Integrating coexpression networks with GWAS to prioritize causal genes in maize

Robert J. Schaefer, Jean Michel Michno, Joseph Jeffers, Owen Hoekenga, Brian Dilkes, Ivan Baxter, Chad L. Myersc

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.

Original languageEnglish (US)
Pages (from-to)2922-2942
Number of pages21
JournalPlant Cell
Volume30
Issue number12
DOIs
StatePublished - Dec 2018

Bibliographical note

Funding Information:
We thank Ben VanderSluis, Henry Ward, and Joanna Dinsmore for their helpful comments and feedback in writing this article. We also thank Abby Cabunoc-Mayes and other members of the Mozilla Science Lab for their mentorshipandhelpinmakingCamocoafreeandopenscientificresource. This work was supported by funding from the National Science Foundation (IOS-1126950, IOS-1444503, and IOS-1450341), the USDA Agricultural Research Service (5070-21000-039-00D), and the USDA National Institute for Food and Agriculture (2016-67012-24841).

Funding Information:
We thank Ben VanderSluis, Henry Ward, and Joanna Dinsmore for their helpful comments and feedback in writing this article. We also thank Abby Cabunoc-Mayes and other members of the Mozilla Science Lab for their mentorshipand help inmakingCamocoa freeandopen scientific resource. This workwas supported by funding from the National Science Foundation (IOS-1126950, IOS-1444503, and IOS-1450341), the USDA Agricultural Research Service (5070-21000-039-00D), and the USDA National Institute for Food and Agriculture (2016-67012-24841).

Publisher Copyright:
© 2018 ASPB.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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