Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments

Abstract

Background: Grain 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 findings: Ten 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/significance: Twelve (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.

Figure 1. QTL and QTL clusters for yield-associated traits in wheat DH lines.

Add_H, additive QTL contributed by Hanxuan 10; Add_L, additive QTL contributed by Lumai 14; Epi, QTLs involved in epistatic effects. The bold line indicates the confidence intervals. All additive QTLs are shown; epistatic QTLs are those with large effects (i.e. the phenotypic variation explained (PVE)>the mean PVE of all main effects) and present in QTL clusters. Map distances in centiMorgans (cM) are shown on the left side of each chromosome.

Figure 2. Phenotypic variation explained (PVE) by different genetic components for yield-associated traits in wheat DH lines.