BLUP Research Articles

GWAS for identification of genomic regions and candidate genes in vegetable crops.

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.

Multi Environment Trials and Adaption of Advanced Bread Wheat (<i>Triticum aestivum </i>L.) Genotypes in Low Moisture Stress Areas of Ethiopia

To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization.

120 Cross-validation of breeding values and future phenotypes for heifer pregnancy in Red Angus cattle using the LR method

Abstract Heifer pregnancy (HP) in beef cattle is a heritable and economically relevant trait but is challenging to predict using best linear unbiased prediction (BLUP) methodology due to the binary nature of the phenotypes. The observed categorical responses (e.g., 1 = pregnant; 0 = non-pregnant) are due to an animal either exceeding a particular threshold for pregnancy or not on an underlying genetic distribution which is why this binary trait is commonly evaluated using a threshold model (TM). However, TM are susceptible to scenarios where only 1 category of observation is predominant. In this study we compare genetic evaluations including “partial” and “whole” data to cross-validate methodologies for the prediction of HP. The objectives of this study were then to evaluate estimators of prediction accuracy, bias, and dispersion. Heifer fertility data were obtained from the Red Angus Association of America. Evaluations for HP were performed using ASREML3.0 and a univariate, BLUP, traditional TM; and a linear continuous BLUP animal model (LM) on both reference (whole) and validation (partial) datasets. Reference data included all animals with HP phenotypes, and validation data had censored the most recent 5 yr of HP phenotypes (~16% of HP phenotypes). Reference and validation estimated breeding values (EBV) were then compared using linear regression (LR) methods. Heritability estimates were 0.05 (± 0.007) and 0.15 (± 0.009) for LM and TM respectively. The average BIF accuracy of censored validation animals (n = 10,175) using a LM on whole and partial datasets were 0.07 and 0.03 respectively, and using a TM on whole and partial datasets were 0.09 and 0.04 respectively. Comparing the mean EBV from the partial and whole datasets can be a good estimator of bias in the model as the difference is expected to be 0. The difference between the mean EBV from partial and mean EBV from whole datasets were –0.003 and –0.016 for LM and TM respectively, suggesting that the TM is a slightly more biased evaluation. The regression of EBV obtained with whole data on EBV estimated with partial data has an expected value of 1 if there is no over/under dispersion which can be a useful tool in determining model correctness and gauge bias in an evaluation. The closer a regression coefficient is to 1, the less biased the evaluation is. The slope of the regression of EBV estimated from whole data on partial data for validation animals were 0.98 (P < 0.0001) and 0.91 (P < 0.0001) for LM and TM respectively. Although both regression coefficients were above 0.90 suggesting both evaluations are performing well, these results suggest that TM models may be a more biased predictor of HP than LM despite a small benefit in prediction accuracy for the trait.

510 Utilizing python programming for modern genetic evaluation systems

Abstract Advancements in agricultural data collection and methodologies for data analysis offer opportunities for beef cattle producers to improve herd genetics. Genetic evaluation systems (GES) can benefit from modern programming approaches to keep up with these advancements. Python, a fast-developing programming language with scientific computing libraries such as NumPy and SciPy, offers flexibility that allows for quick adoption of superior algorithms and new data sources, as well as easy maintenance of the software over the long-term. This study evaluated the use of Python (v3.12.1) for developing a GES capable of executing all the necessary functions expected of modern implementation of GES. A simulated dataset was generated with the R package AlphaSimR to mimic characteristics of the Angus beef cattle breed. The dataset included pedigree information, phenotypic data, and true breeding values (TBVs) for each animal. Phenotypic traits included birth weight (BWT), weaning gain (WG), and post-weaning gain (PWG), with a total of 976,400 animals simulated across 15 generations. The GES software was developed in house, and included components such as data import, pedigree and data preprocessing, calculating inbreeding, constructing the inverse of the numerator relationship matrix, solver initialization, estimation of breeding value, and writing results. The genetic evaluation employed pedigree-based best linear unbiased prediction (BLUP) and a multiple-trait animal model, using a preconditioned conjugate gradient iteration algorithm to solve the mixed model equations. Convergence was assessed using the residual vector norm divided by the right-hand-side norm, with a threshold to declare convergence set to 1x10-5. The software was run on a Linux server with an Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz, 96 GB of RAM, and eight cores. Accuracy was measured using Pearson correlation coefficient between TBVs and estimated breeding values (EBVs). To benchmark computing time and solutions, the same dataset and model were input into a commonly used commercial GES, MiXBLUP (v3.0.1). The solver was the most time-consuming component of the GES, with a run time of 27 min. However, the application of multiprocessing reduced the run time by 72% to 8 min. The Pearson correlation coefficient between EBV and TBV was 0.86, 0.79, and 0.73 (P < 0.01) for BWT, WG, and PWG, respectively. In contrast, MiXBLUP completed breeding value prediction in less than 1 min. The Pearson correlation coefficient between Python and MiXBLUP predictions was 1.0 (P < 0.01). While Python is an ideal language for modern GES, further updates are necessary to optimize performance and operations. Future research will focus on refining computational algorithms, exploring parallel processing techniques, and enhancing user interfaces to ensure easy integration with industry practices.

The number of drones to inseminate a queen with has little potential for optimization of honeybee breeding programs

BackgroundMating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones’ descent, the breeder’s control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs.MethodsBy computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony.ResultsSDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed.ConclusionThe opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen’s spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose.

Phenotypic and Genetic Analyses of Mastitis, Endometritis, and Ketosis on Milk Production and Reproduction Traits in Chinese Holstein Cattle.

Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60-1.50 d), Interval from First Service to Conception (FSTC, 0.20-16.20 d), Calving Interval (CI, 4.00-7.00 d), and Number of Services (NUMS, 0.07-0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

Analysis of genome-wide association studies of low-temperature germination in Xian and Geng rice.

Rice is the leading global staple crop. Low temperatures pose negative impacts on rice's optimal growth and development. Rice cultivars acclimating to low temperatures exhibited improved seedling emergence under direct-seeded sowing conditions, yet little is known about the genes that regulate germination at low temperatures (LTG). In this research investigation, we've performed whole genome sequencing for the 273 rice plant materials. Using the best linear unbiased prediction (BLUP) values for each rice material, we identified 7 LTG-related traits and performed the efficient genetic analysis and genome-wide association study (GWAS). As a result of this, 95 quantitative trait loci (QTLs) and 1001 candidate genes associated with LTG in rice were identified. Haplotype analysis and functional annotation of the candidate genes resulted in the identification of three promising candidate genes (LOC_Os08g30520 for regulating LTG4 and LTG5, LOC_Os10g02625 for regulating LTG6, LTg7 and LTG8, and LOC_Os12g31460 for regulating LTG7, LTg8 and LTG9) involving in the regulation of LTG in rice. This research provides a solid foundation for addressing the LTG issue in rice and will be valuable in future direct-seeded rice breeding programs.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

Mapping of dynamic quantitative trait loci for plant height in a RIL population of foxtail millet (Setaria italica L.).

Plant height (PH) is a crucial trait for strengthening lodging resistance and boosting yield in foxtail millet. To identify quantitative trait loci (QTL) and candidate genes associated with PH, we first developed a genetic map using a recombinant inbred line (RIL) population derived from a cross between Aininghuang and Jingu 21. Then, PH phenotyping data and four variations of best linear unbiased prediction (BLUP) were collected from nine environments and three development stages. Next, QTL mapping was conducted using both unconditional and conditional QTL methods. Subsequently, candidate genes were predicted via transcriptome analysis of parental samples at three developmental stages. The results revealed that the genetic map, based on re-sequencing, consisted of 4,360 bin markers spanning 1,016.06 cM with an average genetic distance of 0.23 cM. A total of 19 unconditional QTL, accounting for 5.23%-35.36% of the phenotypic variation explained (PVE), which included 7 major and 4 stable QTL, were identified. Meanwhile, 13 conditional QTL, explaining 5.88%-40.35% of PVE, including 5 major and 3 stable QTL, were discovered. Furthermore, four consistent and stable QTL were identified. Finally, eight candidate genes were predicted through RNA-seq and weighted gene co-expression network analysis (WGCNA). Those findings provide a crucial foundation for understanding the genetic mechanisms underlying PH development and facilitate molecular marker-assisted breeding of ideal plant types in foxtail millet.

Densification of Genetic Map and Stable Quantitative Trait Locus Analysis for Amino Acid Content of Seed in Soybean (Glycine max L.).

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans.

Combining AMMI and BLUP analysis to select high‐yielding soybean genotypes in Benin

AbstractThirty soybean [Glycine max (L.) Merr.] genotypes, along with three checks, were evaluated over three seasons across five communes in Benin. The experiments were laid out in an alpha lattice design with three replicates. Additive multiplicative mean interaction (AMMI) and best linear unbiased predictor (BLUP) analysis were combined to assess differential agronomic performance and yield stability among genotypes. There was significant variation (p &lt; 0.001) between genotypes for all traits, with highly significant environmental and genotype × environment interaction (GEI) effects on soybean grain yield (p &lt; 0.001). The likelihood ratio test indicated that both genotype and interaction effects were highly significant (p &lt; 0.001). The low R2 (0.21) for GEI reflected the presence of high residual variation in the GEI component, in contrast to the AMMI analysis of variance, which explained a high proportion of the GEI through the first two interaction principal component axes (52%). The very high value of the predictive accuracy (0.89) confirmed the model's reliability in selecting superior genotypes. The low (0.33) genotypic correlation between environments indicated that it was difficult to select superior genotypes for each environment. Based on the superiority index (weighted average absolute scores from BLUP for yield) of BLUP, simultaneous selection led to the identification of Jenguma 2.67 ± 0.06 t ha−1 as the most stable and productive genotype across environments, followed by Favour 2.34 ± 0.08 t ha−1, and Afayak 2.46 ± 0.08 t ha−1. The agronomic performance of soybean in this study suggested great potential for diversifying cotton‐based cropping systems in Benin, thereby improving their sustainability. The effect of these soybean genotypes on the productivity of intercrop combinations and sequences of cash crops, such as cotton, is yet to be investigated.

Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

Multi-model genome-wide association study on key organic naked barley agronomic, phenological, diseases, and grain quality traits

The study objective was to assess the potential benefits of using genomic tools in organic plant breeding programs to enhance selection efficiency. A diversity panel of 247 spring naked barley accessions was characterized under Irish organic conditions over 3 years. Genome-wide association studies (GWAS) were performed on 19 traits related to agronomy, phenology, diseases, and grain quality, using the information on 50 K Single Nucleotide Polymorphisms (SNP). Four models (EMMA, G model, BLINK, 3VMrMLM) were applied to 5 types of Best Linear Unbiased Predictors (BLUP): within-year, mean, aggregated within-year). 1653 Marker-Trait-Associations (MTA) were identified, with 259 discovered in at least two analyses. 3VMrMLM was the best-performing model with significant MTA together explaining the largest proportion of the additive variance for most traits and BLUP types (from 1.4 to 50%). This study proposed a methodology to prioritize main effect MTA from different models’ outputs, using multi-marker regression analyses with markers fitted as fixed or random factors. 36 QTL, considered major, explained more than 5% of the trait variance on each BLUP type. A candidate gene or known QTL was found for 18 of them, with 13 discovered with 3VMrMLM. Multi-model GWAS was useful for validating additional QTL, including 8 only discovered with BLINK or G model, thus allowing a broader understanding of the traits’ genetic architecture. In addition, results highlighted a correlation between the trait value and the number of favorable major QTL exhibited by accessions. We suggest inputting this number in a multi-trait index for a more efficient Marker-Assisted Selection (MAS) of accessions best balancing multiple quantitative traits.

Selection of Saccharum spp. × Saccharum spontaneum progeny for sugar and biomass traits

ABSTRACT This study evaluated an interspecific hybrid (Saccharum spp. × S. spontaneum) population with the aim of developing germplasm for use in both sugar and bioenergy (biomass) production. Specific objectives were to determine the genetic variability and heritability among the interspecific hybrid progenies for traits of economic importance and to use the Best Linear Unbiased Predictions (BLUPs) to determine their genotypic values. The 46 interspecific hybrids used in this study originated from crossing commercial sugarcane cultivars (UT5, F152, and KK07–559) as female and S. spontaneum clones (THS98–41, THS98–91, THS98–95, THS98–94, and THS97–51) as male parents. There were significant residual variances, attributed to the small plot size, but the juice quality traits were measured with relatively higher precision than other traits. Selection recommendations varied for different traits, suggesting selection pressure could be moderate to high for juice quality traits and moderate for yield-related traits. No hybrid genotypes were found to outperform cultivar parents for traits such as commercial cane sugar and low fiber, necessitating further germplasm enhancement for improvement of varieties destined for sugar production. On the contrary, transgressive segregation was observed for cane yield and other traits influencing biomass yield. The production potential of these hybrids could be tested in larger plots across multiple locations.

Imputation of Counterfactual Outcomes when the Errors are Predictable

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (BLUP) of Goldberger in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed PUP is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals.

Regional ionospheric correction generation for GNSS PPP-RTK: theoretical analyses and a new interpolation method

The regional ionospheric corrections have become one of the critical parts of Precise Point Positioning (PPP)-based Real-Time-Kinematic (RTK) services to achieve fast positioning convergence. Several methods for regional ionospheric corrections supporting PPP-RTK have been developed and implemented over recent years, but little attention is given to the theoretical foundation of existing ionospheric correction methods and their performance comparison to find an optimal method in some sense. The optimality criterion of such methods should not only be based on the precision of the ionospheric correction itself, but also on its broadcasting strategies, and implementation aspects. This contribution studies ionospheric correction generation methods within the best linear unbiased predictor (BLUP) framework. Comparing the accuracy performances of the methods, we demonstrate that the Kriging method with trend, as a special case of BLUP, is the most appropriate method for large-scale networks (above 500 km). A strategy for the evaluation of the uncertainty of the grid-interpolated ionospheric corrections is also developed. In contrast to other empirical methods, this new method is rigorous in the sense that it avoids the underestimation of the uncertainty of predicted ionospheric corrections, especially when reference stations are close to a grid point.

Combined selection for productivity, adaptability, and stability in corn genotypes for baby corn production

AbstractBaby corn is the name given to the female inflorescence of corn (Zea mays L.) before fertilization, being harvested in 2–3 days after the issuance of the styles‐stigmata. Currently, in Brazil, there is no specific corn cultivar for baby corn production, which leads to the use of popcorn and sweet corn cultivars because they are more accepted in the food industry. Therefore, the objective of this work was to estimate the genetic parameters and determine the efficiency of simultaneous selection based on productivity, adaptability, and stability of corn genotypes for baby corn production in the state of Espírito Santo, Brazil. The evaluations were conducted in 2019 on family properties in six municipalities in the state of Espírito Santo. Eleven corn genotypes were evaluated for baby corn production, being a double hybrid, four native varieties, and six commercial varieties. The design was randomized blocks with four repetitions each. The genetic parameters were estimated, which were obtained using the best linear unbiased prediction (BLUP), and the selection was carried out by the harmonic mean of the relative performance of genotypic values (HMRPGV). It was observed that the environments influenced the performance of the genotypes, configuring specific adaptation. The genotypes AG 1051, Aliança, and BR 106 presented the best performances in all the environments evaluated, considering, simultaneously, the productivity, adaptability, and stability of marketable ears. The HMRPGV method provided optimized selection of corn genotypes to produce baby corn with high productive potential, predictable behavior, and responsive to environmental stimuli.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10–13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual’s genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual’s predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Unraveling genotype-by-environment interaction in maize using cutting edge statistical tools: Innovative empirical selection for increased yield stability

By prioritizing high yields in ideal conditions, traditional breeding might miss out that genotypes with superior yield and stability. Thus,this study seeks to identify stable genotypes that offer more predictable and reliable yields; under both stress and resourceful environments, leading to not only greater food security but financial stability as well. Incorporating both fixed (AMMI&GGI) and random effects models (BLUP & MTSI), not only leveraged theoretical but also empirical comparisons-weakness of each model is covered by all 4 models-to select elites hybrids Maize. Furthermore, a multi-environment trial assessed 41 hybrids across 4 research stations in 2 winter seasons using RCB design. Likewise, compiled cutting-edge statistical methods for GEI analyses from the “metan” package (R-Software), selected hybrids with consistent performance with meticulous analysis. AMMI(Additive Main effects and Multiplicative Interaction)analysis revealed that of significant (p ≤ 0.05) effects of genotype, environment, & genotype-environment interaction (GEI), explaining yield variance with interaction effects were captured by 3 interaction principal components (IPCs). Moreover,GEI graphics of AMMI biplots elucidates four hybrids(MRM-4062, Super-22, PAC-744, and Gen-4118) with high yield stability and above-average performance. Additionally, eight hybrids (4118, 4558, 5454, NMH1258, PAC 746, PAC 745 Gold, and SUPER 9090) exhibited superior yield, regardless of their stability. Nepalgunj exhibited the lowest GEI value. Conversely, Rampur displayed an above-average GEI, indicating a strong ability to modulate varieties performance. Besides this, GGE biplot revealed two potential mega-environments based on yield similarities. Nine high-yielding hybrids (SUPPER 6768, SUPPER 9396, PAC750, MBS5622, PAC745 Gold, MRM4065, NMH8392, Gen-4118, and Gen-4558) were identified as vertex hybrids, signifying their superior performance across wide environments. Even supposing, mixed effects model, both the best linear unbiased prediction (BLUP) weighted with average absolute score standardized by yields (WAASBY) models, Gen-4118 ranked highest in terms of average yield, followed by NMH-8392, TMH2858, and others, and Multi-Trait Stability Index (MTSI) identified SUPPER-9090 as the most stable and high-yielding genotype, followed by NMH4040, Super-22, Gen-4118, MBS-1144, and NMH1255. In a nutshell, Gen-4118, Super-9090, MRM-4062, NMH1258, NMH-8392, TMH2858, PAC 746, PAC 745 Gold emerge as the highest-yielding and most consistently high-performinghybrids, offering promise for commercial farming across diverse environments. Clearly,this study has potential to mitigate risks for farmers (economic burden, crop failure) and bolster food security/climate resilience. What's more, it paves the way for future research on cold tolerance genes for even greater crop improvement.