Literature DB >> 32594266

A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments.

Xiaoxiang Zhang1, Zhongrong Guan2, Zhaoling Li1, Peng Liu1, Langlang Ma1, Yinchao Zhang1, Lang Pan1, Shijiang He1, Yanling Zhang1, Peng Li1, Fei Ge1, Chaoying Zou1, Yongcong He1, Shibin Gao1,3, Guangtang Pan1, Yaou Shen4,5.   

Abstract

KEY MESSAGE: Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.

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Year:  2020        PMID: 32594266     DOI: 10.1007/s00122-020-03639-4

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  44 in total

1.  Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize.

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Journal:  Genetics       Date:  2014-09-29       Impact factor: 4.562

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Journal:  Theor Appl Genet       Date:  2018-07-24       Impact factor: 5.699

10.  Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize.

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Journal:  BMC Plant Biol       Date:  2016-04-12       Impact factor: 4.215

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  9 in total

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2.  Identification of yield-related genes through genome-wide association: case study of weeping forsythia, an emerging medicinal crop.

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4.  Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies.

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6.  Genome wide association analysis for yield related traits in maize.

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Journal:  Front Plant Sci       Date:  2022-09-26       Impact factor: 6.627

8.  GWAS and WGCNA uncover hub genes controlling salt tolerance in maize (Zea mays L.) seedlings.

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Journal:  Theor Appl Genet       Date:  2021-07-04       Impact factor: 5.699

9.  Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction.

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Journal:  Front Plant Sci       Date:  2021-07-15       Impact factor: 5.753

  9 in total

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