Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

Abstract

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

Mineral nutrients in plants under changing environments: A road to future food and nutrition security

“…The resulting MTAs and associated genes can assist in further MAB for nutrition enhancement in wheat varieties. Recently, more than 400 QTLs associated with 70 traits (including micronutrient contents, agronomic traits, and grain quality) were identified in wheat (Shariatipour et al, 2021). Li et al (2022) identified a set of 411 GWAS-associated genes in 5 QTLs and 2722 differentially expressed genes in responsive to low N, among which OsNIGT1, LOC_Os05g45020, and LOC_Os06g43090 were three novel strong candidates identified for N-deficiency tolerance or NUE in rice.…”

Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment

“…The candidate genes associated with wheat leaf size within the MQTL regions were identified through the analysis of transcriptome data. Given the close evolutionary relationship between Gramineae species genomes [104], candidate genes with unknown functions in the wheat genome were evaluated in the MQTL regions of our study based on their orthologous genes in the rice genome [48]. For example, eight wheat homologs for rice genes, TraesCS1D02G393900, TraesCS1D02G394000, TraesCS1D02G394100, Traes CS4B02G064000, TraesCS5B02G508800, TraesCS7A02 G059000, TraesCS7B02G484200, and TraesCS7D02G 409700 were predicted in the early study [92], which were overlapped with six MQTLs identified in the present study.…”

“…This map contained 14,548 markers, including SSR, DArT, SNP and other types of markers, with a total length of 4813.72 cM, ranging from 155.6 cM to 350.11 cM in the 21 linkage groups. The map was used as reference map for projection of individual QTLs identified in independent populations [48].…”

“…This method has been used to identify consensus regions by examining QTL data from independent studies for their effect and consistency across different genetic backgrounds and environments, and to refine and confirm QTL positions on a consensus map by using mathematical models [34]. In wheat, MQTL analysis has also been successfully used to identify consensus QTL regions for yield-related traits [35][36][37][38], drought and heat tolerance [39][40][41][42], disease resistance [43][44][45][46], grain quality traits [31,[47][48][49], root-related traits [50][51][52], and so on. Likewise, MQTL has also been widely used for the different quantitative traits in different species such as rice (Oryza sativa L.) [53][54][55], maize (Zea mays L.) [56][57][58], barley (Hordeum vulgare L.) [59], and cotton (Gossypium hirsutum L.) [60].…”

Ensuring Nutritional Security in India through Wheat Biofortification: A Review

“…Meta-QTL analysis (MQTL analysis) is a reliable approach for combining available data on QTLs from different mapping populations that helps to understand the genetic mechanism of quantitative characters. Shariatipour et al [ 89 ] used a meta-analysis in wheat with seven independent segregating populations to find the most durable QTLs for traits such as yield, quality characters, and micronutrient concentrations. They observed that QTLs coding for Zn and concentration were located together (57.1%), implying the possibility of simultaneous improvement of both the nutritional traits.…”