Pedigree File Research Articles

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

Drawing human pedigree charts with DrawPed.

Creating pedigree charts is a recurring task in biomedical research, but there are few online tools for drawing complex human pedigrees available and even fewer are free. With DrawPed we aim to close this gap. DrawPed automatically draws pedigree charts from standard PED format pedigree files. Users can also create pedigrees from scratch and interactively edit existing pedigrees. The application can display conditions not captured in a PED file such as deceased persons or suspected consanguinity of parents. Pedigree charts are displayed as SVGs, which are scalable and hence publication-ready. Pedigrees can be exported as PED files for storage, exchange, or use in other applications. DrawPed is open source and freely available at https://www.genecascade.org/DrawPed/.

Качествена оценка на педигрето на Среднородопската овца 2005-2023

The aim of this study was to estimate the quality of the Central Rhodope Sheep pedigree data in years 2005-2023. The data was provided by the Association for breeding Central Rhodope sheep, Karakachan sheep and Rhodope Cigay sheep, which is keeping the breed’s herdbook. The herdbook has 18 720 records for the studied period. Along with the individuals registered only as sires or dams, there were 22686 individuals in total in the pedigree file. The pedigree was processed by the OptiSel software package and then checked for errors with the PedigreeViewer software. All the inconsistencies found in the pedigree, cycles and the gender discrepancies, were solved by erasing the conflicting data. In total 565 sires and 12 156 dams were identified in the pedigree file. The maximum number of generations traced for an animal in the pedigree were six. Since 2014, the average pedigree completeness index is over 98% for the first generation, and for second and third increases from 58% to 100%, and from 38% to 94%, respectively. In the recent five years, the average number of equivalent generations by year of birth is between 2.145 to 3.159, and the average number of full generations 1.839 to 2.537. These results indicate that the cumulated pedigree data might be used for analyses of the population structure and genetic diversity of the Central Rhodope Sheep breed.

Effects of inbreeding on production traits and genetic evaluations in Guzerá beef cattle raised under tropical conditions.

The objectives of this study were to evaluate the influence of inbreeding on growth traits and body measurements, as well as on the estimation of genetic parameters and genetic trends in Guzerá cattle. Phenotypic records of 4,212 animals selected for postweaning weight from Guzerá Breeding Program of Advanced Beef Cattle Research Center were utilized. The pedigree file contained records from 7,213 animals born from 1928 to 2019. The traits analyzed were: birth weight (BW), weights adjusted to 210, 378 and 550 days of age (W210, W378 and W550, respectively), chest girth at 378 and 550 days of age (CG378 and CG550), scrotal circumference (SC), and hip height at 378 and 550 days of age (HH378 and H550). Linear regression was used to evaluate the effects of inbreeding on traits. Genetic parameters were obtained using models including or not the effect of inbreeding as a covariate. Inbreeding had negative effects (P ≤ 0.01) on BW (-0.09kg), W378 (-2.86kg), W550 (-2.95kg), HH378 (-0.10cm), and H550 (-0.29cm). The lowest and highest heritability estimates were obtained for W210 (0.21 ± 0.07) and HH550 (0.57 ± 0.06), respectively. The genetic correlations were strong and positive between all traits, ranging from 0.44 ± 0.08 (SC x HH) to 0.99 ± 0.01 (W378 x W550). Spearman correlations between EBVs obtained with or without inbreeding effect ranged from 0.968 to 0.995 (P < 0.01). The results indicate loss of productive performance in inbred animals. However, the inclusion of inbreeding coefficient in genetic evaluation models did not alter the magnitude of genetic parameters or genetic trends for the traits studied.

Genetic parameters of sheep growth curve traits reared within rangelands under uncertain paternity.

This work aims to improve the selection program of the Timahdit breed through the use of the parameters of the Von Bertalanffy model as selection criteria and the treatment of uncertain paternity found in pastoral systems. A database containing 12,029 animals and a pedigree file integrating the probabilities of the parents with a total of 48,292 animals were used in the analysis. An individual estimation of the parameters of the model studied by the nonlinear regression procedure Proc NLIN of SAS was carried out, followed by the determination of the fixed effects which influence these parameters by means of a general linear model using the GLM procedure of the SAS software. The treatment of uncertain paternity is solved by an R code translating Average Numerator Relationship Matrix Model (ANRM). Then, the variance and (co)variance components were estimated by a Bayesian approach using the BRMS package. The high heritability values obtained, between 0.52 and 0.55 for the parameters studied, suggest good prospects for genetic responses to selection and the maintenance of sustained genetic progress, especially when environmental conditions are unfavorable. The positive correlations between all the parameters studied show that animals with rapid development tend to have lower weight performance. Finally, with optimal selection based on the genetic values associated with these parameters, we can make the desired changes to the growth curve by choosing breeders that achieve high weight performances as quickly as possible, and that would allow improving the feed efficiency for these animals, as well as increasing the profitability of sheep farms.

MSeqDR Quick-Mitome (QM): Combining Phenotype-Guided Variant Interpretation and Machine Learning Classifiers to Aid Primary Mitochondrial Disease Genetic Diagnosis.

The international Mitochondrial Disease Sequence Data Resource Consortium (MSeqDR) Quick-Mitome (QM) is a web-based platform enabling automated variant interpretation of whole-exome sequencing (WES) datasets for the genetic diagnosis of primary mitochondrial diseases (PMD). Designed specifically to address the unique dual genome nature of PMD etiologies, QM includes features for both nuclear and mitochondrial DNA (mtDNA) genome analysis. QM requires VCF variant lists, HPO ID clinical phenotypes, and pedigree files for multiple-sample VCF inputs. QM maps phenotypes to HPO terms before analysis. QM analysis requires 2 to 20 min for 100,000 variants on an 8-vCPU AWS server using Exomiser's "PASS_ONLY" mode for nuclear variants. QM ranks variants based on allele frequency, phenotype-gene association, functional impact, and inheritance mode. Variants are further annotated with multiple data sources such as OMIM, ClinVar, dbNSFP, gnoMAD, MITOMAP, and MSeqDR. In addition to standard Exomiser results, QM generates an Analysis Report and QM Integrated Report with add-on mtDNA-specific analyses, including haplogroup prediction with Phy-Mer, heteroplasmy calculation, and mvTool annotations. We developed the Mitochondrial Disease Variant (MDV) classifier using XGBoost to predict variant pathogenicity for PMD. The MDV classifier was trained on >120 features and performance benchmarking showed that it correctly classified >98% of nuclear gene variants as being pathogenic or benign, and predicted PMD-causing variants with 94% precision. The MSeqDR QM server is an open-access resource for phenotype-driven dual-genome analyses for PMD diagnosis by the global mitochondrial disease community. It is publicly available for non-commercial, non-clinical research use at https://mseqdr.org/quickmitome.php. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Standardizing clinical phenotypes into human phenotype ontology (HPO) terms as the phenotype input for Quick-Mitome (QM) Basic Protocol 2: Prepare the pedigree input for multiple-sample VCF Basic Protocol 3: Quick-Mitome (QM) analysis Basic Protocol 4: Reviewing and understanding the QM Integrated Report and Analysis Report.

Estimation of genetic parameters for parasite resistance and genome-wide identification of runs of homozygosity islands in Florida Cracker sheep

In this study, we estimated genetic parameters for parasite resistance traits and the distribution of runs of homozygosity islands in Florida Cracker sheep. The dataset contained 365 animals with phenotypic records at 38 days post-infection for fecal egg count (FEC), packed cell volume (PCV), FAMACHA score (FAM), and body condition score (BCS). The pedigree file contained 695 animals born between 2016 and 2020 and included 279 individuals with genotypes. Genetic parameters were estimated using a multi-trait model with a Bayesian implementation via Gibbs sampling in the GIBBS3F90 program. Heritability was 0.33 ± 0.09 for FEC, 0.31 ± 0.10 for FAM, 0.22 ± 0.09 for PCV, and 0.19 ± 0.07 for BCS. The genetic correlation between FEC and FAM was 0.51 ± 0.21; the remaining genetic correlations had large posterior standard deviations and yielded 95% posterior intervals including zero or with values out of the parameter space because of our small dataset. Analysis of the distribution of runs of homozygosity islands revealed 113 hot spots with annotated genes related to immune response and parasite resistance traits. Our results suggest that the genetic selection for FAMACHA score can be effective in improving parasite resistance because of its ease of recording, high heritability, and favorable genetic correlation with FEC. Additionally, runs of homozygosity islands related to parasite resistance could harbor important candidate genes for controlling this trait in Florida Cracker sheep.

PSVI-11 Genome-Wide Association Study for 4 Behavior Traits in a Population of Labrador Retrievers Bred as Guide Dogs

Abstract The objective of this study was to assess the genetic architecture and investigate genomic regions associated with Body Handling, Harness sensitivity, Self-modulation, and Noise sensitivity in Labrador Retrievers used as guide dogs. A single-step genome-wide association study (ssGWAS) was used for this purpose. Phenotypic data were collected in 4,841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for all four traits. Phenotypes were selected from the Behavior Checklist, which is a scoring tool used by a skilled scorer to describe aspects of behavior observed during a variety of assessments including formal tests and while training or observing another handler working with the dog. The pedigree file contained 23,593 animals with birth years ranging from 1991 to 2019 from two populations with related ancestors. Genomic data were available for 457 individuals and were obtained by selecting 250K SNPs from whole genome sequences. Associations were calculated as the percentage of variance explained by windows of 80 adjacent SNPs. SNP variances were calculated based on SNP effects that were obtained by back-solving genomic estimated breeding values obtained with the single-step GBLUP method in a genomic selection program with the same population. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Manhattan plots were generated with the variance explained by SNP windows for 3 iterations of wssGBLUP. After visual inspection of the Manhattan plots, the R package GALLO was used to annotate the genes located within the peaks that explained 0.6% or more of the genetic variance. The maximum amount of variance explained by a window of 80 SNPs was 1.1% for Body Handling. This trait presented three other regions explaining more than 0.6% of the genetic variance. Harness sensitivity presented four peaks above the threshold, with the major one explaining 0.75% of the genetic variance. Self-modulation and Noise sensitivity both had their major peak explaining 0.95% of the genetic variance and presented four and three regions above the threshold, respectively. The genes annotated in the candidate regions were not specifically related to behavior, nor were they involved in any pathway with biological relevance to the phenotypes. Moreover, some of the regions were not annotated, and no associated genes were found. The traits studied in this project were shown to be polygenic and complex, and that explains the lack of major genes. It is expected that the quality of associations will improve as the sample size increases. The next steps of this project are to increase the number of genotyped animals, increase the number of markers, and adopt alternative annotation and enrichment tools for the post-GWAS analysis.

118 Variance Components for Age at First Calving and Yearling Weight When Accounting for Age Differences at Fixed Breeding Dates in a Contemporary Group

Abstract The ability of a heifer to calve earlier in a breeding season has implications on the economic success of heifers in a production system. Heifers that calve earlier in a calving season have increased calf performance, improved subsequent fertility, and a longer productive life in the herd. Production systems typically breed heifers during fixed breeding dates, with heifers of variable ages in the same contemporary group. The objective of this study was to determine if heifer yearling weight (YW) or age differences going into a fixed breeding period influenced the additive genetic variance of age at first calving (AFC) explained in a multi-breed population. Heifer fertility data were obtained from International Genetic Solutions. Females with exposure and calf production records were used to identify individuals calving between 630 and 810 days of age. Females with embryo transfer, multiple birth progeny, and an age differential (DIFF) greater than 100 days were excluded. Age differential was defined as the difference in days between an individual’s birth date and the earliest birth date of an animal in a defined contemporary group. Contemporary group was defined as; breeder, herd, year born, birth and yearling group. After data filtering, there were 149,350 calving records. A 3-generation pedigree file was used containing 365,637 animals, with 37,420 unique sires and 219,533 unique dams. Two bivariate multi-trait animal models for AFC and YW with random additive genetic and residual effects and fixed effects of contemporary group, breed proportion, and % retained hybrid vigor were used. The first model consisted of the aforementioned effects while the second model included DIFF as an additional fixed effect. Analyses were performed using ASReml 3.0. When DIFF was not included as a fixed effect, the additive, residual and phenotypic variances for AFC were 126.1, 456.8, and 582.9 d2, respectively, and the genetic correlation between AFC and YW was 0.36 ± 0.02. When DIFF was included as a fixed effect, the additive, residual, and phenotypic variances for AFC were 10.0, 326.0, and 336.0 d2, respectively. The genetic correlation between AFC and YW was 0.19 ± 0.04. In the absence of DIFF, the heritability estimates for AFC and YW were 0.22 ± 0.01 and 0.44 ± 0.01, respectively but were 0.03 ± 0.003 and 0.44 ± 0.01 respectively, when DIFF was included. Age differential was a significant effect on AFC at –0.86 (P &amp;lt; 0.0001). The low additive variance of AFC when accounting for DIFF suggests that the influence of a female's age going into a fixed breeding date explains much of the variation in AFC. The low heritability estimate for AFC and genetic correlation with YW suggests that selection for improved AFC will be difficult using this method and that producers should instead focus on improved management practices to ensure females calve earlier in a calving season.

PSXIII-26 Investigating the Genetic Background of Juniper Consumption in Goats

Abstract Junipers (Juniperus spp.) are woody invasive plants that have caused encroachment problems in the U.S. western rangeland, decreasing forage productivity and biodiversity. A potential solution to this issue is the use of goats in target grazing programs. Selecting goats for juniper consumption is of great interest to rangeland farmers. Therefore, our main objectives were to 1) estimate genetic parameters, including heritability and repeatability, for juniper consumption in Angora (ANG) and Boer x Spanish (BxS) composite population of goats grazing on West Texas rangelands (USA); and, 2) perform a genome-wide association study (GWAS) to identify candidate genes associated with juniper consumption. Fecal samples were collected multiple times between 2008 and 2017 and analyzed using near-infrared reflectance spectroscopy (NIRS) to estimate the percentage of juniper in the diet of goats, using calibration equations previously developed in controlled studies. We analyzed a dataset comprising 2,327 records from 459 ANG and 8,714 records from 1,996 BxS. The pedigree file included 1,601 and 2,426 ANG and BxS goats, respectively. Genomic information for 50,957 single nucleotide polymorphisms (SNP) from 93 ANG and 238 BxS goats was included in the analyses. Sampling date and sex were fitted as categorical fixed effects and age as a linear covariate in the statistical models. Animal direct additive genetic and permanent environment were considered as random effects. Genetic parameters were calculated based on the Restricted Maximum Likelihood method under the single-step genomic BLUP procedure implemented in the BLUPf90+ family of programs. We used de-regressed estimated breeding values and whole-genome sequence data for the GWAS analyses with 8 million SNP for the ANG and 5.3 million SNP for the BxS. The software GCTA was employed for these analyses. Juniper percentage in their diets varied across the years and ranged from 2.14% (± 7.51%) to 14.6% (± 11.0%) for ANG and from 10.5% (± 14.7) to 33.8% (± 18.1) for the BxS population. The heritability estimate for juniper consumption was 0.19 (± 0.038) for ANG and 0.28 (± 0.022) for BxS, indicating that juniper consumption can be increased through selection. The repeatability values were 0.21 for ANG and 0.37 for BxS. The GWAS revealed 1,257 significant SNP located within or close to 220 genes in ANG, and 878 SNP related to 159 genes in BxS, suggesting their association with juniper consumption. These findings suggest that juniper consumption is under genetic control and has a polygenic inheritance pattern influenced by multiple genes of small effect. The heritability and repeatability estimates indicate that genetic improvement of juniper consumption is feasible in both goat populations.

Genomic background of biotypes related to growth, carcass and meat quality traits in Duroc pigs based on principal component analysis.

As the swine industry continues to explore pork quality traits alongside growth, feed efficiency and carcass leanness traits, it becomes imperative to understand their underlying genetic relationships. Due to this increase in the number of desirable traits, animal breeders must also consider methods to efficiently perform direct genetic changes for each trait and evaluate alternative selection indexes with different sets of phenotypic measurements. Principal component analysis (PCA) and genome-wide association studies (GWAS) can be combined to understand the genetic architecture and biological mechanisms by defining biological types (biotypes) that relate these valuable traits. Therefore, the main objectives of this study were to: (1) estimate genomic-based genetic parameters; (2) define animal biotypes utilizing PCA; and (3) utilize GWAS to link the biotypes to candidate genes and quantitative trait loci (QTL). The phenotypic dataset included 2583 phenotypic records from female Duroc pigs from a terminal sire line. The pedigree file contained 193,764 animals and the genotype file included 21,309 animals with 35,651 single nucleotide polymorphisms (SNPs). Eight principal components (PCs), accounting for a total of 99.7% of the population variation, were defined for three growth, eight conventional carcass, 10 pork quality and 18 novel carcass traits. The eight biotypes defined from the PCs were found to be related to growth rate, maturity, meat quality and body structure, which were then related to candidate genes. Of the 175 candidate genes found, six of them [LDHA (SSC1), PIK3C3 (SSC6), PRKAG3 (SSC15), VRTN (SSC7), DLST (SSC7) and PAPPA (SSC1)] related to four PCs were found to be associated with previously defined QTL, linking the biotypes with biological processes involved with muscle growth, fat deposition, glycogen levels and skeletal development. Further functional analyses helped to make connections between biotypes, relating them through common KEGG pathways and gene ontology (GO) terms. These findings contribute to a better understanding of the genetic relationships between growth, carcass and meat quality traits in Duroc pigs, enabling breeders to better understand the biological mechanisms underlying the phenotypic expression of these traits.

Evidential segregation analysis for offspring sex ratio in rabbit and sheep populations

Offspring sex ratio has been found to be altered by environmental and genetic distortions in multiple species, against the Mendelian inheritance rules. However, little is known in livestock populations where it is essential to validate whether a polymorphic major gene with relevant effects may segregate in the target population. However, the current analytical tool (model FREQ) cannot handle new mutations in non-founder individuals, reducing the chance of detecting them. Our new analytical approach aimed to overcome this limitation in the context of evidential inference, a statistical framework based on the likelihood function as a robust objective measure of the strength of statistical evidence without variation from the sample size. Two field data sets from sheep and rabbit populations were used. Models evaluated environmental and inbreeding effects in both species. Our new approach assumed that the mutation primarily arose in an individual of the analyzed data set (model MUTj). Each sire was individually analyzed to determine the most plausible source for the new mutation, if any. The likelihood ratio (LR) against a reference parametrization without mutations (model NULL) was used to test the statistical relevance of systematic effects (LR ≥ 8) and models (LR ≥ 32). Both species revealed relevant departures for offspring sex ratio along the analyzed time frame with strong evidence for the year (LR = 1.4 × 109 in Ripollesa sheep and LR = 85.7 in MARET rabbits) and season (LR = 12.6 in MARET rabbits), although with a fluctuating pattern. The age of the dam reported weak evidence in both species (LR < 4). Inbreeding had a relevant linear impact on both sheep (LR = 60.7) and rabbit (LR = 780.4) populations, whereas the quadratic effect only showed strong evidence in MARET rabbits (LR = 8.3). Regarding the segregation analysis, most models showed an almost homozygous pattern of weak or lacking evidence of new mutations influencing offspring sex ratio. The only species showing strong evidence for the MUTj model (LR > 32) was the Ripollesa sheep, with a ram three to six generations from the founders as the most likely source for a new mutation increasing the odds of daughters. The additive genetic effect of this model for the mutant allele also had strong evidence (LR = 1,195). Therefore, the MUTj parametrization can be a valuable analytical tool to check for the possibility of new mutations along the pedigree files, not only before the founders.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

Genetic associations of ewe body condition score and lamb rearing performance in extensively managed meat sheep

Many small ruminant production systems are relatively low input and rely heavily on animals grazing pastures often in extensive and harsh environmental conditions, unsuitable for any other type of agriculture. New breeding goal traits for lifetime resilience for maternal sheep breeding programmes have been investigated, focusing on body tissue mobilisation phenotypes and their genetic relationships with maternal production traits. Performance records of 8,355 Scottish Blackface ewes, from 2 extensively reared hill sheep flocks, collected over a 20-year period, were used to quantify relationships between body condition score (BCS) and the ewe's ability to successfully rear lambs. Between 14,000 and 25,000 data records per trait were available, measured across the annual sheep reproductive cycle. The pedigree file used for the analyses included sire and dam information for 50,207 animals. Most heritability estimates for the BCS traits - measured at key time points within the annual production cycle, or considering BCS changes between time points - were low, but significantly different from zero, ranging between 0.07 and 0.17. The heritability estimate for the number of lambs at pregnancy scanning was 0.09. Heritabilities for foetal loss from pregnancy scanning to lambing, lamb loss from lambing to weaning and number of lambs weaned were 0.02, 0.02 and 0.06, respectively. These estimates indicate that genetic control over the reproductive and lamb survival traits (expressed as a trait of the dam), is lower than that for BCS (and BCS changes) over the reproductive cycle. The genetic relationships amongst the BCS of ewes at pre-mating, pregnancy scan and at pre-lambing, with litter size at pregnancy scan and number of lambs weaned, were consistent. They indicate that ewes of higher body condition score (potentially over-fat, within the range recorded in these flocks) conceive, and rear, fewer lambs, with genetic correlations ranging between -0.18 and -0.58. However, post-weaning, animals with genetic propensity for greater gain in BCS from weaning to pre-mating produce larger litters and rear more lambs. In addition, the selection of ewes that lose less condition during pregnancy will reduce foetal losses. The results from this study paint a complex picture that should be interpreted in the context of specific management practices for extensively managed hill sheep, at critical times in their reproductive cycle. The traits relating to body tissue mobilisation, as assessed using BCS, are mostly heritable and their inclusion in future breeding programmes, to aid future selection for resilience, should be considered.

Imputation accuracy for genomic selection using embryo biopsy samples in Gir

The aim of this study was to establish a platform for genomic selection of in vitro-fertilized (IVF) Gir embryos. Multiple displacement amplification (MDA)-based embryo biopsy samples were genotyped, and genomic estimated breeding values (GEBV) for milk yield (305MY) were calculated. The concordance of GEBV and accuracy between embryo biopsies and the respective liveborn were assessed. Imputation was performed using two panels (Z-Chip and Bovine HD, Illumina) based on a database of 73,110 lactating cow’s database and pedigree files from 147,131 animals. Biopsied embryos had similar pregnancy rates (39% vs 40%), pregnancy loss rates (18% vs 20%), and pregnancy length compared to Control embryos. After genotyping, low call rate means were detected for biopsy samples compared to the respective calf samples (0.80 vs 0.98). Imputation presented 0.83 (Z-Chip) and 0.96 (HD) accuracy (CORRanim). Embryo GEBV accuracy levels were higher in BovineHD imputation (0.82) than Z-Chip imputation (0.55) or no imputation (0.62), and the correlation between embryo/calf pairs’ accuracy was 0.85 for BovineHD imputation, 0.11 for Z-Chip imputation, and 0.02 for no imputation. GEVB estimates correlation between embryo/calf pairs was 0.87 for BovineHD imputation, 0.80 for Z-Chip imputation, and 0.41 before imputation. The call rate of embryo samples did not affect the correlation between embryo/calf pairs for accuracy and GEBV before and after BovineHD imputation. Embryos obtained on the same farm presented GEBV 305MY differences of up to 800 kg, emphasizing the expected impact of embryo genomic selection for the Gir breed.

Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigs.

Historical data collection for genetic evaluation purposes is a common practice in animal populations; however, the larger the dataset, the higher the computing power needed to perform the analyses. Also, fitting the same model to historical and recent data may be inappropriate. Data truncation can reduce the number of equations to solve, consequently decreasing computing costs; however, the large volume of genotypes is responsible for most of the increase in computations. This study aimed to assess the impact of removing genotypes along with phenotypes and pedigree on the computing performance, reliability, and inflation of GEBV from ssGBLUP for selection candidates. Data from two pig lines, a terminal sire (L1) and a maternal line (L2), were analyzed in this study. Four analyses were implemented: growth and "weaning to finish" mortality on L1, pre-weaning and reproductive traits on L2. Four genotype removal scenarios were proposed: removing genotyped animals without phenotypes and progeny (noInfo), removing genotyped animals based on birth year (Age), the combination of noInfo and Age scenarios (noInfo + Age), and no genotype removal (AllGen). In all scenarios, phenotypes were removed, based on birth year, and three pedigree depths were tested: two and three generations traced back and using the entire pedigree. The full dataset contained 1,452,257 phenotypes for growth traits, 324,397 for weaning to finish mortality, 517,446 for pre-weaning traits, and 7,853,629 for reproductive traits in pure and crossbred pigs. Pedigree files for lines L1 and L2 comprised 3,601,369 and 11,240,865 animals, of which 168,734 and 170,121 were genotyped, respectively. In each truncation scenario, the linear regression method was used to assess the reliability and dispersion of GEBV for genotyped parents (born after 2019). The number of years of data that could be removed without harming reliability depended on the number of records, type of analyses (multitrait vs. single trait), the heritability of the trait, and data structure. All scenarios had similar reliabilities, except for noInfo, which performed better in the growth analysis. Based on the data used in this study, considering the last ten years of phenotypes, tracing three generations back in the pedigree, and removing genotyped animals not contributing own or progeny phenotypes, increases computing efficiency with no change in the ability to predict breeding values.

16 Genetic Parameter Estimates for Age at First Calving in Simmental and Red Angus Heifers

Abstract The objective of this study was to determine genetic parameter estimates for age at first calving (AFC) in Red Angus and Simmental heifers. The ability for a heifer to calve earlier has implications on calf performance and the heifer's successive re-breeding. Heifer pregnancy data were obtained from International Genetic Solutions, where females with exposure records were included in the population. Females with a subsequent record of calving between 630 and 810 days of age were included in the analysis. Females with embryo transfer or multiple birth progeny were excluded. Records from single-animal and contemporary groups with no variation were eliminated and after data filtering, there were 149,379 Simmental with a mean Simmental breed proportion of 0.58 ± 0.25, and 83,191 Red Angus records with a mean Red Angus breed proportion of 0.80 ± 0.13. A 3-generation pedigree file containing 291,530 Simmental animals with 25,585 unique sires and 161,921 unique dams, and a 3-generation pedigree file containing 174,914 Red Angus animals with 19,733 unique sires and 99,464 unique dams were used for the separate analysis. The mean AFC in Simmental and Red Angus was 724.8 ± 28.64 d, and 724.2 ± 29.93 d, respectively. Data for each breed were modeled separately using a single trait animal model with random additive genetic and residual effects, and the fixed effect of contemporary group using ASReml 3.0. Contemporary group was defined as breeder, herd, year born, birth group and yearling group. The additive, residual, and phenotypic variances for Simmental were 120.08, 520.37, and 640.45 d2, respectively. For Red Angus, 85.67, 543.56, and 629.24 d2 were the additive, residual, and phenotypic variances, respectively. The heritability estimates of AFC for Simmental and Red Angus were 0.19 ± 0.0059 and 0.14 ± 0.0076, respectively. These results demonstrate AFC in days is lowly to moderately heritable. Lower residual and higher additive variance in Simmental compared to Red Angus may suggest the Simmental population has larger variation for underlying fertility influences such as age at puberty. The mean breeding values for AFC in Simmental was 6.1 ± 8.0, with a minimum of -24.4 and maximum of 54.7. Whereas Red Angus breeding values for AFC resulted in a mean of 4.0 ± 5.6 and a minimum and maximum of -16.1, and 37.3, respectively. The comparable heritability estimates, low standard error, and range in breeding values suggest selection for improved AFC can be attained. It is worth further exploring the impact of breed effects and heterosis influences on age at first calving. While this study does not account for heifers that did not calve in their first year, tandem selection for heifer fertility and age at calving should be heavily emphasized due to the economic importance of both traits.

PSI-3 Heritability Estimates and Correlations Between Dry Matter Intake and Stayability

Abstract The objective of this study was to determine heritability and genetic correlation estimates between dry matter intake (DMI) and Stayability in Angus cattle originating from the John E. Rouse Beef Improvement Center. From the years 2015–2021, 640 steers were fed in confinement using a GrowSafe System® to measure individual DMI. The duration of feeding varied each year; however, mean test length was 49 ± 0.31 days with a 21-day warm-up period to allow for acclimation to the environment and ration. A bivariate animal model was used. Stayability was evaluated using a binary, probit threshold model while DMI was evaluated as a continuous trait. In addition to the 640 steers with intake, a total of 1,673 pedigree-related cows possessed a Stayability observation. Cows were awarded a ‘1’ if they maintained functionality within the herd for six years. Those not achieving the six-year threshold were given a ‘0’. Mean DMI calculated for the steers was 10.11 ± 0.12 kg/day while the mean for stayability was 0.59 ± .15 (59% of females in this study remained in the herd for at least six years). Fixed effects in the model included contemporary group and age. Contemporary groups were defined as year, pen and test duration for DMI. Contemporary groups for Stayability were solely the year of birth for a given cohort. Individual animal was included as a random effect for both Stayability and DMI to account for the direct genetic effects for both traits. A 3-generation pedigree file containing 3,421 angus influenced animals with 785 unique sires and 2,425 unique dams was used to link the steers to the cows in this study. Residual covariance between DMI and Stayability in this study was fixed at 0. The heritability estimate for DMI was 0.26 ± 0.12 making it moderately heritable with a phenotypic variance of 9.29 kg2 ± 0.55. The heritability estimate for Stayability was quite low 0.02 ± 0.39. The genetic correlation between DMI and Stayability determined by this model was 0.8211 ± 1.2. Though the heritability is low for Stayability, these results suggested that Angus cows that exhibit higher feed intake tend to have more longevity within the herd.

Assessment of Genetic Diversity, Runs of Homozygosity, and Signatures of Selection in Tropical Milking Criollo Cattle Using Pedigree and Genomic Data.

The objective of this study was to evaluate the genetic diversity of the Tropical Milking Criollo cattle (TMC) breed in Mexico through parameters derived from pedigree and genomic information assessment. The pedigree file consisted of 3780 animals. Seventy-nine bovines were genotyped with the medium-density single nucleotide polymorphism chip and considered a reference population for pedigree analysis. The effective population size and the probability of gene origin used to assess the evolution of genetic diversity were calculated with pedigree information. Inbreeding coefficients were evaluated based on pedigree (), the genomic relationship matrix (), and runs of homozygosity () of different length classes. The average inbreeding was 2.82 ± 2.66%, −0.7 ± 3.8%, and 10.9 ± 3.0% for , , and , respectively. Correlation between and was significant only for runs of homozygosity > 4 Mb, indicating the of a population with an average equivalent complete generation of five only recovers the most recent inbreeding. The parameters of the probability of gene origin indicated the existence of genetic bottlenecks and the loss of genetic diversity in the history of the TMC cattle population; however, pedigree and genomic information revealed the existence of current sufficient genetic diversity to design a sustainable breeding program.

Variabilidade genética do gado Guzerat criado no norte do Brasil, com base na análise do pedigree

ABSTRACT Genealogical data comprised 45,711 animals born between 1901 and 2016, with 48,127 animals in the pedigree file. Population structure was analyzed in terms of pedigree completeness, individual inbreeding coefficient (F), generation interval (L), rate of inbreeding (ΔF), effective population size (Ne), effective number of founders (ff), and effective number of ancestors (fa). The herd initially consisted of 13 bulls and 14 cows, and there were variations in the number of selected bulls and cows throughout the analyzed period, with 2,575 bulls, 13,691 cows, and 45,711 births recorded at the end of 2016. In total, 48.81% of the cows had only one progeny. Most dams (47.59%) were between three and seven years old, with a mean L in the population of 7.9 years. According to the results, 52.75% of the cows, 44.92% of the bulls, and 63.71% of the calves of the Guzerat breed in the northern region of Brazil showed some degree of inbreeding, with small-magnitude coefficients (0.56, 0.83, and 0.71% for cows, bulls, and calves, respectively). This fluctuation did not hinder the genetic evolution of the herd in the region. The effective population size does not seem to compromise the maintenance of genetic variability in the breed.