Article
Genetics & Heredity
Abelardo Montesinos-Lopez, Daniel E. Runcie, Maria Itria Ibba, Paulino Perez-Rodriguez, Osval A. Montesinos-Lopez, Leonardo A. Crespo, Alison R. Bentley, Jose Crossa
Summary: Implementing genomic-based prediction models in genomic selection involves understanding how to evaluate prediction accuracy from different models and methods using multi-trait data. This study compared prediction accuracy using six large multi-trait wheat datasets and found that a corrected Pearson's correlation method was more accurate than the traditional method. For grain yield, using a multi-trait model yielded higher prediction performance compared to a single-trait model, with the benefits increasing as genetic correlations between traits strengthen.
G3-GENES GENOMES GENETICS
(2021)
Article
Plant Sciences
Freddy Mora-Poblete, Carlos Maldonado, Luma Henrique, Renan Uhdre, Carlos Alberto Scapim, Claudete Aparecida Mangolim
Summary: In this study, the performance of multi-trait, multi-environment deep learning models and Bayesian models were compared in predicting flowering-related traits in maize. The results showed that multi-trait models had a 14.4% higher prediction accuracy compared to single trait approaches, and using a single trait in a multi-environment scheme improved accuracy by 6.4% compared to multi-trait analysis. Deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. The study also identified candidate genes and marker-trait associations related to flowering time traits. Overall, the findings suggest that deep learning models have the potential to significantly improve prediction accuracy and are effective in genomic selection for flowering-related traits in tropical maize.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Genetics & Heredity
Kismiantini, Abelardo Montesinos-Lopez, Bernabe Cano-Paez, J. Cricelio Montesinos-Lopez, Moises Chavira-Flores, Osval A. Montesinos-Lopez, Jose Crossa
Summary: Genomic selection is an important technique in plant breeding, but its practical implementation is still challenging. This study explores the tuning process using a Gaussian kernel and a multi-trait Bayesian Best Linear Unbiased Predictor model. Three tuning methods were compared, and it was found that using grid search and Bayesian optimization can improve prediction accuracy.
Article
Genetics & Heredity
Osval Antonio Montesinos-Lopez, Abelardo Montesinos-Lopez, Brandon A. Mosqueda-Gonzalez, Jose Cricelio Montesinos-Lopez, Jose Crossa, Nerida Lozano Ramirez, Pawan Singh, Felicitas Alejandra Valladares-Anguiano
Summary: Choosing the right statistical machine learning model is crucial in genomic selection. This study introduces a zero-inflated random forest model, which outperforms conventional random forest and Generalized Poisson Ridge regression models in prediction performance when dealing with excessive zeros in count response variables.
G3-GENES GENOMES GENETICS
(2021)
Article
Genetics & Heredity
Karansher S. Sandhu, Shruti Sunil Patil, Meriem Aoun, Arron H. Carter
Summary: This study explored the potential of using multi-trait genomic selection (GS) models to predict end-use quality traits in soft white wheat. The results showed that multi-trait models outperformed uni-trait models in within-environment and across-location predictions. Machine and deep learning models performed better than traditional GS models for across-location predictions, but their advantages diminished when considering genotype by environment interaction. The highest improvement in prediction accuracy was achieved for flour protein content using the multi-trait MLP model.
FRONTIERS IN GENETICS
(2022)
Article
Plant Sciences
Harsimardeep S. Gill, Jyotirmoy Halder, Jinfeng Zhang, Navreet K. Brar, Teerath S. Rai, Cody Hall, Amy Bernardo, Paul St Amand, Guihua Bai, Eric Olson, Shaukat Ali, Brent Turnipseed, Sunish K. Sehgal
Summary: Genomic prediction is a promising tool for accelerating genetic gain in wheat breeding, but improving prediction accuracy remains a challenge. Multivariate models have shown potential in predicting multiple agronomic traits in advanced breeding lines, with the MT-CV2 and MTME models performing well in predicting traits and reducing phenotyping cost. The results demonstrate the great potential of multivariate genomic selection models in enhancing resource efficiency and implementing genomic selection in breeding programs.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Genetics & Heredity
Zigui Wang, Hao Cheng
Summary: Genomic prediction methods mostly focus on statistical properties and overlook useful biological information. To enhance prediction performance, methods have been developed to incorporate biological information into genomic prediction. The multi-class Bayesian Alphabet methods demonstrate superior performance in genomic prediction.
FRONTIERS IN GENETICS
(2021)
Article
Medicine, General & Internal
Qianwen Liu, Zhaozhong Zhu, Peter Kraft, Qiaolin Deng, Elisabet Stener-Victorin, Xia Jiang
Summary: This study found a genetic correlation between PCOS and obesity-related traits, including adult BMI, childhood BMI, and WHR. Cross-trait meta-analysis identified shared loci between PCOS and obesity-related traits. Mendelian randomization supported the causal role of adult BMI and childhood BMI in PCOS, but not WHR or WHRadjBMI. These findings provide insights into the shared genetic basis of obesity and PCOS and emphasize the role of weight management in PCOS prevention.
Article
Multidisciplinary Sciences
Abram Bunya Kamiza, Sounkou M. Toure, Feng Zhou, Opeyemi Soremekun, Cheickna Cisse, Mamadou Wele, Aboubacrine M. Toure, Oyekanmi Nashiru, Manuel Corpas, Moffat Nyirenda, Amelia Crampin, Jeffrey Shaffer, Seydou Doumbia, Eleftheria Zeggini, Andrew P. Morris, Jennifer L. Asimit, Tinashe Chikowore, Segun Fatumo
Summary: In this study, we used GWAS, MTAG, and flashfm to identify four and 14 novel loci associated with lipid traits in 125,000 individuals of African ancestry. Flashfm reduced the 99% credible set size by 18% compared to single-trait fine-mapping with JAM, and identified more genetic variants with a posterior probability of causality >0.9. In conclusion, we identified additional novel loci associated with lipid traits and flashfm improved the identification of causal genetic variants associated with multiple lipid traits in African ancestry.
NATURE COMMUNICATIONS
(2023)
Article
Medicine, Research & Experimental
Yujie Liang, Xiao Xu, Limei Xu, Zoya Iqbal, Kan Ouyang, Huawei Zhang, Chunyi Wen, Li Duan, Jiang Xia
Summary: This study developed a specific delivery vehicle for gene editing in chondrocytes to attenuate cartilage damage. The results showed that the hybrid delivery vehicle successfully delivered the gene plasmids and suppressed the expression of specific genes in chondrocytes, leading to a decrease in cartilage degradation. This research contributes to alleviating the symptoms and damage of osteoarthritis.
Article
Genetics & Heredity
Qing Lin, Jinyan Teng, Xiaodian Cai, Jiaqi Li, Zhe Zhang
Summary: The genomic prediction models leveraging diverse information from multiple environment phenotypes showed better prediction accuracy, and different prediction strategies could be utilized based on the availability of different environment phenotypes.
Article
Plant Sciences
Yuna Kang, Changhyun Choi, Jae Yoon Kim, Kyeong Do Min, Changsoo Kim
Summary: This study explored the potential of genome-wide association studies (GWAS) and genomewide selection (GS) in breeding ten agricultural traits using genome-wide SNPs. A trait-associated candidate marker was identified through GWAS in a genetically diverse Korean wheat core collection. GS prediction accuracy was assessed using six predictive models and various training populations, and validation was done using Korean wheat cultivars. This research provides a foundation for improving complex traits in wheat breeding programs through genomics-assisted breeding.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Genetics & Heredity
Shaohua Zhu, Tingting Guo, Chao Yuan, Jianbin Liu, Jianye Li, Mei Han, Hongchang Zhao, Yi Wu, Weibo Sun, Xijun Wang, Tianxiang Wang, Jigang Liu, Christian Keambou Tiambo, Yaojing Yue, Bohui Yang
Summary: The accuracy of genomic prediction (GP) or selection (GS) depends on marker density, heritability level, and statistical models used. Increasing marker density improves GP accuracy, and different models are more suitable for traits with different heritability levels. The study highlights the importance of incorporating these factors into real data to optimize GP.
G3-GENES GENOMES GENETICS
(2021)
Article
Computer Science, Information Systems
Vibha Jain, Bijendra Kumar, Aditya Gupta
Summary: The emergence of sixth-generation wireless communication technology has led to the rapid increase of real-time applications. Edge computing driven by Cybertwin is proposed as a promising solution to meet user demand, but it comes with challenges. This work proposes a joint resource allocation and computation offloading scheme using deep reinforcement learning in Cybertwin-enabled 6G wireless networks. The results show that the proposed scheme can reduce latency and energy consumption while improving task completion rate compared to traditional methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Genetics & Heredity
Jonathan Sandoval-Castillo, Luciano B. Beheregaray, Maren Wellenreuther
Summary: Growth is an important trait with ecological, evolutionary, economical, and conservation significance. This study used reduced genome representation data and genome-wide association approaches to identify growth-related genetic variation in Australian snapper. The results revealed the complex polygenic nature of growth in the species and provided insights for captive aquaculture breeding programs and monitoring growth-related evolutionary shifts in wild populations.
G3-GENES GENOMES GENETICS
(2022)
Article
Genetics & Heredity
Osval Antonio Montesinos-Lopez, Jose Cricelio Montesinos-Lopez, Abelardo Montesinos-Lopez, Juan Manuel Ramirez-Alcaraz, Jesse Poland, Ravi Singh, Susanne Dreisigacker, Leonardo Crespo, Sushismita Mondal, Velu Govidan, Philomin Juliana, Julio Huerta Espino, Sandesh Shrestha, Rajeev K. Varshney, Jose Crossa
Summary: This study explores Bayesian multitrait kernel methods for genomic prediction and finds that the Gaussian kernel method outperforms traditional methods in prediction performance, capturing nonlinear patterns more efficiently. Evaluating multiple kernels to select the best one is recommended.
G3-GENES GENOMES GENETICS
(2022)
Article
Plant Sciences
Osval Antonio Montesinos-Lopez, Abelardo Montesinos-Lopez, Ricardo Acosta, Rajeev K. Varshney, Alison Bentley, Jose Crossa
Summary: Genomic selection is a predictive method used in plant breeding that trains machine learning models with a reference population to predict new lines. This study proposes using incomplete block designs for allocating lines to locations, which outperforms random allocation in terms of predictive performance.
Article
Environmental Sciences
Afolabi Agbona, Brody Teare, Henry Ruiz-Guzman, Iliyana D. Dobreva, Mark E. Everett, Tyler Adams, Osval A. Montesinos-Lopez, Peter A. Kulakow, Dirk B. Hays
Summary: Inadequate means to measure early storage root bulking in cassava have prompted a study to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. Different methods of processing the GPR radargram were tested, with a simple model without interaction producing the best prediction accuracy. Results demonstrate the potential for GPR technology to be adopted by cassava breeding programs for selecting early stage root bulking to increase crop yield.
Article
Plant Sciences
Osval Antonio Montesinos-Lopez, Henry Nicole Gonzalez, Abelardo Montesinos-Lopez, Maria Daza-Torres, Morten Lillemo, Jose Cricelio Montesinos-Lopez, Jose Crossa
Summary: Genomic selection is a predictive methodology that is changing plant breeding. In this study, the performance of two algorithms (TGBLUP and GBM) was compared on wheat datasets, and GBM outperformed TGBLUP in terms of prediction accuracy. Further research is encouraged to explore the virtues of GBM in genomic selection.
Article
Genetics & Heredity
Osval Antonio Montesinos Lopez, Brandon Alejandro Mosqueda Gonzalez, Abel Palafox Gonzalez, Abelardo Montesinos Lopez, Jose Crossa
Summary: This paper presents a new software package (SKM) for implementing six popular supervised machine learning algorithms with the optional use of sparse kernels, as well as a function for computing seven different kernels. SKM focuses on user simplicity and computational efficiency, providing a user-friendly format for algorithms and reducing resources needed for kernel machine learning methods.
FRONTIERS IN GENETICS
(2022)
Article
Genetics & Heredity
Osval A. Montesinos-Lopez, Abelardo Montesinos-Lopez, Kismiantini, Armando Roman-Gallardo, Keith Gardner, Morten Lillemo, Roberto Fritsche-Neto, Jose Crossa
Summary: Improved prediction of future seasons or new environments is crucial for plant breeding. This study demonstrates that the partial least squares regression method outperforms the Bayesian genomic best linear unbiased predictor method in predicting future seasons or new environments.
FRONTIERS IN GENETICS
(2022)
Article
Genetics & Heredity
Osval A. Montesinos-Lopez, Abelardo Montesinos-Lopez, Bernabe Cano-Paez, Carlos Moises Hernandez-Suarez, Pedro C. Santana-Mancilla, Jose Crossa
Summary: Genomic selection has revolutionized the way plant breeders select genotypes, using statistical machine learning models to predict phenotypic values of new lines. Multi-trait genomic prediction models leverage correlated traits to improve accuracy. This paper compares the performance of three multi-trait methods and finds that their performance varies under different predictors.
Article
Genetics & Heredity
Osval A. Montesinos-Lopez, Abelardo Montesinos-Lopez, David Alejandro Bernal Sandoval, Brandon Alejandro Mosqueda-Gonzalez, Marco Alberto Valenzo-Jimenez, Jose Crossa
Summary: The genomic selection methodology has revolutionized plant breeding by using statistical machine learning algorithms to predict candidate individuals. However, it faces challenges when predicting future seasons or new environments. This study compared the performance of the multi-trait partial least square (MT-PLS) regression method with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method and found that MT-PLS outperforms MT-GBLUP in predicting future seasons or new environments.
FRONTIERS IN GENETICS
(2022)
Article
Plant Sciences
Raysa Gevartosky, Humberto Fanelli Carvalho, Germano Costa-Neto, Osval A. Montesinos-Lopez, Jose Crossa, Roberto Fritsche-Neto
Summary: This study aimed to design optimized training sets for genomic prediction considering multi-trait multi-environment trials and how those methods may increase accuracy reducing phenotyping costs. The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested.
Article
Genetics & Heredity
Osval A. A. Montesinos-Lopez, Alison R. R. Bentley, Carolina Saint Pierre, Leonardo Crespo-Herrera, Josafhat Salinas Ruiz, Patricia Edwigis Valladares-Celis, Abelardo Montesinos-Lopez, Jose Crossa
Summary: Genomic selection (GS) is a revolutionary plant breeding method that allows the selection of candidate genotypes without the need for field phenotypic evaluation. This study investigated the genomic prediction accuracy of wheat hybrids by incorporating covariates with parental phenotypic information into the model. The results showed that the models with parental information outperformed those without parental information, and the inclusion of covariates significantly improved prediction accuracy compared to marker information. However, the use of parental phenotypic information as covariates is expensive and not always available.
Article
Biotechnology & Applied Microbiology
Osval A. Montesinos-Lopez, Abelardo Kismiantini, Abelardo Montesinos-Lopez
Summary: Genomic selection (GS) is being revolutionized in plant and animal breeding, but its practical implementation faces challenges due to uncontrolled factors. To improve prediction accuracy, this paper proposes two methods: reformulating GS as a binary classification problem, and applying postprocessing to adjust the classification threshold. Both methods outperformed the conventional regression model, with the postprocessing method showing better results.
Article
Genetics & Heredity
Abelardo Montesinos-Lopez, Carolina Rivera, Francisco Pinto, Francisco Pinera, David Gonzalez, Mathew Reynolds, Paulino Perez-Rodriguez, H. Li, Osval A. Montesinos-Lopez, Jose Crossa
Summary: By comparing a novel DL method with conventional GP models, this study found that DL method has higher accuracy in predicting genomic phenotypes in plant breeding research and can account for the complexity of genotype-environment interaction. However, traditional GP models can also achieve high accuracy in certain situations.
G3-GENES GENOMES GENETICS
(2023)
Article
Plant Sciences
Osval A. Montesinos-Lopez, Alison R. Bentley, Carolina Saint Pierre, Leonardo Crespo-Herrera, Leonardo Rebollar-Ruellas, Patricia Edwigis Valladares-Celis, Morten Lillemo, Abelardo Montesinos-Lopez, Jose Crossa
Summary: Genomic selection (GS), proposed by Meuwissen et al. more than 20 years ago, is revolutionizing plant and animal breeding. In our study of 14 real datasets, we found that the average gain in prediction accuracy when genomic information is considered was 26.31%. The quality of the markers and relatedness of the individuals can greatly impact the increase in prediction accuracy.
Article
Environmental Sciences
Afolabi Agbona, Osval A. Montesinos-Lopez, Mark E. Everett, Henry Ruiz-Guzman, Dirk B. Hays
Summary: Many aspects of below-ground plant performance are not fully understood, including their spatial and temporal dynamics in relation to environmental factors. In this study, Ground-Penetrating Radar (GPR) was evaluated for its potential in normalizing spatial heterogeneity and estimating fresh root yield in a cassava field trial. The results showed that the GPR-based autoregressive (AR) model outperformed other models, indicating the potential of GPR in non-destructive yield estimation and field spatial heterogeneity normalization in root and tuber crop programs.
Article
Agronomy
Osval Montesinos-Lopez, Kismiantini, Abelardo Montesinos-Lopez
Summary: Genomic selection is revolutionizing animal and plant breeding, but its implementation faces challenges due to mismatch in training and testing set distributions. This research used the adversarial validation method with probit regression to address the distribution mismatch and select optimal training sets. Evaluations showed that the proposed method effectively detected the mismatch and outperformed existing methods, achieving higher prediction accuracy.