Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Large Scale Joint Analysis of Flowering Time Reveals Independent Temperate Adaptations in Maize

Kelly Swarts, Eva Bauer, Jeffrey C. Glaubitz, Tiffany Ho, Lynn Johnson, Yongxiang Li, Yu Li, Zachary Miller, Cinta Romay, Chris-Carolin Schöen, Tianyu Wang, Zhiwu Zhang, Edward S. Buckler, Peter Bradbury
doi: https://doi.org/10.1101/086082
Kelly Swarts
*Department of Plant Breeding and Genetics, 175 Biotechnology, Cornell University, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eva Bauer
†Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, D-85354 Freising, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jeffrey C. Glaubitz
‡Genomic Diversity Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tiffany Ho
*Department of Plant Breeding and Genetics, 175 Biotechnology, Cornell University, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lynn Johnson
‡Genomic Diversity Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yongxiang Li
**Institute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian, Beijing, China, 100081
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yu Li
**Institute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian, Beijing, China, 100081
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zachary Miller
‡Genomic Diversity Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cinta Romay
‡Genomic Diversity Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chris-Carolin Schöen
†Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, D-85354 Freising, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tianyu Wang
**Institute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian, Beijing, China, 100081
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhiwu Zhang
††Department of Crop and Soil Sciences, Washington State University, 105 Johnson Hall, Pullman, WA 99164, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Edward S. Buckler
*Department of Plant Breeding and Genetics, 175 Biotechnology, Cornell University, Ithaca, NY 14853
§USDA-ARS, 538 Tower Road, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Bradbury
§USDA-ARS, 538 Tower Road, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were ≤0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

Footnotes

  • Kelly Swarts wrote the manuscript and led all analyses, performing those not otherwise indicated, and projected Hapmap 3.21 SNPs onto the GBS samples. Peter Bradbury contributed substantially to the development of BRHM, ran the RMIP analysis, projected the EUNAM populations and contributed intellectually to the discussion and edited the manuscript. Jeff Glaubitz supplied database information for the samples and GBS genotyping and curation for some of the projected datasets. Tiffany Ho ran preliminary machine learning analyses, Zachary Miller ran the Random Forest Classifier analyses and Lynn Johnson set up and curated the database for the machine learning predictors. Yongxiang Li, Yu Li, Tianyu Wang and Zhiwu Zhang contributed the CNNAM datasets and phenotypes. Eva Bauer and Chris-Carolin Schön contributed the EUNAM datasets. Cinta Romay and Edward Buckler contributed the Ames dataset. Cinta Romay additionally added the ZCN genes to the Dong et al flowering candidate list information and selected lines for the temperate and tropical Fst sets and Edward Buckler contributed the USNAM dataset and a substantial intellectual input to the BRHM method, machine learning, interpretations, and manuscript edits.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted November 07, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Large Scale Joint Analysis of Flowering Time Reveals Independent Temperate Adaptations in Maize
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Large Scale Joint Analysis of Flowering Time Reveals Independent Temperate Adaptations in Maize
Kelly Swarts, Eva Bauer, Jeffrey C. Glaubitz, Tiffany Ho, Lynn Johnson, Yongxiang Li, Yu Li, Zachary Miller, Cinta Romay, Chris-Carolin Schöen, Tianyu Wang, Zhiwu Zhang, Edward S. Buckler, Peter Bradbury
bioRxiv 086082; doi: https://doi.org/10.1101/086082
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A Large Scale Joint Analysis of Flowering Time Reveals Independent Temperate Adaptations in Maize
Kelly Swarts, Eva Bauer, Jeffrey C. Glaubitz, Tiffany Ho, Lynn Johnson, Yongxiang Li, Yu Li, Zachary Miller, Cinta Romay, Chris-Carolin Schöen, Tianyu Wang, Zhiwu Zhang, Edward S. Buckler, Peter Bradbury
bioRxiv 086082; doi: https://doi.org/10.1101/086082

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4684)
  • Biochemistry (10362)
  • Bioengineering (7682)
  • Bioinformatics (26340)
  • Biophysics (13534)
  • Cancer Biology (10692)
  • Cell Biology (15445)
  • Clinical Trials (138)
  • Developmental Biology (8501)
  • Ecology (12824)
  • Epidemiology (2067)
  • Evolutionary Biology (16867)
  • Genetics (11401)
  • Genomics (15484)
  • Immunology (10619)
  • Microbiology (25224)
  • Molecular Biology (10225)
  • Neuroscience (54481)
  • Paleontology (402)
  • Pathology (1669)
  • Pharmacology and Toxicology (2897)
  • Physiology (4345)
  • Plant Biology (9252)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2558)
  • Systems Biology (6781)
  • Zoology (1466)