J
John M. Hickey
Researcher at University of Edinburgh
Publications - 175
Citations - 7181
John M. Hickey is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Population & Imputation (genetics). The author has an hindex of 37, co-authored 158 publications receiving 5604 citations. Previous affiliations of John M. Hickey include International Maize and Wheat Improvement Center & University College Dublin.
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Journal ArticleDOI
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
TL;DR: An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.
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Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting and Benchmarking
Hans D. Daetwyler,Mario P. L. Calus,Ricardo Pong-Wong,Gustavo de los Campos,John M. Hickey,John M. Hickey +5 more
TL;DR: Simulation procedures, validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data are reviewed, concluding that no single method can serve as a benchmark for genomic prediction.
Journal ArticleDOI
Genomic prediction in CIMMYT maize and wheat breeding programs
José Crossa,Paulino Pérez,John M. Hickey,John M. Hickey,Juan Burgueño,Leonardo Ornella,J. Jesus Céron-Rojas,Xuecai Zhang,Susanne Dreisigacker,Raman Babu,Yongle Li,David Bonnett,Ky L. Mathews +12 more
TL;DR: Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed, but when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.
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The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes.
TL;DR: A baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.
Journal ArticleDOI
Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
Vanessa S. Windhausen,Gary Atlin,John M. Hickey,José Crossa,Jean-Luc Jannink,Mark E. Sorrells,Babu Raman,Jill E. Cairns,Amsal Tarekegne,Kassa Semagn,Yoseph Beyene,Pichet Grudloyma,Frank Technow,Christian Riedelsheimer,Albrecht E. Melchinger +14 more
TL;DR: In this paper, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations.