Genomic regions responsible for accumulation of grain iron concentration (Fe), grain zinc concentration (Zn), grain protein content (PC) and thousand kernel weight (TKW) were investigated in 286 recombinant inbred lines (RILs) derived from a cross between an old Indian wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops squarrosa [409]//BCN). RILs were grown in six environments and evaluated for Fe, Zn, PC, and TKW. The population showed the continuous distribution for all the four traits, that for pooled Fe and PC was near normal, whereas, for pooled Zn, RILs exhibited positively skewed distribution. A genetic map spanning 2155.3cM was constructed using microsatellite markers covering the 21 chromosomes and used for QTL analysis. 16 quantitative trait loci (QTL) were identified in this study. Four QTLs (QGFe.iari-2A, QGFe.iari-5A, QGFe.iari-7A and QGFe.iari-7B) for Fe, five QTLs (QGZn.iari-2A, QGZn.iari-4A, QGZn.iari-5A, QGZn.iari-7A and QGZn.iari-7B) for Zn, two QTLs (QGpc.iari-2A and QGpc.iari-3A) for PC, and five QTLs (QTkw.iari-1A, QTkw.iari-2A, QTkw.iari-2B, QTkw.iari-5B and QTkw.iari-7A) for TKW were identified. The QTLs together explained 20.0%, 32.0%, 24.1% and 32.3% phenotypic variation, respectively, for Fe, Zn, PC and TKW. QGpc.iari-2A was consistently expressed in all the six environments, whereas, QGFe.iari-7B and QGZn.iari-2A were identified in two environments each apart from pooled mean. QTkw.iari-2A and QTkw.iari-7A, respectively, were identified in four and three environments apart from pooled mean. A common region in the interval of Xgwm359-Xwmc407 on chromosome 2A was associated with Fe, Zn, and PC. One more QTL for TKW was identified on chromosome 2A but in a different chromosomal region (Xgwm382-Xgwm359). Two more regions on 5A (Xgwm126-Xgwm595) and 7A (Xbarc49-Xwmc525) were found to be associated with both Fe and Zn. A QTL for TKW was identified (Xwmc525-Xbarc222) in a different chromosomal region on the same chromosome (7A). This reflects at least a partly common genetic basis for the four traits. It is concluded that fine mapping of the regions of the three chromosomes of A genome involved in determining the accumulation of Fe, Zn, PC, and TKW in this mapping population may be rewarding.
Micronutrient and protein malnutrition is recognized among the major global health issues. Genetic biofortification is a cost-effective and sustainable strategy to tackle malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC), and thousand kernel weight (TKW) were investigated in a set of 163 recombinant inbred lines (RILs) derived from a cross between cultivated wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops tauschii [409]//BCN). The RIL population was genotyped using 100 simple-sequence repeat (SSR) and 736 single nucleotide polymorphism (SNP) markers and phenotyped in six environments. The constructed genetic map had a total genetic length of 7,057 cM. A total of 21 novel quantitative trait loci (QTL) were identified in 13 chromosomes representing all three genomes of wheat. The trait-wise highest number of QTL was identified for GPC (10 QTL), followed by GZnC (six QTL), GFeC (three QTL), and TKW (two QTL). Four novel stable QTL (QGFe.iari-7D.1, QGFe.iari-7D.2, QGPC.iari-7D.2, and QTkw.iari-7D) were identified in two or more environments. Two novel pleiotropic genomic regions falling between Xgwm350–AX-94958668 and Xwmc550–Xgwm350 in chromosome 7D harboring co-localized QTL governing two or more traits were also identified. The identified novel QTL, particularly stable and co-localized QTL, will be validated to estimate their effects on different genetic backgrounds for subsequent use in marker-assisted selection (MAS). Best QTL combinations were identified by the estimation of additive effects of the stable QTL for GFeC, GZnC, and GPC. A total of 11 RILs (eight for GZnC and three for GPC) having favorable QTL combinations identified in this study can be used as potential donors to develop bread wheat varieties with enhanced micronutrients and protein.
Grain softness has been a major trait of interest in wheat because of its role in producing flour suitable for making highquality biscuits, cookies, cakes and some other products. In the present study, marker-assisted backcross breeding scheme was deployed to develop advanced wheat lines with soft grains. The Australian soft-grained variety Barham was used as the donor parent to transfer the puroindoline grain softness gene Pina-D1a to the Indian variety, DBW14, which is hard grained and has PinaD1bPinbD1a genes. Foreground selection with allele-specific PCR-based primer for Pina-D1a (positive selection) was used to identify heterozygous BC 1 F 1 plants. Background selection with 173 polymorphic SSR primers covering all the 21 chromosomes was also carried out, in the foreground-selected BC 1 F 1 plants. BC 1 F 2 plants were selected by ascertaining the presence of Pina-D1a (positive selection) and absence of Pina-D1b (negative selection). Using the approach of positive, negative and background selection with molecular markers, 15 BC 1 F 2 and 31 BC 2 F 1 plants were finally selected. The 15 BC 1 F 2 plants were selfed and the 31 BC 2 F 1 plants were further backcrossed and selfed to raise BC 3 F 1 and BC 2 F 2 progenies, respectively. A part of the BC 2 F 2 seed of each of the 31 plants was analyzed for grain hardness index (GHI) with single-kernel characterization system. The GHI varied from 12.1 to 37.1 in the seeds borne on the 31 BC 2 F 1 plants. The reasons for this variation and further course of action are discussed.
A set of 286 recombinant inbred lines (RILs) along with the parents and a popular wheat variety in India were grown for two consecutive years at three locations belonging to the two major wheat growing zones of India and evaluated for four grain quality traits. Rare recombinants with high trait value appeared for protein content (PC), thousand-kernel weight (TKW), sedimentation value (SV), and kernel hardness (KH). The magnitude of environmental effects was more pronounced than genotypic effects and genotype-environment interaction (GEI). The cumulative contribution of environment and GEI components to the total variance was highest in the expression of PC followed by TKW, SV, and KH. The top five percent (14 RILs) of genotypes with high trait value were subjected to Eberhart and Russell (1966) (ER), genotype and genotype-environment (GGE) and additive main effects and multiplicative interaction (AMMI) stability models. Five RILs were identified as stable in all the three stability models. RIL61 with 38.8%, RIL101 with 8.9%, RIL226 with 26.1% superiority over check variety were the most stable genotypes in all the three stability models for PC, TKW and KH, respectively. RIL113 was found to be stable genotype in ER and GGE models, whereas, RIL231 was the most stable genotype in AMMI and GGE models in the expression of SV. These common stable genotypes with high trait value identified through ER, AMMI and GGE models could be potential donors in active breeding programs to develop high yielding wheat varieties with improved PC, TKW, SV and KH.
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