Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Naser Alkhalifah; Darwin A. Campbell; Celeste M. Falcon; Jack M. Gardiner; Nathan D. Miller; Maria Cinta Romay; Ramona Walls; Renee Walton; Cheng Ting Yeh; Martin Bohn; Jessica Bubert; Edward S. Buckler; Ignacio Ciampitti; Sherry Flint-Garcia; Michael A. Gore; Christopher Graham; Candice Hirsch; James B. Holland; David Hooker; Shawn Kaeppler; Joseph Knoll; Nick Lauter; Elizabeth C. Lee; Aaron Lorenz; Jonathan P. Lynch; Stephen P. Moose; Seth C. Murray; Rebecca Nelson; Torbert Rocheford; Oscar Rodriguez; James C. Schnable; Brian Scully; Margaret Smith; Nathan Springer; Peter Thomison; Mitchell Tuinstra; Randall J. Wisser; Wenwei Xu; David Ertl; Patrick S. Schnable; Natalia De Leon; Edgar P. Spalding; Jode Edwards; Carolyn J. Lawrence-Dill

doi:10.1186/s13104-018-3508-1

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Naser Alkhalifah, Darwin A. Campbell, Celeste M. Falcon, Jack M. Gardiner, Nathan D. Miller, Maria Cinta Romay, Ramona Walls, Renee Walton, Cheng Ting Yeh, Martin Bohn, Jessica Bubert, Edward S. Buckler, Ignacio Ciampitti, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice Hirsch, James B. Holland, David Hooker, Shawn KaepplerJoseph Knoll, Nick Lauter, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, Oscar Rodriguez, James C. Schnable, Brian Scully, Margaret Smith, Nathan Springer, Peter Thomison, Mitchell Tuinstra, Randall J. Wisser, Wenwei Xu, David Ertl, Patrick S. Schnable, Natalia De Leon, Edgar P. Spalding, Jode Edwards, Carolyn J. Lawrence-Dill

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Original language	English (US)
Article number	452
Journal	BMC Research Notes
Volume	11
Issue number	1
DOIs	https://doi.org/10.1186/s13104-018-3508-1
State	Published - Jul 9 2018

Bibliographical note

Funding Information:
We gratefully acknowledge support from: USDA Hatch program funds to mul‑ tiple PIs in this project; the USDA Agricultural Research Service; the Iowa State University Plant Sciences Institute; the Ontario Ministry of Agriculture, Food, and Rural Affairs; the Illinois Corn Marketing Board; the Iowa Corn Promotion Board; the Kansas Corn Commission; the Minnesota Corn Research and Pro‑ motion Council; the Nebraska Corn Board; the Ohio Corn Marketing Program; the Texas Corn Producers Board; and the National Corn Growers Association. We also acknowledge funding from the National Science Foundation under Grant Numbers #DBI‑0735191 and #DBI‑1265383 to support CyVerse (http:// www.cyverse.org) and USDA‑NIFA 2011‑67003‑30342 to SFG, JH, NL, SM, RW, WX, and NDL.

Publisher Copyright:
© 2018 The Author(s).

Keywords

Breeding
Environment
Genome
Genotype
Hybrid
Inbred
Maize
Phenotype
Prediction
Soil

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10.1186/s13104-018-3508-1

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Alkhalifah, N., Campbell, D. A., Falcon, C. M., Gardiner, J. M., Miller, N. D., Romay, M. C., Walls, R., Walton, R., Yeh, C. T., Bohn, M., Bubert, J., Buckler, E. S., Ciampitti, I., Flint-Garcia, S., Gore, M. A., Graham, C., Hirsch, C., Holland, J. B., Hooker, D., ... Lawrence-Dill, C. J. (2018). Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes, 11(1), Article 452. https://doi.org/10.1186/s13104-018-3508-1

Alkhalifah, N, Campbell, DA, Falcon, CM, Gardiner, JM, Miller, ND, Romay, MC, Walls, R, Walton, R, Yeh, CT, Bohn, M, Bubert, J, Buckler, ES, Ciampitti, I, Flint-Garcia, S, Gore, MA, Graham, C, Hirsch, C, Holland, JB, Hooker, D, Kaeppler, S, Knoll, J, Lauter, N, Lee, EC, Lorenz, A, Lynch, JP, Moose, SP, Murray, SC, Nelson, R, Rocheford, T, Rodriguez, O, Schnable, JC, Scully, B, Smith, M, Springer, N, Thomison, P, Tuinstra, M, Wisser, RJ, Xu, W, Ertl, D, Schnable, PS, De Leon, N, Spalding, EP, Edwards, J & Lawrence-Dill, CJ 2018, 'Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets', BMC Research Notes, vol. 11, no. 1, 452. https://doi.org/10.1186/s13104-018-3508-1

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title = "Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets",

abstract = "Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.",

keywords = "Breeding, Environment, Genome, Genotype, Hybrid, Inbred, Maize, Phenotype, Prediction, Soil",

author = "Naser Alkhalifah and Campbell, {Darwin A.} and Falcon, {Celeste M.} and Gardiner, {Jack M.} and Miller, {Nathan D.} and Romay, {Maria Cinta} and Ramona Walls and Renee Walton and Yeh, {Cheng Ting} and Martin Bohn and Jessica Bubert and Buckler, {Edward S.} and Ignacio Ciampitti and Sherry Flint-Garcia and Gore, {Michael A.} and Christopher Graham and Candice Hirsch and Holland, {James B.} and David Hooker and Shawn Kaeppler and Joseph Knoll and Nick Lauter and Lee, {Elizabeth C.} and Aaron Lorenz and Lynch, {Jonathan P.} and Moose, {Stephen P.} and Murray, {Seth C.} and Rebecca Nelson and Torbert Rocheford and Oscar Rodriguez and Schnable, {James C.} and Brian Scully and Margaret Smith and Nathan Springer and Peter Thomison and Mitchell Tuinstra and Wisser, {Randall J.} and Wenwei Xu and David Ertl and Schnable, {Patrick S.} and {De Leon}, Natalia and Spalding, {Edgar P.} and Jode Edwards and Lawrence-Dill, {Carolyn J.}",

note = "Funding Information: We gratefully acknowledge support from: USDA Hatch program funds to mul‑ tiple PIs in this project; the USDA Agricultural Research Service; the Iowa State University Plant Sciences Institute; the Ontario Ministry of Agriculture, Food, and Rural Affairs; the Illinois Corn Marketing Board; the Iowa Corn Promotion Board; the Kansas Corn Commission; the Minnesota Corn Research and Pro‑ motion Council; the Nebraska Corn Board; the Ohio Corn Marketing Program; the Texas Corn Producers Board; and the National Corn Growers Association. We also acknowledge funding from the National Science Foundation under Grant Numbers #DBI‑0735191 and #DBI‑1265383 to support CyVerse (http:// www.cyverse.org) and USDA‑NIFA 2011‑67003‑30342 to SFG, JH, NL, SM, RW, WX, and NDL. Publisher Copyright: {\textcopyright} 2018 The Author(s).",

year = "2018",

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doi = "10.1186/s13104-018-3508-1",

language = "English (US)",

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T2 - 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

AU - Alkhalifah, Naser

AU - Campbell, Darwin A.

AU - Falcon, Celeste M.

AU - Gardiner, Jack M.

AU - Miller, Nathan D.

AU - Romay, Maria Cinta

AU - Walls, Ramona

AU - Walton, Renee

AU - Yeh, Cheng Ting

AU - Bohn, Martin

AU - Bubert, Jessica

AU - Buckler, Edward S.

AU - Ciampitti, Ignacio

AU - Flint-Garcia, Sherry

AU - Gore, Michael A.

AU - Graham, Christopher

AU - Hirsch, Candice

AU - Holland, James B.

AU - Hooker, David

AU - Kaeppler, Shawn

AU - Knoll, Joseph

AU - Lauter, Nick

AU - Lee, Elizabeth C.

AU - Lorenz, Aaron

AU - Lynch, Jonathan P.

AU - Moose, Stephen P.

AU - Murray, Seth C.

AU - Nelson, Rebecca

AU - Rocheford, Torbert

AU - Rodriguez, Oscar

AU - Schnable, James C.

AU - Scully, Brian

AU - Smith, Margaret

AU - Springer, Nathan

AU - Thomison, Peter

AU - Tuinstra, Mitchell

AU - Wisser, Randall J.

AU - Xu, Wenwei

AU - Ertl, David

AU - Schnable, Patrick S.

AU - De Leon, Natalia

AU - Spalding, Edgar P.

AU - Edwards, Jode

AU - Lawrence-Dill, Carolyn J.

N1 - Funding Information: We gratefully acknowledge support from: USDA Hatch program funds to mul‑ tiple PIs in this project; the USDA Agricultural Research Service; the Iowa State University Plant Sciences Institute; the Ontario Ministry of Agriculture, Food, and Rural Affairs; the Illinois Corn Marketing Board; the Iowa Corn Promotion Board; the Kansas Corn Commission; the Minnesota Corn Research and Pro‑ motion Council; the Nebraska Corn Board; the Ohio Corn Marketing Program; the Texas Corn Producers Board; and the National Corn Growers Association. We also acknowledge funding from the National Science Foundation under Grant Numbers #DBI‑0735191 and #DBI‑1265383 to support CyVerse (http:// www.cyverse.org) and USDA‑NIFA 2011‑67003‑30342 to SFG, JH, NL, SM, RW, WX, and NDL. Publisher Copyright: © 2018 The Author(s).

PY - 2018/7/9

Y1 - 2018/7/9

N2 - Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

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KW - Breeding

KW - Environment

KW - Genome

KW - Genotype

KW - Hybrid

KW - Inbred

KW - Maize

KW - Phenotype

KW - Prediction

KW - Soil

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ER -

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Abstract

Bibliographical note

Keywords

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