Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize

Lu, Yanli; Zhang, Shihuang; Shah, Trushar; Xie, Chuanxiao; Zhang, Hao; Li, Xinhai; Farkhari, Mohammad; Ribaut, Jean-Marcel; Cao, Mengji; Tang, Rong; Xu, Yunbi

doi:10.1073/pnas.1006105107

Cited by 196 publications

(135 citation statements)

References 40 publications

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“…Joint linkage association mapping (JLAM) holds great potential for the evaluation of quantitative traits, as it potentially combines the high power of linkage mapping with the good mapping resolution of association mapping (Yu et al, 2008;Lu et al, 2010). The combination of the advantages of both approaches in JLAM is especially promising in applied plant breeding, where many segregating bi-parental populations are routinely generated in the course of the breeding program.…”

Section: Introductionmentioning

confidence: 99%

Comparison of biometrical models for joint linkage association mapping

et al. 2011

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Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

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Section: Introductionmentioning

confidence: 99%

Comparison of biometrical models for joint linkage association mapping

et al. 2011

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“…Until recently, this limited the search space to predetermined candidate genes (Remington and Purugganan 2003). Advances in genomic technology have made it now possible to conduct genome-wide association studies (GWAS) in low-LD populations.Several maize association mapping populations have been constructed, containing various sets of diverse lines (Lu et al 2010;Liu et al 2011;Yan et al 2011;Yu et al 2011). The most widely used of these consists of 302 inbred lines representing the diversity present in public-sector breeding populations around the world (Flint-Garcia et al 2005).…”

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confidence: 99%

A Connected Set of Genes Associated with Programmed Cell Death Implicated in Controlling the Hypersensitive Response in Maize

Olukolu¹,

Negeri²,

Dhawan³

et al. 2013

Genetics

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Rp1-D21 is a maize auto-active resistance gene conferring a spontaneous hypersensitive response (HR) of variable severity depending on genetic background. We report an association mapping strategy based on the Mutant Assisted Gene Identification and Characterization approach to identify naturally occurring allelic variants associated with phenotypic variation in HR. Each member of a collection of 231 diverse inbred lines of maize constituting a high-resolution association mapping panel were crossed to a parental stock heterozygous for Rp1-D21, and the segregating F 1 generation testcrosses were evaluated for phenotypes associated with lesion severity for 2 years at two locations. A genome-wide scan for associations with HR was conducted with 47,445 SNPs using a linear mixed model that controlled for spurious associations due to population structure. Since the ability to identify candidate genes and the resolution of association mapping are highly influenced by linkage disequilibrium (LD), we examined the extent of genome-wide LD. On average, marker pairs separated by .10 kbp had an r 2 value of ,0.1. Genomic regions surrounding SNPs significantly associated with HR traits were locally saturated with additional SNP markers to establish local LD structure and precisely identify candidate genes. Six significantly associated SNPs at five loci were detected. At each locus, the associated SNP was located within or immediately adjacent to candidate causative genes predicted to play significant roles in the control of programmed cell death and especially in ubiquitin pathway-related processes.T HE hypersensitive response (HR) mechanism is a widespread and important plant defense response. Characterized by a rapid, localized cell death around the point of attempted pathogen penetration, it is a form of programmed cell death and is usually associated with an acute local resistance response and up-regulation of defense response pathways (Coll et al. 2011). HR and associated events are generally initiated by the products of resistance (R) genes, which trigger HR upon the recognition of specific pathogenderived molecules or molecular events (Bent and Mackey 2007). The HR and related responses are generally associated with resistance to biotrophic rather than necrotrophic pathogens. Among the multiple classes of R genes, those that encode proteins possessing a nucleotide-binding site (NBS) and a leucine-rich repeat (LRR) are the predominant class (Bent and Mackey 2007).The Rp1 locus on maize chromosome 10 carries multiple tandemly repeated NBS-LRR paralogs, some of which confer resistance to specific races of maize common rust conferred by the fungus Puccini sorghi (Hulbert 1997). The locus is meiotically unstable due to a high frequency of unequal crossovers between paralogs (Sudupak et al. 1993). In one such case, unequal crossing over followed by intragenic recombination resulted in the formation of the chimeric gene Rp1-D21 (Collins et al. 1999;Smith et al. 2010). In the resulting gene product, the recognition an...

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“…Lu et al (2010) adopted joint linkage disequilibrium and integrated mapping to investigate associations between droughtresponse candidate genes and ASI across 305 diverse inbred lines and 217 RILs from three populations. One haplotype, HP71, which includes 10 SNPs from two genes, was identified to be significantly associated with ASI under well-watered condition (ASI-WW).…”

Section: Genes Conferring Tolerance To Abiotic Stress In Maizementioning

confidence: 99%

“…SDG140 is one of the underlying genes of HP71, which encodes a SET domain protein involved in the control of flowering time that correlates with methyltransferase activity. Hp322 was found to be correlated with ASI under water-stressed conditions (ASI-WS), which includes two closely linked SNPs from a gene encoding aldo/keto reductase (AKR) and explains 28.81% of the phenotypic variation using integrated mapping (Lu et al 2010). These two QTPs are expected to be effectively used in breeding programs because of their large effects.…”

Section: Genes Conferring Tolerance To Abiotic Stress In Maizementioning

confidence: 99%

Qualitative and Quantitative Trait Polymorphisms in Maize

Yang

2013

Diagnostics in Plant Breeding

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Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize

Cited by 196 publications

References 40 publications

Comparison of biometrical models for joint linkage association mapping

Comparison of biometrical models for joint linkage association mapping

A Connected Set of Genes Associated with Programmed Cell Death Implicated in Controlling the Hypersensitive Response in Maize

Qualitative and Quantitative Trait Polymorphisms in Maize

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