scholarly journals Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

2020 ◽  
Author(s):  
Mehdi Momen ◽  
Madhav Bhatta ◽  
Waseem Hussain ◽  
Haipeng Yu ◽  
Gota Morota
Keyword(s):  
Factor Analysis ◽  
Bayesian Network ◽  
Grain Yield ◽  
Leaf Rust ◽  
Exploratory Factor Analysis ◽  
Stem Rust ◽  
Latent Variables ◽  
Yellow Rust ◽  
Flag Leaf ◽  
Data Driven

AbstractInferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum Aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

scholarly journals Modeling multiple phenotypes in wheat using data‐driven genomic exploratory factor analysis and Bayesian network learning

Plant Direct ◽  
2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mehdi Momen ◽  
Madhav Bhatta ◽  
Waseem Hussain ◽  
Haipeng Yu ◽  
Gota Morota
Keyword(s):  
Factor Analysis ◽  
Bayesian Network ◽  
Exploratory Factor Analysis ◽  
Data Driven ◽  
Network Learning ◽  
Multiple Phenotypes ◽  
Using Data

scholarly journals Virulence surveillance of wheat black stem rust fungus

2014 ◽  
Vol 11 (2) ◽  
pp. 803-812
Author(s):  
Baghdad Science Journal
Keyword(s):  
Leaf Rust ◽  
Disease Severity ◽  
Stem Rust ◽  
Yellow Rust ◽  
Rust Fungus ◽  
The North ◽  
Rust Diseases ◽  
Northern Regions ◽  
Infection Disease ◽  
Wheat Rust

General survey for wheat rust diseases in Iraqi fields was done during the seasons of 2010, 2011 and 2012. The survey covered different fields in southern, middle and northern regions. Results of the first season indicated that most of Iraqi cultivars such as Tmmoze2, IPA 99 and Mexipak showed different types of susceptibility to both yellow and leaf rust infection. Disease severity increased when the conditions were favorable for infections with using susceptible cultivars. The severity of leaf rust was less in the north region comparing with the middle and south regions. Most of the introduced cultivars such as Sham6 and Cimmyto showed susceptible reaction to yellow and leaf rust. Yellow rust was in epiphytotic form at the Iraqi-Syrian-Turkish triangle where the disease severity was 100%. Low disease severity of stem rust was observed on some cultivars (1-5%), except for the cultivar Mexipak which showed 40%S in Najaf. Rusts at season of 2011 were restricted mostly in Baghdad and the yellow rust was dominant. The AUDPC of 15 wheat cultivars showed that Sawa and Sali were highly susceptible to the three types of rusts while Babil113 and Tamoze2 were resistant. No rusts were detected at season 2012.

scholarly journals Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

2019 ◽  
Vol 30 (2) ◽  
pp. 1033-1040 ◽  
Author(s):  
Yfke P. Ongena ◽  
Marieke Haan ◽  
Derya Yakar ◽  
Thomas C. Kwee
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Internal Consistency ◽  
Latent Variables ◽  
Procedural Knowledge ◽  
Patient Questionnaire ◽  
Successful Implementation ◽  
Personal Interaction ◽  
Five Factors

Abstract Objectives The patients’ view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology. Methods Six domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach’s alpha and composite reliability. Results The exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5). Conclusions This study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale. Key Points • Although AI systems are increasingly developed, not much is known about patients’ views on AI in radiology. • Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients’ views on AI in radiology, revealing five factors. • Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.

scholarly journals Wheat Breeding for Disease Resistance: Review

2019 ◽  
Vol 4 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Gadisa Alemu
Keyword(s):  
Disease Resistance ◽  
Leaf Rust ◽  
Resistance Genes ◽  
Stem Rust ◽  
Rust Resistance ◽  
Yellow Rust ◽  
Wheat Breeding ◽  
High Yield ◽  
Breeding Programs ◽  
Rust Resistance Genes

Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end - use quality. Wheat breeding is focused on high yield, pathogen resistance and abiotic stress tolerance. Among diseases of wheat yellow rust, stem rust, and leaf rust are the most damaging diseases of wheat and other small grain cereals . Disease resistance in wheat breeding with one exception, the diseases of wheat that is important because of their effect on yield. Resistance to all diseases together can is important to avoid an unexpected loss in effectiveness of the resistance of a cu ltivar to a major disease. The genetic resistance to stem rust, leaf rust and yellow rust can be characterized as qualitative and quantitative resistances. Vertical resistance is specific to pathogen isolates based on single or very few genes. Race - specifi c is used to describe resistance that interacts differentially with pathogen races. Quantitative resistance is defined as resistance that varies in continuous way between the various phenotypes of the host population, from almost imperceptible to quite str ong. With the need to accelerate the development of improved varieties, genomics - assisted breeding is becoming an important tool in breeding programs. With marker - assisted selection, there has been success in breeding for disease resistance. Generally, bre eding programs have successfully implemented molecular markers to assist in the development of cultivars with stem, leaf and stripe rust resistance genes. When new rust resistance genes are to be deployed in wheat breeding programs, it unfortunately takes several years before the new sources of resistance will become available in commercial wheat cultivars. This is due to the long process involved in the establishment of pure breeding wheat lines. Biotechnology based techniques are available to accelerate t he breeding process via doubled haploid production.

scholarly journals Assessing the Suitability of Elite Lines for Hybrid Seed Production and as Testers in Wide Crosses With Wheat Genetic Resources

2021 ◽  
Vol 12 ◽  
Author(s):  
Johannes Schneider ◽  
Marcel O. Berkner ◽  
Norman Philipp ◽  
Albert W. Schulthess ◽  
Jochen C. Reif
Keyword(s):  
Field Experiment ◽  
Grain Yield ◽  
Genetic Resources ◽  
Leaf Rust ◽  
Seed Yield ◽  
Yellow Rust ◽  
Hybrid Seed ◽  
Breeding Value ◽  
Wide Crosses ◽  
Hybrid Strategy

The use of genetic resources in breeding is considered critical to ensure future selection gain, but the absence of important adaptation genes often masks the breeding value of genetic resources for grain yield. Testing genetic resources in a hybrid background has been proposed as a solution to obtain unbiased estimates of breeding values for grain yield. In our study, we evaluated the suitability of European wheat elite lines for implementing this hybrid strategy, focusing on maximizing seed yield in hybrid production and reducing masking effects due to susceptibility to lodging, yellow rust, and leaf rust of genetic resources. Over a 3-year period, 63 wheat elite female lines were crossed with eight male plant genetic resources in a multi-environment field experiment to evaluate seed yield on the female side. Then, the resulting hybrids and their parents were tested for plant height, lodging, and susceptibility to yellow rust and leaf rust in a further field experiment at multiple locations. We found that seed yield was strongly influenced by the elite wheat line choice in addition to environment and observed substantial differences among elite tester lines in their ability to reduce susceptibility to lodging, yellow rust, and leaf rust when the hybrid strategy was implemented. Consequently, breeders can significantly increase the amount of hybrid seed produced in wide crosses through appropriate tester choice and adapt genetic resources of wheat with the hybrid strategy to the modern cropping system.

scholarly journals Exploratory Factor Analysis (EFA) Pada Penyerapan Anggaran Pendapatan dan Belanja Negara (APBN) Tahun 2017 di Provinsi Sumatera Barat

2018 ◽  
Vol 3 (3) ◽  
pp. 236-254 ◽  
Author(s):  
Aulia Rahim ◽  
Harsya Saputra
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Latent Variables ◽  
Primary Data ◽  
State Budget ◽  
Budget Planning ◽  
Goods And Services ◽  
Internal Policy ◽  
Verification Process ◽  
Budget Execution

Slow back-loaded pattern highlights challenges and raises a specific concern over the quality of state budget (APBN) implementation in West Sumatera Province. This study aims to investigate factors constraining state budget (APBN) absorption in 2017 which directly causes the existence of such a pattern. A set of questionnaires were used to collect primary data. Exploratory Factor Analysis (EFA) was utilized to identify latent variables underlying the scale and SPSS.22 was employed to test questionnaires from 200 working units that involved in this research. The findings explain that six main factors influencing state budget absorption in West Sumatera were a. Goods and Services Procurement, b. Internal Policy Changes, c. Budget Planning, d. Financial Officers, e.Changes on Government Policy and f. Document Verification Process on Budget Execution. This study recommends that working units should prepare for early procurement to expedite the budget absorption.                    Pola slow back-loaded merupakan tantangan terhadap kualitas pelaksanaan APBN di Sumatera Barat. Studi ini bertujuan untuk menginvestigasi faktor-faktor yang mempengaruhi penyerapan APBN di Sumatera Barat pada tahun 2017 yang secara langsung mempengaruhi pola penyerapan yang terjadi. Satu set kuesioner digunakan untuk mendapatkan data primer yang dibutuhkan. Teknik analisis faktor eksploratori digunakan untuk mengidentifikasi variabel laten yang ada dan SPSS 22 digunakan untuk melakukan tes statistik terhadap kuesioner yang diperoleh dari 200 satker yang terlibat di penelitian ini. Hasil penelitian menjelaskan bahwa enam faktor yang berpengaruh terhadap penyerapan APBN di Provinsi Sumatera Barat yaitu a.Faktor Proses Lelang Pengadaan Barang dan Jasa, b.Faktor Perubahan Kebijakan Internal Satuan Kerja dan K/L, c. Faktor Administrasi Perencanaan Anggaran, d.Faktor Pejabat Perbendaharaan, e. Faktor Perubahan Kebijakan Pemerintah dan f. Faktor Proses Verifikasi Dokumen Untuk Pelaksanaan Anggaran. Studi ini salah satunya merekomendasikan percepatan pelaksanaan pengadaan barang dan jasa untuk mempercepat penyerapan anggaran.

Exploratory and Confirmatory Factor Analysis

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Music Education ◽  
Latent Variables ◽  
Data Driven ◽  
Factor Analyses ◽  
Statistical Tool ◽  
Analysis Techniques ◽  
Confirmatory Factor ◽  
Research Questions

This chapter consists of data-driven examples of how factor analysis as a statistical tool can be applied in music education research. The chapter presents examples of how factor analysis methods can be used to identify latent variables, which in turn can be used to represent a broad set of measured variables. Exploratory and confirmatory factor analysis techniques are compared and illustrated with data-driven examples. The examples highlight some of the major considerations and basic steps for performing factor analyses so that the reader can begin to imagine how to apply this technique to their own research questions.

Bayesian proportional hazards model with latent variables

2017 ◽  
Vol 28 (4) ◽  
pp. 986-1002 ◽  
Author(s):  
Deng Pan ◽  
Kai Kang ◽  
Chunjie Wang ◽  
Xinyuan Song
Keyword(s):  
Risk Factors ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Latent Variables ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Hazards Model

We consider a joint modeling approach that incorporates latent variables into a proportional hazards model to examine the observed and latent risk factors of the failure time of interest. An exploratory factor analysis model is used to characterize the latent risk factors through multiple observed variables. In commonly used confirmatory factor analysis, the number of latent variables and their observed indicators are specified prior to analysis. By contrast, the exploratory factor analysis model allows such information to be fully determined by the data. A Bayesian approach coupled with efficient sampling methods is developed to conduct statistical inference, and the performance of the proposed methodology is confirmed through simulations. The model is applied to a study on the risk factors of chronic kidney disease for patients with type 2 diabetes.

Investigation of linkage drag in near isogenic lines of wheat by testing for seedling reaction to races of stem rust, leaf rust and yellow rust

Euphytica ◽  
1983 ◽  
Vol 32 (2) ◽  
pp. 319-327 ◽  
Author(s):  
A. C. Zeven ◽  
D. R. Knott ◽  
R. Johnson
Keyword(s):  
Leaf Rust ◽  
Stem Rust ◽  
Yellow Rust ◽  
Linkage Drag ◽  
Near Isogenic Lines ◽  
Isogenic Lines

scholarly journals Description of Wheat Rusts and Their Virulence Variations Determined through Annual Pathotype Surveys and Controlled Multi-Pathotype Tests

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Mesfin Kebede Gessese
Keyword(s):  
Leaf Rust ◽  
Stripe Rust ◽  
Resistance Genes ◽  
Stem Rust ◽  
Rust Resistance ◽  
Yellow Rust ◽  
Stripe Rust Resistance ◽  
Wheat Production ◽  
The World ◽  
Rust Resistance Genes

Wheat production started in Australia around 1788 using early maturing varieties adapted to Australian conditions that were able to escape diseases as well as moisture stress conditions. Wheat production is concentrated on mainland Australia in a narrow crescent land considered as the wheat belt occupying an area of about 13.9 million hectares. Rusts are the most important production constraints to wheat production in the world and Australia causing significant yield losses and decreased the qualities of grains. Wheat is affected by three different types of rust diseases: leaf rust, stripe rust or yellow rust, and stem rust. Each species of the rust pathogen has many races or pathotypes that parasitize only on certain varieties of host species, which can only be traced and identified by differential cultivars. Pathotype surveillance is the basis for information on the virulence or pathogenic variations existing in a particular country or wheat growing region of the world. Studies in pathotype variation are conducted in controlled environments using multi-pathotype tests. The currently cultivated commercial wheat varieties of Australia possess leaf rust resistant genes: Lr1, Lr3a, Lr13, Lr13+, Lr14a, Lr17a, Lr17b, Lr20, Lr23, Lr24, Lr26, Lr27, Lr31, Lr34, Lr37, and Lr46; stem rust resistance genes: Sr2, Sr5, Sr8a, Sr8b, Sr9b, Sr9g, Sr11, Sr12, Sr13, Sr15, Sr17, Sr22, Sr24, Sr26, Sr30, Sr36, Sr38, and Sr57; and stripe rust resistance genes: Yr4, Yr9, Yr17, Yr18, Yr27, and Yr33. This paper discusses the historical and current significance of rusts to wheat production in the world with particular reference to Australia viz-a-viz detail description of each of the three rusts and their respective virulence variations through the resistance genes deployed in the commercial cultivars.

Sign in / Sign up

Export Citation Format

Share Document