Accuracy Of Genetic Evaluation Research Articles

Information of Growth Traits Is Helpful for Genetic Evaluation of Litter Size in Pigs.

Litter size is an important trait in pig production. But selection accuracy for this trait is relatively low, compared with production traits. This study, for the first time, investigated the improvement of genetic evaluation of reproduction traits such as litter size in pigs using data of production traits as an additional information source. The data of number of piglets born alive per litter (NBA), age at 100 kg of body weight (Age100), and lean meet percentage (LMP) in a Yorkshire population were analyzed, using either a single-trait model or the multitrait model that allows us to account for environmental correlation between reproduction and production traits in the situation that one individual has only one record for a production trait while multiple records for a reproduction trait. Accuracy of genetic evaluation using single-trait and multitrait models were assessed by model-based accuracy (Rm) and validation accuracy (Rv). Two validation scenarios were considered. One scenario (Valid_r1) was that the individuals did not have a record of NBA, but Age100 and LMP. The other (Valid_r2) was that the individuals did not have a record for all the three traits. The estimate of heritability was 0.279 for Age100, 0.371 for LMP, and 0.076 for NBA. Genetic correlation was 0.308 between Age100 and LMP, 0.369 between Age100 and NBA, and 0.022 between LMP and NBA. Compared with the single-trait model, the multitrait model including Age100 increased prediction accuracy for NBA by 3.6 percentage points in Rm and 5.9 percentage points in Rv for the scenario of Valid_r1. The increase was 1.8 percentage points in Rm and 3.8 percentage points in Rv for the scenario of Valid_r2. Age100 also gained in the multitrait model but was smaller than NBA. However, LMP did not benefit from a multitrait model and did not have a positive contribution to genetic evaluation for NBA. In addition, the multitrait model, in general, slightly reduced level bias but not dispersion bias of genetic evaluation. According to these results, it is recommended to predict breeding values using a multitrait model including growth and reproduction traits.

Comparison of different animal models for estimating genetic parameters for early growth traits and reproductive traits in Tianmu Sainuo sheep.

As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep.

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

221-OR: Retrospective Review of Genetic Mutations and Treatments in Patients with Monogenic Diabetes of the Youth (MODY)

Monogenic diabetes results from specific genetic mutations that result in diabetes mellitus. Clinical phenotypes differ based on the genetic defect, and treatment options are driven by the underlying genetic cause. Thus, accurate genetic diagnosis and evaluation of response to treatment is essential. Retrospective chart review was utilized to identify patients with MODY at a single institution who had confirmed genetic mutations. Demographics, co-morbidities, initial islet cell autoantibodies, diagnosis and treatment, and current treatments were evaluated. Patients diagnosed with MODY (n=92) comprised of 3% of the total patient population diagnosed with diabetes (78% T1DM, 19% T2DM) between 2000 to present day. For patients with MODY, the average age of diabetes diagnosis was 10.3 years and their mean HgbA1c was 8.0% at diagnosis. Three and a half percent of patients with MODY had ketoacidosis at diagnosis and 6.7% had positive islet cell autoantibodies. Among patients with MODY, the most common gene mutations were HNF1A, GCK, and HNF1B. Novel genetic mutations in PDX1, RFX6, BLK and KFL11 were also identified. Current treatment modalities of patients with MODY were compared (insulin, Metformin, Sulfonylureas, Metformin-Glyburide, insulin-Metformin, GLP-1 receptor agonists). For each medication, reduction in HgbA1c was seen at follow-up (0.05-1.15% reduction). All medications except for insulin and Metformin-Glyburide demonstrated a reduction in BMI at follow-up. The greatest reduction in BMI was observed in patients on GLP-1 receptor agonists and insulin-Metformin (3.3% and 8.89% respectively). Our study highlights the importance of genetic testing in patients who present with diabetes without ketoacidosis and that testing positive for islet antibodies does not preclude patients from a MODY diagnosis. Moreover, future investigation of GLP-1 receptor agonist as a treatment for MODY patients is warranted. Disclosure L.Deng: None. A.S.Shah: None. M.Krishnamurthy: None.

Effect of different types and levels of errors in sire pedigree on the accuracy of genetic evaluation in crossbred goats

Effect of different types and levels of errors in sire pedigree on the accuracy of genetic evaluation in crossbred goats

Early-onset colorectal cancer: A review of current knowledge.

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Although most prevalent among older people, its incidence above 50 years old has been decreasing globally in the last decades, probably as a result of better screening. Paradoxically, its incidence in patients below 50 years old [early-onset CRC (EO-CRC)] has been increasing, for reasons not yet fully understood. EO-CRC's increasing incidence is genre independent but shows racial disparities and has been described to occur worldwide. It follows a birth-cohort effect which probably reflects a change in exposure to CRC risk factors. Its incidence is predicted to double until 2030, which makes EO-CRC a serious public health issue. Both modifiable and non-modifiable risk factors have been identified - some are potential targets for preventive measures. EO-CRC is often diagnosed at advanced stages and histological features associated with poor prognosis have been described. EO-CRC presents some distinctive features: Microsatellite in-stability is common, but another subtype of tumours, both microsatellite and chromosome stable also seems relevant. There are no age-specific treatment protocols and studies on EO-CRC survival rates have shown conflicting data. Due to the higher germline pathological mutations found in EO-CRC patients, an accurate genetic risk evaluation should be performed. In this review, we summarize the current evidence on epidemiological, clinical, histopathological and molecular features of EO-CRC and discuss the contribution of genetics and lifestyle risk factors. We further comment on screening strategies and specific dimensions to consider when dealing with a younger cancer patient.

Heritability Estimates of Growth-Related Traits in Oriental River Prawns, Macrobrachium nipponense

Breeding programs in oriental river prawn populations would benefit from a knowledge of heritabilities for growth-related traits to assess their potential for genetic improvement. This study reports the results of heritability estimation and evaluation of epistatic interactions for growth-related traits in oriental river prawn (Macrobrachium nipponense). The results showed that heritability estimates (h2) were low to medium in magnitude, ranging between 0.10 (±0.02) and 0.22 (±0.01) for females and between 0.08 (±0.01) and 0.26 (±0.02) for males. The additive × additive epistasis was directional and synergistic for body weight (growth), body length, and two processing traits (abdominal meat weight and abdominal meat percentage). The presence of biallelic epistasis provides additional new information for the formulation of selection programs. Elimination of some sources of nonadditive effects from phenotypes can lead to more accurate genetic evaluations, and more desirable genetic gains could be achieved through the integration of heritability, additive effects (breeding values), and epistasis in oriental river prawns.

Study of genetic parameters for pre-weaning growth traits in inner Mongolia white Arbas cashmere goats.

Inner Mongolia Arbas white cashmere goats is a dual-purpose breed for producing cashmere and meat. In recent years, its meat has become more and more popular among consumers because of rich nutrients and delicious flavor. Therefore, it is particularly important to study the genetic and non-genetic factors affecting the early growth traits and estimate variance components of pre-weaning growth traits of Inner Mongolia Albas white cashmere goats. A total of 37487 kidding records such as birth weight (BWT), weaning weight (WWT), average daily gain from birth to weaning (ADG) and Kleiber ratio (KR) from 343 sires and 7296 dams were used in this study. The most appropriate model was chosen on the basis of likelihood ratio test by fitting six models which excluding or including maternal genetic, maternal permanent environmental effects. The parameters were estimated under the most appropriate model using AIREML method by WOMBAT software. With the best model (Model 6), heritability estimates were 0.0435, 0.0911, 0.0932 and 0.2339 for BWT, WWT, ADG and KR traits, respectively. Maternal heritability estimates were 0.0143, 0.0246, 0.0220 and 0.0186 for BWT, WWT, ADG, and KR traits respectively. The correlation between different traits was estimated with the most suitable model by using bivariate analysis method. The direct additive genetic correlation among the traits ranged from -0.026 (BWT~KR) to 0.772 (ADG~KR). The maternal permanent environment correlation is between -0.289 (BWT-KR) ~0.900 (WWT-ADG). Results indicated that maternal effects and direct-maternal genetic covariance should be considered in any program aimed at improving pre-weaning growth traits to have an accurate genetic evaluation. In addition, positive and medium to high genetic correlations generally exist among WWT, ADG and KR due to the existence of genetic variation for early growth traits. The results showed that the genetic progress of these traits could be slowly through selection except for KR.

Two approaches to account for genotype-by-environment interactions for production traits and age at first calving in South African Holstein cattle

BackgroundIf not accounted for, genotype x environment (G×E) interactions can decrease the accuracy of genetic evaluations and the efficiency of breeding schemes. These interactions are reflected by genetic correlations between countries lower than 1. In countries that are characterized by a heterogeneity of production systems, they are also likely to exist within country, especially when production systems are diverse, as is the case in South Africa. We illustrate several alternative approaches to assess the existence of G×E interactions for production traits and age at first calving in Holsteins in South Africa. Data from 257,836 first lactation cows were used. First, phenotypes that were collected in different regions were considered as separate traits and various multivariate animal models were fitted to calculate the estimates of heritability for each region and the genetic correlations between them. Second, a random regression approach using long-term averages of climatic variables at the herd level in a reaction norm model, was used as an alternative way to account for G×E interactions. Genetic parameter estimates and goodness-of-fit measures were compared.ResultsGenetic correlations between regions as low as 0.80 or even lower were found for production traits, which reflect strong G×E interactions within South Africa that can be linked to the production systems (pasture vs total mixed ration). A random regression model including average rainfall during several decades in the herd surroundings gave the best goodness-of-fit for production traits. This can be related to a preference for total mixed ration on farms with limited rainfall. For age at first calving, the best model was based on a random regression on maximum relative humidity and maximum temperature in summer.ConclusionsOur results indicate that G×E interactions can be accounted for when genetic evaluations of production traits are performed in South Africa, by either considering production records in different regions as different correlated traits or using a reaction norm model based on herd management characteristics. From a statistical point of view, climatic variables such as average rainfall over a long period can be included in a random regression model as proxies of herd production systems and climate.

The development of effective ruminant breeding programmes in Ireland from science to practice

A genetic improvement programme is a sustainable, cumulative and permanent approach to achieving year-on-year performance gains. Its success is predicated not only on an efficient and effective breeding programme but also on a vision of the traits of importance in the future. A single, industry-owned, centralised database for cattle and sheep has been the foundation for genetic improvement programmes in Ireland. While DNA information has been heralded as a breakthrough for accelerating genetic gain, the basic principles of a successful animal breeding programme still remain the same: (1) a pertinent breeding goal, (2) the appropriate breeding objective to deliver on the breeding goal, (3) an accurate genetic evaluation system, (4) an efficient and effective breeding scheme, and (5) a system to disseminate the elite germplasm to the end user; also of importance is a system for validating the underlying procedures and principles. The constituent traits and their relative emphasis within breeding objectives will continue to be contentious. Traits that will need to be considered more in future ruminant breeding objectives include environmental impact, product quality and animal well-being, including health; while not always explicitly included in Irish breeding objectives for cattle and sheep, indirect improvements for many are expected via the genetic improvement in traits like reproductive performance and survival as well as macro measures of quality such as milk fat and protein concentration and carcass merit. Crucial for the future sustainability of ruminant production systems is the co-evolution of management systems and breeding programmes so that the animal of the future is suited to the most sustainably efficient production system.

Genetic parameters for milk yield, casein percentage, subclinical mastitis incidence and sexual precocity using Bayesian linear and threshold models

Context The economic efficiency of a dairy system is associated with the animal’s productive and reproductive abilities. Therefore, selection criteria should include milk production and quality traits as well as traits related to health and fertility. Since such phenotypes can present non-normal distributions, the use of threshold models is appropriate to study the genetic variation of such traits. Aim To estimate variance components for dairy production and functional traits in a Brazilian Holstein cattle population using linear and threshold models under a Bayesian approach. Methods Data comprised 64 657 test-day records for milk yield (MY, kg/day), casein percentage (CP, % of milk) and subclinical mastitis incidence (SCM), along with 4460 records for sexual precocity (PREC) from 5439 cows. Both SCM and PREC were defined as binary traits. Genetic analyses were based on linear (for MY and CP) and threshold (for SCM and PREC) models using Bayesian estimation. Non-informative and informative priors were considered for variance components, and these models were compared using the deviance information criterion (DIC) and the absolute difference between DIC (Δ). Key results Posterior means of heritability for MY, CP, SCM and PREC were 0.14, 0.39, 0.13 and 0.38 (based on non-informative priors) and 0.13, 0.27, 0.13 and 0.44 (considering informative priors), respectively. The model based on non-informative priors was better (lower DIC) for CP, whereas for PREC, the best model used informative priors. No differences between priors (Δ &lt; 5) were observed for MY and SCM. Conclusions Threshold models were adequate for the analysis of non-normally distributed traits. The use of informative priors can be beneficial if specification is based on results from similar databases and models. Due to their high genetic variation, CP and PREC can be considered as selection criteria in animal breeding programs. In turn, accurate genetic evaluation for MY and SCM will depend on the pedigree and the information from genetically correlated traits. Implications Our study contributes to the understanding of the variation under important dairy production traits in a tropical Holstein population and provides information on the use of Bayesian threshold models as an appropriate method for the evaluation of non-normally distributed phenotypes.

Measuring genetic connectedness between herds based on high density SNP markers.

The accuracy of genetic evaluations in different herds is affected by the degree of genetic connectedness among herds. In this study, we explored the application of high density SNP markers in the assessment of genetic connectedness by comparing the genetic connectedness based on pedigree data and genomic data. Six methods, including PEVD (prediction error variance of differences between estimated breeding values), PEVD (x), VED (variance of estimated difference between the herd effects), CD (generalized coefficient of determination), r (prediction error correlation) and CR (connectedness rating), were implemented to measure the genetic connectedness based on different relationship matrices (A, G, Gs, G0. 5 and H). Our results from both simulated data and SNP chip data indicated that, except for the PEVD (x) and VED methods, the genetic connectedness obtained by PEVD, CD, r and CR based on G. Gs and G0.5 matrices (using genome information only) were superior to those based on A matrix (using pedigree information only). Generally, for most approaches, the genetic connectedness based on H matrix (using both pedigree and genome information) was somewhere between A matrix and G matrices. CD could overestimate the degree of genetic connectedness as it was still very high when CR and r were close to 0. The method r could not accurately reflect the true genetic connectedness of the populations. It generated 0.01 of genetic connectedness for all three pig breeding farms, which were actually genetically different with each other. With increasing of heritability, the degree of genetic connectedness obtained by all methods were increased as well. However, in the case of heritability 0.1, PEVD based on A matrix performed better than based on G matrix, suggesting that traits with medium and high heritability are more suitable for the assessment of genetic connectedness compared to traits with low heritability. Our findings indicated that high-density SNP markers have advantages over pedigree analysis for the measurement of genetic connectedness, and CR is a robust and reliable method to assess genetic connectedness. Further, CR is easily calculated and less affected by heritability of trait. PEVD is good supplement to quantify the prediction errors of estimated breeding values under the specific genetic connectedness. In comparison, G matrix can reflect genetic connectedness better than its extensions Gs and G0.5 matrix.

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss.

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1–4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.

Estimates of (co)variance components and genetic parameters for pre-weaning body weight traits and Kleiber ratio in Sangsari sheep breed

The current study aimed to estimate (co)variance components and heritability for pre-weaning body weight traits and Kleiber ratio in Sangsari sheep. Data used in this research were collected over 30 years (1986–2016) at the breeding station of Sangsari sheep. Traits considered were birth weight (BW), weaning weight (WW), pre-weaning daily gain (PWDG), and Kleiber ratio (KR). Genetic parameters were estimated by the average information restricted maximum likelihood (AI-REML) procedure, using six animal models. The most suitable model was selected using the Akaike information criterion (AIC) for each trait. Direct heritability estimates for BW, WW, PWDG and KR were 0.30 0.04, 0.20 0.03, 0.15 0.02, and 0.13 0.05, respectively. Maternal heritability estimates varied from 0.06 0.02 for PWDG to 0.12 0.03 for BW. The maternal permanent environmental effects for the traits of KR, WW and PWDG account for about 7%, 9% and 12% of the phenotypic variance. The estimates of the direct genetic () and maternal genetic () correlations among pre-weaning growth traits were positive and ranged from 0.21 (BW-KR) to 0.98 (WW-PWDG) and 0.27 (WW-PWDG) to 0.48 (BW-WW), respectively. Therefore, to have an accurate genetic evaluation, maternal effects need to be considered in any program aimed at improving pre-weaning growth traits. In addition, due to the existence of genetic variation for early growth traits and generally positive and medium to high genetic correlations among them, it can be concluded that in the Sangsari breed of sheep, genetic progress for these traits is possible by selection. HIGHLIGHTS Genetic parameters (direct and maternal heritability) were estimated for the Sangsari breed of sheep. The low to moderate estimate of heritability for pre-weaning body weights and Kleiber ratio indicate that genetic improvement of these traits is possible by selection. Considerable genetic correlations among early body weights, pre-weaning daily weight gain, and Kleiber ratio in Sangsari sheep were estimated.

Phenotypic factors associated with lamb live weight and carcass composition measurements in an Irish multi-breed sheep population.

Understanding the phenotypic factors that affect lamb live weight and carcass composition is imperative to generating accurate genetic evaluations and further enables implementation of functional management strategies. This study investigated phenotypic factors affecting live weight across the growing season and traits associated with carcass composition in lambs from a multibreed sheep population. Four live weight traits and two carcass composition traits were considered for analysis namely; birth, preweaning, weaning, and postweaning weight, and ultrasound muscle depth and fat depth. A total of 427,927 records from 159,492 lambs collected from 775 flocks between the years 2016 and 2019, inclusive were available from the Irish national sheep database. Factors associated with live weight and carcass composition were determined using linear mixed models. The heaviest birth, preweaning, and weaning weights were associated with single born lambs (P < 0.001), however by postweaning, there was no difference observed in the weights of single and twin born lambs (P > 0.01). Breed class affected lamb live weight and carcass composition with terminal lambs weighing heaviest and having greater muscle depth than all other breed classes investigated (P < 0.001). Lambs born to first parity dams were consistently lighter, regardless of time of weighing (P < 0.001), while dams lambing for the first time as ewe lambs produced lighter lambs than those lambing for the first time as hoggets (P < 0.001). Greater heterosis coefficients (i.e., >90% and ≤100%) resulted in heavier lambs at weaning compared with lambs with lower levels of heterosis coefficients (P < 0.001). A heterosis coefficient class <10% resulted in lambs with greater muscle depth while recombination loss of <10% increased ultrasound fat depth (P < 0.001). Results from this study highlight the impact of multiple animal level factors on lamb live weight and carcass composition which will enable more accurate bio-economic models and genetic evaluations going forward.

Estimation of genetic parameters for growth traits and Kleiber ratios in Boer x Central Highland goat.

Accurate performance evaluation and genetic parameters estimation are the prerequisites for any successful genetic improvement program. This study was conducted to estimate genetic parameters for growth and Kleiber ratio traits in Boer x Central Highland goats. On-station data collected from 2009 to 2018 were utilized for the study. A general linear model procedure of the Statistical Analysis System (SAS, version 9.0) was used to analyze fixed effects, and genetic parameters were estimated using the WOMBAT software fitted animal model. The log-likelihood ratio test was used for selecting the best fitted model. Based on best fitted models, the total heritability (h2t) estimate for birth weight (BWT), weaning weight (WWT), six-month weight (SMWT), nine-month weight (NMWT), and yearling weight (YWT) were 0.38, 0.12, 0.05, 0.30, and 0.28, respectively. The total heritability (h2t) estimates for weight gain from birth to weaning (ADG1), 3 to 6months (ADG2), 6 to 9months (ADG3), and 9 to 12months of age (ADG4) were 0.09, 0.08, 0.16, and 0.14, respectively. The heritability estimates for Kleiber ratios in different growth phases were found to be low (0.09 to 0.18) based on the selected models. Even with this figure, including the Kleiber ratio in the selection criteria would be imperative to improve the feed efficiency of crossbred goats. The direct genetic correlation estimates among growth traits range from 0.60 ± 0.14 to 0.97 ± 0.12. Except for NMWT, ADG4, and Kleiber ratio from 9months to yearling age, the maternal effect had a significant influence on all considered traits. However, the contribution of the maternal genetic effect was more important for early growth traits. Thus, considering both the direct additive genetic effect and the maternal genetic effect is imperative for accurate genetic evaluation and for high selection response. The moderate heritability estimates for most of the growth traits implies the possibility of selection in conjunction with crossbreeding for a better genetic response. The positive and high genetic correlation estimates among growth traits confirm the possibility of a selection of goats at an early age.

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

Using single-step genomic best linear unbiased prediction to improve the efficiency of genetic evaluation on body weight in Macrobrachium rosenbergii

In a shrimp selective breeding program, the genetic ties among founders or pedigree members are often unclear and could lead to biased estimates of the genetic parameters and predicted genetic gains using the conventional best linear unbiased prediction method based on pedigree (pBLUP). The accuracy of genetic evaluation can be improved by genomic selection with high-throughput molecular markers. This study was designed to estimate genetic parameters and predict genetic gains for body weight using single-step BLUP (ssGBLUP) with genotyped parents in the giant freshwater prawn Macrobrachium rosenbergii. The dataset was from 4 generations comprised 78,700 individuals, which were offspring of four founder populations. A total of 410 genotyped parents with 83,480 single nucleotide polymorphisms (SNPs) loci were obtained after quality control. Variance components were estimated using the animal model with the relationship matrix H combining SNP markers and pedigree data or the relationship matrix A from pedigree data. Prediction accuracies of estimated breeding value (EBV) and genomic EBV (GEBV) were calculated using five-fold cross-validation (CV), depending on the testing dataset from genotyped parents (CV1) or their ungenotyped offspring (CV2). The response to selection for body weight was evaluated by the difference of EBV or GEBV between generations. For 410 genotyped parents, correlation coefficient between lower triangular elements of the A and H matrices were 0.78 after correcting the pedigree. Heritabilities of body weight obtained using the pBLUP model with the A matrix and the ssGBLUP model with the H matrix were 0.11 ± 0.02 and 0.25 ± 0.03, respectively. For the CV1 scenario, the prediction accuracies of EBV and GEBV for body weight were 0.33 and 0.47, respectively; for the CV2 scenario, their accuracies were 0.24 and 0.28, respectively. The accuracy of GEBV for body weight predicted using ssGBLUP compared to pBLUP increased by 42.42% and 16.67% for the CV1 and CV2 scenarios, respectively. After performing 3 selections, the cumulative genetic gains of body weight for pBLUP and ssGBLUP were 4.46 g and 10.88 g, respectively. Response to selection for body weight was underestimated through pBLUP (11.45%) compared to ssGBLUP (27.93%). Our results suggest that ssGBLUP with small number of genotyped parents can improve efficiency of genetic evaluation for body weight using a more accurate relationship matrix compared to pBLUP in M. rosenbergii.

Estimation of genetic parameters for dairy traits in Pag sheep with single and multi – trait test–day models

Abstract The objective of the study was to estimate genetic parameters and other estimable sources of phenotypic variability for daily milk yield (DMY), fat content (FC), and protein content (PC) in the Pag sheep breed reared in predominantly outdoor breeding environment. A total of 79,937 phenotypic records from 7132 ewes were used in the analysis. After pruning of the pedigree, a total of 7870 animals were included in the additive relationship matrix. Single – trait (ST) and multi – trait (MT) approaches were applied in the statistical analysis using fixed regression repeatability test–day model. Parity, litter size, season of lambing, flock, stage of lactation, and age at lambing were fitted as fixed effects, while additive genetic, permanent environment, and flock–test–day as random effects. The estimates of heritabilities with MT model were 0.32, 0.19, and 0.40 for DMY, FC, and PC, respectively. Repeatabilities for DMY, FC, and PC were 0.46, 0.21, and 044, respectively. Negative genetic correlations were found evident between DMY and FC (–0.35), and DMY and PC (–0.36), while positive between FC and PC (0.66). The average reliabilities of breeding values (BVs) for DMY, FC, and PC for ewes were 0.771, 0.788, and 0.846, and for rams 0.675, 0.677, and 0.708, respectively. Estimates of genetic parameters and reliabilities of BVs with ST approach were very similar to MT approach as well as ranking of the animals based on BVs. Rank correlations of BVs between examined approaches (ST and MT) for DMY, FC, and PC were 0.995, 0.987, and 0.999 for ewes, and 0.991, 0.984, and 0.998 for rams, respectively. The use of the MT approach provided small gain in accuracy of genetic evaluation, but we encourage its usage wherever possible, particularly taking into account small size of dairy sheep populations and negligible extra computational costs.

WPSII-4 Population structure and Holstein ancestry analysis of modern Russian Black and White cattle for accurate genetic evaluation in Leningrad region.

WPSII-4 Population structure and Holstein ancestry analysis of modern Russian Black and White cattle for accurate genetic evaluation in Leningrad region.