Estimated Breeding Values Research Articles

Genomic Diversity of U.S. Katahdin Hair Sheep.

In the late 1950s, Katahdin hair sheep were developed as a composite breed of medium size and moderate prolificacy, with potential to express resistance to gastrointestinal nematodes. With increasing popularity and the recent adoption of genomic prediction in their genetic evaluation, there is a risk of decreasing variation with selection based on genomically enhanced estimated breeding values. While Katahdin pedigrees are readily available for monitoring diversity, they may not capture the entirety of genetic relationships. We aimed to characterise the genomic population structure and diversity present in the breed, and how these impact the size of a reference population necessary to achieve accurate genomic predictions. Genotypes of Katahdin sheep from 81 member flocks in the National Sheep Improvement Program (NSIP) were used. After quality control, there were 9704 animals and 31,984 autosomal single nucleotide polymorphisms analysed. Population structure was minimal as a single ancestral population explained 99.9% of the genetic variation among animals. The current Ne was estimated to be 150, and despite differences in trait heritabilities, the effect of Ne on the accuracy of genomic predictions suggested the breed should aim for a reference population size of 15,000 individuals. The average degree of inbreeding estimated from runs of homozygosity (ROH) was 16.6% ± 4.7. Four genomic regions of interest, previously associated with production traits, contained ROH shared among > 50% of the breed. Based on four additional methods, average genomic inbreeding coefficients ranged from 0.011 to 0.012. The current population structure and diversity of the breed reflects genetic connectedness across flocks due to the sharing of animals. Shared regions of ROH should be further explored for incorporation of functional effects into genomic predictions to increase selection gains. Negative impacts on genetic diversity due to genomic selection are not of immediate concern for Katahdin sheep engaged in NSIP.

The Effect of Preselection on the Level of Bias and Accuracy in a Broiler Breeder Population, a Simulation Study.

Many breeding programmes have to perform preselection, as genotyping and phenotyping all potential breeder candidates is often not a feasible option. There is need to understand how preselection affects the quality of the genomic estimated breeding values (EBVs) at final selection and thereby can affect genetic progress. This simulation study evaluated nine different preselection strategies in a broiler breeder programme and their effect on the quality of the (genomic) EBVs and genetic progress for three different traits: body weight (Body Weight), residual feed intake (RFI) and body weight gain (Gain). All birds have Body Weight recorded at preselection, but only the preselected birds were phenotyped for RFI and Gain and genotyped. The following criteria and intensities were studied: preselection based on phenotypic Body Weight (P), on a BLUP index (B) or on an ssGBLUP Index (G). Additionally, all criteria were studied with three different selection intensities, 10% of the males and 30% of the females (P10, B10, G10), 15% of the males and 45% of the females (P15, B15, G15) and 20% of the males and 60% of the females (P20, B20, G20). The accuracy at preselection with G10 was more accurate than B10 for both RFI and Gain (0.71 vs. 0.58 and 0.65 vs. 0.55 respectively), and also G15 was more accurate than B15 for both RFI and Gain (0.72 vs. 0.63 and 0.67 vs. 0.64 respectively); thus, the difference in accuracy reduces with an increasing number of birds being preselected. Differences in accuracy at final selection were mostly notable in the RFI trait between P10, B10 and G10, where G10 showed the highest accuracy (0.82 vs. 0.84 vs. 0.86 respectively). This difference in accuracy for RFI disappeared when more animals were preselected. For Body Weight and Gain, the BLUP preselection resulted in the highest accuracy at final selection when selection intensity decreased. The dispersion bias of EBVs at final selection was most pronounced in the P10 and P15 for Body Weight (0.81 and 0.92) but disappeared at P20 (0.97). The dispersion bias for all other criteria and traits was relatively small. Genetic progress was mostly affected when P10 or P15 was used at preselection, where the progress in Body Weight was noticeably higher, but prominently lower for RFI and Gain. The BLUP and ssGBLUP preselection had very similar genetic progress across traits and showed comparable improvements in the selection index. In conclusion, with high preselection intensity, the use of ssGBLUP at preselection might be favoured as there is an improvement in genetic progress across traits in all scenarios, which is due to the increased preselection accuracy. When preselection intensity decreases, the benefit of using ssGBLUP over BLUP at preselection disappears.

On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness

BackgroundCross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of this work was to evaluate the LR method's performance under pedigree errors and weak connectedness typical in beef cattle genetic evaluations, through simulation.MethodsWe simulated a beef cattle population resembling the Argentinean Brangus, including a quantitative trait selected over six pseudo-generations with a heritability of 0.4. This study considered various scenarios, including: 25% and 40% pedigree errors (PE-25 and PE-40), weak and strong connectedness among herds (WCO and SCO, respectively), and a benchmark scenario (BEN) with complete pedigree and optimal herd connections.ResultsOver six pseudo-generations of selection, genetic gain was simulated to be under- and over-estimated in PE-40 and WCO, respectively, contrary to the BEN scenario which was unbiased. In genetic evaluations with PE-25 and PE-40, true biases of − 0.13 and − 0.18 genetic standard deviations were simulated, respectively. In the BEN scenario, the LR method accurately estimated bias, however, in PE-25 and PE-40 scenarios, it overestimated biases by 0.17 and 0.25 genetic standard deviations, respectively. In herds facing WCO, significant true bias due to confounding environmental and genetic effects was simulated, and the corresponding LR statistic failed to accurately estimate the magnitude and direction of this bias. On average, true dispersion values were close to one for BEN, PE-40, SCO and WCO, showing no significant inflation or deflation, and the values were accurately estimated by LR. However, PE-25 exhibited inflation of EBVs and was slightly underestimated by LR. Accuracies and reliabilities showed good agreement between true and LR estimated values for the scenarios evaluated.ConclusionsThe LR method demonstrated limitations in identifying biases induced by incomplete pedigrees, including scenarios with as much as 40% pedigree errors, or lack of connectedness, but it was effective in assessing dispersion, and population accuracies and reliabilities even in the challenging scenarios addressed.

Stacking beneficial haplotypes from the Vavilov wheat collection to accelerate breeding for multiple disease resistance.

We revealed the neglected genetic relationships of resistance for six major wheat diseases and established a haploblock-based catalogue with novel forms of resistance by multi-trait haplotype characterisation. Genetic potential to improve multiple disease resistance was highlighted through haplotype stacking simulations. Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts, and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation, we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS, and SNB, by up to 48.1%, 35.2%, 29.1%, 12.8%, 18.8%, and 32.8%, respectively. Overall, our results highlight the genetic potential to improve MDR in wheat. The haploblock-based catalogue with novel forms of resistance provides a useful resource to guide desirable haplotype stacking for breeding future wheat cultivars with MDR.

Genomic evaluation of residual feed intake in US Holstein cows: insights into lifetime feed efficiency.

Residual feed intake (RFI) is an important trait of feed efficiency that has been increasingly considered in the breeding objectives for dairy cattle. The objectives of this study were to estimate the genetic parameters of RFI and its component traits, namely, dry-matter intake (DMI), body weight (BW), and energy-corrected milk (ECM), in lactating Holstein cows; we thus developed a system for genomic evaluation of RFI in lactating Holstein cows and explored the associations of the RFI of heifers and cows. The RFI values were calculated from 2,538 first (n = 2,118) and second (n = 420) lactation Holsteins cows between 2020 and 2024 as part of the STgenetics EcoFeed® program. Of the animals, 1,516 were heifers from the same research station with previously established RFI values . After quality control, 61,283 single-nucleotide polymorphisms were used for the analyses. Univariate analyses were performed to estimate the heritabilities of RFI and its components in lactating cows; bivariate analyses were then performed to estimate the genetic correlations between the RFI of heifers and lactating cows using the genomic best unbiased linear prediction method. Animals with phenotypes and genotypes were used as the training population, and animals with only genotypes were considered the prediction population. The reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of the training population's genomic estimated breeding values (GEBVs) and magnitudes of genomic relationships between the individuals in the training and prediction populations. The heritability estimates (mean ± SE) of the RFI, DMI, ECM, and BW were 0.43 ± 0.07, 0.44 ± 0.04, 0.40 ± 0.05, and 0.46 ± 0.04, respectively. The average reliability of the GEBVs for RFI from the training and prediction populations were 44% and 30%, respectively. The genetic correlations for the RFI were 0.42 ± 0.08 between heifers and first lactation cows and 0.34 ± 0.06 between heifers and first and second lactation cows. Our results show that the genetic components of RFI are not fully carried over from heifers to cows and that there is re-ranking of the individuals at different life stages. Selection of animals for feed efficiency on a lifetime basis thus requires accounting for the efficiencies during animal growth and milk production as a lactating cow.

Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction.

The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding.

Genetic aspects of major blood metabolites in the Italian Simmental cattle population

The Simmental breed is widely known for its resilience, robustness, and resistance to disease and therefore the incidence of ketosis in this breed is generally lower compared with Holsteins. Blood concentrations of nonesterified fatty acids (NEFA), BHB, and urea provide valuable information about the metabolic, health, and nutritional status of lactating animals. In the present study we estimated h2 of BHB, NEFA, and urea in blood predicted from milk mid-infrared spectra and assessed their genetic correlation with milk yield and composition traits in the Italian Simmental cattle breed using phenotypes of 3,549 cows in 207 herds. Two sets were considered: early (1,920 records - 1 per cow - between 5 and 35 DIM) and whole (14,378 records - at least 3 per cow - between 5 and 305 d in milk) lactation. In early lactation, h2 were 0.06 for blood BHB as is, 0.06 for log-transformed BHB, 0.18 for NEFA, 0.14 for log-transformed NEFA, and 0.05 for blood urea. In whole lactation, the h2 were 0.09 for blood BHB as is, 0.16 for log-transformed BHB, 0.03 for NEFA, 0.04 for log-transformed NEFA, and 0.04 for blood urea. As far as the genetic correlations were concerned, blood BHB was positively correlated with NEFA and blood urea. Blood BHB and NEFA were generally positively correlated with milk fat-to-protein ratio and milk BHB; blood BHB was negatively correlated with lactose content and positively with SCS. Sires' estimated breeding values for BHB (accuracy ≥0.60) were extrapolated for a-posteriori evaluation of the daughters' observed performance. The progeny of the top 5% of sires exhibited, on average, lower blood BHB, NEFA, and urea compared with the daughters of the bottom 5%. Overall, it is highly recommended to monitor the genetic variability of the metabolic traits in the dual-purpose Italian Simmental breed to monitor the metabolic diseases incidence in future generations.

Genomic selection of resistance to acute hepatopancreatic necrosis disease in Litopenaeus vannamei

Acute hepatopancreatic necrosis disease (AHPND) is a major bacterial disease caused by Vibrio parahaemolyticus (VPAHPND) that results in significant economic losses to Pacific white shrimp Litopenaeus vannamei aquaculture. Breeding of disease-resistant broodstocks is regarded as a fundamental strategy for solving the disease problem. This study evaluated the feasibility of genomic selection (GS) for enhancing the AHPND resistance of L.vannamei. Following the VPAHPND challenge, the survival time was utilized as a phenotypic measure to assess the AHPND resistance. The reference population consisted of 447 genotyped and 288 no-genotyped individuals with phenotypes. The candidate population consisted of 206 uninfected and genotyped individuals. The liquid chip “Yellow Sea Chip No.1” with 40 K single nucleotide polymorphism (SNP) generated genotypes for the reference and candidate populations. Heritabilities of survival time obtained using the average information REML method with a relationship matrix (A or H) and an animal model were 0.16 ± 0.06 and 0.22 ± 0.07, respectively. Predictive accuracies of pedigree-based best linear unbiased prediction (PBLUP), single-step genomic BLUP (ssGBLUP), and single-step Bayesian regression (ssBR) were evaluated using random, full-sib and between-family cross-validation. Compared to PBLUP, the predictive accuracies of ssGBLUP and ssBR for the survival time increased by 15.00 % and 67.50 % in random cross-validation, and 13.33 % and 53.33 % in full-sib cross-validation, respectively. All methods exhibited a reduction in predictive accuracy when moving from the random cross-validation or full-sib cross-validation to the between-family cross-validation. The predictive accuracies of Genomic BLUP (GBLUP) and Bayesian A (BayesA) were evaluated using full-sib cross-validation. Compared to GBLUP, the predictive accuracy of BayesA increased by 33.33 %. ssBR was used to predict the candidates' genomic estimated breeding values (GEBVs). 80 candidates with high GEBVs, 86 with medium GEBVs, and 40 with low GEBVs were selected to breed offspring with a spectrum of resistance levels. In the VPAHPND challenge test, the high resistance offspring had mean survival times that were 10.15 % longer than the moderate resistance offspring and 102.01 % longer than the low resistance offspring. This study validates the practicality and success of GS in improving resistance to AHPND in L.vannamei.

Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.

In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes.

Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population.

Genomic Selection (GS) in tree breeding optimizes genetic gains by leveraging genomic data to enable early selection of seedlings without phenotypic data reducing breeding cycle and increasing selection intensity. Traditional assessments of the potential of GS in forest trees have typically focused on model performance using cross-validation within the same generation but evaluating effectively realized predictive ability (RPA) across generations is crucial. This study estimated RPAs for volume growth (VOL), wood density (WD), and pulp yield (PY) across four generations breeding of Eucalyptus grandis. The training set spanned three generations, including 34,461 trees with three-year growth data, 6,014 trees with wood quality trait data, and 1,918 trees with 12,695 SNPs (single nucleotide polymorphisms) data. Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. RPAs were estimated using three GS targets (individual trees, trees within families, and families), two selection criteria (single- and multiple-trait), and training populations of either all 1,918 genotyped trees or the 67 direct ancestors of the selection candidates. RPAs were higher for wood quality traits (0.33 to 0.59) compared to VOL (0.14 to 0.19) and improved for wood traits (0.42 to 0.75) but not for VOL when trained only with direct ancestors, highlighting the challenges in accurately predicting growth traits. GS was more effective at excluding bottom-ranked candidates than selecting top-ranked ones. The between-family GS approach outperformed individual-tree selection for VOL (0.11 to 0.16) and PY (0.72 to 0.75), but not for WD (0.43 vs. 0.42). Furthermore, higher levels of relatedness and lower genotype by environment (G × E) interaction between training and testing populations enhanced RPAs for VOL (0.39). In summary, despite limited effectiveness in ranking top VOL individuals, GS effectively identified low-performing individuals and families. These multi-generational findings underscore GS's potential in tree breeding, stressing the importance of considering relatedness and G × E interaction for optimal performance.

Comparison of blood parameters in two genetically different groups of horses for functional longevity in show jumping.

In order to find early selection criteria to improve the longevity of show jumping horses, a specific protocol was designed. Before entering competition, young horses selected from extreme stallions for longevity were measured for many traits, including blood parameters. Blood samples were taken from 952 horses aged 2-4years old, sired by two groups of stallions: one with unfavorable (U) and the other with favorable (F) extreme estimated breeding values for functional longevity. These breeding values were previously calculated from data on 202,320 horses that participated in show jumping competitions between 1985 and 2022. Functional longevity was defined as time spent in competition, adjusted for the level of performance. The 59 measured parameters included hematology, proteins, cytokines, liver and kidney function, bone and joint health, oxidative stress and endocrinology. Heritability was estimated using a mixed model that accounted for the effect of age, sex, estimated weight, visit (place and date of collection), and animal random additive value with 10,280 horses in pedigree. A Partial Least Square logistic regression was performed to predict the sire group. Age, sex and estimated weight significantly affected 36, 19 and 16 variables, respectively. The visit had a significant effect on all variables. Heritability estimates were high, with 75% higher than 0.20% and 30% higher than 0.50. The most heritable traits included mean corpuscular volume (0.92, se 0.11), mean corpuscular hemoglobin (0.90, se 0.11), white blood cells (0.55, se 0.13), total alkaline phosphatase (0.68, se 0.12) and percentage of γ-globulin (0.57, se 0.12). The logistic regression that predicted the group of sires favorable for longevity identified 16 significant variables. Key findings included: lower mean corpuscular hemoglobin (p-value < 0.001), lower mean corpuscular volume (p-value < 0.001), lower number of white blood cells (p-value < 0.01), higher percentage of intestinal and bone alkaline phosphatase (p-value < 0.01) for a lower total alkaline phosphatase (p-value < 0.01), higher percentage of α2-globulin (p-value < 0.001) and lower percentage of β1-globulin (p-value < 0.01). Blood parameters measured at rest in young horses may be predictive of their genetic value for functional longevity in show jumping.

Genomic Prediction and Genome-Wide Association Studies for Productivity, Conformation and Heat Tolerance Traits in Tropical Smallholder Dairy Cows.

Genomic selection (GS) and genome-wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h2) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty-two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single-test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat-stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10-fold cross-validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h2 (using the genotype information to build relationships among animals) were moderate (0.20-0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08-0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32-0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47-1.18 Mb on BTA14. Moderate to high h2 and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS.

Adding gastrointestinal parasite resistance to the breeding objective in hair sheep: initial steps.

The U.S. Maternal Hair index was designed by the National Sheep Improvement Program (NSIP) to increase total weight of lamb weaned per ewe lambing (TW). Producers are interested in adding gastrointestinal nematode (GIN) resistance to this breeding objective since parasitism causes substantial economic losses. The NSIP provides producers with estimated breeding values (EBV) for post-weaning fecal egg count (PFEC), an indicator of GIN resistance. Our objective was to gauge the effects of including PFEC as another selection criterion and goal trait. Selection index theory was used to construct 11 indexes. First was a benchmark index with TW as the goal. Weaning weight, and number of lambs born and weaned, were selection criteria. An index was then designed with PFEC added as a selection criterion. In 9 more indices, PFEC was also included as a goal trait, where the economic value of TW was scaled relative to that of PFEC. PFEC received a scaled economic value of -1 with that of TW increasing from +1 to +5 at +0.5 increments. Selection criteria were modelled as EBV or phenotypes. Annual genetic responses in goal traits were predicted. The top 3% of males and 26% of females were selected. Breeding values and phenotypes were simulated for 200 lambs by Cholesky decomposition and used to generate index scores, with 100 replicates run. Concordances with the animals selected in the benchmark scenario were determined. Using EBV as selection criteria, TW increased by 1.46kg/yr in the benchmark scenario. However, unfavorably, PFEC increased by 2.24%/yr. When using phenotypic criteria, TW increased by 0.52kg/yr and PFEC by 0.28%/yr. Once added as a goal trait, PFEC decreased regardless of the scaled economic value of TW. However, responses in TW were also reduced, although less so as its scaled economic value increased. A scaled economic value of +3 for TW appeared reasonable over other choices with 79% of the emphasis placed on TW in the breeding objective. With EBV as selection criteria, PFEC declined by 7.96%/yr with 98% of the response in TW retained. Also, on average, 64% of males and 80% of females were chosen in common with the benchmark. With phenotypic selection criteria, PFEC declined by 5.13%/yr and 94% of the response in TW was retained; 61% of males and 80% of females were chosen in common with the benchmark. Implementing an index with scaled economic values of +3 for TW and -1 for PFEC would balance gains in TW with reductions in PFEC.

The impact of myostatin variants on growth traits in South African Bonsmara beef cattle

Double muscling occurs when the myostatin (MSTN) gene is deactivated due to a series of mutations, leading to uncontrolled muscle growth and excessive muscle fiber accumulation, as the gene can no longer effectively regulate muscle development. This study aimed to assess the impact of MSTN variants and their combinations on growth traits, namely direct birth weight (BWDIR), direct weaning weight (WWDIR), average daily gain (ADG) and feed conversion ratio (FCR) in the South African (SA) Bonsmara. Genomically enhanced estimated breeding value (GEBVs) for traits of interest, and MTSN genotypes for SA Bonsmara animals were available for the study. Thirteen MSTN variants (Nt821, Q204X, F94L, E226X, E291X, C313Y, Nt419, S105C, D182N, Nt414, Nt324, Nt267, and Nt748) were routinely genotyped using the IDBv3 SNP array. Genotypic frequencies of MSTN variants ranged from 1.18% for Q204X to 35.02% for Nt748. No association was observed between the Nt267 variant and any growth traits, while both Nt748 and Nt414 variants affected WWDIR, ADG and FCR (p < 0.05). The results of the effect of multiple variants on growth traits indicated that there was an additive effect when more than one MSTN variant was present in an individual. This study is the first study to report the impact of MSTN variants on traits of economic importance in the SA Bonsmara breed.

Comparing genomic studies in animal breeding and human genetics: Focus on disease-related traits in livestock.

Genomic studies of diseases can be divided into two types: (1) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and (2) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease on the basis of all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.

Experimental evaluation of effectiveness of genomic selection for resistance to northern corn leaf blight in maize.

Northern corn leaf blight (NLB) caused by Setosphaeria turcica (Luttrell) Leonard & Suggs is a severe foliar disease in maize. Resistance to NLB is complexly inherited and controlled by several quantitative trait loci (QTL) distributed across the genome. Phenotype and DNA marker-based selection for resistance to NLB is expected to be effective. Hence, an investigation was carried out to predict the genetic value of selection candidates for resistance to NLB and compare the accuracies of genomic prediction in two F2:3 populations of two crosses (CM212 × MAI172; CM202 × SKV50) derived from contrasting parents. Linkage analysis using 297 polymorphic SNPs in population-1 and 290 polymorphic SNPs in population-2 revealed ten linkage groups spanning 3623.88cM and 4261.92cM with an average distance of 12.40cM and 14.9cM in population-1 and population-2, respectively. Location-wise and pooled data across locations identified common QTLs on linkage groups 1 and 6 in population-1 and 3 and 8 in population-2. The prediction accuracy of the QTL mapping (9.92 and 9.10 for population-1 and population-2, respectively) was based on only a few markers, which explained higher percent phenotypic variation. The prediction accuracies of the genomic estimated breeding values in the present investigation were relatively low in population-1 (0.24 to 0.26) and population-2 (0.29-0.32) compared to the expected accuracies. This could be due to fewer polymorphic markers and a small training/population size. Though the GS prediction accuracies were relatively low, they were significantly higher than QTL mapping, which promises better genetic gain per cycle. The resistant progenies from both populations were advanced to derive inbred lines and crossed with four different testers in line × tester mating design to test for their combining ability and effectiveness of genomic selection. High overall general combining ability was exhibited by 21 inbred lines. Among F1s, 48% were assigned high overall specific combining ability status. Out of the 136 single crosses, seven recorded significant positive standard heterosis over the best check for grain yield. Twenty-five inbreds with high GEBVs were crossed with four testers to obtain 100 F1s and evaluated for their response to NLB. The majority of hybrids displayed moderate to resistant reaction to NLB either in combination with susceptible or resistant testers indicating the effectiveness of selection based on high GEBVs.

Duration, day, and hour postmortem influence the color of farmed Atlantic salmon: Insights into phenotypic measurements

Fillet color of Atlantic salmon plays a large role in consumer purchasing decisions; however, color can be influenced by a variety of factors, including diet and genetics. In a breeding program, selection for color fillets relies on accurate measurements taken during harvest evaluations. The harvest of fish can take place over several days to several weeks, with a large variation in the amount of time taken to process a single fish which could impact the recorded value of fillet color, ultimately affecting estimated breeding values (EBVs) for genetic selection. Therefore, we evaluated the impact of the 1) day of harvest, 2) hour of the day, and 3) duration between entering the processing facility and having color assessed (using SalmoFan and Minolta Chroma Meter, i.e., redness or a*, yellowness or b*, and lightness or L*) on the recorded fillet color of North American Atlantic salmon (Salmo salar) and on the EBVs. A total of 10,380 animals with color measurements were included from eight year classes (2011 to 2019). The color measurements (Minolta Chroma Meter and SalmoFan) took place over an average of three days. The phenotypes of animals were fitted with linear animal mixed models in ASReml-R to determine the magnitude of time related effects on color traits, of which there was a significant impact on color depending on duration, as well as the day measurements were taken (p-value <0.05). Generally, longer harvest days, as the duration to process fish increased, the L* also increased, however, the opposite was observed for shorter harvest days. There was also a significant effect of hour of the day on SalmoFan scores in some year classes (up to a − 1.75 score difference over a 24-h period), where as the hour increased, lower SalmoFan measurements were perceived, possibly due to eye fatigue or other human-operator biases. There were moderate to high heritabilities ranging from 0.298 (L* in the 2018 year class) to 0.864 (a* in the 2014 year class). When days of harvest, hour of the day and the duration were not accounted for in the models, heritabilities for color measurements were generally lower than the models with these time effects. The L* and SalmoFan showed the largest changes in most year classes. The EBVs of color measurements for each individual changed between models where these fixed time effects were included or not, indicating that these effects have the potential to bias breeding decisions. Therefore, it will be essential to consider the duration, day, and hour of measurement when evaluating the phenotypes of individuals for unbiased breeding decisions.

Comparison of predictive ability of single-trait and multitrait genomic selection models for body growth traits in Maiwa yaks

Yaks are grazed extensively on the Qinghai–Tibet Plateau, which has a long history of semi-domestication. The predicted weight of yaks over consecutive years helps make strategic decisions when selecting yak calves for breeding. To achieve more accurate predictions of genomic estimated breeding values, we used a dataset comprising the genotype and weight records of 396 Maiwa yaks collected from 2015 to 2020. We compared the predictive accuracy of the genome best linear unbiased prediction (GBLUP) model with that of six other models. Based on the GBLUP model, we applied two prediction strategies. In the first strategy, the year was treated as a fixed effect in the GBLUP model, and the kinship from all individuals and the markers were treated as random effects. In the second strategy, all individuals were divided into six age groups, with GBLUP performed for each group, and the phenotypes of the closest age groups were treated as fixed effects. Although the GBLUP model provided better prediction accuracy than other single-trait models, most of the predictive capacity was derived from the best linear unbiased estimation. Additionally, incorporating the phenotype of the closest age group as a factor in multitrait prediction enhanced the accuracy of the model. Our findings provide a robust and credible strategy for predicting continuous economic traits in the presence of strong correlations.

First insight of the genome-wide association study and genomic prediction into enteritis disease (Vibrio harveyi) resistance trait in the lined seahorse (Hippocampus erectus).

Enteritis caused by Vibrio is a highly die-off disease that severely impeded substantial production in seahorse aquaculture. In the present study, challenged with LD50 of concentration of Vibrio harveyi, a total of 161 of susceptible and 166 of resistant individuals were allocated into binary survival phenotypes, thus, to firstly investigate the genetic architecture by genome-wide association study (GWAS) analysis, as well as to evaluate the feasibility of genomic selection (GS) in enteritis disease resistance trait of the lined seahorse Hippocampus erectus. Results indicated that the heritability for resistance to Vibrio harveyi was estimated to be 0.10. And a set of 10 significant/suggestive SNPs in a multiple chromosomes localization were identified, explaining 7.76% to 13.28% of genetic variance. Associated 82 of candidate genes were clustered into signal transduction, cell proliferation, response of external stress, bacteria defence, and anti-inflammatory processes. Moreover, the potential performance of genomic selection (GS) in application in selective breeding for enteritis disease resistance seahorses was assessed by genomic prediction (GP). In general, the predictive accuracy of the genomic estimated breeding value (GEBV) of BayesC exceeded the rrBLUP, BayesA, RKHS, and SVM models while with no significant difference. And the GWAS-informative SNPs was significantly superior in efficience than random selected markers by comparison of predictive performance on different selection strategies of SNPs. Overall, the genetic basis of enteritis disease resistance trait in the lined seahorse is a polygenic genetic architecture. SNPs associated with the important genes of cathepsin L1-like previously reported with respect to disease resistance are consider as potential molecular markers of genetic breeding. Furthermore, GS approach is an appropriate, effective, and less-cost application in breeding enteritis disease-resistant seahorses.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.