Abstract
Main conclusion
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data.
Abstract
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.
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Abbreviations
- CNN:
-
Deep convolutional neural network
- DL:
-
Deep learning
- GS:
-
Genomic selection
- MNV:
-
Mean normalized discounted cumulative gain value
- (RR)-BLUP:
-
(Ridge regression)-Best linear unbiased prediction
References
Alipanahi B, Delong A, Weirauch MT, Frey BJ (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 33(8):831–838. https://doi.org/10.1038/nbt.3300
Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12(7):878. https://doi.org/10.15252/msb.20156651
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
Bhering LL, Junqueira VS, Peixoto LA, Cruz CD, Laviola BG (2015) Comparison of methods used to identify superior individuals in genomic selection in plant breeding. Genet Mol Res 14(3):10888–10896. https://doi.org/10.4238/2015.September.9.26
Blondel M, Onogi A, Iwata H, Ueda N (2015) A ranking approach to genomic selection. PLoS One 10(6):e0128570. https://doi.org/10.1371/journal.pone.0128570
Chen Y, Li Y, Narayan R, Subramanian A, Xie X (2016) Gene expression inference with deep learning. Bioinformatics 32(12):1832–1839. https://doi.org/10.1093/bioinformatics/btw074
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
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
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
Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19(9):592–601. https://doi.org/10.1016/j.tplants.2014.05.006
Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4(3):250. https://doi.org/10.3835/plantgenome2011.08.0024
Gianola D, Schön CC (2016) Cross-validation without doing cross-validation in genome-enabled prediction. G3 (Bethesda) 6(10):3107–3128. https://doi.org/10.1534/g3.116.033381
Guzman C, Peña RJ, Singh R, Autrique E, Dreisigacker S, Crossa J, Rutkoski J, Poland J, Battenfield S (2016) Wheat quality improvement at CIMMYT and the use of genomic selection on it. Appl Transl Genom 11:3–8. https://doi.org/10.1016/j.atg.2016.10.004
Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4(1):65–75. https://doi.org/10.3835/plantgenome2010.12.0029
Huang M, Cabrera A, Hoffstetter A, Griffey C, Van Sanford D, Costa J, McKendry A, Chao S, Sneller C (2016) Genomic selection for wheat traits and trait stability. Theor Appl Genet 129(9):1697–1710. https://doi.org/10.1007/s00122-016-2733-z
Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9(2):166–177. https://doi.org/10.1093/bfgp/elq001
Jo T, Hou J, Eickholt J, Cheng J (2015) Improving protein fold recognition by deep learning networks. Sci Rep 5:17573. https://doi.org/10.1038/srep17573
Jonas E, de Koning DJ (2013) Does genomic selection have a future in plant breeding? Trends Biotechnol 31(9):497–504. https://doi.org/10.1016/j.tibtech.2013.06.003
Kelley DR, Snoek J, Rinn JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26(7):990–999. https://doi.org/10.1101/gr.200535.115
Kennedy J, Eberhart R (1995) Particle swarm optimization. ICNN 4:1942–1948. https://doi.org/10.1109/icnn.1995.488968
Kim SG, Harwani M, Grama A, Chaterji S (2016) EP-DNN: a deep neural network-based global enhancer prediction algorithm. Sci Rep 6:38433. https://doi.org/10.1038/srep38433
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Liu F, Li H, Ren C, Bo X, Shu W (2016) PEDLA: predicting enhancers with a deep learning-based algorithmic framework. Sci Rep 6:28517. https://doi.org/10.1038/srep28517
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
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829
Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869. https://doi.org/10.1093/bib/bbw068
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
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
Quang D, Xie X (2016) DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 44(11):e107. https://doi.org/10.1093/nar/gkw226
Quang D, Chen Y, Xie X (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31(5):761–763. https://doi.org/10.1093/bioinformatics/btu703
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
Riedelsheimer C, Technow F, Melchinger AE (2012) Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics 13:452. https://doi.org/10.1186/1471-2164-13-452
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
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0
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
Singh R, Lanchantin J, Robins G, Qi Y (2016) DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics 32(17):i639–i648. https://doi.org/10.1093/bioinformatics/btw427
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
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. JMLR 15:1929–1958
van Eeuwijk FA, Bink MC, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13(2):193–205. https://doi.org/10.1016/j.pbi.2010.01.001
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423. https://doi.org/10.3168/jds.2007-0980
Varshney RK (2016) Exciting journey of 10 years from genomes to fields and markets: some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut. Plant Sci 242:98–107. https://doi.org/10.1016/j.plantsci.2015.09.009
Wang S, Peng J, Ma J, Xu J (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 6:18962. https://doi.org/10.1038/srep18962
Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75(2):249–252. https://doi.org/10.1017/S0016672399004462
Wimmer V, Lehermeier C, Albrecht T, Auinger HJ, Wang Y, Schön CC (2013) Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics 195(2):573–587. https://doi.org/10.1534/genetics.113.150078
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
Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48(2):391. https://doi.org/10.2135/cropsci2007.04.0191
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
Zeng H, Edwards MD, Ge L, Gifford DK, Zeng H, Edwards MD, Ge L, Gifford DK (2016) Convolutional neural network architectures for predicting DNA–protein binding. Bioinformatics 32(12):i121–i127. https://doi.org/10.1093/bioinformatics/btw255
Zhang S, Zhou J, Hu H, Gong H, Chen L, Cheng C, Zeng J (2016) A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res 44(4):e32. https://doi.org/10.1093/nar/gkv1025
Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931–934. https://doi.org/10.1038/nmeth.3547
Zou C, Wang P, Xu Y (2016) Bulked sample analysis in genetics, genomics and crop improvement. Plant Biotechnol J 14(10):1941–1955. https://doi.org/10.1111/pbi.12559
Acknowledgements
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.
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Ma, W., Qiu, Z., Song, J. et al. A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248, 1307–1318 (2018). https://doi.org/10.1007/s00425-018-2976-9
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DOI: https://doi.org/10.1007/s00425-018-2976-9