Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS

Abstract

BackgroundThere are significant limitations in existing methods for the genome-wide identification of genes whose expression patterns affect traits.ResultsThe transcriptomes of five tissues from 27 genetically diverse maize inbred lines were deeply sequenced to identify genes exhibiting high and low levels of expression variation across tissues or genotypes. Transcription factors are enriched among genes with the most variation in expression across tissues, as well as among genes with higher-than-median levels of variation in expression across genotypes. In contrast, transcription factors are depleted among genes whose expression is either highly stable or highly variable across genotypes. We developed a Bayesian-based method for genome-wide association studies (GWAS) in which RNA-seq-based measures of transcript accumulation are used as explanatory variables (eRD-GWAS). The ability of eRD-GWAS to identify true associations between gene expression variation and phenotypic diversity is supported by analyses of RNA co-expression networks, protein–protein interaction networks, and gene regulatory networks. Genes associated with 13 traits were identified using eRD-GWAS on a panel of 369 maize inbred lines. Predicted functions of many of the resulting trait-associated genes are consistent with the analyzed traits. Importantly, transcription factors are significantly enriched among trait-associated genes identified with eRD-GWAS.ConclusionseRD-GWAS is a powerful tool for associating genes with traits and is complementary to SNP-based GWAS. Our eRD-GWAS results are consistent with the hypothesis that genetic variation in transcription factor expression contributes substantially to phenotypic diversity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1328-6) contains supplementary material, which is available to authorized users.

ZmMADS69 functions as a flowering activator through the ZmRap2.7‐ZCN8 regulatory module and contributes to maize flowering time adaptation

“…(c, d) Comparison of Zm MADS 69 expression levels between tropical and temperate inbred lines in the apex based on data from Lin et al . () (c), and the tip of the third leaf based on data from Kremling et al . () (d).…”

“…MADS-box transcription factors are known to play important roles in plant inflorescence and flower development (Smaczniak et al, 2012). Previously, we used eRD-GWAS, which employs gene expression levels, rather than SNPs as explanatory variables in GWAS to identify 26 genes, including ZmMADS69 that are associated with DTA (Lin et al, 2017). Hence, ZmMADS69 was the most likely candidate gene underlying qDTA3-2.…”

Evolutionary and ecological functional genomics, from lab to the wild

“…Specifically, measurements of genome‐wide gene expression variation are helpful in describing the developmental mechanisms that transform genomic information in specific loci to the realized phenotypic responses (Swanson‐Wagner et al ., ; Ren et al ., ). Furthermore, regulatory variation is clearly abundant within and across populations (Lasky et al ., ; Lin et al ., ). And although its evolutionary significance is harder to ascertain, several studies have uncovered heritable gene expression variation among and within species to be the raw material for evolutionary processes (Wang et al ., ; Konishi et al ., ; Cong et al ., ; Jiang and Rausher, ).…”

“…There are numerous studies that employ whole‐genome gene expression analysis not just for model plant species and crops, but also for lesser‐known plant species including Japanese lawn grass (Xie et al ., ), Cunninghamia lanceolata (Cao et al ., ), mangrove fern (Zhang et al ., ), wild oil‐tea camelia (Chen et al ., ), curry tree (Meena et al ., ) and Banksia (He et al ., ). At the same time, computational tools like eRD‐GWAS (expression read depth GWAS mapping) used to uncover expression variation (Lin et al ., ) can now be harnessed much more efficiently to connect genomic variation with phenotypes.…”

Additive partially linear models for ultra‐high‐dimensional regression

“…These advances have caused an escalating demand for statistical tools that can uncover and estimate the effects of gene sequences and gene transcript levels on quantitative traits. A specific example we consider is a maize shoot apical meristem (SAM) study (Leiboff et al, ; Lin et al, ) with 1.3 million SNPs and RNA‐seq read counts for 39,656 genes. Researchers are interested in identifying candidate SNPs and genes whose transcript abundance levels are associated with SAM morphological variation.…”