Deregressed Estimated Breeding Values Research Articles

Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle.

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

Genome-wide association studies for economically important traits in mink using copy number variation

Copy number variations (CNVs) are structural variants consisting of duplications and deletions of DNA segments, which are known to play important roles in the genetics of complex traits in livestock species. However, CNV-based genome-wide association studies (GWAS) have remained unexplored in American mink. Therefore, the purpose of the current study was to investigate the association between CNVs and complex traits in American mink. A CNV-based GWAS was performed with the ParseCNV2 software program using deregressed estimated breeding values of 27 traits as pseudophenotypes, categorized into traits of growth and feed efficiency, reproduction, pelt quality, and Aleutian disease tests. The study identified a total of 10,137 CNVs (6968 duplications and 3169 deletions) using the Affymetrix Mink 70K single nucleotide polymorphism (SNP) array in 2986 American mink. The association analyses identified 250 CNV regions (CNVRs) associated with at least one of the studied traits. These CNVRs overlapped with a total of 320 potential candidate genes, and among them, several genes have been known to be related to the traits such as ARID1B, APPL1, TOX, and GPC5 (growth and feed efficiency traits); GRM1, RNASE10, WNT3, WNT3A, and WNT9B (reproduction traits); MYO10, and LIMS1 (pelt quality traits); and IFNGR2, APEX1, UBE3A, and STX11 (Aleutian disease tests). Overall, the results of the study provide potential candidate genes that may regulate economically important traits and therefore may be used as genetic markers in mink genomic breeding programs.

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

Genome-wide association and functional genomic analyses for various hoof health traits in North American Holstein cattle

Hoof diseases are a major welfare and economic issue in the global dairy cattle production industry, which can be minimized through improved management and breeding practices. Optimal genetic improvement of hoof health could benefit from a deep understanding of the genetic background and biological underpinning of indicators of hoof health. Therefore, the primary objectives of this study were to perform genome-wide association studies, using imputed high-density genetic markers data from North American Holstein cattle, for 8 hoof-related traits: digital dermatitis, sole ulcer, sole hemorrhage, white line lesion, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, and toe ulcer, and a hoof health index. De-regressed estimated breeding values from 25,580 Holstein animals were used as pseudo-phenotypes for the association analyses. The genomic quality control, genotype phasing, and genotype imputation were performed using the PLINK (version 1.9), Eagle (version 2.4.1), and Minimac4 software, respectively. The functional genomic analyses were performed using the GALLO R package and the DAVID platform. We identified 22, 34, 14, 22, 28, 33, 24, 43, and 15 significant markers for digital dermatitis, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, white line lesion disease, and the hoof health index, respectively. The significant markers were located across all autosomes, except BTA10, BTA12, BTA20, BTA26, BTA27, and BTA28. Moreover, the genomic regions identified overlap with various previously reported quantitative trait loci for exterior, health, meat and carcass, milk, production, and reproduction traits. The enrichment analyses identified 44 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously associated with bone development, metabolism, and infectious and immunological diseases. These findings indicate that hoof health traits are highly polygenic and influenced by a wide range of biological processes.

PSVII-8 Genome-Wide Association Studies for Feed Efficiency and Growth Traits in American Mink

Abstract Feed efficiency (FE) traits contribute to the economic output of mink production systems as feed costs comprise the largest proportion of their variable expenses. However, the genetic architecture underlying FE-related traits is largely unknown in American mink. The objective of this study was to identify potential genetic variants and candidate genes associated with feed efficiency and growth traits, including body weight (BW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR). Genome-wide association studies (GWAS) were performed using deregressed estimated breeding values (DEBVs) of 1,255 to 2,160 individuals (as pseudophenotypes), genotyped with the Affymetrix Mink 70K single nucleotide polymorphisms (SNP) array. Association analyses were performed using the mixed linear model in GCTA software. A total of 36 SNPs located on 11 different chromosomes were significantly (FDR < 0.01) associated with eight feed efficiency and growth traits, among which nine SNPs had pleiotropic effects on at least two analyzed traits. The phenotypic variance (of DEBVs) explained by all significant SNPs for BW, ADG, DFI, FCR, RFI, RG, RIG, and KR, were 0.54%, 2.35%, 0.51%, 2.45%, 0.03%, 3.67%, 0.21%, and 2.60%, respectively. Furthermore, 191 genes were identified within 1-Mb windows around these significant SNPs. These regions included candidate genes such as FABP6, ADAMTS18, ADGRB3, GRM8, DSCAM, COL9A1, and CSRP2, previously associated with feed efficiency and growth traits in other livestock species. Gene ontology analyses revealed that these genes were involved in molecular functions such as ATPase activity and ATP binding, which provide the direct energy source for the body. To our knowledge, this is the first GWAS to identify genetic variants and biological mechanisms associated with FE and growth traits in American mink. These findings provide a biological foundation for improving these traits using genomic selection programs to select more efficient mink.

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.

Genome-wide association study identifies functional genomic variants associated with young stock survival in Nordic Red Dairy Cattle

Identifying quantitative trait loci (QTL) associated with calf survival is essential for both reducing economic loss in cattle industry and understanding the genetic basis of the trait. To identify mutations and genes underlying young stock survival (YSS), we performed GWAS using de-regressed estimated breeding values of a YSS index and its component traits defined by sex and age in 3,077 Nordic Red Dairy Cattle (RDC) bulls and 2 stillbirth traits (first lactation and later lactations) in 5,141 RDC bulls. Two associated QTL regions on Bos taurus autosome (BTA) 4 and 6 were identified for the YSS index. The results of 4 YSS component traits indicate that same QTL regions were associated with bull and heifer calf mortality, but the effects were different over the growing period and suggested an additional QTL on BTA23. The GWAS on stillbirth identified 3 additional QTL regions on BTA5, 14, and 24 compared with YSS and its component traits. The conditional test of BTA6 showed at least 2 closely located QTL segregating for YSS component traits and stillbirth. We found 2 independent QTL for stillbirth on BTA23. The post-GWAS revealed LCORL, PPM1K, SSP1, MED28, and LAP3 are putative causal genes on BTA6, and a frame shift variant within LCORL, BTA6:37401770 (rs384548488) could be the putative causal variant. On BTA4, the GRB10 gene is the putative causal gene and BTA4:5296018 is the putative causal variant. In addition, NDUFA9 and FGF23 on BTA5, LYN on BTA14, and KCNK5 on BTA23 are putative causal genes for QTL for stillbirth. The gene analysis also proposed several candidate genes. Our findings shed new light on the candidate genes affecting calf survival, and the knowledge could be utilized to reduce calf mortality and thereby enhance welfare of dairy cattle.

Genome-wide association study of resistance/susceptibility to infectious bovine keratoconjunctivitis in Brazilian Hereford cattle.

Genome-wide association studies were conducted to identify the more informative genomic regions and SNPs, as well as to identify candidate genes associated with infectious bovine keratoconjunctivitis (IBK) resistance/susceptibility in Hereford cattle. A Bayes B statistical approach was initially applied in genome-wide association studies by using deregressed estimated breeding values for IBK resistance/susceptibility. To estimate the combined effect of a genomic region that is potentially associated with QTL, 2504 non-overlapping 1-Mb windows that varied in SNP number were defined, with the most informative 24 windows including 427 SNPs and explaining more than 20% of the estimated genetic variance for IBK resistance/susceptibility. These regions were explored with respect to their biological functions through functional analysis to map potential candidate genes. The significant SNPs were mapped on chromosomes 1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23, and 28, and candidate genes were detected as related to the IBK. Most informative SNPs in term of genetic variance were located in proximity of genes related to phenotypic expression of lesions and biological processes associated to the IBK. Knowledge about phenotypic and genomic variation generated in the present study can be used to on design selection strategies to improve the resistance to IBK of Hereford cattle herds.

Comparison of methods for predicting genomic breeding values for growth traits in Nellore cattle

The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the Conexão DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects: genomic best linear unbiased prediction (GBLUP), BayesCπ, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesCπ and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.

Identifying the complex genetic architecture of growth and fatness traits in a Duroc pig population

In modern pig breeding programs, growth and fatness are vital economic traits that significantly influence porcine production. To identify underlying variants and candidate genes associated with growth and fatness traits, a total of 1 067 genotyped Duroc pigs with de-regressed estimated breeding values (DEBV) records were analyzed in a genome wide association study (GWAS) by using a single marker regression model. In total, 28 potential single nucleotide polymorphisms (SNPs) were associated with these traits of interest. Moreover, VPS4B, PHLPP1, and some other genes were highlighted as functionally plausible candidate genes that compose the underlying genetic architecture of porcine growth and fatness traits. Our findings contribute to a better understanding of the genetic architectures underlying swine growth and fatness traits that can be potentially used in pig breeding programs.

Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle

Genome-wide association studies based on SNP have been completed for multiple traits in dairy cattle; however, copy number variants (CNV) could add genomic information that has yet to be harnessed. The objectives of this study were to identify CNV in genotyped Holstein animals and assess their association with hoof health traits using deregressed estimated breeding values as pseudophenotypes. A total of 23,256 CNV comprising 1,645 genomic regions were identified in 5,845 animals. Fourteen genomic regions harboring structural variations, including 9 deletions and 5 duplications, were associated with at least 1 of the studied hoof health traits. This group of traits included digital dermatitis, interdigital dermatitis, heel horn erosion, sole ulcer, white line lesion, sole hemorrhage, and interdigital hyperplasia; no regions were associated with toe ulcer. Twenty candidate genes overlapped with the regions associated with these traits including SCART1, NRXN2, KIF26A, GPHN, and OR7A17. In this study, an effect on infectious hoof lesions could be attributed to the PRAME (Preferentially Expressed Antigen in Melanoma) gene. Almost all genes detected in association with noninfectious hoof lesions could be linked to known metabolic disorders. The knowledge obtained considering information of associated CNV to the traits of interest in this study could improve the accuracy of estimated breeding values. This may further increase the genetic gain for these traits in the Canadian Holstein population, thus reducing the involuntary animal losses due to lameness.

Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs.

Simple SummaryThe individual birth weight (IBW) of pigs is an important trait regarding its relevance to mortality at weaning, sow prolificacy, and growth performance. This study investigates the variance component estimation, informative window regions, and the efficiency of genomic predictions associated with IBW traits in Yorkshire pigs. The low heritability (0.13) is estimated on the basis of a full model including individual genetic, sow genetic, and common environmental effects. Two common window regions of the genome are identified under three different genotyping platforms found within the ARAP2 and TSN genes concerning the IBW trait. The genomic prediction ability is improved using deregressed estimated breeding values including parental information as a response variable despite Bayesian methods and genotyping platforms for the IBW trait in Korean Yorkshire pigs.This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.

Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs.

Simple SummaryThis study investigated the informative regions and the efficiency of genomic predictions for backfat thickness, days to 90 kg body weight, loin muscle area, and lean percentage in Korean Duroc pigs. The several regions of the genome were identified and a significant marker was found near the MC4R gene for growth and production-related traits. No differences in genomic accuracy were identified on the basis of the Bayesian approaches in these four growth and production-related traits. The genomic accuracy is improved by using deregressed estimated breeding values including parental information as a response variable in Korean Duroc pigs.Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

63 A comparison of clustering methods for cross-validation of genomic predictors when training on phenotypes or deregressed Estimated Breeding Values

Abstract The objective of the study was to evaluate the impact of clustering methods for cross-validation on the accuracy of prediction of molecular breeding values (MBV) in Red Angus cattle (n = 9,763) and in simulation. Individuals were clustered using seven methods [k-means, k-medoids, principal component analysis on the numerator relationship matrix (A) and identical-by-state genomic matrix (G) as data and covariance matrices, and random] and two response variables [deregressed Estimated Breeding Values (DEBV) and adjusted phenotypes]. Genotypes were imputed to a 50K reference panel. Using cross-validation and a Bayes C model, MBV were estimated for traits including birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT) for DEBV and BWT, YWT, and ultrasonically measured intramuscular fat percentage and rib eye area for adjusted phenotypes. A bivariate animal model was used to estimate prediction accuracies calculated using the genetic correlation between estimated MBV and the associated response variable. To quantify the difference between true and estimated accuracies, a simulation mimicking a cattle population was replicated five times. The same clustering methods were used as with the Red Angus data with the addition of forward validation and two genotyping methods (random selection and selection of the top 25% of animals). Predicted accuracies were estimated similarly and true accuracies were estimated using the residual correlation of a bivariate model using MBV and true breeding values (TBV). The Rand index was used to quantify the similarity between clustering methods, showing relationship-based clusters were clearly different from random clusters. In simulation, random genotyping led to higher estimated accuracies than selection of top individuals; however, estimated accuracies over predicted true accuracies with random genotyping but under predicted true accuracies with the selection of top individuals. When forward validation was evaluated within simulation, results suggested DEBV led to less biased estimates of MBV accuracy.

Candidate genes associated with the heritable humoral response to Mycobacterium avium ssp. paratuberculosis in dairy cows have factors in common with gastrointestinal diseases in humans

Infection of cattle with bovine paratuberculosis (i.e., Johne's disease) is caused by Mycobacterium avium ssp. paratuberculosis (MAP) and results in a chronic incurable gastroenteritis. This disease, which has economic ramifications for the cattle industry, is increasing in detected prevalence globally; subclinically infected animals can silently shed the bacterium into the environment for years, exposing contemporaries and hampering disease-control programs. The objective of the present study was to first quantify the genetic parameters for humoral response to MAP in dairy cattle. This was followed by a genome-based association analysis and subsequent downstream bioinformatic analyses from imputed whole genome sequence SNP data. After edits, ELISA test records were available on 136,767 cows; analyses were also undertaken on a subset of 33,818 of these animals from herds with at least 5 MAP ELISA-positive cows, with at least 1 of those positive cows being homebred. Variance components were estimated using univariate animal and sire linear mixed models. The heritability calculated from the animal model for humoral response to MAP using alternative phenotype definitions varied from 0.02 (standard error = 0.003) to 0.05 (standard error = 0.008). The genome-based associations were undertaken within a mixed model framework using weighted deregressed estimated breeding values as a dependent variable on 1,883 phenotyped animals that were ≥87.5% Holstein-Friesian. Putative susceptibility quantitative trait loci (QTL) were identified on Bos taurus autosome 1, 3, 5, 6, 8, 9, 10, 11, 13, 14, 18, 21, 23, 25, 26, 27, and 29; mapping the most significant SNP to genes within and overlapping these QTL revealed that the most significant associations were with the 10 functional candidate genes KALRN, ZBTB20, LPP, SLA2, FI3A1, LRCH3, DNAJC6, ZDHHC14, SNX1, and HAS2. Pathway analysis failed to reveal significantly enriched biological pathways, when both bovine-specific pathway data and human ortholog data were taken into account. The existence of genetic variation for MAP susceptibility in a large data set of dairy cows signifies the potential of breeding programs for reducing MAP susceptibility. Furthermore, the identification of susceptible QTL facilitates greater biological understanding of bovine paratuberculosis and potential therapeutic targets for future investigation. The novel molecular similarities identified between bovine paratuberculosis and human inflammatory bowel disease suggest potential for human therapeutic interventions to be translated to veterinary medicine and vice versa.

The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions.

For genomic predictors to be of use in genetic evaluation, their predicted accuracy must be a reliable indicator of their utility, and thus unbiased. The objective of this paper was to evaluate the accuracy of prediction of genomic breeding values (GBV) using different clustering strategies and response variables. Red Angus genotypes (n = 9,763) were imputed to a reference 50K panel. The influence of clustering method [k-means, k-medoids, principal component (PC) analysis on the numerator relationship matrix (A) and the identical-by-state genomic relationship matrix (G) as both data and covariance matrices, and random] and response variables [deregressed estimated breeding values (DEBV) and adjusted phenotypes] were evaluated for cross-validation. The GBV were estimated using a Bayes C model for all traits. Traits for DEBV included birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT). Adjusted phenotypes included BWT, YWT, and ultrasonically measured intramuscular fat percentage and REA. Prediction accuracies were estimated using the genetic correlation between GBV and associated response variable using a bivariate animal model. A simulation mimicking a cattle population, replicated 5 times, was conducted to quantify differences between true and estimated accuracies. The simulation used the same clustering methods and response variables, with the addition of 2 genotyping strategies (random and top 25% of individuals), and forward validation. The prediction accuracies were estimated similarly, and true accuracies were estimated as the correlation between the residuals of a bivariate model including true breeding value (TBV) and GBV. Using the adjusted Rand index, random clusters were clearly different from relationship-based clustering methods. In both real and simulated data, random clustering consistently led to the largest estimates of accuracy, while no method was consistently associated with more or less bias than other methods. In simulation, random genotyping led to higher estimated accuracies than selection of the top 25% of individuals. Interestingly, random genotyping seemed to overpredict true accuracy while selective genotyping tended to underpredict accuracy. When forward in time validation was used, DEBV led to less biased estimates of GBV accuracy. Results suggest the highest, least biased GBV accuracies are associated with random genotyping and DEBV.

Genetic and genomic analyses of testicular hypoplasia in Nellore cattle.

Reproductive performance is a key indicator of the long-term sustainability of any livestock production system. Testicular hypoplasia (TH) is a morphological and functional reproductive disorder that affects bulls around the world and consequently causes major economic losses due to reduced fertility rates. Despite the improvements in management practices to enhance performance of affected animals, the use of hypoplastic animals for reproduction might contribute to expand the prevalence of this disorder. The aim of this study was to identify genomic regions that are associated with TH in Nellore cattle by performing a genome-wide association study (GWAS) and functional analyses. Phenotypic and pedigree data from 47,563 animals and genotypes (500,689 Single Nucleotide Polymorphism, SNPs) from 265 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. The estimated breeding values (EBVs) were calculated by fitting a single-trait threshold animal model using a Bayesian approach. The SNP effects were estimated using the Bayes C method and de-regressed EBVs for TH as the response variable (pseudo-phenotype). The top-15 ranking windows (5-adjacent SNPs) that explained the highest proportion of variance were identified for further functional and biological network analyses. The posterior mean (95% highest posterior density) of the heritability for TH was 0.16 (0.08; 0.23). The most important genomic windows were located on BTA1, BTA3, BTA4, BTA5, BTA9, BTA22, BTA23, and BTA25. These windows explained together 22.69% of the total additive genetic variance for TH. Strong candidate genes associated with metabolism and synthesis of steroids, cell survival, spermatogenesis process and sperm motility were identified, which might play an important role in the expression of TH. Our findings contribute to a better biological understanding of TH and future characterization of causal variants might enable improved genomic prediction of this trait in beef cattle.

Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle.