BackgroundGrain yield is a key economic driver of successful wheat production. Due to its complex nature, little is known regarding its genetic control. The goal of this study was to identify important quantitative trait loci (QTL) directly and indirectly affecting grain yield using doubled haploid lines derived from a cross between Hanxuan 10 and Lumai 14.Methodology/Principal FindingsTen yield-associated traits, including yield per plant (YP), number of spikes per plant (NSP), number of grains per spike (NGS), one-thousand grain weight (TGW), total number of spikelets per spike (TNSS), number of sterile spikelets per spike (NSSS), proportion of fertile spikelets per spike (PFSS), spike length (SL), density of spikelets per spike (DSS) and plant height (PH), were assessed across 14 (for YP) to 23 (for TGW) year × location × water regime environments in China. Then, the genetic effects were partitioned into additive main effects (a), epistatic main effects (aa) and their environment interaction effects (ae and aae) by using composite interval mapping in a mixed linear model.Conclusions/SignificanceTwelve (YP) to 33 (PH) QTLs were identified on all 21 chromosomes except 6D. QTLs were more frequently observed on chromosomes 1B, 2B, 2D, 5A and 6B, and were concentrated in a few regions on individual chromosomes, exemplified by three striking yield-related QTL clusters on chromosomes 2B, 1B and 4B that explained the correlations between YP and other traits. The additive main-effect QTLs contributed more phenotypic variation than the epistasis and environmental interaction. Consistent with agronomic analyses, a group of progeny derived by selecting TGW and NGS, with higher grain yield, had an increased frequency of QTL for high YP, NGS, TGW, TNSS, PFSS, SL, PH and fewer NSSS, when compared to low yielding progeny. This indicated that it is feasible by marker-assisted selection to facilitate wheat production.
Plant height (PH), a crucial trait related to yield potential in crop plants, is known to be typically quantitatively inherited. However, its full expression can be inhibited by a limited water supply. In this study, the genetic basis of the developmental behaviour of PH was assessed in a 150-line wheat (Triticum aestivum L.) doubled haploid population (Hanxuan 10×Lumai 14) grown in 10 environments (year×site×water regime combinations) by unconditional and conditional quantitative trait locus (QTL) analyses in a mixed linear model. Genes that were expressed selectively during ontogeny were identified. No single QTL was continually active in all periods of PH growth, and QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the original point to the jointing stage) formed a foundation for PH development. Additive main effects (a effects), which were mostly expressed in S1|S0, were more important than epistatic main effects (aa effects) or QTL×environment interaction (QE) effects, suggesting that S1|S0 was the most significant development period affecting PH growth. A few QTLs, such as QPh.cgb-6B.7, showed high adaptability for water-limited environments. Many QTLs, including four A-QTLs (QPh.cgb-2D.1, QPh.cgb-4B.1, QPh.cgb-4D.1, and QPh.cgb-5A.7) coincident with previously identified reduced height (Rht) genes (Rht8, Rht1, Rht2, and Rht9), interacted with more than one other QTL, indicating that the genetic architecture underlying PH development is a network of genes with additive and epistatic effects. Therefore, based on multilocus combinations in S1|S0, superior genotypes were predicted for guiding improvements in breeding for PH.
A recombinant inbred line (RIL) population with 305 lines derived from a cross of Hanxuan 10 9 Lumai 14 was used to identify the dynamic quantitative trait loci (QTL) for plant height (PH) in wheat (Triticum aestivum L.). Plant heights of RILs were measured at five stages in three environments. Total of seven genomic regions covering PH QTL clusters on different chromosomes identified from a DH population derived from the same cross as the RIL were used as the candidate QTLs and extensively analyzed. Five additive QTLs and eight pairs of epistatic QTLs significantly affecting plant height development were detected by unconditional QTL mapping method. Six additive QTLs and four pairs of epistatic QTLs were identified using conditional mapping approach. Among them, three additive QTLs (QPh.cgb-1B.3, QPh.cgb-4D.1, QPh.cgb-5B.2) and three pairs of epistatic QTLs (QPh.cgb-1B.1-QPh.cgb-1B.3, QPh.cgb-2A.1-QPh.cgb-2D.1, QPh.cgb-2D.1 -QPh.cgb-5B.2) were common QTLs detected by both methods. Three QTLs (QPh.cgb-4D.1, QPh.cgb-5B.3, QPh.cgb-5B.4) were expressed under both drought and well-water conditions. The present data are useful for wheat genetic manipulations through molecular marker-assisted selection (MAS), and provides new insights into understanding the genetic mechanism and regulation network underlying the development of plant height in crops. Our result in this study indicated that combining unconditional and conditional mapping methods could make it possible to reveal not only the stable/conserved QTLs for the developmental traits such as plant height but also the dynamic expression feature of the QTLs.
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