1
|
Hong JK, Kim YM, Cho ES, Lee JB, Kim YS, Park HB. Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits. Anim Biosci 2024; 37:622-630. [PMID: 38228129 PMCID: PMC10915216 DOI: 10.5713/ab.23.0264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/31/2023] [Accepted: 11/03/2023] [Indexed: 01/18/2024] Open
Abstract
OBJECTIVE Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). METHODS Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. RESULTS The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. CONCLUSION This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.
Collapse
Affiliation(s)
- Joon-Ki Hong
- Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000,
Korea
| | - Yong-Min Kim
- Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000,
Korea
| | - Eun-Seok Cho
- Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000,
Korea
| | - Jae-Bong Lee
- Korea Zoonosis Research Institute, Jeonbuk National University, Iksan 54531,
Korea
| | - Young-Sin Kim
- Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000,
Korea
| | - Hee-Bok Park
- Department of Animal Resources Science, Kongju National University, Yesan 32439,
Korea
- Resource Science Research Institute, Kongju National University, Yesan 32439,
Korea
| |
Collapse
|
2
|
Montesinos-López OA, Montesinos-López JC, Montesinos-López A, Ramírez-Alcaraz JM, Poland J, Singh R, Dreisigacker S, Crespo L, Mondal S, Govidan V, Juliana P, Espino JH, Shrestha S, Varshney RK, Crossa J. Bayesian multitrait kernel methods improve multienvironment genome-based prediction. G3 (BETHESDA, MD.) 2022; 12:6446035. [PMID: 34849802 PMCID: PMC9210316 DOI: 10.1093/g3journal/jkab406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/18/2021] [Indexed: 11/14/2022]
Abstract
When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.
Collapse
Affiliation(s)
| | | | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Guadalajara 44430, Mexico
- Corresponding author: Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco 44430, Mexico. (A.M.-L.); International Maize and Wheat Improvement Center (CIMMYT). Km 45 Carretera Mexico-Veracruz, CP 52640, Texcoco, Edo de Mexico, Mexico. (J.C.)
| | | | - Jesse Poland
- Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Leonardo Crespo
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Sushismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Velu Govidan
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
| | - Julio Huerta Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Universidad Autónoma de Chapingo, Texcoco 56235, Mexico
| | - Sandesh Shrestha
- Department of Agronomy, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch 6150, Australia
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texoco, Edo. de Mexico, Mexico
- Colegio de Postgraduados, Montecillos, Edo. de México 56230, Mexico
- Corresponding author: Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco 44430, Mexico. (A.M.-L.); International Maize and Wheat Improvement Center (CIMMYT). Km 45 Carretera Mexico-Veracruz, CP 52640, Texcoco, Edo de Mexico, Mexico. (J.C.)
| |
Collapse
|
3
|
Volpato L, Alves RS, Teodoro PE, Vilela de Resende MD, Nascimento M, Nascimento ACC, Ludke WH, Lopes da Silva F, Borém A. Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS One 2019; 14:e0215315. [PMID: 30998705 PMCID: PMC6472761 DOI: 10.1371/journal.pone.0215315] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 03/30/2019] [Indexed: 11/19/2022] Open
Abstract
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; [Formula: see text]) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of [Formula: see text]. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
Collapse
Affiliation(s)
- Leonardo Volpato
- Federal University of Viçosa—Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil
| | - Rodrigo Silva Alves
- Federal University of Viçosa—Department of General Biology, University Campus, Viçosa, Minas Gerais, Brazil
| | - Paulo Eduardo Teodoro
- Federal University of Mato Grosso do Sul—Department of Plant Science, University Campus, Chapadão do Sul, Mato Grosso do Sul, Brazil
| | | | - Moysés Nascimento
- Federal University of Viçosa—Department of Statistics, University Campus, Viçosa, Minas Gerais, Brazil
| | | | - Willian Hytalo Ludke
- Federal University of Viçosa—Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil
| | - Felipe Lopes da Silva
- Federal University of Viçosa—Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil
| | - Aluízio Borém
- Federal University of Viçosa—Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil
| |
Collapse
|