Genome-wide association study of leaf architecture in the maize nested association mapping population

Tian, Feng; Bradbury, Peter J.; Brown, Patrick J.; Hung, Hsiao-Yi; Sun, Qi; Flint-García, Sherry; Rocheford, Torbert; McMullen, Michael D.; Holland, James B.; Buckler, Edward S.

doi:10.1038/ng.746

Cited by 897 publications

(838 citation statements)

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“…Through deep RNA-seq, we obtained an average of 70 million reads for each inbred line, which resulted in the recovery of 1.03 million high-quality SNPs in the maize genome. The identified SNPs are of significance to the maize research community, especially in exploring the genetic architecture of quantitative traits in maize using GWAS, as genomic SNPs were often used in previous GWASs in maize, including leaf architecture 33 , leaf metabolites 34 and disease resistance 35,36 . Most of the newly identified SNPs were mapped to gene regions with an average of 40.3 SNPs per gene, which substantially complemented the maize SNP polymorphisms discovered by genome resequencing 13,14 .…”

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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Section: Discussionmentioning

confidence: 99%

RNA sequencing reveals the complex regulatory network in the maize kernel

et al. 2013

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“…In each trial, the 866 BC 2 S 3 RILs were grown in a single replicate employing an augmented incomplete randomized blocks design widely used to phenotype large populations (Buckler et al ., 2009; Kump et al ., 2011; Tian et al ., 2011; Huang et al ., 2015). Two maize inbred lines W22 and Mo17 were randomly inserted in each incomplete block as checks.…”

Section: Methodsmentioning

confidence: 99%

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Wang

Zhang

et al. 2015

New Phytologist

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Summary The number of leaves and their distributions on plants are critical factors determining plant architecture in maize (Zea mays), and leaf number is frequently used as a measure of flowering time, a trait that is key to local environmental adaptation.Here, using a large set of 866 maize‐teosinte BC 2S3 recombinant inbred lines genotyped by using 19 838 single nucleotide polymorphism markers, we conducted a comprehensive genetic dissection to assess the genetic architecture of leaf number and its genetic relationship to flowering time.We demonstrated that the two components of total leaf number, the number of leaves above (LA) and below (LB) the primary ear, were under relatively independent genetic control and might be subject to differential directional selection during maize domestication and improvement. Furthermore, we revealed that flowering time and leaf number are commonly regulated at a moderate level. The pleiotropy of the genes ZCN8, dlf1 and ZmCCT on leaf number and flowering time were validated by near‐isogenic line analysis. Through fine mapping, qLA1‐1, a major‐effect locus that specifically affects LA, was delimited to a region with severe recombination suppression derived from teosinte.This study provides important insights into the genetic basis of traits affecting plant architecture and adaptation. The genetic independence of LA from LB enables the optimization of leaf number for ideal plant architecture breeding in maize.

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“…One example of an automated SNP panel is KASPar from KBioScience (Hertfordshire, UK), which relies on competitive allele-specific PCR, with fluorescent resonance energy transfer detection. This has been used for a range of genetic studies (for example, Tian et al, 2011). An alternative for SNP typing is the Sequenom MassARRAY platform (Hamburg, Germany), which uses single termination mix multiplexed PCR and identifies different SNPs based on their different masses (applied by Thompson et al, 2009).…”

Section: How Can Ngs Technologies Help Us To Get Around These Limitatmentioning

confidence: 99%

Next-generation hybridization and introgression

2011

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Hybridization has a major role in evolution-from the introgression of important phenotypic traits between species, to the creation of new species through hybrid speciation. Molecular studies of hybridization aim to understand the class of hybrids and the frequency of introgression, detect the signature of ancient hybridization, and understand the behaviour of introgressed loci in their new genomic background. This often involves a large investment in the design and application of molecular markers, leading to a compromise between the depth and breadth of genomic data. New techniques designed to assay a large sub-section of the genome, in association with next-generation sequencing (NGS) technologies, will allow genome-wide hybridization and introgression studies in organisms with no prior sequence data. These detailed genotypic data will unite the breadth of sampling of loci characteristic of population genetics with the depth of sequence information associated with molecular phylogenetics. In this review, we assess the theoretical and methodological constraints that limit our understanding of natural hybridization, and promote the use of NGS for detecting hybridization and introgression between non-model organisms. We also make recommendations for the ways in which emerging techniques, such as pooled barcoded amplicon sequencing and restriction site-associated DNA tags, should be used to overcome current limitations, and enhance our understanding of this evolutionary significant process. Heredity (2012) 108, 179-189; doi:10.1038/hdy.2011.68; published online 7 September 2011Keywords: next-generation sequencing; hybridization; introgression; reticulate evolution; single-nucleotide polymorphisms (SNPs); restriction site-associated DNA (RAD) tags INTRODUCTION Hybridization, the crossbreeding between individuals of different species, and introgression, the transfer of genes between species mediated primarily by backcrossing, have been the focus of evolutionary studies over many decades (see Anderson, 1949;Arnold, 1992;Rieseberg and Carney, 1998). Hybridization is potentially a creative evolutionary process, allowing genetic novelties to accumulate faster than through mutation alone (Anderson and Hubricht, 1938;Martinsen et al., 2001). This may increase allelic variation at selectively neutral loci, and transfer adaptively important genetic variation, which may increase the fitness of the introgressed lineage (Choler et al., 2004;Martin et al., 2006;Castric et al., 2008;Kim et al., 2008). Moreover, hybridization can have a role in speciation. Hybridization in association with whole-genome duplication (polyploidy) is considered a likely route to speciation, particularly in plants ( Hegarty and Hiscock, 2008). The difference in ploidy levels between the polyploid hybrid and diploid progenitors acts as a strong reproductive barrier (Soltis et al., 2004), although there are examples of introgression across ploidy levels (for example, Senecio; Chapman and Abbott, 2010). Hybrid speciation can also occur without a change i...

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Genome-wide association study of leaf architecture in the maize nested association mapping population

Cited by 897 publications

References 33 publications

RNA sequencing reveals the complex regulatory network in the maize kernel

RNA sequencing reveals the complex regulatory network in the maize kernel

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Next-generation hybridization and introgression

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