Spatial kernel models capturing field heterogeneity for accurate estimation of genetic potential
Author:
Affiliation:
1. Graduate School of Agricultural and Life Sciences, The University of Tokyo
2. EARTHNOTE Co. Ltd.
Publisher
Japanese Society of Breeding
Subject
Plant Science,Genetics,Agronomy and Crop Science
Link
https://www.jstage.jst.go.jp/article/jsbbs/71/4/71_20060/_pdf
Reference28 articles.
1. Bernal-Vasquez, A.-M., J. Möhring, M. Schmidt, M. Schönleben, C.-C. Schön and H.-P. Piepho (2014) The importance of phenotypic data analysis for genomic prediction—a case study comparing different spatial models in rye. BMC Genomics 15: 646.
2. Browning, S.R. and B.L. Browning (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81: 1084–1097.
3. Cullis, B.R. and A.C. Gleeson (1991) Spatial analysis of field experiments—an extension to two dimensions. Biometrics 47: 1449–1460.
4. de los Campos, G., D. Gianola, G.J.M. Rosa, K.A. Weigel and J. Crossa (2010) Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92: 295–308.
5. Dilla-Ermita, C.J., E. Tandayu, V.M. Juanillas, J. Detras, D.N. Lozada, M.S. Dwiyanti, C.V. Cruz, E.G.N. Mbanjo, E. Ardales, M.G. Diaz et al. (2017) Genome-wide association analysis tracks bacterial leaf blight resistance loci in rice diverse germplasm. Rice (N Y) 10: 8.
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