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. 2023 Nov 8;12(22):3806.
doi: 10.3390/plants12223806.

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Fu Qian  1   2 , Jianguo Jing  2 , Zhanqin Zhang  1 , Shubin Chen  1 , Zhiqin Sang  1 , Weihua Li  2
Affiliations

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Fu Qian et al. Plants (Basel). .

Abstract

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

Keywords: GWAS; candidate genes; ear traits; maize; meta-QTL; yield.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Correlation analysis of BLUE value of ear traits. ERN, ear row number; KNR, kernel number per row; EL, ear length; ED, ear diameter; CD, cob diameter; EW, ear weight; HKW, hundred kernel weight; VW, volume weight. * and *** indicates the significant levels at p < 0.05 and 0.001, respectively.
Figure 2
Figure 2
Genetic background and population structure analysis of 580 maize inbred lines. (A) Density distribution of SNPs on chromosome. (B) ΔK-value of 580 inbred lines based on 31,826 SNPs. (C) The Bayes cluster analysis of 580 maize inbred lines when K = 2. (D) The Bayes cluster analysis of 580 maize inbred lines when K = 4. (E) Principal component analysis of 580 maize inbred lines. The different groups represented by different colors, and scattered points with the same color are basically clustered together. (F) Kinship heatmap.
Figure 3
Figure 3
Manhattan plot and QQ plot of BLUE value of ear traits. ERN, ear row number; KNR, kernel number per row; EL, ear length; ED, ear diameter; CD, cob diameter; EW, ear weight; HKW, hundred kernel weight; VW, volume weight; BLUE, best linear unbiased estimate. The red dashed line is the threshold line.
Figure 4
Figure 4
Projection and distribution of QTL and MQTL (Meta QTL) identified for ear traits on chromosome 4. Bars on the left side of the chromosome correspond to QTL related to ear traits, black bars within chromosomes represent marker density, colored segments within the chromosome represent MQTL, and on the right side of the chromosome are molecular markers and genetic distances (cM).
Figure 5
Figure 5
Circos plot for the distribution of MQTL (Meta QTL) and significant SNPs of GWAS studies in maize. Colored bars showing the ten maize chromosomes, yellow trajectory represents the physical positions (bp) of MQTL and SNPs on the chromosome, black area represents the physical intervals of MQTL on the chromosome.
See this image and copyright information in PMC

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