Consensus genomic regions associated with grain protein content in hexaploid and tetraploid wheat

Abstract

A meta-analysis of QTLs associated with grain protein content (GPC) was conducted in hexaploid and tetraploid wheat to identify robust and stable meta-QTLs (MQTLs). For this purpose, as many as 459 GPC-related QTLs retrieved from 48 linkage-based QTL mapping studies were projected onto the newly developed wheat consensus map. The analysis resulted in the prediction of 57 MQTLs and 7 QTL hotspots located on all wheat chromosomes (except chromosomes 1D and 4D) and the average confidence interval reduced 2.71-fold in the MQTLs and QTL hotspots compared to the initial QTLs. The physical regions occupied by the MQTLs ranged from 140 bp to 224.02 Mb with an average of 15.2 Mb, whereas the physical regions occupied by QTL hotspots ranged from 1.81 Mb to 36.03 Mb with a mean of 8.82 Mb. Nineteen MQTLs and two QTL hotspots were also found to be co-localized with 45 significant SNPs identified in 16 previously published genome-wide association studies in wheat. Candidate gene (CG) investigation within some selected MQTLs led to the identification of 705 gene models which also included 96 high-confidence CGs showing significant expressions in different grain-related tissues and having probable roles in GPC regulation. These significantly expressed CGs mainly involved the genes/gene families encoding for the following proteins: aminotransferases, early nodulin 93, glutamine synthetases, invertase/pectin methylesterase inhibitors, protein BIG GRAIN 1-like, cytochrome P450, glycosyl transferases, hexokinases, small GTPases, UDP-glucuronosyl/UDP-glucosyltransferases, and EamA, SANT/Myb, GNAT, thioredoxin, phytocyanin, and homeobox domains containing proteins. Further, eight genes including GPC-B1, Glu-B1-1b, Glu-1By9, TaBiP1, GSr, TaNAC019-A, TaNAC019-D, and bZIP-TF SPA already known to be associated with GPC were also detected within some of the MQTL regions confirming the efficacy of MQTLs predicted during the current study.

Nutritional Genomic Approach for Improving Grain Protein Content in Wheat

“…We also identified six putative genes encoding F-box proteins on chromosomes 3A (5) and 3B (1) ( Table 2 and Table S5 ). This is in accordance with the reported high-confidence candidate gene ( TraesCS1B02G046800 ) associated with GPC in wheat governing a F-box-like domain superfamily protein on chromosome 1B [ 52 ]. The F-box domain-containing proteins are key players in the ubiquitin-mediated protein turnover.…”

“…In addition, both are distantly located from the multi-environmentally verified SNPs mapped to the 3A chromosome region between 191,530,793 and 484,643,825 bp [ 47 ]. Moreover, the location comparisons of the stable QTNs identified in our study with those of the three meta-QTLs on chromosome 3A and the two GWAS-validated meta-QTLs on chromosome 3B [ 52 ] also imply that these are different loci. This suggests that chromosomes 3A and 3B harbor one or more genomic regions associated with GPC that had not been reported yet.…”

“…A recent meta-analysis of GPC-associated QTLs in hexaploid and tetraploid wheat conducted on 48 linkage-based mapping studies identified stable QTLs (meta-QTLs) and hotspots on all wheat chromosomes, except for 1D and 4D, of which 19 meta-QTLs and 2 hotspots on 14 chromosomes were shown to be co-localized with significant SNP-based MTAs retrieved from association mapping studies [ 52 ].…”

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight

“…Significant verified SNPs or genomic regions linked to GPC and TKW have been detected on all wheat chromosomes, according to recent research [ 14 , 35 , 56 , 57 ]. We compared the strongly associated SNPs found in this study to previously published loci or markers using the IWGSC RefSeq v.1.0 map as a reference.…”

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

“…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].…”