QTL consistency for agronomic traits across three generations and potential applications in popcorn

Abstract

Favorable agronomic traits are important to improve productivity of popcorn. In this study, a recombinant inbred line (RIL) population consisting of 258 lines was evaluated to identify quantitative trait loci (QTLs) for nine agronomic traits (plant height, ear height, top height (plant height subtracted ear height), top height/plant height, number of leaves above the top ear, leaf area, stalk diameter, number of tassel branches and the length of tassel) under three environments. Meta-analysis was conducted then to integrate QTLs identified across three generations (RIL, F 2:3 and BC 2 F 2) developed from the same crosses. In total, 179 QTLs and 36 meta-QTLs (mQTL) were identified. The percentage of phenotypic variation (R 2) explained by any single QTL varied from 3.86% to 28.4%, and 24 QTLs with contributions over 15%. Nine common QTLs located in the same or similar chromosome regions were detected across three generations. Five meta-QTLs were identified including QTLs in three independent studies. Seven important mQTLs were composed of 11-26 QTLs for 4-7 traits, respectively. Only 11 mQTLs were commonly identified in the same or similar chromosome regions across agronomic traits, popping characteristics (popping fold, popping volume and popping rate) and grain yield components (ear weight per plant, grain weight per plant, 100 grain weight, ear length, kernel number per row, ear diameter, row number per ear and kernel ratio) by meta-QTL analysis. In conclusion, we identified a list of QTLs, some of which with much higher contributions to agronomic traits should be valuable for further study in improving both popping characteristics and grain yield components in popcorn.

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

“…These QTLs confirm the importance of leaf architecture traits in breeding for drought tolerance. In agreement with previous studies, seven of the 34 sQTLs under water‐stressed conditions were of particular note: sQTL1 (bin 1.07; umc1356‐umc1278) for LA, sQTL6 (bin 6.01_6.02; umc2311‐umc1083) for LA, sQTL8 (bin 7.04; bnlg1666‐phi328175) for LA, sQTL12 (bin 2.05_2.06; umc1028‐bnlg1225) for LOV, sQTL16 (bin 9.04_9.06; umc1120‐umc2134) for LOV, sQTL19 (bin 7.01_7.02; mmc0162‐bnlg2160a) for LL and sQTL23 (bin 3.08; umc1320‐umc2174) for LW (Chen et al., ; Dong et al., ; Ku et al., , ; Liu, Jiang, Wang, & Wang, ; Liu et al., ; Lu et al., ). Thus, these intervals may have stable alleles involved in leaf formation and development under drought environments.…”

“…The source of genetic variation in leaf architecture has been explored using QTL mapping in biparental maize mapping populations by many studies, with leaf architecture traits including LA, LOV, LL, LW, LS and LSV (Table ). However, a limited number of QTLs for leaf architecture traits have been identified under water‐stressed conditions (Dong et al., ; Nikolic, Andjelkovic, Dodig, & Ignjatovic‐Micicb, ; Pelleschi, Leonardi, Rocher, & Cornic, ). Despite extensive research, the molecular mechanism for leaf architecture remains poorly understood, calling for in‐depth investigations on the genetic mechanisms controlling leaf architecture in contrasting watering conditions.…”

Comparative QTL analysis for yield components and morphological traits in maize (<i>Zea mays</i> L.) under water-stressed and well-watered conditions

“…Multiple QTLs for PH/PH/KR were identified in bin 1.07 (umc2151-bnlg1556) (Tang et al 2007)/bin 1.07 (umc23-umc58) (Lubberstedt et al 1997, Melchinger et al 1998)/bin 1.07 (umc1335-umc2236) . Using meta-QTL (mQTL) analysis, Dong et al (2015) reported one mQTL for EH and top height-to plant height ratio in bin 1.08-1.10 (bnlg1643-umc2189). Li et al (2011) mapped to one mQTL for EL and kernel number per row in bin 1.07-1.08 (bnlg1556-phi039), and mapped one QTL for ear diameter in bin 1.08-1.09 (bnlg1643-umc2047).…”

“…Under well-watered conditions, except for KR, other nine traits of the common male parent (TS141) were larger than the female parents (Langhuang and Chang7-2) (Supplemental Table 1). This finding shows that these phenotypic traits were highly significantly different between the male the node bearing the highest ear, cm), which were then used in the calculation for EHPH with the following equation (Dong et al 2015):…”

“…Therefore, higher GW and KR can be achieved by maintaining a suitable EHPH under drought stress. However, a limited number of QTLs for EHPH, GW, and KR have been identified under different levels of water availability (Chang et al 2017, Dong et al 2015, Ku et al 2015, and the genetic mechanisms underlying these three traits remains poorly understood, thus warranting in-depth investigations. Furthermore, a better understanding of the genotype-by-environment (G × E) interaction will provide a foundation for the genetic improvement and optimization of genotypes across different environments (EI-Soda et al 2014).…”