The present study involved meta-QTL analysis based on 8,998 QTLs, including 2,852 major QTLs for grain yield (GY) and its following ten component/related traits: (i) grain weight (GWei), (ii) grain morphology related traits (GMRTs), (iii) grain number (GN), (iv) spikes related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/ owering and maturity (DTH/F/M) and (x) grain lling duration (GFD). The QTLs used for this study were retrieved from 230 reports involving 190 mapping populations (1999-2020), which also included 19 studies involving durum wheat. As many as 141 meta-QTLs were obtained with an average con dence interval of 1.37 cM (reduced 8.87 fold), the average interval in the original QTL being > 12.15 cM. As many as 63 MQTLs, each based on at least 10 original QTLs were considered to be the most stable and robust with thirteen identi ed as breeder's meta-QTL. Meta-QTLs (MQTLs) were also utilized for identi cation of as many as 1,202 candidate genes (CGs), which also included 18 known genes.Based on a comparative genomics strategy, a total of 50 wheat homologues of 35 rice, barley and maize yield-related genes were also detected in these MQTL regions. Moreover, taking the advantage of synteny, a total of 24 ortho-MQTLs were detected at co-linear regions between wheat with barley, rice and maize. The present study is the most comprehensive till date, and rst of its kind in providing stable and robust MQTLs and ortho-MQTLs, thus providing useful information for future basic studies and for markerassisted breeding for yield and its component traits in wheat.

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In wheat, meta-QTLs (MQTLs), and candidate genes (CGs) were identi ed for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the ve diseases. As many as 493 QTLs were available from these studies, which were distributed in ve diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103; fusarium head blight (FHB), 184; karnal bunt (KB), 66, and loose smut (LS), 14. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of initial QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were veri ed using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identi ed which were further investigated for differential expression using data from ve transcriptome studies, resulting in 194 differentially expressed genes (DEGs). Among the DEGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for ne mapping of MDR genes and marker-assisted breeding.

In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.

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