Phenomic prediction of maize hybrids

BioSystems

Volumn 146, Issue , 2016, Pages 102-109

  Edlich Muth, Christian ^a,b   Muraya, Moses M ^c   Altmann, Thomas ^c   Selbig, Joachim ^a,b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

^c LEIBNIZ INSTITUTE OF PLANT GENETICS AND CROP PLANT RESEARCH IPK   (Germany)

Author keywords

Hybrid prediction; LASSO; Maize; Phenomics; Regression

Indexed keywords

BIOLOGICAL METHOD; BIOMASS; DEVELOPMENTAL STAGE; EXPERIMENTAL STUDY; HETEROSIS; HYBRID; IMAGE PROCESSING; MACHINE LEARNING; MAIZE; REGRESSION ANALYSIS;

BIOMASS; DEVELOPMENTAL STAGE; DRY WEIGHT; FRESH WEIGHT; HETEROSIS; INBRED STRAIN; MAIZE; PLANT LEAF; PREDICTION; SOWING; STATISTICAL MODEL; ALGORITHM; BIOLOGICAL MODEL; GENETICS; GENOTYPE; GROWTH, DEVELOPMENT AND AGING; HYBRIDIZATION; INBREEDING; PHENOTYPE; TIME FACTOR;

ZEA MAYS;

ALGORITHMS; BIOMASS; GENOTYPE; HYBRID VIGOR; HYBRIDIZATION, GENETIC; INBREEDING; MODELS, GENETIC; PHENOTYPE; TIME FACTORS; ZEA MAYS;

EID: 84969921409     PISSN: 03032647     EISSN: 18728324     Source Type: Journal    
DOI: 10.1016/j.biosystems.2016.05.008     Document Type: Article

References (20)

1. Arend, D., Lange, M., Chen, J., Colmsee, C., Flemming, S., Hecht, D., Scholz, U., e!dal – a framework to store, share and publish research data. BMC Bioinf. 15:1 (2014), 1–13, 10.1186/1471-2105-15-214.
2. Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., Klukas, C., Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26:12 (2014), 4636–4655.
3. Cleveland, E.G.W.S., Shyu, W.M., Local regression. Statistical Models in S, 1992, Wadsworth & Brooks/Cole.
4. Duvick, D.N., Genetic progress in yield of United States maize (Zea mays L.). Maydica 50 (2005), 193–202.
5. Friedman, J., Hastie, T., Tibshirani, R., Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33:1 (2010), 1–22.
6. Hallauer, A.R., Carena, M.J., Miranda Filho, J., Quantitative Genetics in Maize Breeding. 2010, Springer, New York.
7. Junker, A., Muraya, M.M., Weigelt-Fischer, K., Arana-Ceballos, F., Klukas, C., Melchinger, A.E., Meyer, R.C., Riewe, D., Altmann, T., Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Front. Plant Sci., 5, 2015, 770, 10.3389/fpls.2014.00770.
8. Klukas, C., Pape, J.M., Entzian, A., Analysis of high-throughput plant image data with the information system IAP. J. Integr. Bioinf., 9(2), 2012, 191.
9. Klukas, C., Chen, D., Pape, J.M., Integrated analysis platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165:2 (2014), 506–518.
10. Ogutu, J.O., Piepho, H.P., Regularized group regression methods for genomic prediction: bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD. BMC Proc., 8(Suppl. 5), 2014, S7.
11. R Core Team, R: A Language and Environment for Statistical Computing. 2014, R Foundation for Statistical Computing, Vienna, Austria http://www.R-project.org/.
12. Rahaman, M.M., Chen, D., Gillani, Z., Klukas, C., Chen, M., Advanced phenotyping and phenotype data analysis for the plant growth and development study. Front. Plant Sci., 6, 2015, 619, 10.3389/fpls.2015.00619.
13. Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., Altmann, T., Stitt, M., Willmitzer, L., Melchinger, A.E., Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat. Genet. 44:2 (2012), 217–220, 10.1038/ng.1033.
14. Schrag, T.A., Mohring, J., Melchinger, A.E., Kusterer, B., Dhillon, B.S., Piepho, H.P., Frisch, M., Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds. Theor. Appl. Genet. 120:2 (2010), 451–461.
15. Shull, G.H., The composition of a field of maize. J. Heredity os-4:1 (1908), 296–301, 10.1093/jhered/os-4.1.296.
16. Technow, F., Schrag, T.A., Schipprack, W., Bauer, E., Simianer, H., Melchinger, A.E., Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:4 (2014), 1343–1355.
17. Tibshirani, R., Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58 (1994), 267–288.
18. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B., Missing value estimation methods for DNA microarrays. Bioinformatics 17:6 (2001), 520–525.
19. Yang, Y., Zou, H., A fast unified algorithm for solving group-lasso penalize learning problems. Stat. Comput. 25:6 (2015), 1129–1141, 10.1007/s11222-014-9498-5.
20. Yuan, M., Lin, Y., Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B 68 (2006), 49–67.