Genome-wide association study of kernel colour traits and mining of elite alleles from the major loci in maize

Abstract

Background: Maize kernel colour is an important index for evaluating maize quality and value and mainly entails two natural pigments, carotenoids and anthocyanins. To analyse the genetic mechanism of maize kernel colour and mine single nucleotide polymorphisms (SNPs) related to kernel colour traits, an association panel including 244 superior maize inbred lines was used to measure and analyse the six traits related to kernel colour in two environments and was then combined with the about 3 million SNPs covering the whole maize genome in this study. Two models (Q + K, PCA + K) were used for genome-wide association analysis (GWAS) of kernel colour traits.

Results: We identified 1029QTLs, and two SNPs contained in those QTLs were located in coding regions of Y1 and R1 respectively, two known genes that regulate kernel colour. Fourteen QTLs which contain 19 SNPs were within 200 kb interval of the genes involved in the regulation of kernel colour. 13 high-confidence SNPs repeatedly detected for specific traits, and AA genotypes of rs1_40605594 and rs5_2392770 were the most popular alleles appeared in inbred lines with higher levels. By searching the confident interval of the 13 high-confidence SNPs, a total of 95 candidate genes were identified.

Conclusions: The genetic loci and candidate genes of maize kernel colour provided in this study will be useful for uncovering the genetic mechanism of maize kernel colour, gene cloning in the future. Furthermore, the identified elite alleles can be used to molecular marker-assisted selection of kernel colour traits.

Keywords: Candidate gene; Elite allele; Genome-wide association study; Kernel colour; Maize.

Fig. 1

The correlations analysis among the 6 kernel colour traits in two environments. _1: wengyuan experimental station (2020); _2: guangzhou experimental station (2021); The number in the rectangle is the correlation coefficient, Purple indicates negative correlation, Cyan indicates positive correlation, the darker the color the higher the correlation

Fig. 2

Manhattan-plots for GWAS of 6 kernel colour traits in maize.Two GWAS models for the control of false positive (Q-Q plots). The manhattan plots of two models include MLM_PCA + K (left) and MLM_Q + K (right); E1: wengyuan experimental station (2020); E2: guangzhou experimental station (2021)

Fig. 3

Number of significant QTLs and stable QTLs for the concentration of the 6 kernel colour traits in two environments and GWAS models. A, B, C, D, E and F is R, B, RGB, level, G and Gray, respectively. E1: wengyuan experimental station (2020); E2: guangzhou experimental station (2021). Horizontal bars show the number of QTLs for different environments and methods. The colours of circles corresponding to Horizontal bars indicate the environment in which QTLs was detected and the method applied

Fig. 4

The superior and alternative alleles. _1: wengyuan experimental station (2020); _2: guangzhou experimental station (2021). P < 0.05: differences, P < 0.01: significant differences, P < 0.001: highly significant differences

Fig. 5

Location diagram of extracting colour values near the maize kernel style. The red circle is where the colour is extracted

Fig. 6

Standardized colour scale representative kernels from the association mapping families. The ordinal colour scale ranges from A (lightest) to F (darkest), 6 levels