Linear Unbiased Prediction Method Research Articles

Methods of Calculating Prediction Error Variance and Prediction Accuracy for Restricted Best Linear Unbiased Prediction of Breeding Values.

Prediction error variance (PEV) and prediction accuracy (PA) of breeding values (BVs) are essential for formulating breeding plans and predicting response to selection. However, restricted best linear unbiased prediction method (RBLUP method) carries many unknowns: in particular, the formulas for calculating PEV and PA are not clear. New findings were obtained using the RBLUP method. The uniqueness of RBLUP of BVs was proven. The formulas of PEV and PA for the RBLUP of BVs were derived from restricted mixed model equations. A method was also devised for easily calculating the PEV and PA for the RBLUP of BVs. Finally, the relationship between the RBLUP and ordinary BLUP of BVs was derived. It has become easier to calculate the PEV and PA for the RBLUP of BVs. This method is particularly effective for calculating the PEV and PA when applying the RBLUP method to achieve relative desired changes in all traits. This has also made it possible to predict the response to selection using the RBLUP method.

Small Area Estimation of Maluku and Papua Island Child Poverty Levels in 2023

Child welfare issues such as child poverty pose a challenge for Indonesia. The provinces in Maluku and Papua have the highest rates of child poverty. Data on child poverty at regencies/municipalities level is needed to address this issue through targeted policies. The direct estimations have a Relative Standard Error (RSE) value of more than 25 percent, necessitating the use of an indirect method, Small Area Estimation (SAE). This study aims to compare the results of indirect estimates of the percentage of children aged 0-17 living in poverty at the regencies/municipalities level in Maluku and Papua using SAE Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayes Beta (HB Beta) methods. Susenas KOR March 2023 data is used to produce direct estimates, while Podes 2021 data is used to form auxiliary variables. The results indicate that the SAE HB Beta method provides estimates with better RSE compared to SAE EBLUP. All regencies/municipalities in the Maluku and Papua have a fairly good level of accuracy.

Economic Evaluation of Conservation through Use of an Araucaria angustifolia Provenance and Progeny Test.

Araucaria angustifolia is a species known for its valuable wood and nuts, but it is threatened with extinction. The plantation of forests for genetic resource conservation is a complementary strategy designed to reduce the species' genetic variability loss. This study aimed to evaluate the technical and economic viability of A. angustifolia for genetic conservation through use. The analyzed provenance and progeny trial was established in 1982 in Itapeva, Brazil. It was structured using a compact family blocks design with 110 open-pollinated progenies from five natural populations, three replicates, ten plants per subplot, and 3.0 m × 2.0 m spacing. After 33 years, the trial was evaluated for total height, diameter at breast height, wood volume, and survival. The variance components and genetic parameter estimates were performed using Restricted Maximum Likelihood/Best Linear Unbiased Prediction methods (REML/BLUP) methods with the Selegen software (version 2014). The production and management scenarios were obtained using the SisAraucaria software (version 2003). Sensitivity analysis and economic parameter estimates were obtained through various economic evaluation methods using the Planin software (version 1995). In general, the genetic parameters indicated that the population has enough variability for both conservation and breeding purposes, suggesting technical viability for the establishment of a seed orchard. The economic parameters indicated that the commercialization of wood and araucaria nuts proved to be more profitable than wood production by itself. In conclusion, araucaria genetic conservation through use is a technically and economically viable ex situ conservation strategy.

Investigating the genomic background of calving-related traits in Canadian Jersey cattle

Traits related to calving have a significant impact on animal welfare and farm profitability in dairy production systems. Identifying genomic regions associated with calving traits could contribute to refining dairy cattle breeding programs and management practices in the dairy industry. Therefore, the primary objectives of this study were to estimate genetic parameters and perform genome-wide association studies (GWAS) and functional enrichment analyses for stillbirth, gestation length, calf size, and calving ease traits in North American Jersey cattle. A total of 40,503 animals with phenotypic records and 5,398 animals genotyped for 45,101 single nucleotide polymorphisms (SNPs) were included in the analyses. Genetic parameters were estimated based on animal models and Bayesian methods. The effects of SNPs were estimated using the Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) method. The heritability (standard error) estimates ranged from 0.01 (0.01) for stillbirths (SB) in heifers to 0.11 (0.01) for gestation length (GL) in cows. The genetic correlations ranged from -0.58 (0.11) between calving ease (CE) and SB in heifers to 0.44 (0.14) between calving ease and calf size (CZ) in cows. CE showed the highest genetic correlation between heifers and cows, 0.8 (0.22) respectively. The candidate genes identified, including MTHFR, SERPINA5, IGFBP3, and ZRANB1, are involved in key biological processes and metabolic pathways related to the studied traits. Reducing environmental variation and identifying novel indicators of reproduction traits in the Jersey breed are needed given the low heritability estimates for most traits evaluated in this study. In conclusion, this study provides a characterization of the genetic background of calving-related traits in Jersey cattle. The estimates obtained can be used to improve or build selection indexes in Jersey cattle breeding programs in North America.

Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information.

This study aims to compare the accuracy of genomic estimated breeding values (GEBV) estimated using a genomic best linear unbiased prediction (GBLUP) method and GEBV estimates incorporating prior marker information from a genome-wide association study (GWAS) for the weaning weight trait in highland Merino sheep. The objective is to provide theoretical and technical support for improving the accuracy of genomic selection. The study used a population of 1007 highland Merino ewes, with the weaning weight at 3 months as the target trait. The population was randomly divided into two groups. The first group was used for GWAS analysis to identify significant markers, and the top 5%, top 10%, top 15%, and top 20% markers were selected as prior marker information. The second group was used to estimate genetic parameters and compare the accuracy of GEBV predictions using different prior marker information. The accuracy was obtained using a five-fold cross-validation. Finally, both groups were subjected to cross-validation. The study's findings revealed that the heritability of the weaning weight trait, as calculated using the GBLUP model, ranged from 0.122 to 0.394, with corresponding prediction accuracies falling between 0.075 and 0.228. By incorporating prior marker information from GWAS, the heritability was enhanced to a range of 0.125 to 0.407. The inclusion of the top 5% to top 20% significant SNPs from GWAS results as prior information into GS showed potential for improving the accuracy of predicting genomic breeding value.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10–13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual’s genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual’s predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Genomic prediction model optimization for growth traits of olive flounder (Paralichthys olivaceus)

Genomic prediction (GP) has emerged an effective tool for addressing the many shortcomings of traditional selective breeding, thereby enhancing the selection process. In this study, we optimized GP methods using 5-fold cross-validation to estimate genome-estimated breeding values for the weight traits of olive flounder (Paralichthys olivaceus). To accomplish our goal, we determined the parentage of the target broodstock and the ability of 11 prediction models to predict the weight traits of 1.8-year-old olive flounders, which were genotyped using a 70 K single nucleotide polymorphism (SNP) array. Moreover, our optimization efforts toward the predictive ability of genomic best linear unbiased prediction (GBLUP), Bayesian B (BB), and random forest (RF) methods encompassed changes in various aspects such as fixed effects, SNP quantity, population size, and phenotypic data collected at different fish ages. Additionally, we assessed the predictive ability for the total length and body depth of fish using GBLUP, BB, and RF. Among the 11 prediction methods used in this study, the BB (0.675), Elastic Net (0.679), and RF (0.698) methods exhibited the highest predictive abilities, whereas the GBLUP (0.637) method demonstrated the lowest. Incorporating information regarding fish sex as a fixed effect substantially improved the predictive ability of GBLUP and BB. For mean models, utilizing 3000–5000 random SNP markers resulted in a higher predictive ability, similar to that obtained using 50,000 SNPs. Increasing the population size reduced the standard deviation of the predictive ability. Notably, phenotypic records from 1.8-year-old fish exhibited a significantly higher predictive ability than those from the other age groups. Furthermore, GBLUP, BB, and RF provided higher predictive abilities for length (0.655–0.852) and body depth (0.665–0.861). These findings may significantly shape future olive flounder genomic selection programs and offer valuable insights into GP in aquaculture.

Effect of genetic and sex effect on genomic prediction for horn development in Nellore cattle

This study aimed to evaluate the influence of phenotypic classification of horn development, animal sex effect, and non-autosomal SNP (single nucleotide polymorphism) markers on the genetic parameters and genomic prediction ability for horn development in Nellore cattle using the single-step genomic best linear unbiased prediction method. The polled phenotype was evaluated in two (presence and absence of horns), three (scurs and polled offspring from a horned parent, and the polled and horned animals), and four (absence of horn, polled born to a parent with horn, scurs, and presence of horn) phenotypic categories. A total of 12 statistical models were evaluated. The variance components were estimated using the THRGIBBS1F90 software, and a threshold animal model was used for genomic prediction analyses with the single-step genomic BLUP (ssGBLUP) procedure. Accuracy, bias, and dispersion parameters were evaluated based on the linear regression (LR) method. The highest heritability (0.84) was obtained when the polled character was evaluated as a binary trait. The lowest heritability estimates (0.44 to 0.45) for horn development were obtained when the phenotype was classified into three categories. For the same horn development classification method, the heritability estimates were similar regardless of the genomic evaluated models and fixed effects included in the model. For models considering four and three phenotypic categories for horn development, the inclusion of the sex effect as a fixed effect within the CG did not improve the accuracy, bias, and dispersion of genomic predictions for horn development. Analyzing the trait with binary expression, the highest prediction accuracy was observed when the effect of sex was not included in the CG and without the SNPs in the sex chromosomes. These models displayed the highest dispersion, pointing out the low robustness of genomic prediction. In addition, models that use less than four categories to classify the horn development phenotype, with no discrimination between polled and homozygous polled displayed lower prediction ability. The inclusion of non-autosomal SNPs in the analyses for the models considering four phenotypic categories leads to an improvement in prediction accuracy in 5,26 %, bias, and dispersion reduction, 37 % and 4,55 %, respectively, compared with models that only considered autosomal SNPs. The selection using genomic information for the polled trait is feasible, and it is an alternative to obtaining polled Nellore animals. The binary coding of horn development is an unsuitable oversimplification of polled phenotype, and probably, the genetic background of horn development is more complex than previously proposed. The most adequate prediction model to evaluate the horn development in Nellore cattle was considering four phenotypic categories and including non-autosomal SNP in the analyses for genomic prediction purposes of naturally genetically polled animals. Genetic dehorning can be adopted on a large scale as a low-cost and non-invasive approach to increase the frequency of hornless animals using genomic information and mating strategies.

Heritability and predictive ability for the heterobothriosis resistance and growth performance in the tiger pufferfish Takifugu rubripes fed standard or low fishmeal diets

Heterobothriosis resistance and high growth performance are major concerns in the tiger pufferfish (Takifugu rubripes) farming. Our previous small-scale study suggested the feasibility of genomic selection (GS) for these traits but also identified an undesired genetic correlation between them, which could hamper simultaneous genetic improvement. Here, we sought to validate these initial findings through larger samples and crosses and also explore the potential of GS for the individuals fed a normal fishmeal diet and a low fishmeal (LFM) diet. We produced three populations: one was fed a normal diet (PopFM: 1100 individuals) and two were fed an LFM diet (PopLFM1: 993 individuals; PopLFM2: 1017 individuals); all three populations were artificially infected with Heterobothrium okamotoi, a monogenean parasite causing heterobothriosis. We recorded standard lengths of the fish after artificial infection (SLEAI) and the number of parasites on the gill cavity wall (HC) and genotyped genome-wide single nucleotide polymorphisms (SNPs) using genotyping by random amplicon sequencing, direct (GRAS-Di) technology for each sample: PopFM, 12 K SNPs; PopLFM1, 19 K SNPs; PopLFM2, 21 K SNPs. Heritability and prediction ability for each trait were estimated for each population using genomic best linear unbiased prediction (GBLUP), Bayes C, and Bayesian ridge regression methods. SNP effects on each trait were estimated by Bayes C, Bayesian ridge regression, and genome-wide association analyses (GWAS) using the same data sets. We observed moderate to high levels of heritability for HC (0.16–0.57) and SLEAI (0.50–0.77), with predictive ability of 0.25–0.55 for HC and of 0.51–0.73 for SLEAI. These values were slightly larger in LFM diet groups. As reported previously, a weak undesired genetic correlation was observed between the traits, and the correlation was more pronounced in the LFM groups. In conclusion, our results indicate that genetic gain in growth and parasite resistance traits can be expected for the tiger pufferfish through GS, especially for individuals reared on an LFM diet. However, close attention should be paid to the extent of the undesirable genetic correlation between the two traits that may be amplified in the populations fed an LFM diet.

Selection of informative markers using machine learning approaches and genome-wide association studies to improve genomic prediction in Hanwoo cattle: a simulation study

The present study deploys a comparison of Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Genome Wide Association Studies (GWAS) in selecting optimum subsets of single nucleotide polymorphisms (SNPs) to be used in genomic prediction in cattle. The data simulation was carried out for 6,000 animals and 47,841 SNPs which include 43,633 polygenic markers and 4208 quantitative trait loci (QTL) using QMSim software. The genomic prediction was conducted with the best linear unbiased prediction (BLUP) method using the BLUPF90 program. The accuracy of prediction was computed in three different types, namely, Empirical all SNPs, Empirical QTL, and theoretical accuracy, Accuracy PEV . Among the three models, the highest Empirical all SNPs accuracy 0.79 was derived for GBM followed by 0.77 for XGBoost and 0.76 for GWAS. The Empirical QTL accuracy was almost equal for all three models. The maximum theoretical accuracy was obtained for GWAS which was 0.93, whereas GBM and XGBoost obtained 0.86 and 0.85 accuracy levels respectively. Our results indicate that all three models comparably performed in genomic predictions; however, subsets selected by both GBM and GWAS reported higher prediction accuracies compared to the whole SNP set. The number of QTL selected as a proportion of the total number of SNPs was superior in GWAS. These observations can be validated using real data which could enable further optimization of the analysis process.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection.

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

Repetibilidade e número de colheitas para seleção de genótipos de castanheira-da-amazônia em sistema agroflorestal de cultivo

Abstract The objective of this work was to estimate the repeatability coefficients of variables related to fruit and nut production, the minimum number of crops required for genotype selection, and the permanent phenotypic correlation coefficients between variables in a cultivated population of Amazon nut. A total of 40 genotypes from seeds, belonging to an agroforestry system implemented in 1995, in the state of Roraima, Brazil, were evaluated during ten years for six variables related to fruit and nut production. The genetic-statistical analyses were performed using the maximum restricted likelihood and best linear unbiased prediction (REML/BLUP) method, based on the basic model of repeatability, without an experimental design, of the Selegen-REML/BLUP software. Genetic variability between genotypes and a moderate regularity in the repetition of the studied variables between different harvests were detected. The number of harvests required to reach a coefficient of determination of 80% ranged from seven, for the variables average nut weight per fruit and number of fruits per plant, to nine, for average nut weight. The correlation results indicate the possibility of indirect selection for nut production through the evaluation of the number and weight of fruits per plant.

Assessment of sires for first lactation traits using best linear unbiased prediction (BLUP) method in Tharparkar cattle

Assessment of sires for first lactation traits using best linear unbiased prediction (BLUP) method in Tharparkar cattle

A quasi-Bayesian Approach to Small Area Estimation Using Spatial Models

The empirical best linear unbiased prediction (EBLUP) method has been the dominant model-based approach in small area estimation. As an alternative to this frequentist method, the observed best prediction (OBP) method, also frequentist, was proposed by Jiang et al.[ 11 ] where the parameters of the model are estimated by minimizing an objective function which is implied by the total mean squared prediction error. In a recent article, Datta et al.[ 6 ] followed a general Bayesian approach, proposed recently by Bissiri et al.[ 2 ], to develop a quasi-Bayesian method by appropriately calibrating the objective function for the OBP method for the Fay-Herriot model. In a different article, Chung and Datta[ 4 ] demonstrated that in the absence of covariates with good predictive power the small area estimates from the standard Fay-Herriot model can be improved by using spatially dependent random effects. In this article, we develop a quasi-Bayesian small area estimation method using several spatial alternatives to the independent Fay-Herriot random effects model. Evaluation of the proposed method based on an application to estimation of four-person family median incomes for the U.S. states shows its usefulness. Limited but related simulation studies for the median incomes application reinforce our conclusion. AMS Subject Classification: 62F 15, 62D99

Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

Carrying out an index assessment of the live weight of cattle of the kazakh white-headed and hereford breed

The research was carried out to study the dynamics of the live weight of young cattle of the Kazakh white--headed and Hereford breeds (at birth, at weaning, at 12 months, adjusted for 210 and 365 days). An index assessment of live weight (at birth, weaning and at 12 months) was carried out for the formation of a breeding herd in basic farms. The index evaluation of the genetic breeding value by breeding characteristics was carried out using the best linear unbiased prediction (BLUP) method. The method of calculating the index score by statistical method with the construction of a genetic model of the animal has been worked out and the predicted indices of breeding value for 3 productive indicators have been calculated: live weight at birth, at weaning, at 12 months of age. In total, 9,503 farms for breeding Kazakh white--headed cattle and 2,766 farms for breeding Hereford cattle were analyzed. The accuracy of the evaluation of the breeding value index by the BLUP AM method is in cattle of the Kazakh white-headed breed: at birth 0.509-0.557 in bulls and 0.534-0.573 in heifers; at weaning 0.274-0.319 in bulls, 0.354-0.368 heifers; at 12 months 0.336-0.418 and 0.388-0.415, respectively. The accuracy of the evaluation of the breeding value index by the BLUP AM method is in Hereford cattle: at birth 0.531-0.810 in bulls and 0.511-0.565 in heifers; at weaning 0.330-0.608 in bulls, 0.257-0.366 heifers; at 12 months 0.386-0.498 and 0.342-0.414, respectively. The index estimation by the BLUP AM method makes it possible to increase the accuracy of the estimation of cattle of the Kazakh white--headed and Hereford breeds by live weight at birth, at weaning and one-year-old age.

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais.

The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

Genomic Selection for Live Weight in the 14th Month in Alpine Merino Sheep Combining GWAS Information.

Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study's sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335-0.374, the accuracy after five-fold cross-verification was 0.154-0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59-7.79%.

PSVII-2 The Impact of Genomic Information Inclusion on Breeding Value Prediction for a Novel Parasite Resistance Indicator Trait in Brazilian Anglo-Nubian Goats: a Pilot Study

Abstract Infection by Haemonchus contortus is the gastrointestinal parasitosis of greatest economic and social importance in the production of goats, being the leading cause of mortality in the Brazilian goat industry. Genetic selection for resistance to parasites in Anglo-Nubian goats has proven to be a viable alternative to traditional methods of controlling gastrointestinal nematodes, producing animals genetically superior in resistance to parasites. This study aimed to evaluate the impact of including genomic information on the estimation of variance components, genetic parameters, and predictive accuracy of genetic values for the new trait Parasitic Resistance (PR) in Anglo-Nubian goats. The PR is generated by measuring Body Condition Score, FAMACHA chart (ranging from 1 to 5), and Fecal Egg Count on a regular scale. These three traits were used as input variables in the CAPRIOVI software, which uses fuzzy logic to create the PR score, ranging from 0 to 10. Animals with lower scores are more likely to be parasitized and require treatment, while animals with higher scores are less likely to be infected. We used information from 352 animals, totaling 3,855 repeated measurements of PR over the years (2001 to 2020) and 1,500 animals in the pedigree. Animals with less than three measurements for any of the three input traits were removed from the dataset. Contemporary groups were built from information from the year of birth, data collection season, birth season, and physiological stage of the animal, considered as fixed effects, and age at the time of data collection as a covariate in an animal repeatability model. A total of 89 animals were genotyped using the Bead Chip Goats SNP50. After quality control, the remaining 38,659 SNPs (Table 1) were used to build the hybrid matrix (H). The variance components (VC) were estimated using the Bayesian Gibbs Sampling method, and breeding values were predicted using the Best Linear Unbiased Prediction (BLUP) and single-step Best Linear Unbiased Prediction (ssGBLUP) methods. The additive genetic variance and heritability estimation ranged from 0.13 to 0.14 and 0.04 to 0.05, respectively (Table 2). The accuracies obtained for PR with BLUP and ssGBLUP were 0.12 and 0.14, respectively, representing a gain of 16.7%. The genomic information (25 % of all animals with phenotypes) with the repeated records improved the predictive model capability based on accuracy. It also improved the VC estimation for PR in Anglo-Nubian goats raised in the Brazilian semi-arid region. These results highlight the positive impact of genetic evaluation of goats for PR, even with few phenotypes and genotyped animals. In addition to elucidating the additive genetic influence of parasite resistance, it can be a great source of information on the selection and production of Anglo-Nubian goats.

An Assessment of the Genetic Parameters of Boars' Reproductive Traits.

The aim of this study was to estimate genetic parameters for the reproductive traits of boars based on single-nucleotide polymorphism data. A total of 109,836 semen samples from 2249 boars were collected between 2010 and 2022. Five basic traits were assessed: sperm volume, sperm concentration, motility, number of abnormal sperm, and, for the first time for the local population, libido. In addition, two derived traits were assessed: total sperm count and number of functional sperm. Genetic parameters were estimated using the single-step genomic best linear unbiased prediction method (ssGBLUP). Dam and sire breeds were evaluated separately. The five basic traits were evaluated using five-trait models, while the two derived traits were evaluated using single-trait models. The heritability coefficients had lower values for all sperm quality traits with both methods. For the basic traits, the heritability ranged from 0.099 to 0.342. The greatest difference between dam and sire breeds was observed for the heritability of the sperm concentration trait (0.099 and 0.271, respectively). The heritability of the libido trait was twice as high for boars of sire breeds as it was for boars of dam breeds. The genetic parameters estimated with ssGBLUP can be used in routine genetic evaluations to improve the pig breeding process.