A deep convolutional neural network approach for predicting phenotypes from genotypes
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data.
Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted based on genome-wide markers of genotypes. In this study, we present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypes when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional genotypic data. We used a large GS dataset to train DeepGS and compared its performance with other methods. The experimental results indicate that DeepGS can be used as a complement to the commonly used RR-BLUP in the prediction of phenotypes from genotypes. The complementarity between DeepGS and RR-BLUP can be utilized using an ensemble learning approach for more accurately selecting individuals with high phenotypic values, even for the absence of outlier individuals and subsets of genotypic markers. The source codes of DeepGS and the ensemble learning approach have been packaged into Docker images for facilitating their applications in different GS programs.
KeywordsDeep learning Ensemble learning Genomic selection High phenotypic values Machine learning Genotypic marker
Deep convolutional neural network
Mean normalized discounted cumulative gain value
(Ridge regression)-Best linear unbiased prediction
This work was supported by the National Natural Science Foundation of China (31570371), the Agricultural Science and Technology Innovation and Research Project of Shaanxi Province, China (2015NY011), the Youth 1000-Talent Program of China, the Hundred Talents Program of Shaanxi Province of China, the Innovative Talents Promotion Project of Shaanxi Province of China (2017KJXX-67), and the Fund of Northwest A&F University.
Compliance with ethical standards
Conflict of interest
We declare that we have no competing interests.
- Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Singh PK, Singh GP, Prabhu KV (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet 7:221. https://doi.org/10.3389/fgene.2016.00221 CrossRefPubMedPubMedCentralGoogle Scholar
- Crossa J, Jarquín D, Franco J, Pérez-Rodríguez P, Burgueño J, Saint-Pierre C, Vikram P, Sansaloni C, Petroli C, Akdemir D, Sneller C, Reynolds M, Tattaris M, Payne T, Guzman C, Peña RJ, Wenzl P, Singh S (2016) Genomic prediction of gene bank wheat landraces. G3 (Bethesda) 6(7):1819–1834. https://doi.org/10.1534/g3.116.029637 CrossRefGoogle Scholar
- Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, Camacho-González JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011 CrossRefGoogle Scholar
- de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182(1):375–385. https://doi.org/10.1534/genetics.109.101501 CrossRefPubMedPubMedCentralGoogle Scholar
- Marulanda JJ, Mi X, Melchinger AE, Xu JL, Würschum T, Longin CF (2016) Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theor Appl Genet 129(10):1901–1913. https://doi.org/10.1007/s00122-016-2748-5 CrossRefPubMedPubMedCentralGoogle Scholar
- Poland J, Rutkoski J (2016) Advances and challenges in genomic selection for disease resistance. Annu Rev Phytopathol 54:79–98. https://doi.org/10.1146/annurev-phyto-080615-100056 CrossRefPubMedPubMedCentralGoogle Scholar
- Qiu Z, Cheng Q, Song J, Tang Y, Ma C (2016) Application of machine learning-based classification to genomic selection and performance improvement. In: Huang DS, Bevilacqua V, Premaratne P (eds) Intelligent computing theories and applicaton. Proceedings of the 12th international conference on intelligent computing (ICIC 2016), Lecture notes in computer science, vol 9771, pp 412–421. https://doi.org/10.1007/978-3-319-42291-6_41 CrossRefGoogle Scholar
- Resende MF Jr, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190(4):1503–1510. https://doi.org/10.1534/genetics.111.137026 CrossRefPubMedPubMedCentralGoogle Scholar
- Roorkiwal M, Rathore A, Das RR, Singh MK, Jain A, Srinivasan S, Gaur PM, Chellapilla B, Tripathi S, Li Y, Hickey JM, Lorenz A, Sutton T, Crossa J, Jannink JL, Varshney RK (2016) Genome-enabled prediction models for yield related traits in chickpea. Front Plant Sci 7:1666. https://doi.org/10.3389/fpls.2016.01666 CrossRefPubMedPubMedCentralGoogle Scholar
- Schmidt M, Kollers S, Maasberg-Prelle A, Großer J, Schinkel B, Tomerius A, Graner A, Korzun V (2016) Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection. Theor Appl Genet 129(2):203–213. https://doi.org/10.1007/s00122-015-2639-1 CrossRefGoogle Scholar
- Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink JL, McCouch SR (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11(2):e1004982. https://doi.org/10.1371/journal.pgen.1004982 CrossRefPubMedPubMedCentralGoogle Scholar
- Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. JMLR 15:1929–1958Google Scholar
- Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, Hua Y, Gueroussov S, Najafabadi HS, Hughes TR, Morris Q, Barash Y, Krainer AR, Jojic N, Scherer SW, Blencowe BJ, Frey BJ (2015) The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218):1254806. https://doi.org/10.1126/science.1254806 CrossRefPubMedPubMedCentralGoogle Scholar
- Yu X, Li X, Guo T, Zhu C, Wu Y, Mitchell SE, Roozeboom KL, Wang D, Wang ML, Pederson GA, Tesso TT, Schnable PS, Bernardo R, Yu J (2016) Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat Plants 2:16150. https://doi.org/10.1038/nplants.2016.150 CrossRefPubMedPubMedCentralGoogle Scholar