Search Results (31)

(1) Background: Litter size traits are critical for pig breeding efficiency but pose challenges due to low heritability and sex-limited influences. This study aimed to elucidate the genetic architecture and identify candidate genes for these traits in Large White pigs using genomic selection (GS) and genome-wide association analyses (GWAS). (2) Methods: This study utilized phenotypic data from nine litter size traits in Large White sows. Genotyping-by-sequencing (GBS) was performed to obtain genotype data, retaining 153,782 high-quality SNPs after quality control. Genetic evaluation was conducted using single-step genomic best linear unbiased prediction (ssGBLUP), with genetic parameters (heritability and genetic correlations) estimated via an animal model (repeatability model). To assess prediction accuracy, 10-fold cross-validation was employed to compare traditional BLUP with ssGBLUP. Furthermore, a single-step genome-wide association study (ssGWAS) integrated genomic information and pedigree-based relationship matrices to screen for significant SNPs associated with litter size traits across the genome. Functional analysis of key candidate genes was subsequently conducted based on ssGWAS results. (3) Results: Heritabilities for litter traits ranged from 0.01 to 0.06. ssGBLUP improved genomic prediction accuracy by 6.38–13.33% over BLUP. Six genomic windows explaining 1.07–1.77% of genetic variance were identified via ssGWAS, highlighting GPR12 on SSC11 as a key candidate gene linked to oocyte development. (4) Conclusions: This study demonstrates the efficacy of ssGBLUP for low-heritability traits and identifies GPR12 as a pivotal gene for litter size. Prioritizing NHB and LBWT in breeding programs could enhance genetic gains while mitigating adverse effects on piglet health. These findings advance genomic strategies for improving reproductive efficiency in swine. Full article

Background: Single-step genomic BLUP (ssGBLUP) has gained increasing interest from forest tree breeders. ssGBLUP combines phenotypic and pedigree data with marker data to enhance the prediction accuracy of estimated breeding values. However, potential errors in determining progeny relationships among open-pollinated species may result in lower accuracy of estimated breeding values. Unknown parent groups (UPG) and metafounders (MF) were developed to address missing pedigrees in a population. This study aimed to incorporate MF into ssGBLUP models to select the best parents for controlled mating and the best progenies for cloning in a tree breeding population of Eucalyptus globulus. Methods: Genetic groups were defined to include base individuals of similar genetic origin. Tree growth was measured as total height (TH) and diameter at breast height (DBH), while disease resistance was assessed through heteroblasty (the transition from juvenile to adult foliage: ADFO). All traits were evaluated at 14 and 21 months. Two genomic multi-trait threshold linear models were fitted, with and without MF. Also, two multi-trait threshold-linear models based on phenotypic and pedigree information (ABLUP) were used to evaluate the increase in accuracy when adding genomic information to the model. To test the quality of models by cross-validation, the linear regression method (LR) was used. Results: The LR statistics indicated that the ssGBLUP models without MF performed better, as the inclusion of MF increased the bias of predictions. The ssGBLUP accuracy for both validations ranged from 0.42 to 0.68. Conclusions: The best model to select parents for controlled matings and individuals for cloning is ssGBLUP without MF. Full article

Background/Objectives: By simultaneously integrating both genotyped and non-genotyped animals into genetic evaluation, the single-step genomic BLUP method enhanced the accuracy of genetic assessments. This study aimed to compare the increase in prediction reliability (R²) between restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) in the Pura Raza Española (PRE) horse breed. Methods: The dataset comprised reproductive records for seven fertility traits from 47,502 females, with a total of 57,316 animals represented in the pedigree. A total of 4009 animals were genotyped using the EQUIGENE 90K SNP array, and 71,322 SNPs were retained for analysis after quality control. Genetic parameters were estimated using a multivariate model with the BLUPF90+ v2.60 software. Results: Heritability estimates were similar between REML and ssGREML, ranging from 0.07 for IF12 to 0.349 for ALF. An increase in R² was observed with ssGREML compared to REML across all traits, with overall gains ranging from 2.20% to 3.71%. Among genotyped animals, R² values ranged from 17.81% to 24.04%, while significantly lower values (0.80% to 2.34%) were observed in non-genotyped animals. Notably, individuals with low initial R² values under the REML approach exhibited the most significant gains using ssGREML. This improvement was particularly pronounced among stallions with fewer than 40 controlled foals. Conclusions: Our results demonstrated that incorporating genomic data improves the reliability of genetic evaluations for mare fertility in PRE horses. Full article

Understanding the genetic basis of economically important traits is essential for enhancing the productivity, fertility, and health of dairy cattle. This study aimed to identify the pleiotropic genes associated with the 305-day milk yield (MY305), days open (DO), and milk fat-to-protein ratio (FPR) in Thai-Holstein crossbred dairy cattle using a genome-wide association study (GWAS) approach. The dataset included 18,843 records of MY305 and milk FPR, as well as 48,274 records of DO, collected from first-lactation Thai-Holstein crossbred dairy cattle. A total of 868 genotyped animals and 43,284 informative SNPs out of 50,905 were used for the analysis. The single-nucleotide polymorphism (SNP) effects were evaluated using a weighted single-step GWAS (wssGWAS), which estimated these effects based on genomic breeding values (GEBVs) through a multi-trait animal model with single-step genomic BLUP (ssGBLUP). Genomic regions explaining at least 5% of the total genetic variance were selected for candidate gene analysis. Single-step genomic REML (ssGREML) with a multi-trait animal model was used to estimate components of (co)variance. The heritability estimates from additive genetic variance were 0.262 for MY305, 0.029 for DO, and 0.102 for milk FPR, indicating a moderate genetic influence on milk yield and a lower genetic impact on fertility and milk FPR. The genetic correlations were 0.559 (MY305 and DO), −0.306 (MY305 and milk FPR), and −0.501 (DO and milk FPR), indicating potential compromises in genetic selection. wssGBLUP showed a higher accuracy than ssGBLUP, although the improvement was modest. A total of 24, 46, and 33 candidate genes were identified for MY305, DO, and milk FPR, respectively. Pleiotropic effects, identified by SNPs showing significant influence with more than trait, were observed in 14 genes shared among all three traits, 17 genes common between MY305 and DO, 14 genes common between MY305 and milk FPR, and 26 genes common between DO and milk FPR. Overall, wssGBLUP is a promising approach for improving the genomic prediction of economic traits in multi-trait analyses, outperforming ssGBLUP. This presents a viable alternative for genetic evaluation in dairy cattle breeding programs in Thailand. However, further studies are needed to validate these candidate genes and refine marker selection for production, fertility, and health traits in dairy cattle. Full article

This study aimed to evaluate the heritability (h²) estimates of some important udder conformation traits, their relationship to each other and with production, and their phenotypic and genetic trends over a 10-year period in relatively high-yielding Holstein-Friesian cows. A total of 15,032 cows from six herds in Hungary were tested for milk (MY), butterfat (FY), and protein (PY) production yields over 305 days in first lactation. In addition, their udder conformation was scored for udder attachment (FU), rear udder height (RUH), central ligament (CL), udder depth (UD), front teat placement (FTP), and udder texture (UT) on a 1–9 linear udder score scale. REML and BLUP single-step animal model and linear regression model were used for data analysis and estimation. The production traits of the cows were quite reasonable, with 10,179.4 kg milk, 380.3 kg fat, and 333.1 kg protein in a standard lactation of 305 days. The scores of the udder conformation traits (5.4 to 6.1) were slightly above the mean of 5 on a linear scale of 1–9. The h² for MY, FY, and PY were obtained from 0.30 to 0.35, while those for udder traits were from 0.22 to 0.41. Phenotypic (r_p) and genetic (r_g) correlations for the relationship between production and udder conformation were weak or negligible (ranged from −0.33 to +0.15). Most of the associations between different udder traits were generally weak, but moderate positive correlations were observed between FU and UD (r_p = 0.42, r_g = 0.50 or 0.57), and between FU and UT (r_p= 0.36, r_g = 0.33 or 0.35). There were increasing genetic trends in the milk production (b = 2.2 to 16.5), but the studied udder conformation traits did not change over time (b = 0.00 to 0.03). In our study, despite an increase in milk yield over the studied ten-year period, udder conformation traits did not change. Therefore, considering that udder conformation scores were slightly above average and that udder traits were included in the selection index, we believe that it may be necessary to reconsider the udder conformation scoring system and its inclusion in the selection index. Full article

Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs. Full article

This study evaluated the genetic parameters for growth and Vibrio parahaemolyticus (Vp_AHPND) resistance in both the introduced MK strain and the self-constructed GK strain of Penaeus vannamei, investigating the impact of genotyped female parents on trait estimates under a single-parent nested mating design. A total of 32 families from the MK strain and 44 families from the GK strain were analyzed. Fifty-four female parents from both strains were genotyped using the “Yellow Sea Chip No. 1” containing 10.0 K SNPs. In the MK strain, heritability estimates ranged from 0.439 to 0.458 for body weight (Bw) and from 0.308 to 0.489 for survival time (ST) and survival rates at 36 h (36 SR), 50% mortality (SS₅₀), and 60 h (60 SR). In the GK strain, heritability for Bw ranged from 0.724 to 0.726, while ST, 36 SR, SS₅₀, and 60 SR had heritability estimates between 0.370 and 0.593. Genetic correlations between Bw and ST were 0.601 to 0.622 in the MK strain and 0.742 to 0.744 in the GK strain. For Bw and survival rates, correlations ranged from 0.120 to 0.547 in the MK strain and from 0.426 to 0.906 in the GK strain. The genetic correlation between ST and survival rates was not significantly different from 1 (p > 0.05) in both strains. High Pearson correlations (0.853 to 0.997, p < 0.01) were observed among survival rates at different points. Predictive accuracies for Bw, ST, and survival rates using single-step genomic best linear unbiased prediction (ssGBLUP) were comparable to pedigree-based best linear unbiased prediction (pBLUP) in the MK strain, while in the GK strain, ssGBLUP improved predictive accuracies for Bw, ST, and SS₅₀ by 0.20%, 0.32%, and 0.38%, respectively. The results indicate that both growth and Vp_AHPND resistance have significant breeding potential. Although the genetic correlation between weight and resistance varies across different populations, there is a positive genetic correlation between these traits, supporting the feasibility of multi-trait selection. To enhance genetic accuracy, breeding programs should include more genotyped progeny. These findings also suggest that infection frequency and observation time influence resistance performance and breeding selection, emphasizing the need for a tailored resistance evaluation program to improve breeding efficiency and reduce costs. Full article

Taiwan Country chickens are integral to Taiwanese culture and the poultry industry. By establishing a crossbreeding system, breeders must consider the growth-related traits of the dam line to achieve acceptable traits in commercial meat-type chickens. This study compared the accuracy of genomic estimated breeding values (GEBVs) predicted using the pedigree-based best linear unbiased prediction (PBLUP) model and the single-step genomic BLUP (ssGBLUP) model. Additionally, we conducted a genome-wide association study (GWAS) to identify single-nucleotide polymorphisms (SNPs) associated with growth, shank, and body conformation traits to support marker-assisted selection (MAS). The results showed that the ssGBLUP model achieved 4.3% to 16.4% higher prediction accuracy than the PBLUP model. GWAS identified four missense SNPs and four significant SNPs associated with body weight, shank length, and shank width at 12 weeks. These findings highlight the potential of integrating the ssGBLUP model with identified SNPs to improve genetic gain and breeding efficiency and provide preliminary results to assess the feasibility of genomic prediction and MAS in Taiwan Country chicken breeding programs. Further research is necessary to validate these findings and explore their mechanisms and broader application across different breeding programs, particularly for the NCHU-G101 breed of Taiwan Country chickens. Full article

Infection by gastrointestinal nematodes (GINs) in sheep is a significant health issue that affects animal welfare and leads to economic losses in the production sector. Genetic selection for parasite resistance has shown promise in improving animal health and productivity. This study aimed to determine if incorporating genomic data into genetic prediction models currently used in Uruguay could improve the accuracy of breeding value estimations for GIN resistance in the Australian Merino breed. This study compared the accuracy of breeding value predictions using the BLUP (Best Linear Unbiased Prediction) and ssGBLUP (single-step genomic BLUP) models on partial and complete data sets, including 32,713 phenotyped and 3238 genotyped animals. The quality of predictions was evaluated using a linear regression method, focusing on 145 rams. The inclusion of genomic data increased the average individual accuracies by 4% for genotyped and phenotyped animals. For animals with genomic and non-phenotyped data, the accuracy improvement reached 8%. Of these, one group of animals that benefited from an ssGBLUP evaluation came from a facility with a strong connection to the informative nucleus and showed an average increase of 20% in their individual accuracy. Additionally, ssGBLUP slightly outperformed BLUP in terms of prediction quality. These findings demonstrate the potential of genomic information to improve the accuracy of breeding value predictions for parasite resistance in sheep. The integration of genomic data, particularly in non-phenotyped animals, offers a promising tool for enhancing genetic selection in Australian Merino sheep to improve resistance to gastrointestinal parasites. Full article

Reproductive performance is an important trait in poultry production. Traditional methods of improving reproductive traits can only use recorded information from females, making it difficult to effectively assess the reproductive potential of males. Although genomic selection is thought to remedy this shortcoming, most studies now use simulated data or one or two generations of data to assess its effects. Also, the effectiveness of genomic selection for use in the improvement of reproductive traits in ducks has hardly been reported. In this study, data from four consecutive generations of Pekin duck populations were used to assess the effect of genomic selection on reproductive trait improvement. Whole-genome resequencing was performed for genotyping, and pedigree and SNP genetic parameters were evaluated. Using the BLUP (Best Linear Unbiased Prediction), GBLUP (Genomic Best Linear Unbiased Prediction), and ssGBLUP (Single-step Genomic Best Linear Unbiased Prediction) models, we assessed selection progress for body weight at 6 weeks, age at first egg, and egg number from 25 to 44 weeks over multiple generations. Ten-fold cross-validation was used to evaluate the genomic prediction performance. The results indicated that the heritability of growth traits decreased after routine selection, while reproductive and egg quality traits maintained moderate heritability (0.2–0.4). Selection progress showed a one-day advancement in age at first egg and an increase of one egg per generation from the 13th to 15th generations. The GBLUP model performance significantly outperformed BLUP, but ssGBLUP showed minimal improvement due to comprehensive genotyping. In conclusion, this study provides crucial insights for optimizing breeding strategies and improving economic efficiency in Pekin duck breeding. Full article

In this study, 1,616,549 Holstein-Friesian females were genotyped for genomic evaluation of genetic merit (BV_Genomic). Genotyping was performed using the EuroGenomics MD v3.0 chipset on the Illumina microarray scanner platform operated by an accredited Illumina laboratory. In addition, international and national reference populations were used for traditional BLUP breeding value (BV) estimation for both individuals (BV_BLUP) and parents (BV_Pedigree). A single-step BLUP animal model was used for this estimation. A sample of 190 first lactation progeny cows from a single herd, reared and kept under consistent environmental conditions, was used to validate the three types of BV estimation methods. Correlation and regression analysis were used to study the association between the phenotypic performance and the results of three different estimation models. The average production of the 305-day standard lactation was 10,910.5 kg milk, 397.86 kg butterfat and 365.33 kg protein. Comparative analyses showed that BV_BLUP had the highest accuracy, followed by BV_Genomic, while BV_Pedigree was the least reliable, R² = 0.37 to 0.48; 0.09 to 0.23; 0.02 to 0.06, respectively. Full article

The development of multi-omics has increased the likelihood of further improving genomic prediction (GP) of complex traits. Gene expression data can directly reflect the genotype effect, and thus, they are widely used for GP. Generally, the gene expression data are integrated into multiple random effect models as independent data layers or used to replace genotype data for genomic prediction. In this study, we integrated pedigree, genotype, and gene expression data into the single-step method and investigated the effects of this integration on prediction accuracy. The integrated single-step method improved the genomic prediction accuracy of more than 90% of the 54 traits in the Duroc × Erhualian F₂ pig population dataset. On average, the prediction accuracy of the single-step method integrating gene expression data was 20.6% and 11.8% higher than that of the pedigree-based best linear unbiased prediction (ABLUP) and genome-based best linear unbiased prediction (GBLUP) when the weighting factor (w) was set as 0, and it was 5.3% higher than that of the single-step best linear unbiased prediction (ssBLUP) under different w values. Overall, the analyses confirmed that the integration of gene expression data into a single-step method could effectively improve genomic prediction accuracy. Our findings enrich the application of multi-omics data to genomic prediction and provide a valuable reference for integrating multi-omics data into the genomic prediction model. Full article

Vitiligo is a depigmentation autoimmune disorder characterized by the progressive loss of melanocytes leading to the appearance of patchy depigmentation of the skin. The presence of vitiligo in horses is greater in those with grey coats. The aim of this study was therefore to perform a genome-wide association study (GWAS) to identify genomic regions and putative candidate loci associated with vitiligo depigmentation and susceptibility in the Pura Raza Español population. For this purpose, we performed a wssGBLUP (weighted single step genomic best linear unbiased prediction) using data from a total of 2359 animals genotyped with Affymetrix Axiom™ Equine 670 K and 1346 with Equine GeneSeek Genomic Profiler™ (GGP) Array V5. A total of 60,136 SNPs (single nucleotide polymorphisms) present on the 32 chromosomes from the consensus dataset after quality control were employed for the analysis. Vitiligo-like depigmentation was phenotyped by visual inspection of the different affected areas (eyes, mouth, nostrils) and was classified into nine categories with three degrees of severity (absent, slight, and severe). We identified one significant genomic region for vitiligo around the eyes, eight significant genomic regions for vitiligo around the mouth, and seven significant genomic regions for vitiligo around the nostrils, which explained the highest percentage of variance. These significant genomic regions contained candidate genes related to melanocytes, skin, immune system, tumour suppression, metastasis, and cutaneous carcinoma. These findings enable us to implement selective breeding strategies to decrease the incidence of vitiligo and to elucidate the genetic architecture underlying vitiligo in horses as well as the molecular mechanisms involved in the disease’s development. However, further studies are needed to better understand this skin disorder in horses. Full article

The current study aimed to provide a precise assessment of the genetic parameters associated with growth and white spot syndrome virus (WSSV) resistance traits in Pacific white shrimp (Litopenaeus vannamei). This was achieved through a controlled WSSV challenge assay and the analysis of phenotypic values of five traits: body weight (BW), overall length (OL), body length (BL), tail length (TL), and survival hour post-infection (HPI). The analysis included test data from a total of 1017 individuals belonging to 20 families, of which 293 individuals underwent whole-genome resequencing, resulting in 18,137,179 high-quality SNP loci being obtained. Three methods, including pedigree-based best linear unbiased prediction (pBLUP), genomic best linear unbiased prediction (GBLUP), and single-step genomic BLUP (ssGBLUP) were utilized. Compared to the pBLUP model, the heritability of growth-related traits obtained from GBLUP and ssGBLUP was lower, whereas the heritability of WSSV resistance was higher. Both the GBLUP and ssGBLUP models significantly enhanced prediction accuracy. Specifically, the GBLUP model improved the prediction accuracy of BW, OL, BL, TL, and HPI by 4.77%, 21.93%, 19.73%, 19.34%, and 63.44%, respectively. Similarly, the ssGBLUP model improved prediction accuracy by 10.07%, 25.44%, 25.72%, 19.34%, and 122.58%, respectively. The WSSV resistance trait demonstrated the most substantial enhancement using both genomic prediction models, followed by body size traits (e.g., OL, BL, and TL), with BW showing the least improvement. Furthermore, the choice of models minimally impacted the assessment of genetic and phenotypic correlations. Genetic correlations among growth traits ranged from 0.767 to 0.999 across models, indicating high levels of positive correlations. Genetic correlations between growth and WSSV resistance traits ranged from (−0.198) to (−0.019), indicating low levels of negative correlations. This study assured significant advantages of the GBLUP and ssGBLUP models over the pBLUP model in the genetic parameter estimation of growth and WSSV resistance in L. vannamei, providing a foundation for further breeding programs. Full article

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