Genome-wide expression quantitative trait loci (eQTL) analysis in maize

Holloway, Beth; Luck, Stanley; Beatty, Mary; Rafalski, J. Antoni; Li, Bailin

doi:10.1186/1471-2164-12-336

Cited by 66 publications

(56 citation statements)

References 44 publications

(45 reference statements)

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“…A slight majority (54.0%) of eQTLs were local at an FDR of 0.1. However, consistent with other studies (Holloway et al, 2011), the ratio of local to distant eQTLs was sensitive to the FDR threshold, with local eQTLs becoming proportionally more abundant at more stringent thresholds (Table 1).…”

Section: Natural Genetic Variation In Gene Expression (G)supporting

confidence: 91%

“…Here, mRNA transcript abundance for each gene in the genome is treated as a trait for genetic mapping. Over the last decade, eQTL mapping has been used to understand the genetic architecture of variation in gene expression in model systems, crops, and humans (Brem et al, 2002;DeCook et al, 2006;West et al, 2007;Smith and Kruglyak, 2008;Kliebenstein, 2009;Holloway et al, 2011;Grundberg et al, 2011;Maranville et al, 2011;Zhang et al, 2011;Barreiro et al, 2012;Cubillos et al, 2012aCubillos et al, , 2012bSnoek et al, 2012). More recently, studies have begun to explore how eQTL effects on transcript abundance can interact with the environment (Li et al, 2006;Smith and Kruglyak, 2008;Grundberg et al, 2011;Maranville et al, 2011;Barreiro et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Expression Quantitative Trait Locus Mapping across Water Availability Environments Reveals Contrasting Associations with Genomic Features in Arabidopsis

et al. 2013

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The regulation of gene expression is crucial for an organism's development and response to stress, and an understanding of the evolution of gene expression is of fundamental importance to basic and applied biology. To improve this understanding, we conducted expression quantitative trait locus (eQTL) mapping in the Tsu-1 (Tsushima, Japan) × Kas-1 (Kashmir, India) recombinant inbred line population of Arabidopsis thaliana across soil drying treatments. We then used genome resequencing data to evaluate whether genomic features (promoter polymorphism, recombination rate, gene length, and gene density) are associated with genes responding to the environment (E) or with genes with genetic variation (G) in gene expression in the form of eQTLs. We identified thousands of genes that responded to soil drying and hundreds of main-effect eQTLs. However, we identified very few statistically significant eQTLs that interacted with the soil drying treatment (GxE eQTL). Analysis of genome resequencing data revealed associations of several genomic features with G and E genes. In general, E genes had lower promoter diversity and local recombination rates. By contrast, genes with eQTLs (G) had significantly greater promoter diversity and were located in genomic regions with higher recombination. These results suggest that genomic architecture may play an important a role in the evolution of gene expression.

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Section: Natural Genetic Variation In Gene Expression (G)supporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Expression Quantitative Trait Locus Mapping across Water Availability Environments Reveals Contrasting Associations with Genomic Features in Arabidopsis

et al. 2013

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“…Fourth, the maize materials for genotyping by the three platforms are not from the same plants, the residual heterozygosis of inbred lines may also be a factor influencing the concordant rate. Regulation of expression variation may be broadly defined by traditional linkage studies 22,39 . In experimental populations from two parental lines, eQTL mapping resolution is limited by population size.…”

Section: Discussionmentioning

confidence: 99%

RNA sequencing reveals the complex regulatory network in the maize kernel

et al. 2013

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RNA sequencing can simultaneously identify exonic polymorphisms and quantitate gene expression. Here we report RNA sequencing of developing maize kernels from 368 inbred lines producing 25.8 billion reads and 3.6 million single-nucleotide polymorphisms. Both the MaizeSNP50 BeadChip and the Sequenom MassArray iPLEX platforms confirm a subset of high-quality SNPs. Of these SNPs, we have mapped 931,484 to gene regions with a mean density of 40.3 SNPs per gene. The genome-wide association study identifies 16,408 expression quantitative trait loci. A two-step approach defines 95.1% of the eQTLs to a 10-kb region, and 67.7% of them include a single gene. The establishment of relationships between eQTLs and their targets reveals a large-scale gene regulatory network, which include the regulation of 31 zein and 16 key kernel genes. These results contribute to our understanding of kernel development and to the improvement of maize yield and nutritional quality.

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“…In plants a number of studies have been conducted on a range of species including Arabidopsis thaliana, barley (Hordeum vulgare), maize (Zea mays), poplar (Populus spp. ), and wheat (Triticum aestivum) (Decook et al 2006;Jordan et al 2007;Keurentjes et al 2007;West et al 2007;Potokina et al 2008;Drost et al 2010;Druka et al 2010;Holloway et al 2011;Zhang et al 2011). Using a genetical genomics approach, gene expression by environment interaction has been studied in Caenorhabditis elegans and yeast, but to date no studies in plants have taken advantage of gene expression measurements in more than one environment to help in understanding G·E (Li et al 2006;Smith and Kruglyak 2008).…”

mentioning

confidence: 99%

Systems Genetics of Environmental Response in the Mature Wheat Embryo

Munkvold

Laudencia‐Chingcuanco

Sorrells

2013

Genetics

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Quantitative phenotypic traits are influenced by genetic and environmental variables as well as the interaction between the two. Underlying genetic · environment interaction is the influence that the surrounding environment exerts on gene expression. Perturbation of gene expression by environmental factors manifests itself in alterations to gene co-expression networks and ultimately in phenotypic plasticity. Comparative gene co-expression networks have been used to uncover biological mechanisms that differentiate tissues or other biological factors. In this study, we have extended consensus and differential Weighted Gene Co-Expression Network Analysis to compare the influence of different growing environments on gene co-expression in the mature wheat (Triticum aestivum) embryo. This network approach was combined with mapping of individual gene expression QTL to examine the genetic control of environmentally static and variable gene expression. The approach is useful for gene expression experiments containing multiple environments and allowed for the identification of specific gene co-expression modules responsive to environmental factors. This procedure identified conserved coregulation of gene expression between environments related to basic developmental and cellular functions, including protein localization and catabolism, vesicle composition/trafficking, Golgi transport, and polysaccharide metabolism among others. Environmentally unique modules were found to contain genes with predicted functions in responding to abiotic and biotic environmental variables. These findings represent the first report using consensus and differential Weighted Gene Coexpression Network Analysis to characterize the influence of environment on coordinated transcriptional regulation. Q UANTITATIVE phenotypic traits are governed by genetics, environment, and interactions between the two (G·E). The extent of G·E for a particular trait in part determines heritability and our understanding of the underlying biology. Transcriptional regulation of genes plays a large role in the response of an organism to the environment, and this modulation of gene expression is itself genetically controlled and subject to G·E interactions (Landry et al. 2006;Li et al. 2006;Smith and Kruglyak 2008). A number of studies have used broad transcriptional profiling in segregating or natural populations to identify genetic loci that control gene expression, commonly referred to as genetical genomics or gene expression QTL (eQTL) mapping (Jansen and Nap 2001;Brem et al. 2002;Schadt et al. 2003;Brem and Kruglyak 2005;Zhang et al. 2011). In plants a number of studies have been conducted on a range of species including Arabidopsis thaliana, barley (Hordeum vulgare), maize (Zea mays), poplar (Populus spp.), and wheat (Triticum aestivum) (Decook et al. 2006;Jordan et al. 2007;Keurentjes et al. 2007;West et al. 2007;Potokina et al. 2008;Drost et al. 2010;Druka et al. 2010;Holloway et al. 2011;Zhang et al. 2011). Using a genetical genomics approach, gene expr...

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Genome-wide expression quantitative trait loci (eQTL) analysis in maize

Cited by 66 publications

References 44 publications

Expression Quantitative Trait Locus Mapping across Water Availability Environments Reveals Contrasting Associations with Genomic Features in Arabidopsis

Expression Quantitative Trait Locus Mapping across Water Availability Environments Reveals Contrasting Associations with Genomic Features in Arabidopsis

RNA sequencing reveals the complex regulatory network in the maize kernel

Systems Genetics of Environmental Response in the Mature Wheat Embryo

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