Genomic Breeding Values Research Articles

EVALUATION OF THE ADAPTIVE POTENTIAL OF PROMISING SHALLOT ACCESSIONS

Ecological stability and high adaptive potential are the main factors for stable performance and yield of agricultural crops under changing environmental conditions; therefore the selection of stress-resistant genotypes is relevant at all stages of breeding. Purpose. To evaluate and select promising breeding accession of shallots by adaptive potential for the "green leaf yield" trait and its components. Methods. Field, laboratory, computational and analytical methods were used in the study. The study was conducted in outdoors at the Institute of Vegetable and Melon Growing of NAAS in 2017–2020. Five breeding hybrid accessions were investigated. Lira was the check variety. Results. Having assessed the adaptive potentials of 5 breeding accessions of shallot, we selected a promising environmentally stable accession, D-57, which was noticeable for a high yield of green leaves (5.6 kg/m2) in combination with the highest general adaptive capacity (GAC = 1.3), high stability (bi = 0.4) and genomic breeding value (GBV = 2.7) for this trait. As to the adaptive potential for the "plant height" trait, the D-57 accessions also stood out; it was characterized by high general adaptive capacity (GAC = 1.3), relative stability (Sgi = 10.9 %) and genomic breeding value (GBV = 23.4). This accession was also the best in terms of the "plant weight": trait with the genomic breeding value (GBV = 42.4), it had the largest plant weight including a bulb (75.4 g), combined high general (GAC = 17.3) and specific adaptive capacity (SAC= 129.3) with high plasticity (bi = 2.6). Conclusions. The comprehensive study of the adaptive potential of 5 promising forms of shallots distinguished the D-57 accession, which was superior to the check variety, Lira, in terms of green leaf yield, plant height and weight (including a bulb) and had a high adaptive potential and genomic breeding value for these traits.

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.

Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize.

The study of yield and resistance/tolerance to pest are related traits fundamental for maize breeding programs. Genomic selection (GS), which uses all marker information to calculate genomic breeding values, is presented as an emerging alternative to phenotypic and marker-assisted selections for improving complex traits controlled by many genes with small effects. Therefore, although phenotypic selection (PS) has been effective for increasing resistance and yield under high infestation with maize stem borers, higher genetic gains are expected to be obtained through GS based on the complex architecture of both traits. Our objective was to test whether GS is more effective than PS for improving resistance and/or tolerance to maize stem borers and grain yield. For this, we compared different selection programs based on phenotype and genotypic value for a single trait, resistance or yield, and for both traits together. We obtained that GS achieved the highest genetic gain for yield, meanwhile phenotypic selection for yield was the program that achieved the highest reduction of tunnel length, but was ineffective for increasing yield. However, phenotypic or genomic selection for increased resistance may be more effective in improving both traits together; although the gains per cycle would be small for both traits.

Identification of candidate lethal haplotypes and genomic association with post-natal mortality and reproductive traits in Nellore cattle

The wide use of genomic information has enabled the identification of lethal recessive alleles that are the major genetic causes of reduced conception rates, longer calving intervals, or lower survival for live-born animals. This study was carried out to screen the Nellore cattle genome for lethal recessive haplotypes based on deviation from the expected population homozygosity, and to test SNP markers surrounding the lethal haplotypes region for association with heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY). This approach requires genotypes only from apparently normal individuals and not from affected embryos. A total of 62,022 animals were genotyped and imputed to a high-density panel (777,962 SNP markers). Expected numbers of homozygous individuals were calculated, and the probabilities of observing 0 homozygotes was obtained. Deregressed genomic breeding values [(G)EBVs] were used in a GWAS to identify candidate genes and biological mechanisms affecting HR, STAY and PNM. In the functional analyses, genes within 100 kb down and upstream of each significant SNP marker, were researched. Thirty haplotypes had high expected frequency, while no homozygotes were observed. Most of the alleles present in these haplotypes had a negative mean effect for PNM, HR and STAY. The GWAS revealed significant SNP markers involved in different physiological mechanisms, leading to harmful effect on the three traits. The functional analysis revealed 26 genes enriched for 19 GO terms. Most of the GO terms found for biological processes, molecular functions and pathways were related to tissue development and the immune system. More phenotypes underlying these putative regions in this population could be the subject of future investigation. Tests to find putative lethal haplotype carriers could help breeders to eliminate them from the population or manage matings in order to avoid homozygous.

Impact of inclusion non-additive effects on genome-wide association and variance’s components in Scottish black sheep

Context It’s well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection. Aim This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep. Methods This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used. Key results The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model. Conclusions The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16–24 weeks in Scottish Blackface lambs. Implications It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Genomic studies of the additive and dominant genetic control on production traits of Euterpe edulis fruits

In forest genetic improvement programs for non-domesticated species, limited knowledge of kinship can compromise or make the estimation of variance components and genetic parameters of traits of interest unfeasible. We used mixed models and genomics (in the latter, considering additive and non-additive effects) to evaluate the genetic architecture of 12 traits in juçaizeiro for fruit production. A population of 275 genotypes without genetic relationship knowledge was phenotyped over three years and genotyped by whole genome SNP markers. We have verified superiority in the quality of the fits, the prediction accuracy for unbalanced data, and the possibility of unfolding the genetic effects into their additive and non-additive terms in the genomic models. Estimates of the variance components and genetic parameters obtained by the additive models may be overestimated since, when considering the dominance effect in the model, there are substantial reductions in them. The number of bunches, fresh fruit mass of bunch, rachis length, fresh mass of 25 fruits, and amount of pulp were strongly influenced by the dominance effect, showing that genomic models with such effect should be considered for these traits, which may result in selective improvements by being able to return more accurate genomic breeding values. The present study reveals the additive and non-additive genetic control of the evaluated traits and highlights the importance of genomic information-based approaches for populations without knowledge of kinship and experimental design. Our findings underscore the critical role of genomic data in elucidating the genetic control architecture of quantitative traits, thereby providing crucial insights for driving species' genetic improvement.

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.

Efficient large-scale single-step evaluations and indirect genomic prediction of genotyped selection candidates

BackgroundSingle-step genomic best linear unbiased prediction (ssGBLUP) models allow the combination of genomic, pedigree, and phenotypic data into a single model, which is computationally challenging for large genotyped populations. In practice, genotypes of animals without their own phenotype and progeny, so-called genotyped selection candidates, can become available after genomic breeding values have been estimated by ssGBLUP. In some breeding programmes, genomic estimated breeding values (GEBV) for these animals should be known shortly after obtaining genotype information but recomputing GEBV using the full ssGBLUP takes too much time. In this study, first we compare two equivalent formulations of ssGBLUP models, i.e. one that is based on the Woodbury matrix identity applied to the inverse of the genomic relationship matrix, and one that is based on marker equations. Second, we present computationally-fast approaches to indirectly compute GEBV for genotyped selection candidates, without the need to do the full ssGBLUP evaluation.ResultsThe indirect approaches use information from the latest ssGBLUP evaluation and rely on the decomposition of GEBV into its components. The two equivalent ssGBLUP models and indirect approaches were tested on a six-trait calving difficulty model using Irish dairy and beef cattle data that include 2.6 million genotyped animals of which about 500,000 were considered as genotyped selection candidates. When using the same computational approaches, the solving phase of the two equivalent ssGBLUP models showed similar requirements for memory and time per iteration. The computational differences between them were due to the preprocessing phase of the genomic information. Regarding the indirect approaches, compared to GEBV obtained from single-step evaluations including all genotypes, indirect GEBV had correlations higher than 0.99 for all traits while showing little dispersion and level bias.ConclusionsIn conclusion, ssGBLUP predictions for the genotyped selection candidates were accurately approximated using the presented indirect approaches, which are more memory efficient and computationally fast, compared to solving a full ssGBLUP evaluation. Thus, indirect approaches can be used even on a weekly basis to estimate GEBV for newly genotyped animals, while the full single-step evaluation is done only a few times within a year.

Genetic parameters and multi-trait genomic prediction for hemoparasites infection levels in cattle

Babesiosis and anaplasmosis are tick-borne diseases that substantially affect the economic outcomes of livestock production in tropical countries. This study aimed to evaluate the genetics of resistance to infection caused by these parasites through the estimation of heritabilities and genetic correlations of infection levels among Babesia bigemina (BigIL), B. bovis (BovIL), and Anaplasma marginale (AmIL), and tick counts (TC). The predictive ability of single and multi-trait genomic prediction models was evaluated through various combinations of these traits. To our knowledge, this is the first genomic study to examine BigIL and AmIL. Infection levels of BigIL (n = 1,882), BovIL (n = 1,858), and AmIL (n = 1,523) were estimated from blood samples using real-time PCR. TC phenotypes (n = 5,867) were obtained by counting the number of parasites larger than 4.5 mm from the right-hand side of each animal. Genotypic data were available for 3,977 animals which were then imputed up to ∼777,000 SNP and, after quality control, 502,398 SNP remained for downstream analyses. Variance components for BigIL and AmIL and the genetic correlations between traits were estimated using a Bayesian approach. The single-step best linear unbiased prediction was used to estimate genomic breeding values (GBV). The heritability estimates for BigIL and AmIL were low at 0.094 and 0.090, respectively, suggesting high environmental influence levels for both traits. The genetic correlations between tick count and infection levels for BigIL (0.239), BovIL (0.160), and AmIL (-0.019) were low, as well as the correlation between AmIL and BovIL (0.043). The genetic correlations between BigIL and BovIL (0.524) and BigIL and AmIL (0.793) were high, which contributed to improved GBV accuracies when these traits were combined in multi-trait models in comparison to single-trait models. These results suggested that multi-trait genomic prediction models of infection levels for tick-borne diseases are preferable to single-trait models. Additionally, our results indicated that the TC data and the GBV based on them are not useful for predicting infection levels of BigIL, BovIL, and AmIL.

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBVHMU vs. GBVLMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBVLMU cows, whereas GBVHMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

45 Effects of Genomic Residual Feed Intake on Performance, Feed Efficiency and Greenhouse Gas Emissions in Holstein Heifers

Abstract The objective of this study was to examine the differences in performance, feed efficiency, and greenhouse gas emissions in Holstein heifers with divergent genomic residual feed intake (RFIg). Holstein heifers (n = 55; initial BW = 351 ± 64 kg) were classified as either low RFIg (n = 29) or high RFIg (n = 26) based on RFIg values determined from their genomic breeding values (Ecofeed index, STgenetics). Heifers were blocked by RFIg, stratified by BW and assigned to 1 of 2 pens, each equipped with 4 electronic feedbunks (GrowSafe Systems), with 1 pen equipped with a GreenFeed real-time gaseous-exchange measurement system (GEM; C-Lock). Heifers were fed a corn-silage based total mixed ration twice daily. Individual dry matter intake (DMI) was collected for 84 d and BW measured weekly. Phenotypic residual feed intake (RFIp) was calculated as the residual from the regression of DMI on average daily gain (ADG) and mid-test BW0.75. As only 1 of the 2 pens had a GEM system, during the 84-day study heifers were rotated between the two pens at 14 day intervals resulting in each group of heifers having three 14-day gaseous exchange measurement periods. A time series analysis of gaseous exchange measurements, with a repeated measure of measurement period, fixed effect of RFIg class and random effect of pen was conducted. A mixed model with RFIg class as fixed effect and pen as a random effect was used for analysis of the other response variables. There were no differences in initial or final BW and ADG for heifers with divergent RFIg; however, the low RFIg heifers consumed 7.3% less (P &amp;lt; 0.05) feed per day than their high RFIg counterparts. Consequently, low RFIg heifers exhibited a more favorable (P &amp;lt; 0.05) RFIp, with a RFIp of -0.188 and 0.211 kg/d for low and high RFIg heifers, respectively. There were no RFIg class by measurement period interactions (P &amp;gt; 0.10) for the greenhouse gas emissions measured. Low RFIg heifers had 7.7% less (P &amp;lt; 0.05) methane emissions (g/d) and 6.1% less (P &amp;lt; 0.05) carbon dioxide production (g/d) than their high RFIg counterparts. However, methane yield (g methane/kg DMI) was similar (P &amp;gt; 0.05) for low and high RFIg heifers. These results indicate that selection for low RFIg has the potential to identify cattle with favorable feed efficiency phenotypes. Additionally, selection based on RFIg provides the opportunity to produce cattle with fewer greenhouse gas emissions. Further research is warranted into selection based on RFIg through genomic indexes to improve feed efficiency and mitigate the environmental impact of cattle production.

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole-genome SNP chip panels.

The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single-trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single-trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.

Induced endometritis in early lactation compromises production and reproduction in dairy cows

Objectives of this experiment were to study the effect of infusing utero-pathogenic bacteria to induce endometrial inflammation on productive performance in early lactation and subsequent reproduction. Although endometritis is associated with perturbed reproduction, numerous factors may contribute to the observed association. It was hypothesized that induced endometrial inflammation, resulting in localized and systemic inflammatory responses, compromises production and reproduction. Holstein cows without clinical disease and with less than 18% polymorphonuclear leukocytes (PMN) in endometrial cytology on d 31 ± 3 postpartum had their estrous cycle synchronized. Cows were blocked by parity and genomic breeding value for cow conception rate and, within block, assigned randomly to remain as untreated controls (CON; n = 37) or to receive an intrauterine infusion of 5.19 × 108 cfu Escherichia coli and 4.34 × 108 cfu Trueperella pyogenes during the luteal phase to induce endometrial inflammation (INF; n = 48). Endometrial cytology was taken on d 2 and 7 after treatment to evaluate the proportion of PMN. Rectal temperature, dry matter intake, and yields of milk and components were measured in the first 7 d after treatment. Blood serum was analyzed for concentration of haptoglobin. Leukocytes were isolated from blood on d 2 and 7 after treatment and on d 19 after artificial insemination (AI) and mRNA was quantified for a select group of genes. Cows received AI and reproduction was followed for 300 d postpartum. Bacterial infusion induced endometrial inflammation with increased proportions of PMN in the endometrial cytology on d 2 (4.4 ± 0.7 vs. 26.3 ± 2.8%) and 7 (10.9 ± 1.7 vs. 17.4 ± 2.1%) after treatment, resulting in increased mean prevalence of subclinical endometritis (>10% PMN; 23.3 ± 6.3 vs. 80.9 ± 5.1%). Rectal temperature did not differ between CON and INF, but the concentration of haptoglobin in serum tended to increase in INF compared with CON (113 ± 14 vs. 150 ± 16 µg/mL). Induced endometrial inflammation reduced yields of milk (44.9 ± 0.8 vs. 41.6 ± 0.8 kg/d), protein (1.19 ± 0.03 vs. 1.12 ± 0.03 kg/d), and lactose (2.17 ± 0.04 vs. 2.03 ± 0.04 kg/d) and tended to reduce dry matter intake (20.7 ± 0.5 vs. 19.4 ± 0.6 kg/d) in the first 7 d after treatment. Indeed, the reduction in milk yield lasted 4 wk. However, treatment did not affect yields of energy-corrected milk or fat because treatment with INF increased the concentration of fat in milk (3.54 ± 0.10 vs. 3.84 ± 0.10%). Induced endometrial inflammation reduced pregnancy per AI at all inseminations (33.4 ± 5.1 vs. 21.6 ± 3.7%) and the hazard of pregnancy (0.61; 95% CI = 0.36-1.04), which extended the median days open by 24 d. Blood leukocytes from INF cows had increased mRNA expression of the pro-inflammatory gene IL1B on d 2 and 7 after treatment, but reduced expression of the IFN-stimulated genes ISG15 and MX2 on d 19 after AI. Induced endometrial inflammation depressed production and caused long-term negative effects on reproduction in lactating dairy cows.

Mixed reference population in genomic evaluation for clinical mastitis in Czech Holstein cattle

The accuracy of genomic breeding values (GEBV) for clinical mastitis (CM) of Czech Holstein cattle was analysed. The single-step genomic method and mixed genomic reference populations were employed. Dataset included 92,388 Holstein cows and 160,426 lactations. CM lactation incidence was 19.05%. Cows calved between 2017 and 2022 in 119 herds. A total of 4,969 Holstein sires and 35,814 Holstein females were genotyped. Three genomic matrices were used, two of them encompassing females. The linear animal model with repeatability included fixed effects of herd,year-season and parity_age at calving. The highest average accuracy of GEBV occurred for genotyped cows. SD of GEBV accuracy for bulls was significantly higher than these for cows. The accuracy of genotyped cows with health phenotypes ranged from 0.06 to 0.51. For the genotyped bulls born in 2021 and 2022, maximal accuracy was 0.37; for genotyped heifers, maximal accuracy was 0.42. The highest average GEBV accuracy occurred for the reference population with genotyped bulls and genotyped cows with phenotypes. The average accuracy for the young genotyped bulls increased by 1p. p. when phenotyped cows were considered in the reference genomic population. The cow's GEBV accuracy benefited from including their genotypes in the prediction. It has been confirmed that due to the expansion of the genomic reference population to include a group of genotyped cows with phenotypes, the individual reliability of GEBV for CM has increased.

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values.

The impact of inclusion of genome-wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information from animal genotypes is incorporated into the genetic evaluations. New genetic parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well-linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un-phenotyped animals.

Impact of mixing between parallel year groups on genomic prediction in Atlantic salmon breeding programmes under random selection

A commercial breeding programme in Atlantic salmon utilises a four-year generation interval with four parallel breeding populations. In this study, we develop a computer simulation of a salmon breeding programme and explore the impact of gene flow between the parallel year groups on the accuracy of genomic prediction within and between breeding lines. We simulated four parallel lines for 10 discrete generations with random selection and different mixing rates between parallel year groups. The genetic distance between fish (as a measure of diversity) and the accuracy of estimated genomic breeding values were used as criteria of comparison. With no mixing the genetic distance increased between populations, the genetic variation within populations decreased and there was no increase in accuracy when combining data across populations. Even a low percentage of mixing decreased the genetic distance between populations and increased the genetic variation within populations. The higher the percentage of mixing the faster the lines became more similar. The accuracy of prediction climbed as the percentage of mixing increased. The increase in accuracy from the combined evaluation approach compared to the within evaluation approach was greater with an increased percentage of mixing. In conclusion, if there is no gene flow between populations the lines drift apart and there is no value in combining information across populations for genomic breeding value prediction. Only a low amount of mixing between lines brings the lines closer together and facilitates the use of information across lines to improve breeding value prediction. Optimising gene flow between lines should be an integral part of salmon breeding programme design.

First large-scale genomic prediction in the honey bee

Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyzes the accuracy and bias of pedigree-based and genomic breeding values for honey yield, three workability traits, and two traits for resistance against the parasite Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the accuracy of pedigree-based estimated breeding values was 0.12 for honey yield, and ranged from 0.42 to 0.61 for the workability traits. The inclusion of genomic marker data improved these accuracies to 0.23 for honey yield, and a range from 0.44 to 0.65 for the workability traits. The inclusion of genomic data did not improve the accuracy of the disease-related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. For all traits except the Varroa resistance traits, the bias with genomic methods was on a similar level compared to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees.

A Comprehensive Strategy Combining Feature Selection and Local Optimization Algorithm to Optimize the Design of Low-Density Chip for Genomic Selection

Design of low-density SNP chips provides an opportunity for wide application of genomic selection at lower cost. A novel strategy referred to as the “block-free” method is proposed in this study to select a subset of SNPs from a high-density chip to form a low-density panel. In this method, Feature Selection using a Feature Similarity (FSFS) algorithm was first performed to remove highly correlated SNPs, and then a Multiple-Objective, Local-Optimization (MOLO) algorithm was used to pick SNPs for the low-density panel. Two other commonly used methods called the “uniform” method and the “block-based” method were also implemented for comparison purposes. A real pig dataset with 7967 individuals from three breeds containing 43,832 SNPs was used for comparison of the methods. In terms of genotype imputation accuracy and genomic prediction accuracy, our strategy was superior in most cases when the densities were lower than 1K. The genotype imputation accuracy from the low-density chip compared to the original high-density chip was higher than 90% in all pig breeds as the density increased to 1K. In addition, the accuracies of predicted genomic breeding values (GEBV) calculated using the imputed panel were nearly 90% of estimates from the original chip for all traits and breeds. Our strategy is effective to design low-density chips by making full use of information of close relationships for genomic selection in animals and plants.

Associations between insulin-like factor 3, scrotal circumference and semen characteristics in young Norwegian Red bulls.

With the integration of genomic selection in the cattle artificial insemination (AI) industry, bulls are selected for their semen production capacity and fertility at a younger age than previously. Norwegian Red bull calves selected as candidates to become future AI bulls based on their genomic breeding value are kept in a performance testing station from around the age of 3-12months, allowing for sample collection and analysis of different parameters during their pre- and peripubertal period. Insulin-like factor 3 (INSL3) is a small peptide hormone specifically secreted by the mature Leydig cells of the testes. In the foetus, it induces the first phase of testicular descent and is considered to reflect Leydig cell development during puberty; it could therefore be an interesting early indicator of future semen production capacity. The main objective of our study was to evaluate the relationship between INSL3, scrotal circumference (SC), and semen characteristics. This is the first time INSL3 was measured in the Norwegian Red population. We collected blood samples for analysis of INSL3 from 142 Norwegian Red bulls at the performance testing station and measured their SC on the same day. Altogether, measurements were made at four time points: upon arrival at the performance testing station (quarantine (Q): 2-5months) and later at approximately 6, 9 and 12months of age. Information on season and place of birth were made available from the database of the breeding company Geno, together with data on semen characteristics from the test station and the AI station. The median SCs for age groups Q, 6, 9, and 12 were 15, 21.5, 29, and 34cm, respectively. INSL3 was shown to be positively correlated with SC (R = 0.4) but not with any of the semen characteristics. Similarly, we found no correlation between SC and sperm characteristics from data on ejaculates analysed at the performance testing station and AI station. The mean sperm volume for the 31 selected bulls with at least 10 ejaculates produced in the AI station increased from 2.3ml at the performance testing station to 6.4ml at the AI station. The corresponding increase in mean sperm concentration was from 497 million/ml to 1049 million/ml. We conclude that INSL3 exhibits high inter-individual variability in the Norwegian Red bull population, which cannot be explained by the parameters measured in this study. At present, INSL3 cannot be used as a biomarker of sperm production in this breed.

Assessing accuracy of genomic breeding values of selection candidates under biosecurity restrictions by progeny testing in Chinese shrimp Fenneropenaeus chinensis

Assessing accuracy of genomic breeding values of selection candidates under biosecurity restrictions by progeny testing in Chinese shrimp Fenneropenaeus chinensis