Large‐scale identification of expression quantitative trait loci in Arabidopsis reveals novel candidate regulators of immune responses and other processes. Issue 10 (4th May 2020) - Wang, Xingang; Ren, Min; Liu, Danni; Zhang, Dabao; Zhang, Cuijun; Lang, Zhaobo; Macho, Alberto P.; Zhang, Min; Zhu, Jian‐Kang
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. Abstract : In this work, we developed a novel strategy to perform genome‐wide association studies (GWASs) using publicly available genomic and transcriptomic data from hundreds of Arabidopsis accessions. This allows the identification of expression quantitative trait loci (eQTL) with increased sensitivity, accuracy, and computational efficiency in comparison with previous methods.