A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Abstract

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice

“…The CI of 18 MQTL identified in this study was 3.44 times lower than that of the initial QTL studies indicating a possible close linkage of associated markers to the MQTL, thus enabling flexibility of introgressing MQTL in marker-assisted breeding. This finding is consistent with our previous report [ 39 ] and other studies in rice and wheat [ 30 , 57 , 58 ]. However, for individual MQTL, we found that MQTL3.2 had a large confidence interval (40.7 cM) although did not exceed the CI of the initial QTL (Supplementary Table 2 ).…”

“…In the present study, we conducted a meta-analysis of 120 previously reported QTL underlying submergence-related traits from 16 independent studies and discovered that these 120 QTL were unevenly spread across the rice chromosomes, with a large number of QTL located on chromosomes 7, 1, and 5 and the least QTL on chromosomes 4, 8, and 11 (Fig. 1 ) which agreed with our previous study [ 39 ] and studies conducted by Wu et al [ 56 ] and Khahani et al [ 37 ]. One of the interesting findings of this study is that the PVE by most of the initial QTL was low indicating that minor effect loci are at play in influencing rice submergence tolerance.…”

“…A high-density consensus map was constructed using the R package LPmerge employed in our previously developed reference map [ 39 ] and one constructed by Prakash et al [ 40 ]. Also, markers linked to individual QTL were included in the construction of the consensus map to ensure high number of initial QTL is projected.…”

Identification of Salt-Sensitive and Salt-Tolerant Genes through Weighted Gene Co-Expression Networks across Multiple Datasets: A Centralization and Differential Correlation Analysis

“…Moreover, the auxin response family members were found to be regulated by other transcription factors, such as the MYB transcription factor, the bHLH transcription factor, and the heat shock transcription factor (HSF) [ 59 , 60 , 61 ]. OsIAA3 is located in MQTL1.1, which refers to the traits of spikelet fertility, grain number per panicle and 1000-grain weight [ 62 ]. It was reported to regulate root growth and grain size in rice [ 63 , 64 , 65 , 66 ].…”

New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

“…Increasing yield potential has been a long-term objective in rice breeding and is imperative to overcome the global food crisis [ 4 – 9 ]. Rice yield is the most targeted complex trait that is a direct function of multiple factors including number and size of grains, productive tillers per plant, plant count per unit area, size of panicles and plant height [ 2 , 7 , 10 – 12 ] thus, the aforesaid component traits are prerequisite for achieving the desired yield in rice. This warrants the dissection of yield-related quantitative traits, discovery of the novel genetic factors and elucidate their molecular basis to meet the increased global rice demand.…”