J Integr Plant Biol. ›› 2020, Vol. 62 ›› Issue (10): 1469-1484.DOI: 10.1111/jipb.12930

• New Technology • Previous Articles     Next Articles

Large‐scale identification of expression quantitative trait loci in Arabidopsis reveals novel candidate regulators of immune responses and other processes

Xingang Wang1,4†, Min Ren3†, Danni Liu3, Dabao Zhang3, Cuijun Zhang1,2, Zhaobo Lang1,2, Alberto P. Macho2 *, Min Zhang3* and Jian‐Kang Zhu1,2*   

  1. 1Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, Indiana, 47907, USA
    2Shanghai Center for Plant Stress Biology, Center of Excellence for Molecular Plant Sciences, Shanghai 200032, China
    3Department of Statistics, Purdue University, West Lafayette, Indiana, 47907, USA
    4Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA

    These authors contributed equally to this work.
    Email: Jian-Kang Zhu (jkzhu@purdue.edu, Dr. Zhu is responsible for the distribution of the materials associated with this article); Min Zhang (minzhang@purdue.edu); Alberto P. Macho (alberto.macho@sibs.ac.cn)
  • Received:2019-12-04 Accepted:2020-03-24 Online:2020-04-04 Published:2020-10-01

Abstract: The extensive phenotypic diversity within natural populations of Arabidopsis is associated with differences in gene expression. Transcript levels can be considered as inheritable quantitative traits, and used to map expression quantitative trait loci (eQTL) in genome‐wide association studies (GWASs). In order to identify putative genetic determinants for variations in gene expression, we used publicly available genomic and transcript variation data from 665 Arabidopsis accessions and applied the single nucleotide polymorphism‐set (Sequence) Kernel Association Test (SKAT) method for the identification of eQTL. Moreover, we used the penalized orthogonal‐components regression (POCRE) method to increase the power of statistical tests. Then, gene annotations were used as test units to identify genes that are associated with natural variations in transcript accumulation, which correspond to candidate regulators, some of which may have a broad impact on gene expression. Besides increasing the chances to identify real associations, the analysis using POCRE and SKAT significantly reduced the computational cost required to analyze large datasets. As a proof of concept, we used this approach to identify eQTL that represent novel candidate regulators of immune responses. The versatility of this approach allows its application to any process that is subjected to natural variation among Arabidopsis accessions.

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