Genome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice

Abstract

Background Improving yield and yield-related traits is the crucial goal in breeding programmes of cereals. Meta-QTL (MQTL) analysis discovers the most stable QTLs regardless of populations genetic background and field trial conditions and effectively narrows down the confidence interval (CI) for identification of candidate genes (CG) and markers development. Results A comprehensive MQTL analysis was implemented on 1052 QTLs reported for yield (YLD), grain weight (GW), heading date (HD), plant height (PH) and tiller number (TN) in 122 rice populations evaluated under normal condition from 1996 to 2019. Consequently, these QTLs were confined into 114 MQTLs and the average CI was reduced up to 3.5 folds in compare to the mean CI of the original QTLs with an average of 4.85 cM CI in the resulted MQTLs. Among them, 27 MQTLs with at least five initial QTLs from independent studies were considered as the most stable QTLs over different field trials and genetic backgrounds. Furthermore, several known and novel CGs were detected in the high confident MQTLs intervals. The genomic distribution of MQTLs indicated the highest density at subtelomeric chromosomal regions. Using the advantage of synteny and comparative genomics analysis, 11 and 15 ortho-MQTLs were identified at co-linear regions between rice with barley and maize, respectively. In addition, comparing resulted MQTLs with GWAS studies led to identification of eighteen common significant chromosomal regions controlling the evaluated traits. Conclusion This comprehensive analysis defines a genome wide landscape on the most stable loci associated with reliable genetic markers and CGs for yield and yield-related traits in rice. Our findings showed that some of these information are transferable to other cereals that lead to improvement of their breeding programs.

Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions

“…1 B). The distribution of QTLs on different rice chromosomes with the highest number of QTLs on chromosomes 1 and 3 was similar to previous reports 14 , 19 , 20 . The number of QTLs on each chromosome exhibited a positive correlation (r = 0.73) with the length of chromosome.…”

“…1 B,C) supported by at least two QTLs deduced from different populations and considerably reduced the respective confidence intervals (CI) in comparison to the initial QTLs (Table 3 ). Therefore, MQTL analysis can efficiently confine the number of QTLs and narrow down the genomic regions controlling different traits 19 .…”

“…Several MQTL studies for drought stress have been conducted in cereals such as wheat 16 , maize 8 , 17 , and barley 5 , 18 . While a recent rice MQTL study considered various traits under unstressed conditions 19 , relatively few reports address water deficit conditions in rice: MQTL studies by Swamy et al and Trijatmiko et al focused on yield integrating data from 15 and 13 experiments 20 , 21 , respectively, and Khowaja et al and Yang et al reported some MQTLs for plant height and heading date based on QTLs published until 2009 and 2011, respectively 22 , 23 .…”

Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study

“…The first irrigation regime was a hundred percent field capacity until harvest, and the second had no irrigation after anthesis. The two locations were the research farm of Shahed university, located at Shahr-e-Rey, 15 www.nature.com/scientificreports/ of -15° C to a maximum of 41 °C, and the climate was characterized by a mean annual precipitation of 224 mm (the highest of which is 49% in winter and 21% in spring). Whereas in the second research field, the minimum and maximum temperatures were − 20 °C and 42°C, respectively, with annual precipitation of 247.3 mm (the highest of which is 43% and 36% in winter and spring, respectively).…”

“…This method has been used to determine consensus regions of the genome across multiple QTL studies for their effects and consistency across different genetic backgrounds and environments, also to refine and confirm QTL positions on a consensus map via mathematical models. Reducing the CI of the MQTLs in comparison with QTLs is another indispensable perks of MQTL analysis 11 – 15 . More recently, a number of studies have applied the QTL meta-analysis method to different traits in wheat, including root morphological traits 13 , 16 , grain traits 11 , fusarium head blight resistance 14 , 17 , adaptation to drought and heat stress 18 and leaf rust resistance 19 .…”