BLUP Analysis Research Articles

Identification of Climate-Smart Bread Wheat (Triticum aestivum L.) Germplasm for Optimum Moisture Areas of Ethiopia

Ethiopia's Bread Wheat Breeding Program conducts annual multi-environment yield trials to develop advanced wheat genotypes for Ethiopian wheat cultivation, ensuring a steady supply of new and improved varieties to meet production and marketing challenges. The objective of this research was to assess potential yield and the interactions between genotype and environment in wheat across multiple environments, as every cultivar has a distinct response to soil and climate. The BLUP analysis reveals that the 22AA and 22KU trials in 2022, along with 21KU trials in 2021 yielded high grain production, indicating optimal testing locations for distinguishing bread wheat genotypes and agroecologies. The study found that seven out of sixteen trials exhibited a higher genetic variance for yield, indicating high genotype discriminating power, with estimates ranging from 0.043 to 0.989 for genetic variance, 0.084 to 1.147 for error variance, and 72.7 to 96.4 for heritability. EBW202471 and EBW202473 are stable genotypes with good yield performance across correlated locations, with EBW202471 showing the highest yield (4.98 t/ha) and Deka showing a lower yield (4.07 t/ha). Three wheat genotypes, EBW202471, EBW202472, and EBW202473, were found to be moderately resistant to moderately susceptible to stem and yellow rust among 20 wheat genotypes. Finally, the two genotypes, EBW202471 and EBW202473, were advanced to National Performance Trials to evaluate their performance alongside top genotypes from regional federal research centers and to be released as new varieties.

A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties.

The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R2) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R2=0.9; b=1.05), Cakrabuana (R2=0.98; b=0.99), and Padjajaran (R2=0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83ton ha-1, 98.12 days; 7.37ton ha-1, 95.52 days; and 7.29ton ha-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.

Nano Urea Fertilization for More Wheat Yield Under North Western Plains Zone for Irrigated Conditions Assessed by AMMI and BLUP Analysis

Highly significant variations due to treatments, locations and TxL interactions were observed for wheat yield by analysis of variance during field evaluation of nano urea formulations at number of locations in the north western plans zone of the country. First component of AMMI analysis shared of about 51.3% while second accounted for 29.9%. Significant variations due to locations, TxL interactions and treatments effects were observed for thousands grains weight. Around 82.9% of the interaction effects accounted by two significant components while total of significant components were up to 96.9%. Maximum yield was observed at Karnal followed by Hisar more over maximum yield for 56.1 was expressed by T3 treatment. ASV1 & ASV measures based on 81.3% of interaction effects selected T5, T8 and T8, T5 treatments. Average of thousands grains weight found T3, T8, T9 treatments as suitable for maximum realization and as per MASV1 and MASV settled T6, T2 treatments for thousands grains weight. Adaptability measures corresponding to BLUP estimates of yield i.e. PRVG, PRVG*Gu, HMPRVG*Gu, HMPRVG measures found T3, T4, T2 treatments for yield. Superiority index measures considering average thousands grains weight and stability in 65 and 35 ratios for weighted average settled for T3, T7, T8 treatments formulation of nano urea in the study. Biplot analysis observed Pantnagar center had expressed strong bondage with superiority index measures while considering mean, GAI and HM of treatments based on fixed and random effects of treatments. WAASB, W3, W2, W5 had maintained direct association with MASV, MASV1 on right hand side and with ASV1, W1, IPC4 on left side for yield. Treatments T13, T5 and T4 would express unstable yield as compared to T11, T6, T7 placed near to origin of biplot analysis. Thousands grains weight found that Karnal, Hisar centers with IPV2 formed the first cluster while Gurdaspur with Gwalior joined hands with superiority index measures in second cluster of biplot analysis. Next cluster was of adaptability measures PRVG, HMPRVG, PRVG*Gu, HMPRVG*Gu, mean, GAI, HM with Jammu, Delhi and Ludhiana centres. Next cluster of IPC4, IPC6 with Pantnagar observed near to large cluster of adaptability measures PRVG, HMPRVG, PRVG*Gu, HMPRVG*Gu.

RAIZ’s eucalypt genetic improvement program: The path taken and new challenges

RAIZ is a forest and paper research institute, part of The Navigator Company, a Portuguese pulp and paper company. Its eucalypt genetic improvement program started in the mid-1980s, focused mainly on E. globulus, which is well suited to the local soil and climate conditions and has outstanding wood qualities for pulp and paper production. Initially, main selection criteria were growth, survival to drought conditions, wood density and pulp yield. Cloning was the main form to transfer results into operational plantations and, as a result, rooting ability was also a key trait for successful large-scale nursery plant production. These requirements were maintained for several decades during which the program has achieved substantial gains and the E. globulus clonal forest was a reality. Meanwhile, interspecific hybridization has been tested mainly to achieve higher drought and pest resistance. As a result, the clonal program includes several hybrids. Currently, 11 clones, including three hybrids, are in nursery large-scale production, with operational gains of 30-50% over unimproved stocks, in terms of pulp produced per hectare. In the last decade, more than 250 controlled-crosses were carried out, including 70 interspecific crosses. More than 400 new clones, including 50 hybrids, and approximately 400 seed lots, including 100 hybrids and other species lots, have been tested across over 140 sites. Advanced BLUP analysis techniques are used since mid-1990’s to support strategic planning decisions, including more than 125,000 records for survival, 85,000 records for height and diameter, 57,000 pilodyn and 6,000 NIR records, collected from 219 trials, and covering about 45,000 individual genotypes. Nevertheless, efforts to face climate changes must be intensified, especially to mitigate the effects of drought and pests that currently affect a substantial portion of the forest estate. In the last 3 years, more than 100 new genotypes were selected from hybrids progeny trials involving species such as E. grandis, E. viminalis, E. saligna, E. rudis, E. urophylla, and E. tereticornis. Their multiplication and testing are undergoing, and results are expected within 5 years. In addition, an expanded approach to further diversify and strengthen of eucalypt genetic pool is being implemented, introducing species like E. cladocalyx, E. gomphocephala, E. benthamii, E. melliodora, E. punctata, or E. dunnii, aiming to enhance resilience to environmental challenges and ensuring the sustainability of Portugal's eucalyptus forests.

Comparative genetic analysis of frequentist and Bayesian approach for reproduction, production and life time traits showing favourable association of age at first calving in Tharparkar cattle.

The present study was aimed primarily for estimating various genetic parameters (heritability, genetic correlations) of reproduction (age at first calving [AFC], first service period [FSP]); production (first lactation milk, solid-not fat, and fat yield) and lifetime traits (lifetime milk yield, productive life [PL], herd life [HL]) in Tharparkar cattle to check the association of reproduction traits with lifetime traits through two different methods (Frequentist and Bayesian) for comparative purpose. Animal breeding data of Tharparkar cattle (n = 964) collected from Livestock farm unit of ICAR-NDRI Karnal for the period 1990 through 2019 were analyzed using a Frequentist least squares maximum likelihood method (LSML; Harvey, 1990) and a multitrait Bayesian-Gibbs sampler approach (MTGSAM) for genetic correlations estimation of all the traits. Estimated breeding values of sires was obtained by BLUP and Bayesian analysis for the production traits. Heritability estimates of most of the traits were medium to high with the LSML (0.20±0.44 to 0.49±0.71) and Bayesian approach (0.24±0.009 to 0.61±0.017), respectively. However, more reliable estimates were obtained using the Bayesian technique. A higher heritability estimate was obtained for AFC (0.61±0.017) followed by first lactation fat yield, first lactation solid-not fat yield, FSP, first lactation milk yield (FLMY), PL (0.60±0.013, 0.60±0.006, 0.57±0.024, 0.57±0.020, 0.42±0.025); while a lower estimate for HL (0.38±0.034) by MTGSAM approach. Genetic and phenotypic correlations were negative for AFC-PL, AFC-HL, FSP-PL, and FSP-HL (-0.59±0.19, -0.59±0.24, -0.38±0.101 and -0.34±0.076) by the multi-trait Bayesian analysis. Breed and traits of economic importance are important for selection decisions to ensure genetic gain in cattle breeding programs. Favourable genetic and phenotypic correlations of AFC with production and lifetime traits compared to that of FSP indicated better scope of AFC for indirect selection of life-time traits at an early age. This also indicated that the present Tharparkar cattle herd had sufficient genetic diversity through the selection of AFC for the improvement of first lactation production and lifetime traits.

Nano urea Fertilization on Wheat Morphology under Restricted Irrigation Conditions Evaluated under Multi Locations Trails by AMMI and BLUP Analysis

Nano urea Fertilization on Wheat Morphology under Restricted Irrigation Conditions Evaluated under Multi Locations Trails by AMMI and BLUP Analysis

Estimates of genetic parameters and genetic trend for growth traits and Kleiber ratio of Holstein-Friesian calves

Growth traits of calves, which are quantitative characteristics determining cattle business profitability, vary according to genetic and environmental factors. In other words, growth traits depend on the genetics of the individual and vary with farm management. The aim of this study was to investigate the effective environmental factors, genetic parameters, and genetic trends for some growth traits and the Kleiber ratio (KR) in Holstein-Friesian calves. For this purpose, the records of 724 calves, progeny of 566 dams and 29 sires, reared between 2017 and 2019 on a private dairy farm in Türkiye, were used. MTDFREML software was utilized to estimate genetic parameters and genetic trends of growth traits and KR. In this study, regarding weight, the mean of birth weight (BW), 60-day weight (W60), and 90-day weight (W90) were 39.76 ± 6.15 kg, 69.23 ± 10.93 kg, and 95.76 ± 16.48 kg, respectively. Concerning weight gain, 1-60 daily weight gain (DWG1-60), 60-90 daily weight gain (DWG60-90), and 1-90 daily weight gain (DWG1-90) were 0.49 ± 0.16 kg, 0.91 ± 0.34 kg, and 0.63 ± 0.17 kg, respectively. With respect to KR, 1-60 daily KR (KR1-60), 60-90 daily KR (KR60-90), and 1-90 daily KR (KR1-90) were 2.03 ± 0.48, 2.93 ± 0.89, and 2.02 ± 0.34, respectively. As a result of the GLM analysis, only the effect of the birth season on all traits was significant (p < 0.05 or p < 0.01). In addition, it was detected that sex had a significant effect on BW and W60 (p < 0.05 or p < 0.01). For all traits, only the effect of parity on KR1-60 was not significant. In REML analysis, direct heritability differed between 0.26 ± 0.16 and 0.81 ± 0.27 obtained at DWG1-90 and DWG1-60, respectively. Also, the highest repeatability (0.100) was obtained in DWG1-60. It was detected that mass selection could be used in all traits for breeding program. In BLUP analysis, the current population had an increasing trend for BW and W90 and a decreasing trend for W60. However, there was no significant change in other weight gain traits and KR over the years. Calves with high breeding values for BW, W60, W90, DWG1-60, DWG60-90, and DWG1-90 should be chosen for selection programs. But for KR1-60, KR60-90, and KR1-90, calves with low breeding values should be selected for efficiency. Also, KR evaluated would contribute to the literature and other research should be studied regarding KR.

Genetic evaluation of Kankrej bulls by different sire evaluation methods

The genetic evaluation of Kankrej bulls was done by using three different methods viz., BLUP, Wombat sire, andWombat animal models. First lactation 305-days yields of 348 daughters born to 9 sires and calved from 2016 to 2020 were used for the analyses. For BLUP analysis, season and year of calving were considered as fixed effects, andsire was considered as random effect. The overall average estimate of 2414.40 kg was obtained in BLUP method while 2430.29 kg was obtained in both Wombat sire and animal models. The results revealed that the Wombat animal model discriminated against the sires to the maximum extent followed by Wombat sire and BLUP models. Based on the results, two Kankrej bulls viz., K.8.32 and K.8.62 can be selected as proven bulls for nominated mating for improving the milk productivity of Kankrej cattle.

QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome.

As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.

Accounting for population structure in genomic predictions of Eucalyptus globulus.

Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP ("HBLUP") analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the "Green Triangle" region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.

Impact of genetic selection and enhanced feeding on growth rate of Nilagiri and Sandyno sheep and their lambs

Weaned ewe lambs born during 2009-2014 were ranked according to their breeding value ascertained by BLUP analysis for weaning weight. Ten weaned ewe lambs each in Nilagiri and Sandyno breeds, on the top of the ranking were selected each year and placed as a separate elite group and were supplemented with increased level of concentrate feed from weaning with 8 h of routine grazing. Weight at 6, 9 and 12 months was recorded. Weight at birth, weaning, 6th and 12th months of lambs born to elite ewes were recorded up to February 2020 lambing. The data were analyzed by least square analysis. The body weight at 6, 9 and 12 months of age were significantly higher in elite ewes (17.22±0.21, 22.17±0.24, 26.19±0.27 vs 14.29±0.10, 18.09±0.13, 21.38±0.14). Elite Sandyno lambs weighed significantly higher than elite Nilagiri lambs at all stages. Highest ADG was observed in Sandyno elite ewe lambs during 6 months to 9 months period (62.98±2.80 g). The ADG was significantly higher in elite ewes during this period and from 9 months to 12 months (54.79±1.85 and 44.38±1.61 vs 37.52±0.98 and 33.70±0.87). The average birth weight, weaning weight and weaning percentage of born elite Nilagiri lambs (2.88±0.15, 11.52±0.77 and 93.54% vs 2.63±0.15, 10.02±0.76 and 90.82%) and Sandyno lambs (3.41±0.17, 12.91±0.86 and 94.69% vs 3.04±0.17, 11.31±0.84 and 91.73%) were significantly higher. The survivability of twin lambs was significantly higher in born elite lambs. The findings will help in promoting genetic selection and improved nutritional management in sheep farms from weaning to improve growth rate, better lamb crop and good survivability. Marginal increase in the feed would bring a substantial improvement in the performance of the sheep.

Результаты оценки племенной ценности линий при помощи методов сравнения со сверстницами и BLUP на поголовье крупного рогатого скота Мурманской области

Abstract. The aim of the study is to compare two methods for assessing the breeding value of cows (comparison with peers and BLUP) during the analyzing process of the influence of lines on milk production. It is necessary to identify the most accurate and objective method for further use in the system for assessing of genealogical groups by a set of characteristics. Methods. We used one- and two-way analysis of variance. Comparative analysis of breeding values for milk production of genealogical groups (lines) using the methods of comparison with peers and BLUP. The ranking of genealogical groups by the results of assessing of breeding value and the calculating of the Spearman’s rank correlation coefficient were made. Results. According to the results of the analysis of variance, the strength of the influence of the environmental factor “year and season” on the milk productivity of first-calf cows was +0.62 of the total variance (P &lt; 0.01). Hereditary factors were assessed by the method of two-way analysis of variance of an uneven complex. The strength of the influence of the lines on the milk productivity of first-calf cows was 0.011 of the total variance (P &lt; 0.05). The use of the method of comparison with peers, the dependence on a sufficient number of representatives of the genealogical group was revealed. BLUP analysis of lines changed their ranking. The number of animals in the studied groups does not affect the distribution of ranks with this method of assessment. The rank correlation between BLUP and peer comparison methods was 0.65. It has been established that the BLUP assessment is currently the most objective and takes into account the bias of the assessment under the influence of an insufficient number of animals. The novelty of the study is the first time the use of the BLUP method was tested on the livestock population in the Murmansk region to assess the breeding value based on the milk productivity of the lines.

AMMI with BLUP analysis for stability assessment of wheat genotypes under multi locations timely sown trials in Central Zone of India

AMMI analysis explained the highly significant effects of the environment, GxE interaction, and genotypes for wheat genotypes evaluated under restricted irriation timely sown multi-location trials in the Central zone of the country during 2018-19 and 2019-20. About 77.1%, 12.2% & 2.3% had been contributed by environments, interactions and genotypes of the total sum of squares due to treatments respectively in the first year.

BLUP and stability analysis of multi-environment trials of potato varieties in sub-tropical Indian conditions

Potato is an important crop in India with area spread across Himalayan hills in the North to hot tropical conditions in South, albeit major area in sub-tropical Indo-Gangetic plains. The first common requirement in all regions is that the variety should have high performance for tuber yield along with essential agronomic traits. The present study was carried out to identify an ideal variety with wide adaptability for tuber yield and dry matter. Six varieties were evaluated in 9, 11 and 10 locations in the years 2014–15, 2015–16 and 2016–17, respectively for TY, MY and DM. The data were analysed with ANOVA, mixed models, BLUPs and GGE biplot as well as univariate stability statistics. Combined analysis of variance showed significant genotype, environment and genotype × environment interactions. The relative magnitudes of G, E and G×E variances accounted for 6.76–8.91, 51.85–76.65 and 12.41–23.19 per cent for TY and 2.86–4.66, 65.87–72.85 and 13.74–20.04 per cent for DM. Although the genotypes contributed significantly, major part of the variation was explained by environments for all the three traits. Mean across locations and years, and BLUP values of varieties for all the three traits showed similar results with Kufri Khyati as the best variety for TY and MY, whereas Kufri Jyoti and Kufri Garima were best for DM. Based on GGE biplot and univariate stability statistics, Kufri Khyati was the ideal high yielding wide adaptable variety in all the three years and Kufri Jyoti was the ideal variety based on mean dry matter and stability. The environments were very diverse and their clustering suggested three groups, which can be used as three separate zones for varietal evaluation and regional deployment of varieties.

Prediction of genetic gain in Ficus variegata progeny trial based on breeding value

Abstract. Haryjanto L, Prastyono, Hadiyan Y, Susanto M. 2019. Prediction of genetic gain in Ficus variegata progeny trial based on breeding value. Biodiversitas 20: 2994-2999. Prediction of genetic gain in Ficus variegata Blume progeny trial based on breeding values. The aim of this study was to estimate the breeding value based on components variance and genetic gain prediction of Ficus variegata Blume using REML/BLUP. A total of 17 families of Ficus variegata Blume were evaluated at three years after planting at progeny trial in Mangunan, Bantul, Yogyakarta laid out in Randomized Completely Block Design (RCBD). Estimation of variance family (height and diameter) were ± 5% of total variance. Narrow-sense individual heritability was moderate (0.27 and 0.21 respectively) indicated promising genetic improvement. Strong genetic correlation of height and diameter (rG=0.9) indicated that improving a trait will improving others. BLUP analysis for diameter trait showed that the best individual value from best family selection was appropriated with restriction 10 family and 5 individual per family and genetic gain prediction was 15.83% gave acceptable rate of inbreeding 2% per generation.

Prediction accuracy of direct and indirect approaches, and their relationships with prediction ability of calibration models

Milk infrared spectra are routinely used for phenotyping traits of interest through links developed between the traits and spectra. Predicted individual traits are then used in genetic analyses for estimated breeding value (EBV) or for phenotypic predictions using a single-trait mixed model; this approach is referred to as indirect prediction (IP). An alternative approach [direct prediction (DP)] is a direct genetic analysis of (a reduced dimension of) the spectra using a multitrait model to predict multivariate EBV of the spectral components and, ultimately, also to predict the univariate EBV or phenotype for the traits of interest. We simulated 3 traits under different genetic (low: 0.10 to high: 0.90) and residual (zero to high: ±0.90) correlation scenarios between the 3 traits and assumed the first trait is a linear combination of the other 2 traits. The aim was to compare the IP and DP approaches for predictions of EBV and phenotypes under the different correlation scenarios. We also evaluated relationships between performances of the 2 approaches and the accuracy of calibration equations. Moreover, the effect of using different regression coefficients estimated from simulated phenotypes (βp), true breeding values (βg), and residuals (βr) on performance of the 2 approaches were evaluated. The simulated data contained 2,100 parents (100 sires and 2,000 cows) and 8,000 offspring (4 offspring per cow). Of the 8,000 observations, 2,000 were randomly selected and used to develop links between the first and the other 2 traits using partial least square (PLS) regression analysis. The different PLS regression coefficients, such as βp, βg, and βr, were used in subsequent predictions following the IP and DP approaches. We used BLUP analyses for the remaining 6,000 observations using the true (co)variance components that had been used for the simulation. Accuracy of prediction (of EBV and phenotype) was calculated as a correlation between predicted and true values from the simulations. The results showed that accuracies of EBV prediction were higher in the DP than in the IP approach. The reverse was true for accuracy of phenotypic prediction when using βp but not when using βg and βr, where accuracy of phenotypic prediction in the DP was slightly higher than in the IP approach. Within the DP approach, accuracies of EBV when using βg were higher than when using βp only at the low genetic correlation scenario. However, we found no differences in EBV prediction accuracy between the βp and βg in the IP approach. Accuracy of the calibration models increased with an increase in genetic and residual correlations between the traits. Performance of both approaches increased with an increase in accuracy of the calibration models. In conclusion, the DP approach is a good strategy for EBV prediction but not for phenotypic prediction, where the classical PLS regression-based equations or the IP approach provided better results.

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

Genomic prediction of reproduction traits for Merino sheep.

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

Short communication: The combined use of linkage disequilibrium–based haploblocks and allele frequency–based haplotype selection methods enhances genomic evaluation accuracy in dairy cattle

The construction and use of haploblocks [adjacent single nucleotide polymorphisms (SNP) in strong linkage disequilibrium] for genomic evaluation is advantageous, because the number of effects to be estimated can be reduced without discarding relevant genomic information. Furthermore, haplotypes (the combination of 2 or more SNP) can increase the probability of capturing the quantitative trait loci effect compared with individual SNP markers. With regards to haplotypes, the allele frequency parameter is also of interest, because as a selection criterion, it allows the number of rare alleles to be reduced, and the effects of those alleles are usually difficult to estimate. We have proposed a simple pipeline that simultaneously incorporates linkage disequilibrium and allele frequency information in genomic evaluation, and here we present the first results obtained with this procedure. We used a population of 2,235 progeny-tested bulls from the Montbéliarde breed for the tests. Phenotype data were available in the form of daughter yield deviations on 5 production traits, and genotype data were available from the 50K SNP chip. We conducted a classical validation study by splitting the population into training (80% oldest animals) and validation (20% youngest animals) sets to emulate a real-life scenario in which the selection candidates had no available phenotype data. We measured all reported parameters for the validation set. Our results proved that the proposed method was indeed advantageous, and that the accuracy of genomic evaluation could be improved. Compared with results from a genomic BLUP analysis, correlations between daughter yield deviations (a proxy for true) and genomic estimated breeding values increased by an average of 2.7 percentage points for the 5 traits. Inflation of the genomic evaluation of the selection candidates was also significantly reduced. The proposed method outperformed the other SNP and haplotype-based tests we had evaluated in a previous study. The combination of linkage disequilibrium-based haploblocks and allele frequency-based haplotype selection methods is a promising way to improve the efficiency of genomic evaluation. Further work is needed to optimize each step in the proposed analysis pipeline.

Evaluation of genetic components in traits related to superovulation, in vitro fertilization, and embryo transfer in Holstein cattle

The objectives of this study were to estimate variance components and identify regions of the genome associated with traits related to embryo transfer in Holsteins. Reproductive technologies are used in the dairy industry to increase the reproductive rate of superior females. A drawback of these methods remains the variability of animal responses to the procedures. If some variability can be explained genetically, selection can be used to improve animal response. Data collected from a Holstein dairy farm in Florida from 2008 to 2015 included 926 superovulation records (number of structures recovered and number of good embryos), 628 in vitro fertilization records (number of oocytes collected, number of cleaved embryos, number of high- and low-quality embryos, and number of transferrable embryos), and 12,089 embryo transfer records (pregnancy success). Two methods of transformation (logarithmic and Anscombe) were applied to count variables and results were compared. Univariate animal models were fitted for each trait with the exception of pregnancy success after embryo transfer. Due to the binary nature of the latter trait, a threshold liability model was fitted that accounted for the genetic effect of both the recipient and the embryo. Both transformation methods produced similar results. Single-step genomic BLUP analyses were performed and SNP effects estimated for traits with a significant genetic component. Heritability of number of structures recovered and number of good embryos when log-transformed were 0.27 ± 0.08 and 0.15 ± 0.07, respectively. Heritability estimates from the in vitro fertilization data ranged from 0.01 ± 0.08 to 0.21 ± 0.15, but were not significantly different from zero. Recipient and embryo heritability (standard deviation) of pregnancy success after embryo transfer was 0.03 (0.01) and 0.02 (0.01), respectively. The 10-SNP window explaining the largest proportion of variance (0.37%) for total structures collected was located on chromosome 8 beginning at 55,663,248 bp. Similar regions were identified for number of good embryos, with the largest proportion of variance (0.43%) explained by a 10-SNP window on chromosome 14 beginning at 26,713,734 bp. Results indicate that there is a genetic component for some traits related to superovulation and that selection should be possible. Moreover, the genetic component for superovulation traits involves some genomic regions that are similar to those for other fertility traits currently evaluated.