Meta-QTL analysis explores the key genes, especially hormone related genes, involved in the regulation of grain water content and grain dehydration rate in maize

Abstract

Background Low grain water content (GWC) at harvest of maize (Zea mays L.) is essential for mechanical harvesting, transportation and storage. Grain drying rate (GDR) is a key determinant of GWC. Many quantitative trait locus (QTLs) related to GDR and GWC have been reported, however, the confidence interval (CI) of these QTLs are too large and few QTLs has been fine-mapped or even been cloned. Meta-QTL (MQTL) analysis is an effective method to integrate QTLs information in independent populations, which helps to understand the genetic structure of quantitative traits. Results In this study, MQTL analysis was performed using 282 QTLs from 25 experiments related GDR and GWC. Totally, 11 and 34 MQTLs were found to be associated with GDR and GWC, respectively. The average CI of GDR and GWC MQTLs was 24.44 and 22.13 cM which reduced the 57 and 65% compared to the average QTL interval for initial GDR and GWC QTL, respectively. Finally, 1494 and 5011 candidate genes related to GDR and GWC were identified in MQTL intervals, respectively. Among these genes, there are 48 genes related to hormone metabolism. Conclusions Our studies combined traditional QTL analyses, genome-wide association study and RNA-seq to analysis major locus for regulating GWC in maize.

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

“…Taking into account the physical positions of markers, MQTLs were found to be scattered throughout almost the entire span of the chromosomes, consistent with the physical positions of MQTLs identified in previous meta-analysis studies. Differences in the physical positions of MQTLs reported in earlier studies [ 33 , 70 ] and the present study may be attributable to the usage of different genome assemblies for extracting the physical positions of MQTLs. The current study implemented the latest reference genome assembly ( Zm -B73-REFERENCE-NAM-5.0), whereas earlier meta-analysis studies employed the earlier reference genome assembly (Maize_B73_RefGen_v4) [ 33 , 71 ].…”

Genome-wide association studies of Striga resistance in extra-early maturing quality protein maize inbred lines

“…Three maize genes (GRMZM5G813206, GRMZM2G167220, and GRMZM2G467069) that could play important roles on lateral root and crown root development of maize were also identified. Wang et al (2022) carried out MQTL analysis using 282 QTLs from 25 experiments and identified 11 and 34 MQTLs associated with grain dry matter and low grain water content, respectively. Sheoran et al (2022) using a total of 542 QTLs, detected 32 mfeta-QTL possessing 1,907 candidate genes for different abiotic (drought, water logging, heat, and cold) stresses.…”

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

“…Meta-analyses of QTLs associated with a variety of traits have been recently conducted in different crops such as wheat ( Kumar et al, 2021 ; Kumar et al, 2022 A. C. ; Kumar et al, 2023 S. ; Saini et al, 2021 ; Saini et al, 2022 ; Tanin et al, 2022 ), rice ( Sandhu et al, 2021 ; Kumari et al, 2023 ), barley ( Akbari et al, 2022 ), common bean ( Shafi et al, 2022 ), pigeon pea ( Halladakeri et al, 2023 ), including maize ( Kaur et al, 2021 ; Sheoran et al, 2022 ; Wang et al, 2022 ; Gupta et al, 2023 ; Karnatam et al, 2023 ), for diverse traits, including both yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) and quality traits ( Jin et al, 2013 ; Dong et al, 2015 ). However, there is currently no comprehensive study on the genomic regions influencing both grain quality and yield in maize.…”