Dataset Information

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize.

ABSTRACT: Maize yield components including row number, kernel number per row, kernel thickness, kernel width, kernel length, 100-kernel weight, and volume weight affect grain yield directly. Previous studies mainly focused on dissecting the genetic basis of per se performances for yield-related traits, but the genetic basis of general combining ability (GCA) for these traits is still unclear. In the present study, 328 RILs were crossed as males to two testers according to the NCII mating design, resulting in a hybrid panel composed of 656 hybrids. Both the hybrids and parental lines were evaluated in four environments in 2015 and 2016. Correlation analysis showed the performances of GCA effects were significantly correlated to the per se performances of RILs for all yield-related traits (0.17 ? r ? 0.64, P > 0.01). Only 17 of 95 QTL could be detected for both per se performances of RILs and GCA effects for eight yield-related traits. The QTL qKN7-1 and qHKW1-3, which could explain more than 10% of the variation in the GCA effects of KN and HKW, were also detected for per se performances for the traits. The pleiotropic loci qRN3-1 and qRN6, which together explained 14.92% of the observed variation in GCA effects for RN, were associated with the GCA effects of KW and HKW, but not with per se performances for these traits. In contrast, Incw1, which was related to seed weight in maize, was mapped to the region surrounding MK2567 at the qHKW5-2 locus, but no GCA effect was detected. The QTL identified in present study for per se performances and corresponding GCA effects for yield-related traits might be useful for maize hybrid breeding.

SUBMITTER: Lu X

PROVIDER: S-EPMC7387702 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize.

Lu Xin X Zhou Zhiqiang Z Yuan Zhaohui Z Zhang Chaoshu C Hao Zhuanfang Z Wang Zhenhua Z Li Mingshun M Zhang Degui D Yong Hongjun H Han Jienan J Li Xinhai X Weng Jianfeng J

Frontiers in plant science 20200722

Maize yield components including row number, kernel number per row, kernel thickness, kernel width, kernel length, 100-kernel weight, and volume weight affect grain yield directly. Previous studies mainly focused on dissecting the genetic basis of <i>per se</i> performances for yield-related traits, but the genetic basis of general combining ability (GCA) for these traits is still unclear. In the present study, 328 RILs were crossed as males to two testers according to the NCII mating design, re ...[more]

PMID: 32793248

Similar Datasets

Project description:Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

Project description:BackgroundUtilization of heterosis in maize could be critical in maize breeding for boosting grain yield. However, the genetic architecture of heterosis is not fully understood. To dissect the genetic basis of yield-related traits and heterosis in maize, 301 recombinant inbred lines derived from 08 to 641 × YE478 and 298 hybrids from the immortalized F2 (IF2) population were used to map quantitative trait loci (QTLs) for nine yield-related traits and mid-parent heterosis.ResultsWe observed 156 QTLs, 28 pairs of loci with epistatic interaction, and 10 significant QTL × environment interactions in the inbred and hybrid mapping populations. The high heterosis in F1 and IF2 populations for kernel weight per ear (KWPE), ear weight per ear (EWPE), and kernel number per row (KNPR) matched the high percentages of QTLs (over 50%) for those traits exhibiting overdominance, whereas a notable predominance of loci with dominance effects (more than 70%) was observed for traits that show low heterosis such as cob weight per ear (CWPE), rate of kernel production (RKP), ear length (EL), ear diameter (ED), cob diameter, and row number (RN). The environmentally stable QTL qRKP3-2 was identified across two mapping populations, while qKWPE9, affecting the trait mean and the mid-parent heterosis (MPH) level, explained over 18% of phenotypic variations. Nine QTLs, qEWPE9-1, qEWPE10-1, qCWPE6, qEL8, qED2-2, qRN10-1, qKWPE9, qKWPE10-1, and qRKP4-3, accounted for over 10% of phenotypic variation. In addition, QTL mapping identified 95 QTLs that were gathered together and integrated into 33 QTL clusters on 10 chromosomes.ConclusionsThe results revealed that (1) the inheritance of yield-related traits and MPH in the heterotic pattern improved Reid (PA) × Tem-tropic I (PB) is trait-dependent; (2) a large proportion of loci showed dominance effects, whereas overdominance also contributed to MPH for KNPR, EWPE, and KWPE; (3) marker-assisted selection for markers at genomic regions 1.09-1.11, 2.04, 3.08-3.09, and 10.04-10.05 contributed to hybrid performance per se and heterosis and were repeatedly reported in previous studies using different heterotic patterns is recommended.

Dataset Information

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize.

Publications

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets