Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth in Pinus radiata D. Don Under Water Stress

The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype’s photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.

Genetic Parameters and Selection in Full-Sib Families of Tall Fescue Using Best Linear Unbiased Prediction (BLUP) Analysis of Animal Breeding and Genetic

Background: Better understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regards, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure. Method: Forty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers’ along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017-2020). Results: Considerable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits via developing an index may lead to indirect genetic improvement of DFY. Conclusion: Our results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21M, 1M, and 20L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

​Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program

Background: Additive main and multiplicative interaction (AMMI) analysis had been exploited for multi environment trials for most of the crops. Usage of the best linear unbiased prediction (BLUP), along with AMMI tools, of the genotypes would improve the estimation of interaction effects. Methods: AMMI based measures of adaptability have been enriched with the incorporation of BLUP of genotypes by new Superiority index that allowed variable weights for stability and yield of genotypes. Result: Stability measure weighted average of absolute scores (WAASB) based on all significant interaction principal components ranked suitability of KB1754, RD3000, NDB1445 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield and stability arranged DWRB201, NDB1445, RD2552 as of stable high yield performance of barley genotypes. Corrected measure Modified AMMI Stability Value (MASV1) found RD2552, DWRB201, KB1762 and Modified AMMI Stability Value (MASV) ranked DWRB201, RD2552, KB1762. ASTAB measure achieved the desirable lower values for DWRB201 DWRB207, HUB268 genotypes. Biplot graphical analysis based on 60.7% of variation of the stability measures observed MASV1, ASTAB (AMMI based stability parameter), EV(Averages of the squared eigenvector values), SIPC (Sums of the absolute value of the IPC scores), Za (Absolute value of the relative contribution of IPCs to the interaction), W3, WAASB and MASV had been clubbed together. For the second year lower value of WAASB measure had observed for RD3016, KB1815 HUB273. Ranking of genotypes as per Superiority index found RD3017, RD2907, HUB274 as of stable high yield performance. Genotypes RD3017, RD2907 and NDB1173 pointed out by MASV1 while RD3017, RD2907, NDB1173 identified by MASV as the genotypes of choice. RD3017 NDB1173, RD2907 genotypes were selected as per values of ASTAB measure. Total of 71.8% of variation of the considered measures in biplot analysis expressed larger cluster comprised of AMMI based measures and a separate cluster of Superiority indexes as per mean, Geometric Adaptability Index (GAI) and HMGV also observed.

Identifying crisphead lettuce genotypes for a wider range of environments

This work aims to select crisphead lettuce (Lactuca sativa L) genotypes superior in production, stability, and adaptability using a mixed model method: restricted maximum likelihood/best linear unbiased prediction. Ten genotypes were grown in different municipalities of the State of São Paulo, Brazil, and seasons of the year, resulting in twelve different environments. The experiment has a randomized complete block design with four repetitions. Genotypes comprise eight breeding lines and two commercial cultivars, Vanda and Vera. The evaluated traits include total production in g/plant, commercial production in g/plant, and numbers of leaves/plant. Analysis of joint deviance indicated that the genotypes responded differently to the environments evaluated. The crisphead lettuce breeding lines that were most productive, stable, and adapted to the twelve lettuce-growing environments, even outperforming the commercial Vanda and Vera cultivars, were lines L8, L2, and L6.

COMPLEMENTARY TOOLS UTILIZED IN THE PEA (Pisum sativum L.) BREEDING PROGRAM AT UNIVERSIDAD NACIONAL DE ROSARIO

Conventional breeding can be complemented by different strategies that increase the efficiency of the methodologies and the current rate of increase in yields in order to meet demand. The use of molecular markers with the aim of developing linkage maps of the species, the use of Blup (Best Linear Unbiased Prediction) for an efficient selection of progenitors to hybridize, the use of in vitro culture to artificially increase the number of F1 plants or the use of digital phenotyping for efficient digital characterization that can be performed during the periodic and routine regeneration of accessions in germplasm collections. Key words: Molecular markers, Blup, in vitro culture, digital phenotyping.

Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle

There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits—bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round—in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.

An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology

Extensively grazed cattle are often mustered only once a year. Therefore, birthdates are typically unknown or inaccurate. Birthdates would be useful for deriving important traits (growth rate; calving interval), breed registrations, and making management decisions. Epigenetic clocks use methylation of DNA to predict an individual’s age. An epigenetic clock for cattle could provide a solution to the challenges of industry birthdate recording. Here we derived the first epigenetic clock for tropically adapted cattle using portable sequencing devices from tail hair, a tissue which is widely used in industry for genotyping. Cattle (n = 66) with ages ranging from 0.35 to 15.7 years were sequenced using Oxford Nanopore Technologies MinION and methylation was called at CpG sites across the genome. Sites were then filtered and used to calculate a covariance relationship matrix based on methylation state. Best linear unbiased prediction was used with 10-fold cross validation to predict age. A second methylation relationship matrix was also calculated that contained sites associated with genes used in the dog and human epigenetic clocks. The correlation between predicted age and actual age was 0.71 for all sites and 0.60 for dog and human gene epigenetic clock sites. The mean absolute deviation was 1.4 years for animals aged less than 3 years of age, and 1.5 years for animals aged 3–10 years. This is the first reported epigenetic clock using industry relevant samples in cattle.

Genomic studies with pre-selected markers reveal dominance effects influencing growth traits in Eucalyptus nitens

Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait relevant markers may be important compared to non-selected markers. Here we used pre-selected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN) and kraft pulp yield (KPY) were analysed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, non-genotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is non-linear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.

M-quantile Chambers-Dunstan Untuk Pendugaan Area Kecil

Metode Small Area Estimations (SAE) digunakan sebagai pendekatan yang reliabel dalam mengatasi kendala ketidakcukupan sampel pada survei sampel. BPS memproduksi statistik area kecil menggunakan metode SAE popular seperti Empirical Best Linear Unbiased Prediction dalam model Fay-Herriot (EBLUP-FH). Metode EBLUP-FH sebagai pendekatan parametrik memerlukan asumsi normalitas dan terbebas dari outliers pada kedua komponen random effect-nya. Namun, hal tersebut sulit dipenuhi karena seringkali data di lapangan berperilaku ekstrim. Metode SAE M-quantile Chambers-Dunstan (CD) merelaksasi asumsi parametrik dan robust dalam inferensi terhadap outliers. Penelitian ini mengkaji metode M-quantile CD dalam meningkatkan robustness pendugaan area kecil melalui penerapannya pada data riil untuk estimasi rata-rata pengeluaran rumah tangga per kapita tingkat kecamatan di DI Yogyakarta tahun 2018. Penelitian ini menggunakan data Susenas 2018 dan Podes 2018. Hasil implementasi pada data riil menunjukkan model M-quantile CD berhasil memperbaiki presisi EBLUP-FH. Dengan mengimplementasikan M-quantile CD diharapkan estimasi data berperilaku ekstrim lebih akurat untuk pengambilan kebijakan di daerah.