Signatures of selection in Angus and Hanwoo beef cattle using imputed whole genome sequence data.

In this study, we detected signatures of selection in Hanwoo and Angus beef cattle using allele frequency and haplotype-based methods based on imputed whole genome sequence variants. Our dataset included 13,202 Angus animals with 10,057,633 imputed SNPs and 10,437 Hanwoo animals with 13,241,550 imputed SNPs. The dataset was subset down to 6,873,624 SNPs in common between the two populations to identify within population (runs of homozygosity, extended haplotype homozygosity) and between population signals of selection (allele fixation index, extended haplotype homozygosity). Assuming these selection signals were complementary to each other, they were combined into a decorrelated composite of multiple signals to identify regions under selection for each of the breeds. 27 genomic regions spanning 25.15 Mb and harboring 360 genes were identified in Angus on chromosomes 1,3, 4, 5, 6, 7, 8, 12, 13, 14, 16, 20, 21 and 28. Similarly, in Hanwoo, 59 genes and 17 genomic regions spanning 5.21 Mb on chromosomes 2, 4, 5, 6, 7, 8, 9, 10, 13, 17, 20 and 24 were identified. Apart from a small region on chromosome 13, there was no major overlap of selection signals between the two breeds reflecting their largely different selection histories, environmental challenges, breeding objectives and breed characteristics. Positional candidate genes identified in selected genomic regions in Angus have been previously associated with growth, immunity, reproductive development, feed efficiency and adaptation to environment while the candidate genes identified in Hanwoo included important genes regulating meat quality, fat deposition, cholesterol metabolism, lipid synthesis, neuronal development, and olfactory reception.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches.

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Evaluation of Whole-Genome Sequence Imputation Strategies in Korean Hanwoo Cattle.

This study evaluated the accuracy of sequence imputation in Hanwoo beef cattle using different reference panels: a large multi-breed reference with no Hanwoo (n = 6269), a much smaller Hanwoo purebred reference (n = 88), and both datasets combined (n = 6357). The target animals were 136 cattle both sequenced and genotyped with the Illumina BovineSNP50 v2 (50K). The average imputation accuracy measured by the Pearson correlation (R) was 0.695 with the multi-breed reference, 0.876 with the purebred Hanwoo, and 0.887 with the combined data; the average concordance rates (CR) were 88.16%, 94.49%, and 94.84%, respectively. The accuracy gains from adding a large multi-breed reference of 6269 samples to only 88 Hanwoo was marginal; however, the concordance rate for the heterozygotes decreased from 85% to 82%, and the concordance rate for fixed SNPs in Hanwoo also decreased from 99.98% to 98.73%. Although the multi-breed panel was large, it was not sufficiently representative of the breed for accurate imputation without the Hanwoo animals. Additionally, we evaluated the value of high-density 700K genotypes (n = 991) as an intermediary step in the imputation process. The imputation accuracy differences were negligible between a single-step imputation strategy from 50K directly to sequence and a two-step imputation approach (50K-700K-sequence). We also observed that imputed sequence data can be used as a reference panel for imputation (mean R = 0.9650, mean CR = 98.35%). Finally, we identified 31 poorly imputed genomic regions in the Hanwoo genome and demonstrated that imputation accuracies were particularly lower at the chromosomal ends.

Cluster analysis to explore additive-genetic patterns for the identification of sheep resistant, resilient and susceptible to gastrointestinal nematodes.

Infection caused by gastrointestinal nematodes is an important issue for animal health and production. Controlling worm infections improves the sustainability of the sheep industry. Genetic selection of animals that are resistant to gastrointestinal nematodes is another strategy to render sheep production more sustainable by decreasing the use of anthelmintics. The aims of this study were (1) to explore the additive-genetic pattern of EBVs for Famacha© (FAM), packed-cell volume (PVC), and fecal egg counts (FEC) of Santa Ines sheep, (2) to propose a classification of animals that are resistant, resilient and susceptible to gastrointestinal nematodes based on their additive-genetic patterns, and (3) to identify the most suitable animals for selection based on their genetic pattern. A dataset of 2,241 records from 747 animals was used to predict the breeding values for indicator traits of resistance to gastrointestinal nematodes with THRGIBBS1F90 and to carry out cluster analyses was used R software. Three clusters of animals were found in the population using hierarchical cluster analysis of the breeding values for FAM, PCV and FEC. Each cluster was characterized by different additive-genetic patterns identified by k-means non-hierarchical cluster analysis. Among a total of 747 animals, 196 were classified as resistant, 288 as resilient, and 263 as susceptible. Cluster analysis is a valuable tool for data screening that permits to evaluate only selection candidates based on their additive-genetic pattern for gastrointestinal nematode resistance. EBVs for FEC were decisive to divide the population into resilient, resistant and susceptible animals. It is also important to include the EBVs for PCV and FAM to adequately distinguish resistant from resilient animals. Finally, the resistant cluster consisted of the most desirable animals to be used as selection candidates in order to genetically improve resistance to infection with gastrointestinal nematodes. This cluster contained animals with the most appropriate additive-genetic pattern to achieve the breeding goal, with positive breeding values for PCV and negative breeding values for FAM and FEC.

Feed efficiency and maternal productivity of Bos indicus beef cows.

This study evaluated 53 primiparous cows (36.8±1.23 months old and 484±40.9 kg of body weight) performance tested (GrowSafe® System) from 22±5 to 190±13 days of lactation in order to obtain daily dry matter intake (DMI). The animals received a high-forage diet (forage-to-concentrate ratio of 90:10). Milk production of the cows was evaluated three times by mechanical milking and the energy-corrected milk yield (ECMY) was calculated. Energy status (through the indicators glucose, cholesterol, triglycerides, and ß-hydroxybutyrate), protein status (indicators albumin, urea, and creatinine), mineral status (indicators calcium, phosphorus, and magnesium), and hormonal status (indicators insulin and cortisol) were estimated four times throughout lactation. The residual feed intake (RFI) of cows was calculated considering DMI, average daily gain (ADG) and mid-test metabolic weight (BW0.75) obtained in early lactation (from 22±5 to 102±7 days), and the animals were classified as negative (most efficient) or positive RFI (least efficient). The RFI model explained 53% of the variation in DMI. The mean DMI, ADG, ECMY, and calf weight as a percentage of cow weight were 12.47±2.70 kg DM/day, 0.632±0.323 kg/day, 10.47±3.23 kg/day, and 36.6±5.39%, respectively. Negative RFI cows consumed 11.5% less DM than positive RFI cows, with performance and metabolic profile being similar to those of positive RFI cows, except for a lower milk protein content and higher blood cholesterol concentration. In conclusion, negative (most efficient) and positive RFI (least efficient) Nellore cows, fed an ad libitum high-forage diet, produced similar amounts of milk, fat and lactose and had similar subcutaneous fat thickness, weight, calf weight as a percentage of cow weight, and blood metabolite concentrations (except for cholesterol). Therefore, there are economic benefits to utilizing RFI in a cow herd since cattle had decreased DMI with similar overall performance, making them more profitable due to lower input costs.

Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle.

This study compared imputation from lower-density commercial and customized panels to high-density panels and a combined panel (Illumina and Affymetrix) in Nelore beef cattle. Additionally, linkage disequilibrium and haplotype block conformation were estimated in individual high-density panels and compared with corresponding values in the combined panel after imputation. Overall, 814 animals were genotyped using BovineHD BeadChip (IllumHD), and 93 of these animals were also genotyped using the Axion Genome-Wide BOS 1 Array Plate (AffyHD). In general, customization considering linkage disequilibrium and minor allele frequency had the highest accuracies. The IllumHD panel had higher values of linkage disequilibrium for short distances between SNPs than AffyHD and the combined panel. The combined panel had an increased number of small haplotype blocks. The use of a combined panel is recommended due to its increased density and number of haplotype blocks, which in turn increase the probability of a marker being close to a quantitative trait locus of interest. Considering common SNPs between IllumHD and AffyHD for the customization of a low-density panel increases the imputation accuracy for IllumHD, AffyHD and the combined panel.

Genomic structure of a crossbred Landrace pig population.

Single nucleotide polymorphism (SNP) markers are used to study population structure and conservation genetics, which permits assessing similarities regarding the linkage disequilibrium and information about the relationship among individuals. To investigate the population genomic structure of 300 females and 25 males from a commercial maternal pig line we analyzed linkage disequilibrium extent, inbreeding coefficients using genomic and conventional pedigree data, and population stratification. The average linkage disequilibrium (r2) was 0.291 ± 0.312 for all adjacent SNPs, distancing less than 100 Kb (kilobase) between markers. The average inbreeding coefficients obtained from runs of homozygosity (ROH) and pedigree analyses were 0.119 and 0.0001, respectively. Low correlation was observed between the inbreeding coefficients possibly as a result of genetic recombination effect accounted for the ROH estimates or caused by pedigree identification errors. A large number of long ROHs might indicate recent inbreeding events in the studied population. A total of 36 homozygous segments were found in more than 30% of the population and these ROH harbor genes associated with reproductive traits. The population stratification analysis indicated that this population was possibly originated from two distinct populations, which is a result from crossings between the eastern and western breeds used in the formation of the line. Our findings provide support to understand the genetic structure of swine populations and may assist breeding companies to avoid a high level of inbreeding coefficients to maintain genetic diversity, showing the effectiveness of using genome-wide SNP information for quantifying inbreeding when the pedigree was incomplete or incorrect.

Cluster analysis of breeding values for milk yield and lactation persistency in Guzerá cattle / Análises de agrupamento dos valores genéticos para produção de leite e persistência da lactação em bovinos da raça Guzerá

ABSTRACT: The aim of this study was to explore the pattern of genetic lactation curves of Guzerá cattle using cluster analysis. Test-day milk yields of 5,274 first-lactation Guzerá cows were recorded in a progeny test. A total of 34,193 monthly records were analyzed with a random regression animal model using Legendre polynomials to fit additive genetic and permanent environmental random effects and mean trends. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for monthly test-day milk yield, peak yield, lactation persistency, and partial cumulative and 305-day yields. The heritability estimates for test-day milk yields ranged from 0.24 to 0.52. Cluster analysis identified animals in the population that belong to different groups according to milk production level and lactation persistency.

RESUMO: Objetivou-se neste estudo explorar o padrão das curvas de lactação genéticas de bovinos da raça Guzerá, empregando análises de agrupamento. Os 34.193 registros mensais de produção de leite foram provenientes de 5.274 vacas da raça Guzerá, participantes do teste de progênie. As análises foram realizadas com um modelo de regressão aleatória com polinômios de Legendre, composto pelos efeitos aleatórios genético aditivo, de ambiente permanente e o residual, e a curva média de lactação da população. Análise de agrupamento hierárquico e não hierárquico foram realizados com base nos VG para a produção acumulada até os 305 dias, pico e persistência da lactação, e períodos parciais da lactação. As estimativas de herdabilidade para produção de leite no dia do controle variaram entre 0,24 a 0,52. A análise de agrupamento identificou os animais da população que pertencem a diferentes grupos de acordo com o nível de produção de leite e persistência da lactação.

Association of Apolipoprotein B and Adiponectin Receptor 1 Genes with Carcass, Bone Integrity and Performance Traits in a Paternal Broiler Line.

Apolipoprotein B (APOB) and Adiponectin Receptor 1 (ADIPOR1) are related to the regulation of feed intake, fat metabolism and protein deposition and are candidate genes for genomic studies in birds. In this study, associations of two single nucleotide polymorphisms (SNPs) g.102A>T (APOB) and g.729C>T (ADIPOR1) with carcass, bone integrity and performance traits in broilers were investigated. Genotyping was performed on a paternal line of 1,454 broilers. The SNP detection was carried out by PCR-RFLP technique using the restriction enzymes HhaI for the SNP g.729C>T and MslI for the SNP g.102A>T. The association analyses of the two SNPs with 85 traits were performed using the restricted maximum likelihood (REML) and Generalized Quasi-Likelihood Score (GQLS) methods. For REML the model included the random additive genetic effect of animal and fixed effects of sex, hatch and SNP genotypes. In the GQLS method, a logistic regression was used to associate the genotypes with phenotypes adjusted for fixed effects of sex and hatch. The SNP g.729C>T in the ADIPOR1 gene was associated with thickness of the femur and breast skin yield. Thus, the ADIPOR1 gene seems implicated in the metabolism and/or fat deposition and bone integrity in broilers.

Genetic parameters for body weight, carcass chemical composition and yield in a broiler-layer cross developed for QTL mapping.

The objective of this study was to estimate genetic and phenotypic correlations of body weight at 6 weeks of age (BW6), as well as final carcass yield, and moisture, protein, fat and ash contents, using data from 3,422 F2 chickens originated from reciprocal cross between a broiler and a layer line. Variance components were estimated by the REML method, using animal models for evaluating random additive genetic and fixed contemporary group (sex, hatch and genetic group) effects. The heritability estimates (h(2)) for BW6, carcass yield and percentage of carcass moisture were 0.31 ± 0.07, 0.20 ± 0.05 and 0.33 ± 0.07, respectively. The h(2) for the percentages of protein, fat and ash on a dry matter basis were 0.48 ± 0.09, 0.55 ± 0.10 and 0.36 ± 0.08, respectively. BW6 had a positive genetic correlation with fat percentage in the carcass, but a negative one with protein and ash contents. Carcass yield, thus, appears to have only low genetic association with carcass composition traits. The genetic correlations observed between traits, measured on a dry matter basis, indicated that selection for carcass protein content may favor higher ash content and a lower percentage of carcass fat.

Genetic parameters for body weight, carcass chemical composition and yield in a broiler-layer cross developed for QTL mapping

The objective of this study was to estimate genetic and phenotypic correlations of body weight at 6 weeks of age (BW6), as well as final carcass yield, and moisture, protein, fat and ash contents, using data from 3,422 F2 chickens originated from reciprocal cross between a broiler and a layer line. Variance components were estimated by the REML method, using animal models for evaluating random additive genetic and fixed contemporary group (sex, hatch and genetic group) effects. The heritability estimates (h²) for BW6, carcass yield and percentage of carcass moisture were 0.31 ± 0.07, 0.20 ± 0.05 and 0.33 ± 0.07, respectively. The h² for the percentages of protein, fat and ash on a dry matter basis were 0.48 ± 0.09, 0.55 ± 0.10 and 0.36 ± 0.08, respectively. BW6 had a positive genetic correlation with fat percentage in the carcass, but a negative one with protein and ash contents. Carcass yield, thus, appears to have only low genetic association with carcass composition traits. The genetic correlations observed between traits, measured on a dry matter basis, indicated that selection for carcass protein content may favor higher ash content and a lower percentage of carcass fat.