Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

Abstract

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

Genes Impacting Grain Weight and Number in Wheat (Triticum aestivum L. ssp. aestivum)

“…The recommendations for breeding would be to perform such a cross with locally adapted cultivars and utilize a Genome Wide Association analysis on the resulting population to identify which genes are associated with the most improvement to GN or GW in the adapted environment. Genotype by environment interaction plays a large role in determining the bes method by which to pursue a yield enhancement [12][13][14]. There are a wide variety o growing environments around the world, but they can be aggregated into two distinctl different situations.…”

“…There are a wide variety o growing environments around the world, but they can be aggregated into two distinctl different situations. There are irrigated environments, in which the nutrients and wate Genotype by environment interaction plays a large role in determining the best method by which to pursue a yield enhancement [12][13][14]. There are a wide variety of growing environments around the world, but they can be aggregated into two distinctly different situations.…”

“…Many of the genes discussed in this review were shown to influence either the GN or GW through recombinant inbred line (RIL) experiments, only to find that they had no effect in subsequent experiments in different environments. Recent studies have demonstrated this genotype by environment interaction by repeating RIL experiments on a variety of known genes and quantitative trait loci (QTL) that effect the GN or GW, only to find significant results for less than half of the previously identified genes or QTL [12][13][14]. This does not indicate that the genes discussed are not useful to increase yield, but rather that the magnitude of the effect will be different under different environments and in different genetic backgrounds.…”

Genome-Wide Association Study of Kernel Traits Using a 35K SNP Array in Bread Wheat (Triticum aestivum L.)

“…All cultivars were grown in six environments with different locations and years at Yuzhong farm station, Lanzhou, Gansu (35°51′N, 104°07′E; altitude 1900 m) in 2015–2016 (E1) and 2017–2018 (E3), and Tongwei farm station, Dingxi, Gansu (35°11′N, 105°19′E; altitude 1750 m) in 2015–2016 (E2), 2017–2018 (E4), 2018–2019 (E5), and 2019–2020 (E6). The two cultivation sites are characterized by a typical arid inland climate in Northwestern China, where the annual average temperature is about 7.0°C, the annual rainfall is less than 400 mm, with nearly 60% falling from July to September, but the annual evaporation capacity is more than 1,500 mm ( Miao et al, 2022 ). A randomized complete block design was conducted with three replications, where the row length of each plot was 1 m and the row spacing was 20 cm, and 60 seeds were sown in each row.…”

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

“…Furthermore, a meta-analysis of QTLs attempts to clarify if QTLs encompassing different loci from different studies are the same loci or if they represent the same position on a linkage map of species under study. To date, numerous MQTLs for important agronomic traits have been identified in different crops such as wheat ( Acuña-Galindo et al., 2015 ; Darzi-Ramandi et al., 2017 ; Soriano and Alvaro, 2019 ; Liu et al., 2020 ; Saini et al, 2021 ; Amo and Soriano, 2022 ; Miao et al., 2022 ; Saini et al., 2022 ; Yang et al., 2021 ), soybean ( Zhao-Ming et al., 2011 ; Hwang et al., 2016 ; Qin et al., 2018 ), maize ( Semagn et al., 2013 ; Wang et al., 2013 ; Martinez et al., 2016 ; Chen et al., 2017 ; Zhao et al., 2018 ; Kaur et al., 2021 ), barley ( Zhang et al., 2017 ; Khahani et al., 2019 ), peanut ( Lu et al., 2018 ), and sorghum ( Aquib and Nafis, 2021 ). However, reports on Meta - QTLs for grain yield in rice under field conditions are limited.…”