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.

Faktor yang Mempengaruhi Return Saham dengan Harga Saham sebagai Variabel Moderasi (Studi Kasus pada Perusahan Perbankan yang Terdaftar di BEI)

This study aims to know the factors that impact stock return with Market Price as the moderating variable of the banking company listed on the IDX from 2015 to 2020. The data is retrieved from idx.co.id. The population of this article is 43 banking companies, and to select the sample for this article has used purposive sampling and has selected 11 companies. The analysis method of this article has used descriptive statistics. The data has gone through BLUE (best linear unbiased estimator) test, such as normality test, autocorrelation test, multicollinearity test, and heteroscedasticity test before doing the hypothesis test. Further, the analysis data has used F-test, t-test, the equation of multiple linear regression, determination coefficient, and moderation. The study's findings are that, partially, LDR does not affect SR, ROA does not affect SR, and BOPO does not affect SR. PBV can not moderate the effect of LDR, ROA, and BOPO on stock return. The determination coefficient is 0.048 (4.8%), which means that the LDR, ROA, and BOPO have impacted SR as much as 4.8%, and the remaining is affected by other factors. The contribution of the research is to help the investors select the right stock.

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.

Preprocessing Tools for Data Preparation

This data preparation chapter is of paramount importance for implementing statistical machine learning methods for genomic selection. We present the basic linear mixed model that gives rise to BLUE and BLUP and explain how to decide when to use fixed or random effects that give rise to best linear unbiased estimates (BLUE or BLUEs) and best linear unbiased predictors (BLUP or BLUPs). The R codes for fitting linear mixed model for the data are given in small examples. We emphasize tools for computing BLUEs and BLUPs for many linear combinations of interest in genomic-enabled prediction and plant breeding. We present tools for cleaning, imputing, and detecting minor and major allele frequency computation, marker recodification, frequency of heterogeneous, frequency of NAs, and three methods for computing the genomic relationship matrix. In addition, scaling and data compression of inputs are important in statistical machine learning. For a more extensive description of linear mixed models, see Chap. 10.1007/978-3-030-89010-0_5.

Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

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.

Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

The objectives were to investigate prediction of malting quality (MQ) phenotypes in different locations using information from metabolomic spectra, and compare the prediction ability using different models and different sizes of training population (TP). A total of 2,667 plots of 564 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each individual plot. Metabolomic features (MFs) used were 24,018 NMR intensities measured on each wort sample. Models involved in the statistical analyses were a metabolomic best linear unbiased prediction (MBLUP) model and a partial least squares regression (PLSR) model. Predictive ability within location and across locations were compared using cross-validation methods. The proportion of variance in MQ traits that could be explained by effects of MFs was above 0.9 for all traits. The prediction accuracy increased with increasing TP size but when the TP size reached 1,000, the rate of increase was negligible. The number of components considered in the PLSR models can affect the performance of PLSR models and 20 components were optimal. The accuracy of individual plots and line means using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation ranged from 0.517 to 0.817.In conclusion, it is possible to carry out metabolomic prediction of MQ traits using MFs, the prediction accuracy is high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

BEST LINEAR UNBIASED LATENT VALUES PREDICTORS FOR FINITE POPULATION LINEAR MODELS WITH DIFFERENT ERROR SOURCES

We develop best linear unbiased predictors (BLUP) of the latent values of labeled sample units selected from a finite population when there are two distinct sources of measurement error: endogenous, exogenous or both. Usual target parameters are the population mean, the latent values associated to a labeled unit or the latent value of the unit that will appear in a given position in the sample. We show how both types of measurement errors affect the within unit covariance matrices and indicate how the finite population BLUP may be obtained via standard software packages employed to fit mixed models in situations with either heteroskedastic or homoskedastic exogenous and endogenous measurement errors.