Combined GWAS and QTL analysis for dissecting the genetic architecture of kernel test weight in maize

Zhang, Xiaoxiang; Guan, Zhongrong; Wang, Lei; Fu, Jun; Zhang, Yinchao; Li, Zhaoling; Ma, Langlang; Liu, Peng; Zhang, Yanling; Liu, Min; Zou, Chaoying; He, Yongcong; Lin, Hung-Ching; Pan, Guangtang; Shen, Yaou

doi:10.1007/s00438-019-01631-2

Cited by 40 publications

(28 citation statements)

References 54 publications

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“…QTLs associated with sabinene and terpinen-4-ol in roots have also been detected by a previous GWAS in the same genomic region 23 . A consistency of QTLs detected by QTL mapping with those identified by GWAS was observed in several crops such as soybean 36 , maize 37 , tea 38 and rice 39 .…”

Section: Discussionsupporting

confidence: 55%

The carrot monoterpene synthase gene cluster on chromosome 4 harbours genes encoding flavour-associated sabinene synthases

et al. 2020

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In plants, low molecular weight terpenes produced by terpene synthases (TPS) contribute to multiple ecologically and economically important traits. The present study investigates a carrot terpene synthase gene cluster on chromosome 4 associated with volatile monoterpene production. Two carrot mutants, yellow and cola, which are contrasting in the content of low molecular weight terpenes, were crossed to develop an F2 mapping population. The mapping analysis revealed overlapping QTLs on chromosome 4 for sabinene, α-thujene, α-terpinene, γ-terpinene, terpinen-4-ol and 4-carene. The genomic region of this locus includes a cluster of five terpene synthase genes (DcTPS04, DcTPS26, DcTPS27, DcTPS54 and DcTPS55). DcTPS04 and DcTPS54 displayed genotype- and tissue-specific variation in gene expression. Based on the QTL mapping results and the gene expression patterns, DcTPS04 and DcTPS54 were selected for functional characterization. In vitro enzyme assays showed that DcTPS54 is a single-product enzyme catalysing the formation of sabinene, whereas DcTPS04 is a multiple-product terpene synthase producing α-terpineol as a major product and four additional products including sabinene, β-limonene, β-pinene and myrcene. Furthermore, we developed a functional molecular marker that could discriminate carrot genotypes with different sabinene content in a set of 85 accessions.

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Section: Discussionsupporting

confidence: 55%

The carrot monoterpene synthase gene cluster on chromosome 4 harbours genes encoding flavour-associated sabinene synthases

et al. 2020

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“…In total, 43 782 SNPs were retained for GWAS. The analysis of genetic structure in our previous study showed that this panel can be divided into three subpopulations (Zhang et al 2020). Thus, ‘PCA = 3’ was adopted in GWAS of this study.…”

Section: Methodsmentioning

confidence: 99%

“…The primers of PCR amplification are shown in Table S1. Sequence alignment was conducted using DNAMAN software (Zhang et al 2020). DnaSP v5.0 was applied to identify sequence diversity.…”

Section: Methodsmentioning

confidence: 99%

“…Among these loci, zma‐miR164e was also shown to control seed development in Arabidopsis (Liu et al 2020). A kernel test weight (KTW)‐associated SNP was detected by combined QTL mapping and GWAS, and three candidate genes were validated to affect KTW in a candidate gene association study (Zhang et al 2020). This study used an association panel and an IBM Syn10 DH population to identify the QTL and the SNPs related to KMC by combined linkage mapping and GWAS in different environments.…”

Section: Introductionmentioning

confidence: 99%

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Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content

Zhang

Guan

et al. 2020

Physiologia Plantarum

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The free moisture in crop kernels after being naturally dried is referred to as kernel moisture content (KMC). Maize KMC reflects grain quality and influences transportation and storage of seeds. We used an IBM Syn10 DH maize population consisting of 249 lines and an association panel comprising 310 maize inbred lines to identify the genetic loci affecting maize KMC in three environments. Using the IBM population detected 13 QTL on seven chromosomes, which were clustered into nine common QTL. Genome-wide association analysis (GWAS) identified 16 significant SNPs across the 3 environments, which were linked to 158 genes across the three environments. Combined QTL mapping and GWAS found two SNPs that were located in two of the mapped QTL, respectively. Twenty-three genes were linked with the loci co-localized in both populations. Of these 181 genes, five have previously been reported to be associated with KMC or to regulate seed development. These associations were verified by candidate gene association analysis. Two superior alleles and one favorable haplotype for Zm00001d007774 and Zm00001d047868 were found to influence KMC. These findings provide insights into molecular mechanisms underlying maize KMC and contribute to the use of markerassisted selection for breeding low-KMC maize.

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“…The combination of meta-QTL intervals and signi cant SNP provide reliable information to identify candidate genes Due to the challenging detection of rare variants in GWAS and high false-positive rates in QTL mapping, the combination of association mapping and linkage analysis has been wildly used for revealing the genetic architecture of complex quantitative traits (Andersen et al 2005;Li et al 2016b;Visscher 2008). Seventeen candidate genes for kernel test weight were identi ed in maize by the combination of association mapping and linkage analysis (Zhang et al 2019c). Nineteen candidate genes for plant and ear height were identi ed in maize by combining association mapping and linkage analysis (Li et al 2016b).…”

Section: Go and Kegg Enrichment Analysis Of Candidate Genesmentioning

confidence: 99%

Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

Shudi

Pan

Yin

et al. 2020

Preprint

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Background: Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants.Results: In our study, 884 QTLs associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTLs were identified, including 19 meta-QTLs for fiber length (FL); 18 meta-QTLs for fiber strength (FS); 11 meta-QTLs for fiber uniformity (FU); 11 meta-QTLs for fiber elongation (FE); and 15 meta-QTLs for micronaire (MIC). Combined with 8,589 significant SNPs associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development.Conclusions: This study provides not only stable QTLs used for marker-assisted selection (MAS), but also candidate genes to uncover the molecular mechanisms for cotton fiber development.

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Combined GWAS and QTL analysis for dissecting the genetic architecture of kernel test weight in maize

Cited by 40 publications

References 54 publications

The carrot monoterpene synthase gene cluster on chromosome 4 harbours genes encoding flavour-associated sabinene synthases

The carrot monoterpene synthase gene cluster on chromosome 4 harbours genes encoding flavour-associated sabinene synthases

Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content

Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

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