Joint Regression Analysis Research Articles

Joint regression analysis of clustered current status data with latent variables.

Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.

Evaluation and identification of high yielding and stable cowpea genotypes using GGE biplot and joint regression analysis for varietal development in East and Southern Africa

Malawi, Mozambique, and Tanzania's cowpea breeding programs have been collaborating on pre-breeding trials in different locations, focusing on key traits such as grain yield. However, there is a lack of information regarding the nature of genotype-environment interaction patterns among these environments. The study was conducted to assess and identify high-yielding and stable cowpea genotypes for the development of improved varieties in these three countries. Thirty genotypes were evaluated in ten environments using an incomplete block design with three replications. Grain yield data was analyzed using genotype (G) × genotype-environment interaction (GGE) biplot and joint regression analysis. The joint analysis of variance showed highly significant differences (P < 0.001) for the main effects of genotypes and environment, as well as their interaction effects. This suggested that the environments assessed were distinct and that the genotypes exhibited varying performances in response to environmental variations. The environmental indices showed that only four environments were favorable for high grain yield, and overall, Guariba was the topmost yielder (1973.5 kg ha−1) across all environments. The analysis identified IT10K-817-7 and MWcp08 as superior and stable genotypes, making them highly desirable and potentially recommended across test environments for their expected ability to achieve economically viable yields. Overall, Chitedze Research Station in Malawi and Nametil Research Station in Mozambique were both discriminating and representative, indicating that they are good test environments for selecting best-performing genotypes that can perform well across a wide range of conditions. These environments can be recommended as the best environments for future cowpea genotype evaluations targeting the release of varieties in the three countries (Malawi, Mozambique and Tanzania) where the METs were carried out.

Performance and Stability for Grain Yield and Its Components of Some Rice Cultivars under Various Environments

Refine current agricultural practices considering environmental changes are crucial for finding tolerant rice varieties that can meet the demands of human consumption. To this end, stability analysis assesses a crop genotype’s ability to adapt to various conditions. Therefore, the objective of this study was to (1) examine the interaction between rice genotypes and environmental conditions; (2) evaluate the stability of twelve rice genotypes using various stability methods; (3) identify representative environments for multi-environment testing; and (4) determine superior genotypes for specific environments. The evaluated rice cultivars were Sakha 101, Sakha 104, Sakha 105, Sakha 106, Sakha 107, Sakha 108, Giza 177, Giza 178, Giza 179, Giza 182, Egyptian Yasmine, and Sakha super 300. The experiment followed a strip-plot design, with three replications. The findings revealed significant differences among the rice varieties across various environments for the majority of the assessed characteristics. The joint regression analysis of variance demonstrated highly significant differences among rice cultivars for all the studied traits in terms of genotype-by-environment interaction (G × E). The statistical significance of the interaction between genetic and environmental factors was evident for all variables demonstrating heritable variation among the rice cultivars, specifically Sakha 108, Sakha 104, Giza 177, and Giza 178, concerning grain yield per feddan. These rice cultivars exhibited stability parameters that were not significantly different from unity for the regression coefficient (bi) and from zero for the deviations from regression (S2di) for those traits. Overall, stability criteria are essential for ensuring reliable rice production, meeting human consumption, advancing genetic improvement, and promoting environmental sustainability in agriculture.

Joint regression analysis of multiple traits based on genetic relationships.

Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties. Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency. GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.

Genotype × Environment Interaction: A Comparison between Joint Regression Analysis and Weighted Biplot Models

This work examines the obstacles presented by genotype-environment interaction (GEI) in plant breeding and the significance of accurate analysis in selecting superior genotypes. Although current models, such as the JRA and GGE biplot models, have their limitations, especially when dealing with multi-environment data with a single trait and several environments, our approach addresses the challenges of GEI in plant breeding. We introduce new models and conduct a comprehensive comparison with the existing ones. The inclusion of mega-environment analysis and the evaluation of individual test environments within each mega-environment adds depth to this study, aiming to provide a more nuanced understanding of the causes and effects of GEI. We intend to validate and test the proposed models on real-world datasets to assess their effectiveness and practical applicability in the field of plant breeding. Additionally, communicating the benefits and potential limitations of your proposed models will contribute to the broader understanding and adoption of improved methods for analyzing GEI in plant breeding. We conclude that joint use of the JRA and GGE Biplot models has proven effective in exploring genotype × environment interaction, particularly for multi-environment data (MET). JRA model provides a most robust and reliable representation of patterns in the data related to genotypes and environments.

Genotype-by-environment interaction analysis for cotton seed yield using various biometrical methods under irrigation regimes in a semi-arid region

ABSTRACT The main objective of this study was to identify high-yielding and stable cotton genotypes under normal irrigation regimes and drought stress conditions using some biometrical methods including combined analysis of variance (ANOVA), joint regression analysis (JRA), the additive main effect and multiplicative interaction (AMMI), and genotype (G) main effect plus genotype-by-environment (GE) interaction (GGE) biplot. Cotton seed yield (CSY) was found to be significantly affected by genotypes, environments, and Genotype-by-environment interaction (GEI) using combined ANOVA, JRA, and AMMI. AMMI was superior, explaining 79% of the total variability caused by GEI under drought stress conditions compared to 66% and 25% for the GGE biplot and JRA, respectively. The CSY was found to be significantly lower under drought stress conditions vs normal irrigation regimes, ranging from 9.03% (G24) to 29.91% (G15) across the five tested environments. The JRA, AMMI, and GGE biplot methods were positively correlated for classifying the genotypes for static stability. The GGE biplot was the most effective and acceptable for identifying stable genotypes and optimal environments in both irrigation regimes. All the methods compared were concordant in separating, ranking, and identifying that the G5 and G20 genotypes were highly stable across the environment as being higher-yielding and drought-tolerant.

Identification of promising and stable genotypes of wheat (Triticum aestivum L.) for grain yield under varied climatic conditions of north-western Himalayas using joint regression analysis

Identification of promising and stable genotypes of wheat (Triticum aestivum L.) for grain yield under varied climatic conditions of north-western Himalayas using joint regression analysis

Trends and patterns of antibiotics use in Serbia from 2006 to 2021: Pre-COVID-19 period versus COVID-19 pandemic

Global rise in antibiotic utilization has been strongly associated with the resistance of bacteria to antibiotics. The COVID-19 saw an increase in the use of antibiotics in some countries. The aim of this study was to evaluate antibiotic utilization from 2006 to 2021 in the Republic of Serbia. Data on antibiotic use were retrieved from the national annual reports on the official website of the Medicines and Medical Devices Agency of Serbia during the period 2006 to 2021. To evaluate trends in the use of antibiotics in Serbia, linear, and joint regression analyses were performed. The analysis of the antibiotics use over a sixteen-year period included a total of 50 antibiotics. A significant increase during the COVID-19 pandemic was observed for glycylcyclines that is, new-generationtetracyclines (tigecycline), third-generation cephalosporins (ceftazidime, ceftriaxone, and cefixime), respiratory fluoroquinolones (levofloxacin and moxifloxacin), carbapenems (ertapenem), and oxazolidinones (linezolid) utilization. Moreover, an almost negligible use of new β-lactam/β-lactamase inhibitors during the prepandemic period increased significantly during the COVID-19 pandemic period. A significant increasing trend in the use of specific antibiotics classified as the "Watch" and "Reserve" antibiotics during the pandemic period was observed.

Deciphering transcriptomic signatures explaining the phenotypic plasticity of nonheading lettuce genotypes under artificial light conditions.

Elucidating the mechanisms and pathways involved in genotype-environment (G×E) interactions and phenotypic plasticity is critical for improving plant growth. Controlled environment agricultural systems allow growers to modulate the environment for particular genotypes. In this study, we evaluated the effects of interactions among 14 genotypes and four artificial light environments on leaf lettuce phenotypes and dissected the underlying molecular mechanism via transcriptome-based modeling. Variations in morphological traits and phytochemical concentrations in response to artificial light treatments revealed significant G×E interactions. The appropriate genotype and artificial light combinations for maximizing phenotypic expression were determined on the basis of a joint regression analysis and the additive main effect and multiplicative interaction model for these G×E interactions. Transcriptome-based regression modeling explained approximately 50%-90% of the G×E variations. Further analyzes indicated Red Lettuce Leaves 4 (RLL4) regulates UV-B and blue light signaling through the effects of the HY5-MBW pathway on flavonoid biosynthesis and contributes to natural variations in the light-responsive plasticity of lettuce traits. Our study represents an important step toward elucidating the phenotypic variations due to G×E interactions in nonheading lettuce under artificial light conditions.

Joint Regression Analysis of Variance for Seed Yield and its Attributing Traits by Eberhart and Russell (1966) Model in Black Gram (Vigna mungo L.) Under Rainfed Conditions of NW Himalaya’s

Joint Regression Analysis of Variance for Seed Yield and its Attributing Traits by Eberhart and Russell (1966) Model in Black Gram (Vigna mungo L.) Under Rainfed Conditions of NW Himalaya’s

Provenances from introduced stands of Northern Red Oak (Quercus rubra L.) outperform those from the natural distribution

Northern Red Oak (Quercus rubra L.), which is native to North America, covers over 350.000 ha in Europe and is currently the most common non-native deciduous tree species in Germany. Due to its high growth potential and higher drought tolerance in relation to native oak species, its valuable ecological characteristics and the versatile use of its wood for lumber or furniture it is one of the potential alternative tree species in a future warmer climate.In this study, growth performance of Q. rubra (particularly basal area [m2 ha-1] at age 33) is analysed, using data from a provenance trial established in 1991 with three test sites located in North, East and Central Germany. We focus on 12 common provenances, six of North American origin and six sourced from introduced stands from Germany, to evaluate patterns of provenance-environment interaction (P × E). Using effect size statistics, we found a significant percent gain in productivity of German compared to North American provenances on two humid sites. Two provenances from South-west Germany further convince with a good stem form. North American provenances from Canada show an equivalent growth to those from Germany on a drier test site, while provenances from the United States show poor growth in all environments. We assessed the provenance sensitivity across the tested environments by characterising growth differences with the joint regression analysis (JRA), which confirmed a significant P × E and the growth superiority of German provenances. The application of the Weighted Average of the Absolute Scores (WAAS) as a stability parameter enables the classification of provenances into generalists and specialists. While the German provenances can be assumed to be generalists with above average performance (4 provenances) or specialists with a high productivity on humid sites (2 provenances) and therefore seem suitable for recommendations, those from Canada performed well in the dry environment, which could be promising if planted at sites with decreasing precipitation regimes. All of these aspects shall contribute to a broader use of Northern Red Oak especially in Germany in the process of forest restoration.

Susceptibility of UK oat (Avena sativa) varieties to infection by Fusarium species and subsequent HT‐2 and T‐2 toxin contamination

AbstractThe aim of this study was to compare the susceptibility of oats to Fusarium langsethiae infection, as measured by combined HT‐2 and T‐2 mycotoxin concentration (HT2 + T2) in harvested oat grain samples. Over 10 years (2004–2013), samples from single replicates of each UK Recommended List oat trial were analyzed for HT‐2 and T‐2. For spring oats, there were small but statistically significant differences between varieties, whereas for winter oats, they had a broader range and higher mean of HT2 + T2 concentration compared with spring oats. For winter oats, the short‐strawed varieties had consistently high HT2 + T2 levels compared with other varieties, whereas naked varieties were at the lower end of the range, and short, naked varieties had intermediate levels. A separate set of harvested oat grain samples of eight common varieties from 17 field experiments were analyzed by modified joint regression analysis. Results showed that environment had the strongest impact on HT‐2 and T‐2 concentrations but that the varietal susceptibility to HT‐2 and T‐2 contamination was highly stable across environments. This methodology can be used to calculate a Fusarium (HT2 + T2) resistance score for oats to aid grower selection of suitable varieties, as is available for Fusarium (DON) resistance for wheat varieties in many countries.

Identification of stable oat wild relatives among Avena species for seed and forage yield components using joint regression analysis

Crop wild relatives are a good source of useful alleles for climate change adaptation. In present investigation, seven oat wild relatives (OWRs) including different Avena species tested against one commercial check of cultivated species A. sativa across three consecutive cropping seasons (Rabi 2018-19 to 2020-21) prevalent in North-western Himalayas at CSKHPKV, Palampur. Stability analysis was performed using Eberhart and Russell model for sixteen seed, forage yield and quality attributes. The mean squares for GE interaction were significant for most of the traits indicating differential response of genotypes to different environments. Both linear and non-linear components significantly contributed to genotype × environment interactions. Higher mean over commercial check, above average responsiveness with wider adaptable or stable genotypes identified were HFO 103 (A. orientalis) for seeds per panicle, HFO 498 (A. longiglumis) for harvest index and all the genotypes for protein content. Thus, these selected OWRs found stable and well adapted to all the types of climatic conditions of humid sub-temperate climate could be exploited in future breeding programme

Identification of stable oat wild relatives among Avena species for seed and forage yield components using joint regression analysis

Crop wild relatives are a good source of useful alleles for climate change adaptation. In present investigation, seven oat wild relatives (OWRs) including different Avena species tested against one commercial check of cultivated species A. sativa across three consecutive cropping seasons (Rabi 2018-19 to 2020-21) prevalent in North-western Himalayas at CSKHPKV, Palampur. Stability analysis was performed using Eberhart and Russell model for sixteen seed, forage yield and quality attributes. The mean squares for GE interaction were significant for most of the traits indicating differential response of genotypes to different environments. Both linear and non-linear components significantly contributed to genotype × environment interactions. Higher mean over commercial check, above average responsiveness with wider adaptable or stable genotypes identified were HFO 103 (A. orientalis) for seeds per panicle, HFO 498 (A. longiglumis) for harvest index and all the genotypes for protein content. Thus, these selected OWRs found stable and well adapted to all the types of climatic conditions of humid sub-temperate climate could be exploited in future breeding programme.

Study of correlation between daily calorie intake (DCI) and body composition in type 2 DM patients using bioimpedance analysis as a screening tool

Evaluation of body composition (BC) represents a valuable screening tool to assess nutritional status among diabetic patients. The most commonly used methods to evaluate BC in the clinical practice are based on bicompartmental models such as dual energy X-ray absorptiometry (DXA) etc. Factors like daily caloric intake (DCI), percentage of total body water (%TBW), muscle mass and bone mass and the changes that occur in these components with age could be related to the development of type 2 diabetes mellitus (T2DM). The objective of the study is to use Bioimpedance Analysis (BIA) as a screening tool to assess the correlation between these parameters in type 2 diabetic patients. This study was conducted on 200 T2DM patients aged 25-45 years for a duration of 6 months in an urban out-patients clinic after obtaining proper informed consent and ethics committee approval. A multifrequency body composition monitor TANITA MC 980 was used to analyse muscle mass, bone mass and %TBW. The impedance readings were entered into medically researched formulae to calculate body composition. The results were correlated with DCI. The study results showed a strong correlation between DCI and muscle mass (p&amp;#60;0.01). Scatter diagram analysis showed that muscle mass has a significant correlation with DCI at p&amp;#60;0.01. No significant correlation was observed between bone mass, and metabolic age with DCI. Joint regression analysis by ANOVA showed a significant correlation between %TBW and muscle mass with DCI at p&amp;#60;0.05. This study showed a strong correlation between DCI and increasing muscle mass and basal metabolic rate among diabetic patients. It also shows that BIA is a useful tool for clinical studies in assessing the correlation between DCI and body composition among type 2 diabetic patients.

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.

Maximum Likelihood and L2 Environmental Indices in Joint Regression Analysis

Summary This paper describes an iterative analysis of incomplete genotype × environment data. L 2 environmental indices were introduced to enable the use of Joint Regression Analysis (JRA) in analyzing experiments with incomplete blocks. We now show how, once normality of yields is assumed, the introduction of L2 environmental indices provides a theoretical framework for Joint Regression Analysis. Using this framework, maximum likelihood estimators are obtained and likelihood ratio tests are derived. It is noted that the technique allows unequal weighting of data, and the special case of complete blocks is discussed.

Multivariate Analysis of Agronomic Traits in Newly Developed Maize Hybrids Grown under Different Agro-Environments.

Developing high-yielding maize hybrids is essential under the fast-growing global population and abrupt global climate change. Planting density is one of the imperative components for enhancing maize productivity. This study assessed newly developed maize hybrids under three planting densities on two sowing dates. The evaluated hybrids were 40 maize genotypes comprised of 36 F1-developed hybrids and 4 commercial high-yielding check hybrids. The developed hybrids were generated from selected maize inbred lines according to their adaptive traits to high planting density, such as prolificacy, erect leaves, short plants, early silking, anthesis-silking interval, and small tassel size. The applied planting densities were high, intermediate, and low, with 95,000, 75,000, and 55,000 plants/ha, respectively, under timely and late sowing. The high planting density displayed the uppermost grain yield compared with the intermediate and low densities at both sowing dates. The developed hybrid G36 exhibited the highest agronomic performance under high planting density at timely and late sowing. Additionally, G38, G16, G37, G23, G5, G31, G18, G7, G2, G20, G29, and G17 displayed high agronomic traits at both sowing dates. Joint regression and AMMI analyses revealed significant genotype, agro-environment, and genotype × agro-environment interaction effects for grain yield. The AMMI biplot displayed that G39 was closest to the ideal stable hybrid, and the hybrids G36, G18, G38, G17, G2, and G37 were considered desirable stable hybrids. Moreover, the GGE biplot indicated that a high planting density at an optimal sowing date could be considered a representative environment for discriminating high-yielding maize hybrids. The designated promising hybrids are recommended for further inclusion in maize breeding due to their stability and high yields.

Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model

PurposeThe purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model.Design/methodology/approachThe joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market.FindingsEmpirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model.Social implicationsThe proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks.Originality/valueA novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.

Genetic Advance and Grain Yield Stability of Moroccan Durum Wheats Grown under Rainfed and Irrigated Conditions

The development of high-yielding durum wheat genotypes requires the knowledge of the genetic variation for grain yield and its components. This study was performed to determine genetic gain and to evaluate the genotype × environment effect for grain yield and related traits in Moroccan durum wheat. A historical series of varieties developed during the last three decades was grown under two water regimes (rainfed and irrigated) during three growing seasons. Traits evaluated in the present work included grain yield, spikes per m2, kernels per spike, spikelets per spike, kernels per spikelet, spike length, kernel weight, plant height, harvest index, and fertile tillering. Results from ANOVA analyses revealed that grain yield and related traits were controlled to a large extent by water regime and growing season. Grain yield advance per year was estimated in 78 kg ha−1 under irrigated conditions, with no significant change under rainfed ones. Overall, the results indicated that most of the yield components changed by breeding activities during the last three decades. AMMI and joint regression analyses revealed that intermediate varieties have a wide adaptation, and old varieties were specifically acclimated to water-limit environments, while modern varieties were performed only under favorable conditions.