Meta-QTL Analysis in Rice and Cross-Genome Talk of the Genomic Regions Controlling Nitrogen Use Efficiency in Cereal Crops Revealing Phylogenetic Relationship

Abstract

The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

“…Several meta-QTL analyses for various traits in cereal crops such as maize 39 , rice 41,46,20 , and barley have already been conducted 35 . In wheat, meta-QTL studies have been undertaken for four different abiotic stresses, including PHS 54 , SS 30 , DS 32 , and HS 33 , but no meta-analysis has been conducted for the traits contributing to WS and AS stresses.…”

“…The current study additionally investigated chromosomal regions with MQTLs that are conserved across several cereals. These ortho-MQTL regions were identified by integrating the two different approaches utilized by Saini et al 26 and Sandhu et al 20 , respectively. Ortho-MQTLs have recently been conducted in wheat for different traits including quality traits 28 , thermotolerance 33 , salinity stress tolerance 30 , nitrogen use efficiency 24 , grain yield and component traits 22 .…”

“…To see if there is any co-localization of QTLs, all of the QTLs reported for the stresses should be presented in an integrated manner and mapped out. Meta-QTL analysis can efficiently integrate information from a multitude of QTL mapping studies, allowing for a high level of statistical power over a large amount of data 18,19,20 .…”

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

“…Meta-analysis has been performed for a number of traits in a wide range of species, including wheat, rice, maize, sorghum, barley, tomato, etc. (Zhang et al 2017;Ayenan et al 2018;Kaur et al 2021;Sandhu et al 2021;Aquib and Na s 2022;Saini et al 2022c). However, there are no studies available on meta-analysis of QTLs in pigeonpea.…”

“…Meta analysis can be performed using either Meta-QTL or BioMercator software by formulating and inserting particular sets of algorithms for accurate estimation and recalculation of the genetic position (in cM) for a speci ed set of QTLs (Wang et al 2014). The MQTL analysis has already been conducted in different crops including wheat (Kumar et al 2021;Pal et al 2021Pal et al , 2022Saini et al 2021Saini et al , 2022aGudi et al 2022), rice (Sandhu et al 2021), maize (Kaur et al 2021), sorghum (Aquib and Na s 2022), barley (Zhang et al 2017), and tomato (Ayenan et al 2018). Furthermore, MQTL analysis has also been conducted in some legume crops for different traits such Fe and Zn content and resistance to white mold in french bean (Vasconcellos et al 2017;Izquierdo et al 2018), yield-related traits, seed protein content and partial resistance to Aphanomyces root rot in pea (Hamon et al 2013;Klein et al 2020), leaf spot resistance, plant height, seed number, and seed yield in peanut (Lu et al 2018;Lv et al 2018;Chen et al 2019), and major quality traits in soybean (Chen et al 2021).…”