Linear Unbiased Estimator Research Articles

Analyses of Wheat Resistance to Fusarium Head Blight Using Different Inoculation Methods

Fusarium head blight is a devastating wheat disease that causes yield reduction and mycotoxins contamination, leading to multiple negative consequences for the economy, health, and food safety. Despite the tremendous efforts that have been undertaken over the last several decades to harness the disease, the problem remains a challenging issue. Due to global warming, its impact has become increasingly severe in Baltic and Nordic countries. The improvement of wheat resistance is hampered by complicated genetic inheritance, the scarcity of adapted resistant breeding materials, and difficulties in obtaining accurate and reproducible data due to the high interaction and dependency of the disease development on the environment. In this study, the resistance of 335 genotypes, 9 of which were of exotic origin and the remainder of which were adapted to the environments of Lithuania, Latvia, Estonia, or Norway, was studied in 8 trials using spray and point inoculation with spore suspensions and grain spawn inoculation under field and/or greenhouse conditions. The best linear unbiased estimates (BLUEs) of each genotype within the individual trials and the adjusted means across the trials were determined to reduce the environmental effects. Genotypes that exhibited excellent Type I or Type II resistance and overall resistance were identified.

Data assimilation for noise mapping using CNOSSOS - A Danish Case Study

Noise maps play a significant role in developing noise mitigation policies. However, maps based on noise simulation models are likely to be uncertain due to underlying model uncertainties and parameterization. This research work explores the feasibility of data assimilation techniques to address this issue. A noise measurement campaign was carried out at two locations in the Central Jutland Region of Denmark. Consequently, 76 days (1 September-15 November 2019) (N = 1824 hours) of measured hourly A-weighted, energy equivalent, sound pressure level data (LAeq1h in dBA) was available for this case study. In addition, traffic counting data was available. The LAeq1h (dBA) levels were then simulated using the EU noise prediction standard, CNOSSOS. The comparison of daily averaged measured and simulated noise levels reveals the Root Mean Squared Error (RMSE) in the range of 1.4 - 2.8 dBA. Subsequently, data assimilation techniques, such as the Best Linear Unbiased Estimator (BLUE) and Bayesian Inverse Inference, are used to produce improved, hourly, so-called analysis maps with the minimum error covariance. The oral presentation and short article will present details of model inputs and simulations, data assimilation as well as the strengths and limitations of this research.

Exploring Novel Genomic Loci and Candidate Genes Associated with Plant Height in Bulgarian Bread Wheat via Multi-Model GWAS.

In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat.

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

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

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize.

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Addressing Autocorrelation, Multicollinearity, and Heavy-Tail Errors in the Linear Regression Model

The most popular estimator for estimating parameters of linear regression models is the Ordinary Least Squares (OLS) Estimator. The OLS is considered the best linear unbiased estimator when certain assumptions are not violated. However, when autocorrelation, multicollinearity, and heavy-tail error are jointly present in the dataset, the OLS estimator is inefficient and imprecise. In this paper, we developed an estimator of linear regression model parameters that jointly handle multicollinearity, autocorrelation, and heavy tail errors. The new estimator, LADHLKL, was derived by combining the Hildreth-Lu (HL), the Kibria Lukman (KL), and the Least Absolute Deviation (LAD) estimators. The LADHLKL poses both the characteristics of the LAD, HL, and KL estimators which makes it resistant to both problems. We examined the properties of the proposed estimator and compared its performance with other existing estimators in terms of mean square error. An application to real-life data and simulation study revealed that the proposed estimator dominates other estimators in all the considered conditions in terms of mean square error.

A New Approach to Composite Estimation for Repeated Surveys with Rotating Panels

Composite estimation in repeated surveys with rotating panels refers to methods of estimation that exploit correlations in the data in the sample overlap between survey times to improve the precision of estimates. In this article a novel approach to composite estimation is proposed, in which composite regression estimators of current totals for a number of key variables are generated from a simultaneous calibration of the sampling weights of the overlapping samples of the current and previous survey time. In this procedure, in addition to the usual calibration to known population totals, differences of estimates for the key variables based on the full sample and the common sample from the two consecutive times are calibrated to each other. The resulting multivariate composite regression estimator, which is constructed as an approximate best linear unbiased estimator, incorporates effectively information from the samples of both survey times for enhanced estimation efficiency. Unlike other composite regression estimators, the proposed estimator does not require micro-matching of data in the overlap sample, and, therefore, is free of potential issues associated with it. It is also considerably more practical than existing composite regression estimators and the traditional AK-composite estimator.

Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials

Key Message Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models.The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky–Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02–0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment (G×E)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(G\ imes E)$$\\end{document} interaction variance.

Curation of historical phenotypic wheat data from the Czech Genebank for research and breeding

Climate change and population growth are putting increasing pressure on global food security. The development of high-yielding varieties for important crops such as wheat is crucial to meet these challenges. The basis for this is extensive exploitation of beneficial genetic variation resting in genebanks around the world. Selecting suitable donor genotypes from the vast number of wheat accessions stored in genebanks is a difficult task and depends critically on the density of information on the performance of individual accessions. Therefore, this study aimed to access phenotypic data from the Czech genebank, storing over 13,000 wheat accessions. We curated and analyzed data on heading date, plant height, and thousand grain weight for more than one-third of all available accessions regenerated across 70 years. The data underwent analysis using a linear mixed model, revealing high quality of curated data with heritability reaching 99%. The raw data, but also derived data such as the best linear unbiased estimations, are now available for the wheat collection of the Czech genebank for research and breeding.

Unifying the prediction strategies of Theil-Goldberger and Kibria-Lukman within linear mixed models

. Linear mixed models employ the best linear unbiased estimator and the best linear unbiased predictor to estimate the parameter vectors for fixed and random effects. However, due to the undesirable variance properties of the best linear unbiased estimator in the presence of multicollinearity, alternative estimators and predictors are preferred. The Theil-Goldberger’s and the Kibria-Lukman’s prediction approaches are commonly used for prediction under multicollinearity in linear mixed models. To address the issue of multicollinearity, this article introduces the mixed Kibria-Lukman estimator and predictor by combining these prediction approaches. To assess their effectiveness, the proposed mixed Kibria-Lukman estimator/predictor is compared with other estimators/predictors, including the best linear unbiased estimator/the best linear unbiased predictor and mixed estimators/predictors, using the matrix mean square error criterion. Furthermore, the performance of the newly defined prediction approach is demonstrated through the analysis of greenhouse gases data and a Monte-Carlo simulation study.

Pengaruh Upah Minimum, TPT, Dependency Ratio dan Pertumbuhan Ekonomi Terhadap Tingkat Kemiskinan di Provinsi Jawa Timur

Poverty is an issue related to the welfare of mankind. More in-depth research is needed on factors that can affect poverty. Aiming to achieve the influence, Minimum Wage, Open Unemployment Rate, dependency ratio and Economic Growth on the Poverty Rate in East Java Province with a 13-year series from 2010 to 2022. The study used quantitative research using secondary data obtained from the Central Bureau of Statistics of East Java Province. Using multiple linear regression with BLUE (Best Linear Unbiased Estimate) assumptions. The results of the study simultaneously known sig value. test F as much as 0.001 which means less than𝛼 = 0.05, as well as the value of F count 15.427 F table 3.84. From these results it can be concluded that all independent variables, Minimum Wage (X1), Open Unemployment Rate (X2), Dependency Ratio (X3) and Economic Growth (X4), simultaneously or simultaneously have a significant influence on the dependent variable, Poverty Level (Y). Partially, the Open Unemployment Rate, Dependency Ratio and Economic Growth do not have a significant effect on the Poverty Level, while the Minimum Wage has a significant effect on the Poverty Level.

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020.

Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999-2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha-1 year-1 (1.94% year-1) and 26 kg ha-1 year-1 (0.42% year-1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of -2.65% year-1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with -1.19% and -0.27% year-1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of -0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya's highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development.

Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information.

This study aims to compare the accuracy of genomic estimated breeding values (GEBV) estimated using a genomic best linear unbiased prediction (GBLUP) method and GEBV estimates incorporating prior marker information from a genome-wide association study (GWAS) for the weaning weight trait in highland Merino sheep. The objective is to provide theoretical and technical support for improving the accuracy of genomic selection. The study used a population of 1007 highland Merino ewes, with the weaning weight at 3 months as the target trait. The population was randomly divided into two groups. The first group was used for GWAS analysis to identify significant markers, and the top 5%, top 10%, top 15%, and top 20% markers were selected as prior marker information. The second group was used to estimate genetic parameters and compare the accuracy of GEBV predictions using different prior marker information. The accuracy was obtained using a five-fold cross-validation. Finally, both groups were subjected to cross-validation. The study's findings revealed that the heritability of the weaning weight trait, as calculated using the GBLUP model, ranged from 0.122 to 0.394, with corresponding prediction accuracies falling between 0.075 and 0.228. By incorporating prior marker information from GWAS, the heritability was enhanced to a range of 0.125 to 0.407. The inclusion of the top 5% to top 20% significant SNPs from GWAS results as prior information into GS showed potential for improving the accuracy of predicting genomic breeding value.

Genome-wide association study of stem structural characteristics that extracted by a high-throughput phenotypic analysis "LabelmeP rice" in rice.

Stem is important for assimilating transport and plant strength; however, less is known about the genetic basis of its structural characteristics. In this study, a high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles, which allows the establishment of a stem cross-section phenotype dataset containing anatomical information of 1738 images from hand-cut transections of stems collected from 387 rice germplasm accessions grown over two successive seasons. Then, the phenotypic diversity of the rice accessions was evaluated. Genome-wide association studies identified 94, 83, and 66 significant single nucleotide polymorphisms (SNPs) for the assayed traits in 2 years and their best linear unbiased estimates, respectively. These SNPs can be integrated into 29 quantitative trait loci (QTL), and 11 of them were common in 2 years, while correlated traits shared 19. In addition, 173 candidate genes were identified, and six located at significant SNPs were repeatedly detected and annotated with a potential function in stem development. By using three introgression lines (chromosome segment substitution lines), four of the 29 QTLs were validated. LOC_Os01g70200, located on the QTL uq1.4, is detected for the area of small vascular bundles (SVB) and the rate of large vascular bundles number to SVB number. Besides, the CRISPR/Cas9 editing approach has elucidated the function of the candidate gene LOC_Os06g46340 in stem development. In conclusion, the results present a time- and cost-effective method that provides convenience for extracting rice stem anatomical traits and the candidate genes/QTL, which would help improve rice.

Analisis Faktor–Faktor yang Mempengaruhi Impor Kapas di Indonesia

The purpose of this study is to determine and examine if cotton imports into Indonesia are influenced by cotton prices, cotton consumption, cotton production and exchange rate.. This study falls under the category of quantitative research. Data on cotton prices, cotton consumption, cotton production, exchange rate, and cotton imports make up this study population. The study sample consists of yearly data from Indonesia organized into timeseries that are consecutive and span the years 2003 through 2020. The secondary data documentation approach is the data gathering strategy used in this study. The analysis technique used is multiple linear regression analysis which meets the BLUE (Best Linear Unbiased Estimate) assumption. Based on the statistical t test results, the variable Cotton Prices (X1) 3,299 > ttable 2,160, Cotton Consumption (X2) = 6,398 > ttable 2,160, Cotton Production (X3) = 4,324 > ttable 2,160, and that the Exchange Rate (X4) = 3,128 > ttable 2,160 has an effect on Cotton Imports (Y) in Indonesia. The result of statistical testing indicates that H0 is rejected when Fcount > Ftable. The research found that although the four variables Cotton Prices (X1), Cotton Consumption (X2), Cotton Production (X3), and Exchange Rates (X4) had a simultaneous and limited influence on Imports of Cotton (Y), the Cotton Prices (X1) variable had the most impact.

A new parameter estimation method for the extended power Lindley distribution based on order statistics, with application

In this paper, we propose inference procedures for the estimation of parameters by using order statistics. First, we derive some new expressions for single and product moments of the order statistics from the extended power Lindley distribution. We then use these moments to obtain the best linear unbiased estimates (BLUEs) of the location and scale parameters based on Type-II right-censored samples. A real data set is analysed to illustrate the flexibility and importance of the model.

Investigating the Effects of Education and Labour Market Challenges on Income Inequality

Abstract This paper examines the impact of education and labour market challenges on the income inequality in European Union (27 Member States) within the period 2012-2022, this being calculated using the Panel EGLS method. Even if the effects are clearly visible from a theoretical point of view, in the latest years there were not many authors focusing their studies on the effects of the unemployment and early dropout from school and training on income inequality. In this regard, updating the figures, the impact coefficients and the theoretical background increase the understanding of the statistical processes and their results in the new economic context. Our results confirmed a positive relationship between unemployment rate and income inequality (measured by Gini coefficient), this being also the highest impact found, but also a positive link between the early leavers from education and training rate and income inequality. In addition, we used additional variables to catch the current economic challenges that are related to demographic changes and high energy prices. In this context, we found positive effects exerted by housing cost overburden rate and old-age dependency ratio on income inequality. Even if the model is limited to four income inequality drivers, we have demonstrated that the calculated coefficients are the best linear unbiased estimators.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains.

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.

Genetic progress in cowpea [Vigna unguiculata (L.) Walp.] stemming from breeding modernization efforts at the International Institute of Tropical Agriculture.

Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48g/year (SD), 0.68g/year (MD), and 0.55g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding.