Other Statistics and Probability Commons^™

Topological Regression As An Interpretable And Efficient Tool For Quantitative Structureactivity Relationship Modeling, Ruibo Zhang, Daniel Nolte, Cesar Sanchez-Villalobos, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Quantitative structure-activity relationship (QSAR)modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application inmolecular design.We propose a similaritybased regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learningbased QSAR models and provide better intuitive interpretation by extracting an approximate …

Bagging Improves The Performance Of Deep Learning-Based Semantic Segmentation With Limited Labeled Images: A Case Study Of Crop Segmentation For High-Throughput Plant Phenotyping, Yinglun Zhan, Yuzhen Zhou, Geng Bai, Yufeng Ge

Department of Statistics: Faculty Publications

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the …

Editorial: Ipps 2022 - Plant Phenotyping For A Sustainable Future, Elias Kaiser, Philipp Von Gillhaussen, Jennifer Clarke, Ulrich Schurr

Department of Statistics: Faculty Publications

Plants are a venue for addressing the challenges facing humanity. The need for a reliable supply of food, feed, materials, chemicals and energy as well as ways to manage agroecology and climate change are among the challenges that we can address through the sustainable use of plants and plant ecosystems. The research community needs to integrate plant systems approaches, from molecular to organismal to applications in the field and ecosystems, to increase productivity sustainably while using fewer land, water, and nutrient resources. In the past two decades, plant phenotyping research has developed a highly valuable portfolio of technologies, processes and …

Discussion On “Spatial+: A Novel Approach To Spatial Confounding” By Dupont, Wood, And Augustin, Brian J. Reich, Shu Yang, Yawen Guan

Department of Statistics: Faculty Publications

Congratulations to the authors for this thoughtful and timely contribution to the spatial confounding literature. The intuitive nature of the method and simplicity of the estimation procedure will surely make Spatial+ popular with practitioners, and the theoretical developments are a major advance for researchers in this area. There is much to discuss! We have formatted our discussion in two sections: in Section 2 we consider the assumptions and statistical properties of Spatial+, and in Section 3 we examine how Spatial+ fits in the wider literature on spatial causal inference.

Prebiotic Proanthocyanidins Inhibit Bile Reflux–Induced Esophageal Adenocarcinoma Through Reshaping The Gut Microbiome And Esophageal Metabolome, Katherine M. Weh, Connor L. Howard, Yun Zhang, Bridget A. Tripp, Jennifer Clarke, Amy B. Howell, Joel H. Rubenstein, Julian A. Abrams, Maria Westerhoff, Laura A. Kresty

Department of Statistics: Faculty Publications

The gut and local esophageal microbiome progressively shift from healthy commensal bacteria to inflammation-linked pathogenic bacteria in patients with gastroesophageal reflux disease, Barrett’s esophagus, and esophageal adenocarcinoma (EAC). However, mechanisms by which microbial communities and metabolites contribute to reflux-driven EAC remain incompletely understood and challenging to target. Herein, we utilized a rat reflux-induced EAC model to investigate targeting the gut microbiome–esophageal metabolome axis with cranberry proanthocyanidins (C-PAC) to inhibit EAC progression. Sprague-Dawley rats, with or without reflux induction, received water or C-PAC ad libitum (700 μg/rat/day) for 25 or 40 weeks. C-PAC exerted prebiotic activity abrogating reflux-induced dysbiosis and mitigating …

Bingroup2: Statistical Tools For Infection Identification Via Group Testing, Christopher R. Bilder, Brianna D. Hitt, Brad J. Biggerstaff, Joshua M. Tebbs, Christopher S. Mcmahan

Department of Statistics: Faculty Publications

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm’s operating characteristics, such as the expected number of …

Good Practices And Common Pitfalls In Climate Time Series Changepoint Techniques: A Review, Robert B. Lund, Claudie Beaulieu, Rebecca Killick, Qiqi Lu, Xueheng Shi

Department of Statistics: Faculty Publications

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change changepoint conclusions. This paper is a review of the topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features …

Data Sharing And Ontology Use Among Agricultural Genetics, Genomics, And Breeding Databases And Resources Of The Agbiodata Consortium, Jennifer L. Clarke, Laurel D. Cooper, Monica F. Poelchau, Tanya Z. Berardini, Justin Elser, Andrew D. Farmer, Stephen Ficklin, Sunita Kumari, Marie-Angélique Laporte, Rex T. Nelson, Rie Sadohara, Peter Selby, Anne E. Thessen, Brandon Whitehead, Taner Z. Sen

Department of Statistics: Faculty Publications

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and …

Pre-Sleep Feeding, Sleep Quality, And Markers Of Recovery In Division I Ncaa Female Soccer Players, Casey E. Greenwalt, Elisa Angeles, Matthew D. Vukovich, Abbie E. Smith-Ryan, Chris W. Bach, Stacy T. Sims, Tucker Zeleny, Kristen E. Holmes, David M. Presby, Katie J. Schiltz, Marine Dupuit, Liliana I. Renteria, Michael J. Ormsbee

Department of Statistics: Faculty Publications

Pre-sleep nutrition habits in elite female athletes have yet to be evaluated. A retrospective analysis was performed with 14 NCAA Division I female soccer players who wore a WHOOP, Inc. band – a wearable device that quantifies recovery by measuring sleep, activity, and heart rate metrics through actigraphy and photoplethysmography, respectively – 24 h a day for an entire competitive season to measure sleep and recovery. Pre-sleep food consumption data were collected via surveys every 3 days. Average pre-sleep nutritional intake (mean ± sd: kcals 330 ± 284; cho 46.2 ± 40.5 g; pro 7.6 ± 7.3 g; fat 12 …

Increasing Racial Diversity In The North American Plant Phenotyping Network Through Conference Participation Support, David Lebauer, Alexander Bucksch, Jennifer Clarke, Jesse Potts, Sonali Roy

Department of Statistics: Faculty Publications

A key goal of the North American Plant Phenotyping Network (NAPPN) annual conference is to cultivate a new generation of scientists from diverse backgrounds. As part of their effort to diversify the plant phenomics research community, NAPPN acquired funding to cover all attendance costs for participants from historically black colleges and universities (HBCU) for the 2022 annual meeting. Seven award recipients represented the first attendees from HBCUs in the conference’s 6-year history. In this commentary, we report on the impact of the conference awards, including lessons learned, and the future of the award.

Near-Term Effects Of Perennial Grasses On Soil Carbon And Nitrogen In Eastern Nebraska, Salvador Ramirez Ii, Marty R. Schmer, Virginia L. Jin, Robert B. Mitchell, Kent M. Eskridge

Department of Statistics: Faculty Publications

Incorporating native perennial grasses adjacent to annual row crop systems managed on marginal lands can increase system resiliency by diversifying food and energy production. This study evaluated (1) soil organic C (SOC) and total N stocks (TN) under warm-season grass (WSG) monocultures and a low diversity mixture compared to an adjacent no-till continuous-corn system, and (2) WSG total above-ground biomass (AGB) in response to two levels of N fertilization from 2012 to 2017 in eastern Nebraska, USA. The WSG treatments consisted of (1) switchgrass (SWG), (2) big bluestem (BGB), and (3) low-diversity grass mixture (LDM; big bluestem, Indiangrass, and sideoat …

Integrating And Optimizing Genomic, Weather, And Secondary Trait Data For Multiclass Classification, Vamsi Manthena, Diego Jarquín, Reka Howard

Department of Statistics: Faculty Publications

Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel …

Federated Learning Framework Integrating Refined Cnn And Deep Regression Forests, Daniel Nolte, Omid Bazgir, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as …

Federated Learning Framework Integrating Refined Cnn And Deep Regression Forests, Daniel Nolte, Omid Bazgir, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as …

Socioeconomic Factors In The Diagnosis And Treatment Of Malignant Melanoma In Hispanic Vs. Non-Hispanic Patients: A National Cancer Database (Ncdb) Study, Julia Griffin, Sarah J. Aurit, Timothy Malouff, Peter Silberstein

Department of Statistics: Faculty Publications

Background: The incidence of melanoma is rapidly increasing in the United States. There is a paucity of research of how melanoma affects the Hispanic population, the quickest growing population.

Objective: To identify and understand how socioeconomic factors affect a Hispanic patients health outcome and treatment of malignant melanoma with comparisons to white, non-Hispanic (WNH) patients.

Methods: A retrospective study utilizing the National Cancer Database (NCDB) was completed investigating Hispanic patients (n=2282) and WNH patients (n=190,469) with Stage I-IV malignant melanoma. Outcome and socioeconomic variables were identified and compared across groups. Data was analyzed with SPSS and SAS …

Estimating The Prevalence Of Two Or More Diseases Using Outcomes From Multiplex Group Testing, Md S. Warasi, Joshua M. Tebbs, Christopher S. Mcmahan, Christopher R. Bilder

Department of Statistics: Faculty Publications

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, …

Penguins Go Parallel: A Grammar Of Graphics Framework For Generalized Parallel Coordinate Plots, Susan Vanderplas, Yawei Ge, Antony Unwin, Heike Hofmann

Department of Statistics: Faculty Publications

Parallel Coordinate Plots (PCP) are a valuable tool for exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this article, we propose Generalized Parallel Coordinate Plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observations …

Viscoelastic Properties Of Human Facial Skin And Comparisons With Facial Prosthetic Elastomers, Mark W. Beatty, Alvin G. Wee, D. B. Marx, Lauren Ridgway, Bobby Simetich, Thiago Carvalho De Sousa, Kevin Vakilzadian, Joel Schulte

Department of Statistics: Faculty Publications

Prosthesis discomfort and a lack of skin-like quality is a source of patient dissatisfaction with facial prostheses. To engineer skin-like replacements, knowledge of the differences between facial skin properties and those for prosthetic materials is essential. This project measured six viscoelastic properties (percent laxity, stiffness, elastic deformation, creep, absorbed energy, and percent elasticity) at six facial locations with a suction device in a human adult population equally stratified for age, sex, and race. The same properties were measured for eight facial prosthetic elastomers currently available for clinical usage. The results showed that the prosthetic materials were 1.8 to 6.4 times …

Early Detection Of Covid-19 In Female Athletes Using Wearable Technology, Liliana I. Rentería, Casey E. Greenwalt, Sarah Johnson, Shiloah Shiloah Kviatkovsky, Marine Dupuit, Elisa Angeles, Sachin Narayanan, Tucker Zeleny, Michael J. Ormsbee

Department of Statistics: Faculty Publications

Background: Heart rate variability (HRV), respiratory rate (RR), and resting heart rate (RHR) are common variables measured by wrist-worn activity trackers to monitor health, fitness, and recovery in athletes. Variations in RR are observed in lower-respiratory infections, and preliminary data suggest changes in HRV and RR are linked to early detection of COVID-19 infection in nonathletes.

Hypothesis: Wearable technology measuring HRV, RR, RHR, and recovery will be successful for early detection of COVID-19 in NCAA Division I female athletes.

Study Design: Cohort study.

Level of Evidence: Level 2.

Methods: Female athletes wore WHOOP, Inc. bands …

Socio‑Economic Inequalities In Minimum Dietary Diversity Among Bangladeshi Children Aged 6–23 Months: A Decomposition Analysis, Satyajit Kundu, Pranta Das, Ashfikur Rahman, Hasan Al Banna, Kaniz Fatema, Akhtarul Islam, Shobhit Srivastava, T. Muhammad, Rakhi Dey, Ahmed Hossain

Department of Statistics: Faculty Publications

This study aimed to measure the socio-economic inequalities in having minimum dietary diversity (MDD) among Bangladeshi children aged 6–23 months as well as to determine the factors that potentially contribute to the inequity. The Bangladesh Demographic and Health Survey (BDHS) 2017–2018 data were used in this study. A sample of 2405 (weighted) children aged 6–23 months was included. The overall weighted prevalence of MDD was 37.47%. The concentration index (CIX) value for inequalities in MDD due to wealth status was positive and the concentration curve lay below the line of equality (CIX: 0.1211, p < 0.001), where 49.47% inequality was contributed by wealth status, 25.06% contributed by the education level of mother, and 20.41% contributed by the number of ante-natal care (ANC) visits. Similarly, the CIX value due to the education level of mothers was also positive and the concentration curve lay below the line of equality (CIX: 0.1341, p < 0.001), where 52.68% inequality was contributed by the education level of mother, 18.07% contributed by wealth status, and 14.69% contributed by the number of ANC visits. MDD was higher among higher socioeconomic status (SES) groups. Appropriate intervention design should prioritize minimizing socioeconomic inequities in MDD, especially targeting the contributing factors of these inequities.

Identification Of Disease Resistance Parents And Genome-Wide Association Mapping Of Resistance In Spring Wheat, Muhammad Iqbal, Kassa Semagn, Diego Jarquin, Harpinder Randhawa, Brent D. Mccallum, Reka Howard, Reem Aboukhaddour, Izabela Ciechanowska, Klaus Strenzke, José Crossa, J. Jesus Céron-Rojas, Amidou N’Diaye, Curtis Pozniak, Dean Spaner

Department of Statistics: Faculty Publications

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) …

Evaluating Dimensionality Reduction For Genomic Prediction, Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard

Department of Statistics: Faculty Publications

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the …

Evaluating Dimensionality Reduction For Genomic Prediction, Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard

Department of Statistics: Faculty Publications

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the …

Bayesian Analysis For The Lomax Model Using Noninformative Priors, Daojiang He, Dongchu Sun, Qing Zhu

Department of Statistics: Faculty Publications

The Lomax distribution is an important member in the distribution family. In this paper, we systematically develop an objective Bayesian analysis of data from a Lomax distribution. Noninformative priors, including probability matching priors, the maximal data information (MDI) prior, Jeffreys prior and reference priors, are derived. The propriety of the posterior under each prior is subsequently validated. It is revealed that the MDI prior and one of the reference priors yield improper posteriors, and the other reference prior is a second-order probability matching prior. A simulation study is conducted to assess the frequentist performance of the proposed Bayesian approach. Finally, …

Combining Phenotypic And Genomic Data To Improve Prediction Of Binary Traits, Diego Jarquin, Arkaprava Roy, Bertrand S. Clarke, Subhashis Ghosal

Department of Statistics: Faculty Publications

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here ‘main traits’) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or ‘phenotypes’) that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any …

Comparative Antiplatelet Effects Of Chlorthalidone And Hydrochlorothiazide, Khalid Bashir, Tammy Burns, Samuel J. Pirruccello, Sarah J. Aurit

Department of Statistics: Faculty Publications

Chlorthalidone (CTD) may be superior to hydrochlorothiazide (HCTZ) in the reduction of adverse cardiovascular events in hypertensive patients. The mechanism of the potential benefit of CTD could be related to antiplatelet effects. The objective of this study was to determine if CTD or HCTZ have antiplatelet effects. This study was a prospective, double-blind, randomized, three-way crossover comparison evaluating the antiplatelet effects of CTD, HCTZ, and aspirin (ASA) in healthy volunteers. The effects of these treatments on platelet activation and aggregation were assessed using a well-established method with five standard platelet agonists. Thirty-four patients completed the three-way crossover comparing pre- and …

Spatio-Temporal Models Of Infectious Disease With High Rates Of Asymptomatic Transmission, Aminur Rahman, Angela Peace, Ramesh Kesawan, Souparno Ghosh

Department of Statistics: Faculty Publications

The surprisingly mercurial Covid-19 pandemic has highlighted the need to not only accelerate research on infectious disease, but to also study them using novel techniques and perspectives. A major contributor to the dificulty of containing the current pandemic is due to the highly asymptomatic nature of the disease. In this investigation, we develop a modeling framework to study the spatio-temporal evolution of diseases with high rates of asymptomatic transmission, and we apply this framework to a hypothetical country with mathematically tractable geography; namely, square counties uniformly organized into a rectangle. We first derive a model for the temporal dynamics of …

Reply To Response By Fbi Laboratory Filed In Illinois V. Winfield And Affidavit By Biederman Et Al. (2022) Filed In Us V. Kaevon Sutton (2018 Cf1 009709), Susan Vanderplas, Kori Khan, Heike Hofmann, Alicia Carriquiry

Department of Statistics: Faculty Publications

1 Preliminaries

1.1 Scope

The aim of this document is to respond to issues raised in Federal Bureau of Investigation¹ and Alex Biedermann, Bruce Budowle & Christophe Champod.²

1.2 Conflict of Interest

We are statisticians employed at public institutions of higher education (Iowa State University and University of Nebraska, Lincoln) and have not been paid for our time or expertise when preparing either this response or the original affidavit.³ We provide this information as a public service and as scientists and researchers in this area.

1.3 Organization

The rest of the document precedes as follows: we begin …

Genomic Prediction Accuracy Of Stripe Rust In Six Spring Wheat Populations By Modeling Genotype By Environment Interaction, Kassa Semagn, Muhammad Iqbal, Diego Jarquin, Harpinder Randhawa, Reem Aboukhaddour, Reka Howard, Izabela Ciechanowska, Momna Farzand, Raman Dhariwal, Colin W. Hiebert, Amidou N’Diaye, Curtis Pozniak, Dean Spaner

Department of Statistics: Faculty Publications

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with …

Comparing Artificial-Intelligence Techniques With State-Of-The-Art Parametric Prediction Models For Predicting Soybean Traits, Susweta Ray, Diego Jarquin, Reka Howard

Department of Statistics: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a significant source of protein and oil and is also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein, and oil content is important to feed the ever-growing population. As opposed to high-cost phenotyping, genotyping is both cost and time efficient for breeders because evaluating new lines in different environments (location–year combinations) can be costly. Several genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional …