Genomic Estimated Breeding Values Research Articles

The characteristics of bovine satellite cells with highly scored genomic estimated breeding value

The characteristics of bovine satellite cells with highly scored genomic estimated breeding value

Accurate genomic selection using low-density SNP panels preselected by maximum likelihood estimation

Genomic selection (GS) poses a challenge for the prediction of the genomic estimated breeding value (GEBV) using a low-density SNP panel. Several methods have been proposed for SNP preselection. However, these methods often suffer from either significant computational complexity or erratic accuracy in GS. In this study, we developed an approach called MLE-rank based on maximum likelihood estimation to preselect a set of SNPs for GS. First, we generated 90 simulated datasets and compared the performance of MLE-rank with uniform distribution and preselection based on a genome-wide association study (GWAS). For simulated datasets, compared to uniform distribution, both MLE-rank and GWAS preselection reduced the SNP density by a factor of 10 while maintaining prediction accuracy. Additionally, compared to the other two methods, MLE-rank's prediction accuracy was significantly improved with the medium- and high-heritability datasets. Then, we further evaluated these three preselection approaches using real disease-resistant phenotypes of leopard coral grouper (Plectropomus leopardus) and Japanese flounder (Paralichthys olivaceus). We found that the 3 k SNPs preselected by MLE-rank had a stable and effective prediction effect. The uniform distribution requires 70 k, while the GWAS preselection method requires 3 k (P. leopardus) and 50 k (P. olivaceus) to achieve similar prediction accuracy. Finally, we evaluated the prediction accuracy of MLE-rank using candidate populations of flounders and their progeny survival rates, with uniform distribution and GWAS preselection as benchmarks. In the results for this dataset, MLE-rank was found to have the same predictive effect for low-density SNP panels as it did for high-density SNPs, regardless of whether GWAS preselection or uniform distribution was used. Taken together, the results we have observed indicate that we have ensured that MLE-rank does not reduce prediction accuracy for any of the datasets. MLE-rank showed superior performance in reducing the number of SNPs. Moreover, we observed a relative standard deviation in prediction accuracy when using a low density of SNPs selected by MLE-rank compared to a high density determined through a uniform distribution strategy. In conclusion, MLE-rank not only reduces the number of SNPs used for GS but also exhibits high predictive accuracy. This could potentially lead to a decrease in genotyping costs and promote the wider application of GS in fish breeding.

Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding.

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein's function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat

Septoria tritici blotch (STB) is a destructive foliar diseases threatening wheat grain yield. Wheat breeding for STB disease resistance has been identified as the most sustainable and environment-friendly approach. In this work, a panel of 316 winter wheat breeding lines from a commercial breeding program were evaluated for STB resistance at the seedling stage under controlled conditions followed by genome-wide association study (GWAS) and genomic prediction (GP). The study revealed a significant genotypic variation for STB seedling resistance, while disease severity scores exhibited a normal frequency distribution. Moreover, we calculated a broad-sense heritability of 0.62 for the trait. Nine single- and multi-locus GWAS models identified 24 marker-trait associations grouped into 20 quantitative trait loci (QTLs) for STB seedling-stage resistance. The seven QTLs located on chromosomes 1B, 2A, 2B, 5B (two), 7A, and 7D are reported for the first time and could potentially be novel. The GP cross-validation analysis in the RR-BLUP model estimated the genomic-estimated breeding values (GEBVs) of STB resistance with a prediction accuracy of 0.49. Meanwhile, the GWAS assisted wRR-BLUP model improved the accuracy to 0.58. The identified QTLs can be used for marker-assisted backcrossing against STB in winter wheat. Moreover, the higher prediction accuracy recorded from the GWAS-assisted GP analysis implies its power to successfully select superior candidate lines based on their GEBVs for STB resistance.

Genetic parameters and genomic prediction for nutritional quality-related traits of Pacific abalone (Haliotis discus hannai)

The Pacific abalone (Haliotis discus hannai) is one of the most widely cultivated shellfish species in China. Nutritional quality-related traits are a major objective in aquatics as they determine the value of products and consumer preference. Estimating genetic parameters and the potential of genomic selection is significant in designing breeding programs, which has not been performed in abalone. In the study, we developed near infrared reflectance spectroscopy (NIRS) models for collagen content (CC) and taurine content (TC) in Pacific abalone, estimated the genetic parameters for glycogen content (GLY), total protein content (TPC), CC, and TC, and assessed the prediction ability of GBLUP and three Bayesian methods for the four nutritional traits. The genetic analysis included all 274 individuals and 69,530 high quality SNPs. The prediction correlation of the NIRS models were 0.9702 and 0.9289 for CC and TC, respectively. The heritability of GLY, TPC, CC, and TC was 0.33 ± 0.13, 0.42 ± 0.14, 0.02 ± 0.10 and 0.32 ± 0.13, respectively, while the heritability of growth traits in our study ranged from 0.34 ± 0.14 to 0.47 ± 0.14. Negative correlations were observed between GLY and both TPC (−0.96 ± 0.03) and TC (−0.90 ± 0.07), whereas moderate correlations were found between GLY and growth traits. The prediction ability for GLY, TPC, and TC was 0.21 ± 0.04 to 0.22 ± 0.03, 0.27 ± 0.02 to 0.29 ± 0.03, 0.20 ± 0.03 to 0.24 ± 0.02, respectively, using four genomic selection (GS) models (GBLUP, BayesA, BayesB, and BayesCpi). The results were encouraging and showed comparable performance for the predictability of genomic estimated breeding values, demonstrating the potential for GS in selection breeding of the nutritional quality-related traits in Pacific abalone.

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

A review of machine learning models applied to genomic prediction in animal breeding.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

ABSTRACT Traditionally, the p-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the p-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.

Genetic architecture of tuber-bound free amino acids in potato and effect of growing environment on the amino acid content

Free amino acids in potato tubers contribute to their nutritional value and processing quality. Exploring the natural variation in their accumulation in tubers across diverse genetic backgrounds is critical to potato breeding programs aiming to enhance or partition their distribution effectively. This study assessed variation in the tuber-bound free amino acids in a diversity panel of tetraploid potato clones developed and maintained by the Texas A&M Potato Breeding Program to explore their genetic basis and to obtain genomic-estimated breeding values for applied breeding purposes. Free amino acids content was evaluated in tubers of 217 tetraploid potato clones collected from Dalhart, Texas in 2019 and 2020, and Springlake, Texas in 2020. Most tuber amino acids were not affected by growing location, except histidine and proline, which were significantly lower (− 59.0%) and higher (+ 129.0%), respectively, at Springlake, Texas (a location that regularly suffers from abiotic stresses, mainly high-temperature stress). Single nucleotide polymorphism markers were used for genome-wide association studies and genomic selection of clones based on amino acid content. Most amino acids showed significant variations among potato clones and moderate to high heritabilities. Principal component analysis separated fresh from processing potato market classes based on amino acids distribution patterns. Genome-wide association studies discovered 33 QTL associated with 13 free amino acids. Genomic-estimated breeding values were calculated and are recommended for practical potato breeding applications to select parents and advance clones with the desired free amino acid content.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.

Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

Marker-assisted and Genomic Selection of Beef Cattle

This article provides an overview of modern genetic technologies for improving production traits and predicting breeding value in beef cattle. In particular, in marker-assisted selection the most promising is the selectionby desirable genotypes in the genes of myostatin (MSTN), calpain (CAPN), calpastatin (CAST), growth hormone (GH), leptin (LEP), thyroglobulin (TG), fatty acid binding protein (FABP), retinoic acid C-receptor (RORC), diacyl-glycerol acyltransferase (DGATI), sterol-Co desaturase (SCD). A modern and much more advanced approach is the Single Step Genomic Best Linear Unbiased Predictions (ssGBLUP) method, which calculates a Genomic Estimated Breeding Value (GEBV) using DNA chip genotyping, phenotype and pedigree data. Genome-wide association studies (GWAS), based on the use of genetic markers distributed throughout the genome and in non-equilibrium linkage with at least one of the quantitative traits, are currently recognised as more informative for finding new genes for beef cattle productivity. New genes associated with live weight at different stages of ontogenesis, average daily live weight gain, residual feed intake, carcass weight and flesh content have been identified. Most of the identified genes control cell division, lipid and carbohydrate metabolism. The accumulated data on full-genome association studies and exome sequencing led to new methods of genetic analysis – gene ontology and gene networks. The use of gene networks provided the first detailed understanding of the genetic basis for the formation of complex phenotypic traits based on the complex interaction of regulatory networks of «major» and «peripheral» genes controlling the development of a particular trait.

Health assessment of Holstein calves born after in vitro fertilization, biopsy-based genotyping at the blastocyst stage and subsequent embryo transfer

Establishing methods for evaluating genomic estimated breeding values of bovine embryos can potentially increase the efficiency of breeding programs by transferring only embryos with a high genomic estimated breeding value. This may be achieved by analyzing DNA from trophectoderm biopsies. However, manipulation of bovine embryos is associated with a risk of impaired conceptus health. More knowledge on the health implications of embryonic handling procedures is required. In this study, we followed pregnancies after transfer of in vitro-produced (IVP) embryos and assessed the health of the offspring during the first 2 weeks of life. Three groups of calves were studied: i) freshly transferred non-biopsied embryos (39 transfers, 17 calves; Group B-/C-); ii) biopsied and freshly transferred IVP embryos (42 transfers, 21 calves; Group B+/C-); iii) biopsied and cryopreserved IVP embryos (17 transfers, 6 calves; Group B+/C+). Blood biochemical and hematologic values were compared between groups and to a control group of 13 calves produced by conventional artificial insemination. The pregnancy rate on day 50 and the calving rate did not differ among the groups, but the average gestation length of the B+/C+ group was significantly shorter and with wider variation than the two other groups. There was a tendency toward a higher average body weight at birth in group B+/C+ (45.1 kg) and the standard deviation in body weight was larger (11.7 kg) compared to the B-/C- (39.5 kg; 3.2 kg) and B+/C- (41.8 kg; 6 kg) groups. Body weight on day 14 was higher in the B+/C+ calves compared to the other groups. There was no difference in the biochemical and hematological values at birth between the groups and these were within the normal range. However, when compared to a group of calves produced by standard artificial insemination, significantly higher concentrations were found for the hepatic-related enzymes ALAT, ASAT, ALP, and GGT in group B-/C-and B+/C-, while only higher ALP concentrations were found in B+/C+ calves. The biochemical findings indicate higher heterogeneity in IVP calves compared to calves produced by artificial insemination. The more manipulated IVP embryos also showed increased heterogeneity in body weight at birth, with a shift toward heavier calves, which calls for closer attendance at parturition to handle dystocia in a timely manner and minimize fetal losses.

A statistical package for evaluation of hybrid performance in plant breeding via genomic selection

Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin (Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.

Association of Phenotypic Markers of Heat Tolerance with Australian Genomic Estimated Breeding Values and Dairy Cattle Selection Indices.

Dairy cattle predicted by genomic breeding values to be heat tolerant are known to have less milk production decline and lower core body temperature increases in response to elevated temperatures. In a study conducted at the University of Melbourne's Dookie Robotic Dairy Farm during summer, we identified the 20 most heat-susceptible and heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on their phenotypic responses (changes in respiration rate, surface body temperature, panting score, and milk production). Hair samples were collected from the tip of the cows' tails following standard genotyping protocols. The results indicated variation in feed saved and HT genomic estimated breeding values (GEBVs) (p ≤ 0.05) across age, indicating a potential for their selection. As expected, the thermotolerant group had higher GEBVs for HT and feed saved but lower values for milk production. In general, younger cows had superior GEBVs for the Balanced Performance Index (BPI) and Australian Selection Index (ASI), whilst older cows were superior in fertility, feed saved (FS), and HT. This study demonstrated highly significant (p ≤ 0.001) negative correlations (-0.28 to -0.74) between HT and GEBVs for current Australian dairy cattle selection indices (BPI, ASI, HWI) and significant (p ≤ 0.05) positive correlations between HT and GEBVs for traits like FS (0.45) and fertility (0.25). Genomic selection for HT will help improve cow efficiency and sustainability of dairy production under hot summer conditions. However, a more extensive study involving more lactating cows across multiple farms is recommended to confirm the associations between the phenotypic predictors of HT and GEBVs.

Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population.

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV.

Genomic selection improves inner shell purpleness in triangle sail mussel Hyriopsis cumingii (Lea, 1852)

Purple pearls, which are exclusively produced by the freshwater pearl mussel, Hyriopsis cumingii (Lea, 1852), are of great economic and ornamental value. Selection mussels with purple inner shell as donor mussel can realize oriented cultivation of purple pearls. This study sought to explore the genomic genetic parameters of inner shell color traits as well as genomic-based methods to improve inner shell purpleness using genomic selection (GS). Genotype by sequencing (GBS) technology was used to generate genome-wide SNPs (n = 97,090) for reference population (n = 496) and candidate population (n = 268). In reference population, the four inner shell color phenotypes, lightness (L*), redness (a*), yellowness (b*), and color difference (dE*) were 59.97 ± 7.27, 2.66 ± 2.57, 1.79 ± 4.77, and 40.65 ± 7.46, respectively, and the genomic heritability of four traits were moderately high (0.39–0.67). GBLUP and four Bayesian models were applied to assess the prediction accuracy, where BayesB performed the best and it was chosen to develop a cost-effective GS method using low-density SNPs. Lastly, one GS method that consisted of BayesB model and 5 K GWAS-informative SNPs was successfully established and the genomic estimated breeding value (GEBV) of each candidate individual was predicted. The mussels with the highest GEBVs (n = 30) were selected as broodstocks for breeding the purple strain (PS), while the rest for control strain (CS). Significant differences in inner shell color a* and dE* were detected between PS and CS, as well as the proportion of deep purple individuals rose from 9.57% and 12.38% to 51.16% and 26.92% in Wuyi (WY) and Chongming (CM) sites, respectively. These results demonstrated that GS was an effective selective breeding approach for enhancing the purpleness of the inner shell in H. cumingii, and opened the door for future commercial GS uses on other aquaculture species.

Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep

BackgroundGenomic prediction of breeding values (GP) has been adopted in evolutionary genomic studies to uncover microevolutionary processes of wild populations or improve captive breeding strategies. While recent evolutionary studies applied GP with individual single nucleotide polymorphism (SNP), haplotype-based GP could outperform individual SNP predictions through better capturing the linkage disequilibrium (LD) between the SNP and quantitative trait loci (QTL). This study aimed to evaluate the accuracy and bias of haplotype-based GP of immunoglobulin (Ig) A (IgA), IgE, and IgG against Teladorsagia circumcincta in lambs of an unmanaged sheep population (Soay breed) based on Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian [BayesA, BayesB, BayesCπ, Bayesian Lasso (BayesL), and BayesR] methods.ResultsThe accuracy and bias of GPs using SNP, haplotypic pseudo-SNP from blocks with different LD thresholds (0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.00), or the combinations of pseudo-SNPs and non-LD clustered SNPs were obtained. Across methods and marker sets, higher ranges of genomic estimated breeding values (GEBV) accuracies were observed for IgA (0.20 to 0.49), followed by IgE (0.08 to 0.20) and IgG (0.05 to 0.14). Considering the methods evaluated, up to 8% gains in GP accuracy of IgG were achieved using pseudo-SNPs compared to SNPs. Up to 3% gain in GP accuracy for IgA was also obtained using the combinations of the pseudo-SNPs with non-clustered SNPs in comparison to fitting individual SNP. No improvement in GP accuracy of IgE was observed using haplotypic pseudo-SNPs or their combination with non-clustered SNPs compared to individual SNP. Bayesian methods outperformed GBLUP for all traits. Most scenarios yielded lower accuracies for all traits with an increased LD threshold. GP models using haplotypic pseudo-SNPs predicted less-biased GEBVs mainly for IgG. For this trait, lower bias was observed with higher LD thresholds, whereas no distinct trend was observed for other traits with changes in LD.ConclusionsHaplotype information improves GP performance of anti-helminthic antibody traits of IgA and IgG compared to fitting individual SNP. The observed gains in the predictive performances indicate that haplotype-based methods could benefit GP of some traits in wild animal populations.

Scaling factor assessment in single-step GBLUP evaluations for small genotyped populations: A case study on Iranian Holstein cattle

The estimation of genomic breeding values (GEBV) with the single-step genomic BLUP (ssGBLUP), which combines genomic (G) and pedigree relationship matrices in the single-step relationships matrix (H), may require the specification of two scaling parameters, τ and ω. In this study, we evaluate different scenarios to determine the best combinations of these scaling factors (τ for G−1 and ω for A22−1), and emphasize their relevance for single-step genome-wide association studies (ssGWAS). We use a dataset of about 3.1 million Iranian Holstein cattle, including 827,396 and 469,676 primiparous cows with records on milk yield (MY) and somatic cell score (SCS), respectively, and 2,189 genotyped bulls. The choice of the scaling factors was in particular relevant for dispersion (measured by regression coefficient b1 in a linear regression). Using default value s of τ=ω=1 resulted in a slight dispersion in SCS (b1=1.038) compared to MY (b1=0.88). Optimal combinations of scaling factors for each trait were selected based on the validation metrics, accuracy and b1, and the outputs of post genomic analysis (like ssGWAS). Concerning the latter, the behavior of P-values and especially prediction error variance (PEV) of SNP effects when changing τ and ω, provided helpful information complementing the metrics accuracy and b1. Overall, our results confirm the importance of using the τ, and ω parameters in both (ss)GBLUP and (ss)GWAS, and suggest that the combinations of (τ, ω) equal to (1.2, 0.7) and (1, 1) are the best choices for MY and SCS on the considered dataset.

Genetic gain and inbreeding from simulation of different genomic mating schemes for pig improvement

BackgroundGenomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values (GEBV) to accelerate the speed of genetic improvement in domestic animals. But after multi-generation selection, the rate of inbreeding and the occurrence of homozygous harmful alleles might increase, which would reduce performance and genetic diversity. To mitigate the above problems, we can utilize genomic mating (GM) based upon optimal mate allocation to construct the best genotypic combinations in the next generation. In this study, we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population. These factors included: the algorithm used to derive inbreeding coefficients; the trait heritability (0.1, 0.3 or 0.5); the kind of GM scheme (focused average GEBV or inbreeding); the approach for computing the genomic relationship matrix (by SNP or runs of homozygosity (ROH)). The outcomes were compared to three traditional mating schemes (random, positive assortative or negative assortative matings). In addition, the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.ResultsGenomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain. The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs. The GROH-based GM schemes with the maximum genetic gain resulted in 0.9%–2.6% higher rates of genetic gain ΔG, and 13%–83.3% lower ΔF than positive assortative mating regardless of heritability. The rates of inbreeding were always the fastest with positive assortative mating. Results from a purebred Large White pig population, confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.ConclusionCompared with traditional mating schemes, genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population. Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.