Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits

Abstract

Background Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.

Integration of GWAS, linkage analysis and transcriptome analysis to reveal the genetic basis of flowering time-related traits in maize

“…Therefore, maize flowering time is an important characteristic that determines local environmental adaptation and is easily affected by the local environment. Similar to the findings of a previous study (Shi et al, 2022), the interaction effect of genotype and the environment on maize flowering time-related traits was significant in this study. Revealing the potential genetic basis of flowering time will aid in the selection of stable varieties in different local environments and will improve maize yields.…”

“…For example, ZmWRKY14 , which is a regulator of maize leaf number, flowering time and biomass yield, has been identified based on GWAS and linkage analysis ( Li et al., 2021 ). By using the same strategy, seventeen candidate genes significantly associated with maize flowering time and leaf number have been found ( Shi et al., 2022 ). In this study, 25 important candidate genes were found by integrating the results of GWASs, QTL mapping, and transcriptome analysis.…”

“…The GWAS approach has been shown to be an effective strategy for mining genetic loci for flowering time in maize (Buckler et al, 2009;Li et al, 2016;Shi et al, 2022). For example, a total of 18 SNPs and 19 candidate genes involved in maize flowering time were found in an association panel that consisted of 252 inbred lines (Vanous et al, 2018), andLi et al (2016) identified nearly 1000 SNPs and 220 candidate genes using an extremely large association panel.…”

“…Since it was first domesticated 9000 years ago, its planting area has expanded ( Matsuoka et al., 2002 ), and this spread to various parts of the globe has resulted in rich phenotypic and genetic variation ( Li et al., 2016 ). Among the phenotypic variations in maize, the variation in flowering time is an important factor in determining adaptation to local environments and is also a key selection standard for maize breeding and germplasm innovation ( Shi et al., 2022 ).…”

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach

“…Additionally, 9 SNPs (82%) associated with ASI were found on chromosomes 1, 3, and 4; for FF trait, 70% (7/10) of SNPs were found on chromosomes 2, 7, and 8; and for FM trait, 4 SNPs (44%) were distributed on chromosomes 1 and 7 ( Supplementary Table S3 ). Previous studies have also identified significant SNPs associated with flowering traits in maize on similar chromosomes ( Li et al., 2016 ; Liu et al., 2019 ; Maldonado et al., 2019 ; Shi et al., 2022 ), suggesting that these regions may contain genes that play a critical role in controlling flowering time variation in maize. The phenotypic variation explained by significant SNPs in this study ranged from 6.7 to 9.6% and was evenly distributed among traits, indicating that many significant SNPs of small effects contribute to genetic variation in flowering time in maize ( Maldonado et al., 2019 ).…”