Journal of Animal Breeding and Genetics最新文献

Aneuploidy is a genetic condition characterized by the loss or gain of one or more chromosomes. Aneuploidy affecting the sex chromosomes can lead to infertility in otherwise externally phenotypically normal cattle. Early identification of cattle with sex chromosomal aneuploidy is important to minimize the costs associated with rearing infertile cattle and futile breeding attempts. As most livestock breeding programs routinely genotype their breeding populations using single nucleotide polymorphism (SNP) arrays, this study aimed to assess the feasibility of integrating an aneuploidy screening tool into the existing pipelines that handle dense SNP genotype data. A further objective was to estimate the prevalence of sex chromosome aneuploidy in a population of 146,431 juvenile cattle using available genotype intensity data. Three genotype intensity statistics were used: the LogR Ratio (LRR), R-value (the sum of X and Y SNP probe intensities), and B-allele frequency (BAF) measurements. Within the female-verified population of 124,958 individuals, the estimated prevalence rate was 0.0048% for XO, 0.0350% for XXX, and 0.0004% for XXY. The prevalence of XXY in the male-verified population was 0.0870% (i.e., 18 out of 20,670 males). Cytogenetic testing was used to verify 2 of the XXX females who were still alive. The proposed approach can be readily integrated into existing genomic pipelines, serving as an efficient, large-scale screening tool for aneuploidy. Its implementation could enable the early identification of infertile animals with sex-chromosome aneuploidy.

Improving the nutrient efficiency in pork production is required to reduce the resource competition between human food and animal feed regarding diet components edible for humans and to minimize emissions relevant to climate or the environment. Thereby, protein utilization efficiency and its equivalent nitrogen utilization efficiency (NUE) play a major role. Breeding for more nitrogen (N) efficient pigs bears a promising strategy to improve such traits, however, directly phenotyping NUE based on N balance data is neither cost-efficient nor straightforward and not applicable for routine evaluations. Blood urea nitrogen (BUN) levels in the pig are suitable to predict the NUE and, therefore, might be an indicator trait for NUE because BUN is a relatively easy-to-measure trait. This study investigated the suitability of NUE as a selection trait in future breeding programs. The relationships to classical growth performance and feed efficiency traits were analysed as well as the relationship to BUN to infer the role of BUN as an indicator trait to improve NUE via breeding. The analyzes were based on a Landrace F1 cross population consisting of 502 individuals who descended from 20 Piétrain sires. All animals were genotyped for 48,525 SNPs. They were phenotyped in two different fattening phases, i.e., FP1 and FP2, during the experiment. Uni- and bivariate analyses were run to estimate variance components and to determine the genetic correlation between different traits or between the same trait measured at different time points. Moderate heritabilities were estimated for all traits, whereby the heritability for NUE was h² = 0.293 in FP1 and h² = 0.163 in FP2 and BUN had the by far highest heritability (h² = 0.415 in FP1 and h² = 0.460 in FP2). The significant genetic correlation between NUE and BUN showed the potential of BUN to be considered an indicator trait for NUE. This was particularly pronounced when NUE was measured in FP1 (genetic correlations $��r_g�=�-�0.631�$ and $��r_g�=�-�0.688�$ between NUE and BUN measured in FP1 and FP2, respectively). The genetic correlations of NUE and BUN with important production traits suggest selecting pigs with high growth rates and low BUN levels to breed more efficient

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI–REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI–REML without genomics, AI–REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI–REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used.

Maintaining genetic diversity and variation in livestock populations is critical for natural and artificial selection promoting genetic improvement while avoiding problems due to inbreeding. In Laos, there are concerns that there has been a decline in genetic diversity and a rise in inbreeding among native goats in their village-based smallholder system. In this study, we investigated the genetic diversity of Lao native goats in Phin, Songkhone and Sepon districts in Central Laos for the first time using Illumina's Goat SNP50 BeadChip. We also explored the genetic relationships between Lao goats with 163 global goat populations from 36 countries. Our results revealled a close genetic relationship between Lao native goats and Chinese, Mongolian and Pakistani goats, sharing ancestries with Guangfen, Jining Grey and Luoping Yellow breeds (China) and Teddi goats (Pakistan). The observed (Ho) and expected (He) heterozygosity were 0.292 and 0.303 (Laos), 0.288 and 0.288 (Sepon), 0.299 and 0.308 (Phin) and 0.289 and 0.305 (Songkhone), respectively. There was low to moderate genetic differentiation (F_ST: 0.011–0.043) and negligible inbreeding coefficients (F_IS: −0.001 to 0.052) between goat districts. The runs of homozygosity (ROH) had an average length of 5.92–6.85 Mb, with short ROH segments (1–5 Mb length) being the most prevalent (66.34%). Longer ROH segments (20–40 and >40 Mb length categories) were less common, comprising only 4.81% and 1.01%, respectively. Lao goats exhibit moderate genetic diversity, low-inbreeding levels and adequate effective population size. Some genetic distinctions between Lao goats may be explained by geographic and cultural features.

The quality of alpaca textile fibre has great potential, especially if objectionable fibres (coarse and medullated fibres) that cause itching are reduced, considering that objectionable fibres can be identified by diameter and medullation types. The objective of this study was to estimate genetic parameters for medullar types and their respective diameters to evaluate the possibility of incorporating them as selection criteria in alpaca breeding programmes. The research used 3149 alpaca fibre samples collected from 2020 to 2022, from a population of 1626 Huacaya type alpacas. The heritability and correlations of the percentages of non-medullated (NM), fragmented medulle (FM), uncontinuous medullated (UM), continuous medullated (CM), and strongly medullated (SM) fibres were analysed, also the fibre diameter (FD) for each of the medullation types. The heritability estimated for medullation types were 0.25 ± 0.01, 0.18 ± 0.01, 0.10 ± 0.01, 0.20 ± 0.01 and 0.11 ± 0.01 for NM, FM, UM, CM and SM, respectively. The genetic correlations for medullation categories ranged from 0.15 ± 0.03 to 0.66 ± 0.02 (in absolute values). The heritabilility estimated for fibre diameter (FD) of each of the medullation types were 0.29 ± 0.03, 0.27 ± 0.02, 0.35 ± 0.02, 0.30 ± 0.02, 0.25 ± 0.02 and 0.10 ± 0.02 for FD, FD_NM, FD_FM, FD_UM, FD_CM and FD_SM, respectively. The genetic correlations for fibre diameter of the medullation types ranged from 0.04 ± 0.04 to 0.97 ± 0.01. FD, NM and FM are the main traits to be used as selection criteria under a genetic index, since they would reduce fibre diameter, and also increase NM and FM, and, in addition reducing indirectly CM, SM, and SM_FD. Therefore, the quality of alpaca fibre could be improved.

The genome-wide analysis of runs of homozygosity (ROH) islands can be an effective strategy for identifying shared variants within a population and uncovering important genomic regions related to complex traits. The current study performed ROH analysis to characterize the genome-wide patterns of homozygosity, identify ROH islands and annotated genes within these candidate regions using whole-genome sequencing data from 100 American mink (Neogale vison). After sequence processing, variants were called using GATK and Samtools pipelines. Subsequent to quality control, 8,373,854 bi-allelic variants identified by both pipelines remained for further analysis. A total of 34,652 ROH segments were identified in all individuals, among which shorter segments (0.3–1 Mb) were abundant throughout the genome, approximately accounting for 84.39% of all ROH. Within these segments, we identified 63 ROH islands housing 156 annotated genes. The genes located in ROH islands were associated with fur quality (EDNRA, FGF2, FOXA2 and SLC24A4), body size/weight (MYLK4, PRIM2, FABP2, EYS and PHF3), immune capacity (IL2, IL21, PTP4A1, SEMA4C, JAK2, CCNA2 and TNIP3) and reproduction (ADAD1, KHDRBS2, INSL6, PGRMC2 and HSPA4L). Furthermore, Gene Ontology and KEGG pathway enrichment analyses revealed 56 and 9 significant terms (FDR-corrected p-value < 0.05), respectively, among which cGMP-PKG signalling pathway, regulation of actin cytoskeleton, and calcium signalling pathway were highlighted due to their functional roles in growth and fur characteristics. This is the first study to present ROH islands in American mink. The candidate genes from ROH islands and functional enrichment analysis suggest possible signatures of selection in response to the mink breeding targets, such as increased body length, reproductive performance and fur quality. These findings contribute to our understanding of genetic characteristics, and provide complementary information to assist with implementation of breeding strategies for genetic improvement in American mink.

There may be an increased risk of metabolic disorders, such as rumen acidosis, in cattle fed high-concentrate diets, particularly those from Bos taurus indicus genotypes, which have shown to be more sensitive to ruminal acidification. Therefore, this study aimed to estimate (co)variance components and identify genomic regions and pathways associated with ruminal acidosis in feedlot Nellore cattle fed high-concentrate diets. It was utilized a dataset containing a total of 642 Nellore bulls that were genotyped from seven feedlot nutrition studies. The GGP Indicus 35k panel was used with the single step genome-wide association study methodology in which the effects of the markers were obtained from the genomic values estimated by the GBLUP model. A bivariate model to estimate genetic correlations between the economically important traits and indicator traits for acidosis was used. The traits evaluated in this study that were nutritionally related to rumen acidosis included average daily gain (ADG), final body weight, time spent eating (TSE), time spent ruminating, rumenitis score (RUM), rumen absorptive surface area (ASA), rumen keratinized layer thickness (KER) and hot carcass weight (HCW). The identified candidate genes were mainly involved in the negative or non-regulation of the apoptotic process, salivary secretion, and transmembrane transport. The genetic correlation between HCW and ASA was low positive (0.27 ± 0.23), and between ADG and ASA was high moderate (0.58 ± 0.59). A positive genetic correlation between RUM and all performance traits was observed, and TSE correlated negatively with HCW (−0.33 ± 0.21), ASA (−0.75 ± 0.48), and KER (−0.40 ± 0.27). The genetic association between economically important traits and indicator traits for acidosis suggested that Nellore cattle may be more sensitive to acidosis in feedlot systems.

Mora M, González P, Quevedo JR, Montañés E, Tusell L, Bergsma R, Piles M. Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms. J Anim Breed Genet. 2023 Nov;140(6):638–652. doi: 10.1111/jbg.12815. PMID: 37403756.

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This work received funding for open access charge: CRUE-Universitat Politècnica de València. MM is a recipient of a ‘Formación de Personal Investigador (FPI)’ associated with the research project RTI2018-097610R-I00. This research was also supported by the project PID2021-128173OR-C21 (GENEF3) (grant no. PID2019-110742RB-I00).

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This study aimed to estimate two reproductive efficiency indices in sheep based on the ratio between litter weight (at birth and weaning) and dam weight, as well as their genetic parameters. Phenotypic and pedigree data comprising the period from 1990 to 2018 were obtained from the Santa Inês sheep database of Embrapa Tabuleiros Costeiros. For estimation of the genetic parameters of the indices, a repeatability model was applied in single- and two-trait analyses by a Bayesian approach. The mean reproductive efficiency index was 0.069 ± 0.0163 and 0.43 ± 0.0955 at birth and weaning, respectively. These values indicate that, on average, ewes give birth to 69 g of lamb per kg body weight and wean 430 g of lamb per kg body weight. Described here for the first time, the heritability estimate obtained in single- and two-trait analyses was 0.24 for the index based on birth weights and ranged from 0.13 to 0.15 for the index based on weaning weights. The estimates indicate the possibility of genetic gain by selection and are similar to those reported for reproductive traits in sheep, representing an option for selection criterion. The genetic correlation between indices was positive and moderate (0.26). The repeatability estimates were high (0.49 for the birth weight index and 0.71 for the weaning weight index). These values indicate good prediction of future performance with few observations. The weaning weight index might be a good culling criterion of females.

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.