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

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.

Abstract

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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Acknowledgements

This work was supported by the Major Project of China on New varieties of GMO Cultivation (2016ZX08003003).

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This study was conceived by YS and ZG. XZ, ZG, ZL, LM, YZ, LP, SH, YZ, PL, PL, CZ, FG, and YH participated in the management of the experiment and yield-related trait measurements in this work. Collections of all plant materials were performed by SG and GP. XZ and PL conducted the data analysis. XZ wrote the initial manuscript, and YS reviewed the manuscript and made significant revision contributions. All authors read and approved the final manuscript.

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Correspondence to Yaou Shen.

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Fig. S1:

Genetic map for the maize IBM Syn10 DH population. (PDF 135 kb)

Fig. S2:

Distributions of investigated agronomic traits for the association population and IBM Syn10 DH population in different environments. (PDF 688 kb)

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Zhang, X., Guan, Z., Li, Z. et al. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133, 2881–2895 (2020). https://doi.org/10.1007/s00122-020-03639-4

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