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Montesinos-López A, Gutiérrez-Pulido H, Ramos-Pulido S, Montesinos-López JC, Montesinos-López OA, Crossa J. Bayesian discrete lognormal regression model for genomic prediction. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:21. [PMID: 38221602 DOI: 10.1007/s00122-023-04526-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/11/2023] [Indexed: 01/16/2024]
Abstract
KEY MESSAGE Genomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, that with a Gibbs sampler to explore the corresponding posterior distribution and make the predictions. Two datasets of resistance disease is used in the wheat crop and are then evaluated against the traditional Gaussian model and a lognormal model. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | - Humberto Gutiérrez-Pulido
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | - Sofía Ramos-Pulido
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México
| | | | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, C. P. 56237, Texcoco, Edo. de México, México.
- Colegio de Postgraduados, C. P. 56230, Montecillos, Edo. de México, México.
- Centre for Crop & Food Innovation, Food Futures Institute, Murdoch University, Murdoch, 6150, Australia.
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Azevedo CF, Ferrão LFV, Benevenuto J, de Resende MDV, Nascimento M, Nascimento ACC, Munoz PR. Using visual scores for genomic prediction of complex traits in breeding programs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 137:9. [PMID: 38102495 DOI: 10.1007/s00122-023-04512-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
KEY MESSAGE An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.
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Affiliation(s)
- Camila Ferreira Azevedo
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Luis Felipe Ventorim Ferrão
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Juliana Benevenuto
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Marcos Deon Vilela de Resende
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Department of Forestry Engineering, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Embrapa Café, Brasília, Distrito Federal, Brazil
| | - Moyses Nascimento
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Patricio R Munoz
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA.
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Xiong H, Chen H, Xu L, Liu H, Fan L, Tang Q, Cho H. A survey of data element perspective: Application of artificial intelligence in health big data. Front Neurosci 2022; 16:1031732. [PMID: 36389224 PMCID: PMC9641178 DOI: 10.3389/fnins.2022.1031732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/06/2022] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples’ daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.
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Affiliation(s)
- Honglin Xiong
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Hongmin Chen
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Li Xu
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Li Xu,
| | - Hong Liu
- Business School, University of Shanghai for Science and Technology, Shanghai, China
- Hong Liu,
| | - Lumin Fan
- Business School, University of Shanghai for Science and Technology, Shanghai, China
- Operation Management Department, East Hospital Affiliated to Tongji University, Shanghai, China
| | - Qifeng Tang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
- National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China
- Shanghai Data Exchange Corporation, Shanghai, China
| | - Hsunfang Cho
- National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China
- Shanghai Data Exchange Corporation, Shanghai, China
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