scale and very close to reliabilities obtained from models without metafounders. The reliabilities using contrasts with ssGBLUP also resulted in meaningful values. CONCLUSIONS: This work provides a general method to obtain reliabilities for both BLUP and ssGBLUP when several base populations are included through metafounders.

Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches and their associations with growth, reproductive, and carcass traits in Nellore cattle.

This study aimed to estimate prediction ability and genetic parameters for residual feed intake (RFI) calculated using a regression equation for each test (RFItest) and for the whole population (RFIpop) in Nellore beef cattle. It also aimed to evaluate the correlations between RFIpop and RFItest with growth, reproductive, and carcass traits. Genotypic and phenotypic records from 8354 animals were used. An analysis of variance (ANOVA) was performed to verify the adequacy of the regression equations applied to estimate the RFItest and RFIpop. The (co)variance components were obtained using the single-step genomic best linear unbiased prediction under single and two-trait animal model analyses. The genetic and phenotypic correlations between RFItest and RFIpop with dry matter intake, frame, growth, reproduction, and carcass-related traits were evaluated. The prediction ability and bias were estimated to compare the RFItest and RFIpop genomic breeding values (GEBV). The RFIpop ANOVA showed a higher significance level (p < 0.0001) than did the RFItest for the fixed effects. The RFIpop displayed higher additive genetic variance estimated than the RFItest, although the RFIpop and RFItest displayed similar heritabilities. Overall, the RFItest showed higher residual correlations with growth, reproductive, and carcass traits, while the RFIpop displayed higher genetic correlations with such traits. The GEBV for the RFItest was slightly biased than GEBV RFIpop. The approach to calculate the RFI influenced the decomposition and estimation of variance components and genomic prediction for RFI. The application of RFIpop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias for RFI.

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 ( G $$ \mathbf{G} $$ ) and pedigree relationship matrices for all ( A $$ \mathbf{A} $$ ) and genotyped ( A 22 $$ {\mathbf{A}}_{22} $$ ) animals. The procedure ensures G $$ \mathbf{G} $$ and A 22 $$ {\mathbf{A}}_{22} $$ are compatible so that both matrices refer to the same genetic base ('tuning'). Then G $$ \mathbf{G} $$ is combined with a proportion of A 22 $$ {\mathbf{A}}_{22} $$ ('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 A 22 $$ {\mathbf{A}}_{22} $$ . 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 G $$ \mathbf{G} $$ to be similar to those in A 22 $$ {\mathbf{A}}_{22} $$ and another one that adjusts based on the average difference between all elements of G $$ \mathbf{G} $$ and A 22 $$ {\mathbf{A}}_{22} $$ . Over 6 million Angus growth records and 5.9 million 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 G $$ \mathbf{G} $$ before blending, regardless of blending parameter used, had a negligible impact on genomic predictions and SNP effects in this study.

Certification program of Addiction Centres for hepatitis C virus elimination in Spain. HepCelentes Project. / Programa de certificación de Centros de Adicciones para la eliminación del virus de la hepatitis C en España. Proyecto HepCelentes.

Microelimination strategies for the hepatitis C virus (HCV) in vulnerable populations, such as users of Addiction Centres (AC), are key for the elimination of hepatitis C. The aim of the HepCelentes project was to design a certification program for AC from the generation of a guide with the criteria to favour the prevention, diagnosis, control, and treatment of HCV in Spain. The project was structured in 4 phases: normalisation, implementation, certification, and communication. In the first phase, developed between July and December 2020, a Steering Committee was created (formed by representatives of scientific societies, healthcare professionals from AC, primary care centres and hospital units, and patient associations) that, from of an exhaustive bibliographic review, generated by consensus an accreditation guide for AC. The guide consists of 22 criteria (15 mandatory and 7 recommended) structured based on the requirements to be met by AC, justification for the selection, level of action (management, prevention, diagnosis and treatment/follow-up), measurement of the indicator, objective level to be achieved, evidence of compliance, clarifications to improve understanding, and mandatory / recommendation (depending on their relevance to achieve HCV elimination and its feasibility for implementation in real practice). The development of a certification system for the AC, based on consensus and coordination of multidisciplinary teams, is intended to favour the management of hepatitis C and its elimination in AC users, supporting the international, national, and regional elimination strategies.

Las estrategias de microeliminación del virus de la hepatitis C (VHC) en poblaciones vulnerables, como los usuarios de los centros de adicciones (CA), son fundamentales para lograr la eliminación de la hepatitis C. El objetivo del proyecto HepCelentes fue diseñar un programa de certificación para los CA, a partir de la generación de una guía con los criterios para favorecer la prevención, diagnóstico, control y tratamiento del VHC en España. El proyecto se estructuró en 4 fases: normalización, implementación, certificación y comunicación. En la primera fase, desarrollada entre julio y diciembre de 2020, se creó un Comité de Normalización (formado por representantes de sociedades científicas, profesionales sanitarios de CA, centros de atención primaria, unidades hospitalarias, y asociaciones de pacientes) que, a partir de una revisión bibliográfica exhaustiva, generó por consenso una guía de certificación de los CA. La guía consta de 22 criterios (15 obligatorios y 7 recomendados) estructurados en base a la definición del criterio, justificación de su selección, nivel de actuación (gestión, prevención, diagnóstico y tratamiento/seguimiento), fórmula de medición, nivel objetivo a alcanzar, evidencias de su cumplimiento, aclaraciones para mejorar su comprensión y obligatoriedad/recomendación (en función de la relevancia en la eliminación y capacidad de implementación). El desarrollo de un sistema de certificación para los CA, a partir del consenso y la coordinación de equipos multidisciplinares, pretende favorecer el manejo de la hepatitis C y su eliminación en los usuarios de los CA, apoyando las estrategias de eliminación internacionales, nacionales y autonómicas.

Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP.

BACKGROUND: Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. RESULTS: Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards. CONCLUSIONS: Theoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.

Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.

This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.

Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information.

The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS.

Lifting COVID-19 mitigation measures in Spain (May-June 2020).

INTRODUCTION: The state of alarm was declared in Spain due to the COVID-19 epidemic on March 14, 2020, and established population confinement measures. The objective is to describe the process of lifting these mitigation measures. METHODS: The Plan for the Transition to a New Normality, approved on April 28, contained four sequential phases with progressive increase in socio-economic activities and population mobility. In parallel, a new strategy for early diagnosis, surveillance and control was implemented. A bilateral decision mechanism was established between the Spanish Government and the autonomous communities (AC), guided by a set of qualitative and quantitative indicators capturing the epidemiological situation and core capacities. The territorial units were established ad-hoc and could be from Basic Health Zones to entire AC. RESULTS: The process run from May 4 to June 21, 2020. AC implemented plans for reinforcement of core capacities. Incidence decreased from a median (50% of territories) of 7.4 per 100,000 in 7 days at the beginning to 2.5 at the end. Median PCR testing increased from 53% to 89% of suspected cases and PCR total capacity from 4.5 to 9.8 per 1000 inhabitants weekly; positivity rate decreased from 3.5% to 1.8%. Median proportion of cases with traced contacts increased from 82% to 100%. CONCLUSION: Systematic data collection, analysis, and interterritorial dialogue allowed adequate process control. The epidemiological situation improved but, mostly, the process entailed a great reinforcement of core response capacities nation-wide, under common criteria. Maintaining and further reinforcing capacities remained crucial for responding to future waves.

Assessing the accuracy of imputation in the Gyr breed using different SNP panels.

The aim of this study was to evaluate the accuracy of imputation in a Gyr population using two medium-density panels (Bos taurus - Bos indicus) and to test whether the inclusion of the Nellore breed increases the imputation accuracy in the Gyr population. The database consisted of 289 Gyr females from Brazil genotyped with the GGP Bovine LDv4 chip containing 30 000 SNPs and 158 Gyr females from Colombia genotyped with the GGP indicus chip containing 35 000 SNPs. A customized chip was created that contained the information of 9109 SNPs (9K) to test the imputation accuracy in Gyr populations; 604 Nellore animals with information of LD SNPs tested in the scenarios were included in the reference population. Four scenarios were tested: LD9K_30KGIR, LD9K_35INDGIR, LD9K_30KGIR_NEL, and LD9K_35INDGIR_NEL. Principal component analysis (PCA) was computed for the genomic matrix and sample-specific imputation accuracies were calculated using Pearson's correlation (CS) and the concordance rate (CR) for imputed genotypes. The results of PCA of the Colombian and Brazilian Gyr populations demonstrated the genomic relationship between the two populations. The CS and CR ranged from 0.88 to 0.94 and from 0.93 to 0.96, respectively. Among the scenarios tested, the highest CS (0.94) was observed for the LD9K_30KGIR scenario. The present results highlight the importance of the choice of chip for imputation in the Gyr breed. However, the variation in SNPs may reduce the imputation accuracy even when the chip of the Bos indicus subspecies is used.

Reproductive performance of Angus, Hereford, Salers and Nellore crossbred females: Additive and non-additive effects.

Reproductive traits in breeding herds can be improved through crossbreeding, which results in breed differences, heterosis and breed complementarity. The aim of this study was to estimate group additive genetic and dominance effects for three reproductive traits; probability of artificial insemination (AIP); calving success (CS); and days to calving (DC) for Hereford (H), Angus (A), Nellore (N) and Salers (S) breeds under grazing conditions. Data were obtained from an experiment carried out during 1992-2002 by the Faculty of Agronomy, Universidad de la Republica (UdelaR), Uruguay and Caja Notarial de Seguridad Social. The data set contained reproductive information of 1,164 females from 11 different genetic groups (GG) consisting of crosses between H, N, S and A. AIP, CS and DC were examined in first-calf heifers, while CS and DC were examined in second-calf and 3- to 7-year-old cows. Least square means for each GG and group additive genetic and dominance effects were estimated for each trait. F1 crossbreed females performed better for artificial insemination probability than purebred females. Crossbred A/H heifers had the highest AIPs and CS rates, while crossbred N/H 3- to 7-year-old cows recorded the highest averages for CS and DC. Estimates of group additive genetic effects did not differ amongst A, S, N and H; however, dominance increased the AIP and CS of the heifers.

Emerging issues in genomic selection.

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.

Selection for Test-Day Milk Yield and Thermotolerance in Brazilian Holstein Cattle.

Intense selection for milk yield has increased environmental sensitivity in animals, and currently, heat stress is an expensive problem in dairy farming. The objectives were to identify the best model for characterizing environmental sensitivity in Holstein cattle, using the test-day milk yield (TDMY) combined with the temperature-humidity index (THI), and identify sires genetically superior for heat-stress (HS) tolerance and milk yield, through random regression. The data comprised 94,549 TDMYs of 11,294 first-parity Holstein cows in Brazil, collected from 1997 to 2013. The yield data were fitted to Legendre orthogonal polynomials, linear splines and the Wilmink function. The THI (the average of two days before the dairy control) was used as an environmental gradient. An animal model that fitted production using a Legendre polynomials of quartic order for the days in milk and quadratic equations for the THI presented a better quality of fit (Akaike's information criterion (AIC) and Bayesian information criterion (BIC)). The Spearman correlation coefficient of greatest impact was 0.54, between the top 1% for TDMY and top 1% for HS. Only 9% of the sires showed plasticity and an aptitude for joint selection. Thus, despite the small population fraction allowed for joint selection, sufficient genetic variability for selecting more resilient sires was found, which promoted concomitant genetic gains in milk yield and thermotolerance.