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

Abstract

A meta‐analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto “PigeonPea_ConsensusMap_2022,” saturated with 10,522 markers, which resulted in the prediction of 34 meta‐QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37 times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high‐confidence MQTLs with CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker‐trait associations or significant SNPs identified in previous genome‐wide association studies. Furthermore, synteny and ortho‐MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL‐assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

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.…”

“…The validity of QTL mapping findings is influenced by various factors, such as the specific experimental conditions, size and composition of the mapping population, density of genetic markers employed, statistical methodologies utilized, and the presence of genotype-environment and epistatic interactions ( Goffinet and Gerber, 2000 ; Mackay, 2001 ; Arcade et al, 2004 ; Halladakeri et al, 2023 ). Additionally, it is common for QTLs to span relatively large genetic intervals, which can pose challenges when transferring desired QTLs through marker-assisted breeding (MAB).…”

“…This approach helps account for genotype-environment interactions, ensuring that the identified QTLs exhibit consistent effects across different genetic backgrounds and environmental conditions. Consensus QTLs that consistently demonstrate significant associations with the target trait across multiple populations and environments are regarded as particularly suitable candidates for integration into MAB programs ( Goffinet and Gerber, 2000 ; Halladakeri et al, 2023 ).…”

“…Meta-QTL analysis represents a conceptual approach that combines QTL discoveries from various studies and refines the positions of QTLs on a consensus map ( Goffinet and Gerber, 2000 ). When numerous QTLs associated with the specific trait are analysed together through meta-analysis, the resulting consensus QTLs are referred to as “meta-QTLs (MQTLs).” This approach can not only identify redundant QTLs and candidate genes (CGs), but also facilitate the selection of promising QTLs for breeding applications involving MAB ( Goffinet and Gerber, 2000 ; Sandhu et al, 2021 ; Saini et al, 2022 ; Sheoran et al, 2022 ; Tanin et al, 2022 ; Kumar A. et al, 2023 ; Halladakeri et al, 2023 ; Karnatam et al, 2023 ).…”

Deciphering the genetic landscape of seedling drought stress tolerance in wheat (Triticum aestivum L.) through genome-wide association studies

“…High-confidence MTAs identified through GWAS serve as potential targets for extracting CGs associated with the trait of interest. Several studies utilized GWAS-identified MTAs to extract the potential CGs associated with: (i) agronomic traits including days to anthesis, days to maturity, tiller number, spike length, spikelet number, grain number per spike, grain weight, and grain yield ( Gill et al., 2022 ; Gudi et al., 2024 ); (ii) physiological traits such as chlorophyll fluorescence, chlorophyll content, vegetation index, gas exchange, and stomatal conductance ( Hamdani et al., 2019 ; Gudi et al., 2023 ); (iii) stress tolerance such as drought, heat, salinity, etc ( Tanin et al., 2022 , Tanin et al., 2023 ; Tian et al., 2023 ); (iv) biochemical compounds such as proline, abscisic acid, and hydrogen peroxides (H 2 O 2 ) ( Verslues et al., 2014 ; Kamruzzaman et al., 2022 ); and (v) quality traits including grain protein content, sedimentation volume, kernel hardiness, solvent retention capacity, Fe content, and Zn content ( Gudi et al., 2022b ; Halladakeri et al., 2023 ). Similarly, in the present study we used 13 high-confidence MTAs explaining >10% phenotypic variance and having the LOD scores >5 to extract 216 CGs models.…”