Plant Mol Biol Rep DOI 10.1007/s11105-014-0726-0
ORIGINAL PAPER
QTL Mapping of Yield and Yield Components under Normal and Salt-stress Conditions in Bread Wheat (Triticum aestivum L.) Amin Azadi & Mohsen Mardi & Eslam Majidi Hervan & Seyed Abolghasem Mohammadi & Foad Moradi & Mohammad Taghi Tabatabaee & Seyed Mostafa Pirseyedi & Mohsen Ebrahimi & Farzad Fayaz & Mehrbano Kazemi & Sadegh Ashkani & Babak Nakhoda & Ghasem Mohammadi-Nejad
# Springer Science+Business Media New York 2014
Abstract A population of 186 recombinant inbred lines of bread wheat (Superhead#2/Roshan) was evaluated to identify quantitative trait loci (QTL) for yield and yield components under normal (2 ds m–1) and salt-stress (10–12 ds m–1) conditions. A genetic map was constructed with 451 markers, including, 23 simple sequences repeats (SSRs) and 428 diversity arrays technology markers (DArTs). The main-effect QTL were identified by composite interval mapping (CIM) analysis using QTL Cartographer v2.5 and Qgene v4.3.2 and a mixedmodel-based composite interval mapping (MCIM) method using QTLNetwork v2.1. A total of 98 significant QTL were detected at two testing locations on 20 chromosomes. Of these, only 40 QTL were detected by at least two of these software programs. A total of 24 QTL on ten chromosomes were identified for grain yield, most of which had a minor effect, contributing less than 10 % of the total phenotypic
variation. Two grain-yield QTL intervals were detected on 1A1 and 3B, which contributed 11.02 % and 10.3 % to the total phenotypic variation, respectively. Roshan alleles were associated with an increase in grain yield under stress conditions on 1A1, 2B3, 3B, 6B1, 1D, 2D1. Among the 20 chromosomes, chromosome 3B with 27 QTL and two distinctive cluster regions was the most important. SSR markers gwm282, gwm247, gwm566, and gwm33 were tightly linked to QTL for the same or different traits under normal, stress or both conditions, and accounted for about 17 %, 43 %, 43 % and 20 % of the total phenotypic variation, respectively. These markers are suitable for marker-assisted selection to improve grain yield under normal and salt-stress conditions. Keywords Recombinant inbred lines . Wheat . QTL . Grain yield . Salt stress
Electronic supplementary material The online version of this article (doi:10.1007/s11105-014-0726-0) contains supplementary material, which is available to authorized users. A. Azadi : S. Ashkani Department of Agronomy and Plant Breeding, Shahre-Rey Branch, Islamic Azad University, Tehran, Iran M. Mardi (*) : S. M. Pirseyedi : M. Kazemi Department of Genomics, Agricultural Biotechnology Research Institute of Iran (ABRII), Karaj, Iran e-mail:
[email protected]
M. T. Tabatabaee Yazd Agricultural Research Center, Yazd, Iran M. Ebrahimi Department of Agronomy & Plant Breeding, College of Abouraihan, University of Tehran, Pakdasht, Iran
E. M. Hervan : F. Moradi : B. Nakhoda Department of Molecular Physiology, Agricultural Biotechnology Research Institute of Iran (ABRII), Karaj, Iran
F. Fayaz College of Agriculture, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
S. A. Mohammadi Department of Agronomy & Plant Breeding, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
G. Mohammadi-Nejad Department of Agronomy & Plant Breeding, Shahid Bahonar University of Kerman, Kerman, Iran
Plant Mol Biol Rep
Introduction Bread wheat is the most important food crop in many countries. Optimizing wheat yield is important, but the achievement of this goal is under constant challenge by exposure of the crops to both biotic and abiotic stresses. Among the various abiotic stresses plants may face, salinity limits the productivity of crops all over the world. Soils that contain a high concentration of salt are found in many countries, including Iran. It has been estimated that about 20 % of global agricultural land (Flowers and Yeo 1995) are soils with high salinity. Estimates suggest that about 34 million ha, including 4.1 million ha of irrigated land in Iran, are salt-affected (Qadir et al. 2008). An effective approach to developing breeding programs might be to identify and map genes that respond to stress using molecular markers, and to determine the relationship of these genes to phenotypic traits. Grain yield is a particularly complex trait, which usually has low heritability (Quarrie et al. 2005) and is influenced significantly by the environment (Cuthbertet al. 2008). Due to the importance and complex nature of yield and yield components, mapping these traits is a critical factor for most breeding programs. Most of the quantitative trait loci (QTL) for the yield of such crops as wheat and barley that have been identified account for less than 10 % of the total phenotypic variation (McCartney et al. 2005; Cuthbert et al. 2008; Xue et al. 2009). QTL mapping of yield-related traits in wheat (Triticum aestivum L.), which is an allohexaploid species with a large genome, has produced various results due to the choice of parents for crossing, environmental effects, the use of different software programs to analyze data, and the nature of the quantitative traits themselves. Several studies have attempted to map QTL for grain yield and yield components of wheat under non-stress conditions (Kato et al. 2000; Börner et al. 2002; McCartney et al. 2005; Huang et al. 2004 and 2006; Marza et al. 2006; Narasimhamoorthy et al. 2006; Kumar et al. 2007; Kuchel et al. 2007; Cuthbert et al. 2008; Heidari et al. 2011). Cuthbert et al. (2008) used 186 doubled haploids (DHs) at six locations to identify QTL for different agronomic parameters. They detected 53 QTL on 12 chromosomes, with 5 QTL for grain yield detected on chromosomes 1A, 2D, 3B, and 2 loci on chromosome 5A. McCartney et al. (2005) studied a population of 182 DH wheat individuals in multiple environments and detected 34 QTL for six agronomic traits. Börner et al. (2002) used a set of 114 recombinant inbred lines (RILs) grown in several different environments and identified 64 major QTL for different morphological, agronomic, and disease-resistance traits. In addition, a considerable number of reports have been published on the mapping of QTL associated with tolerance to salinity in rice seedlings grown in greenhouses (Koyama et al. 2001; Lin et al. 2004; Thomson et al. 2010; Wang et al. 2012a, b), as well as in wheat seedlings
(Lindsay et al. 2004; Ma et al. 2007; Edwards et al. 2008; Ogbonnaya et al. 2008; Genc et al. 2010; Xu et al. 2012). However, a few studies have investigated the effects of QTL in wheat, barley and rice on yield and yield components in the field under salt-stress conditions (Quarrie et al. 2005; Manneh et al. 2007; Xue et al. 2009; Diaz de Leon et al. 2011). Diaz de Leon et al. (2011) identified 22 and 36 QTL under normal and salt-stress conditions, respectively (EC=1.0 and 12.0 ds m−1, respectively). Richards (1983) reported that screening large populations for salinity tolerance in the field is difficult, due to the great heterogeneity of soils with high salinity. Lindsay et al. (2004) identified a locus, named Nax1, (Na exclusion) on chromosome 2AL, using AFLP, RFLP and microsatellite markers, that accounted for approximately 38 % of phenotypic variation in mapping population. Three simple sequence repeats (SSR) markers (gwm249, wmc170 and gwm313) linked to the Nax1 locus. A gene for salt tolerance in bread wheat, Kna1, was mapped to the chromosome 4DL (Dubcovsky et al. 1996). In the study reported herein, two bread wheat cultivars were used: Roshan and Superhead#2. Roshan is a local bread wheat cultivar with high height that is tolerant to both drought and salinity. In contrast, Superhead#2 is a dwarf cultivar with a high grain yield that is susceptible to salinity and drought stresses. Superhead#2 was developed at the Seed and Plant Improvement Institute (SPII), Karaj, Iran. The objectives of the study were to identify (1) positive parent alleles for QTL that were specific to normal environments, (2)positive parent alleles for QTL that were specific to salt stress conditions, and (3) markers that are associated significantly with yield and yield components, using single marker analysis.
Materials and Methods Plant Materials This study used 186 F8 recombinant inbred lines (RILs), derived from a cross between Superhead#2 (a high-yield and salt-sensitive variety from SPII) and Roshan (a local salttolerant cultivar) via single seed descent at the Agricultural Biotechnology Research Institute of Iran (ABRII, Karaj, Iran). Field Trials The mapping population was evaluated at two locations in Iran: Yazd (31°53′ N and 54°22′ E) and Kerman (30°17′ N and 57°05′ E). Yield components were studied using a randomized complete block design under normal and salt stress conditions with two replications in each site. The traits that were investigated included grain yield (Yld), thousand-grain weight (Tgw), grain number per spike (Gnu), and spike length (SL) at all sites. In addition, spike weight (Sw), spikelet number per
Plant Mol Biol Rep
spike (Spn) and biological yield (Byld, straw+grain, t/ha) were studied in Yazd. The data were recorded for RILs and their parents using 20 random plants in each plot. In order to assess grain yield, each plot was harvested in its entirety. A common seed source was used at the four sites. Each plot consisted of six 2 m-long rows, each spaced 20 cm apart. The levels of electrical conductivity of the normal and high-salinity soils were approximately 2 and 10–12 ds m–1, respectively. The normal and highly saline soils were irrigated to the same level, but the concentration of salt in the irrigation water of the soil with high salinity being maintained at 120 mM NaCl throughout the experimental period. The frequency distributions of traits for the186 F8 RILs in the salinity treatment were generated using Excel. ANOVA was performed using the GLM procedure of SAS software (SAS Institute 1990). At first, each site was analyzed individually, then a combined analysis of variance over location was performed. Pearson’s correlation coefficient (for the normal and salt-stress conditions separately) among traits was calculated using SAS V6.12. DNA Extraction and Marker Analysis Leaf tissue of each RIL was used for DNA extraction using the Triticarte plant DNA extraction protocol (http://www. triticarte.com.au/content/DNA-preparation.html). A total of 107 SSR markers, including 26 Wheat Microsatellite Consortium (WMC; Gupta et al. 2002), 67 Gatersleben Wheat Microsatellite (GWM; Röder et al. 1998), 12 Beltsville Agricultural Research Center (BARC; Song et al. 2002, 2005) and 2 Clermont-Ferrand D genome (CFD; Guyomarch et al. 2002) markers, were assessed for the existence of polymorphism between parents. Sequences of SSR markers were obtained from the GrainGenes website (http:// wheat.pw.usda.gov/) and previously published reports. Amplification was performed with a thermal cycler (Applied Biosystems, Foster City, CA) using initial denaturation (one cycle) at 94 °C for 1 min, followed by 30 cycles of 1 min at 94 °C, 30 s for annealing (temperature depending on the primer annealing conditions) and 45 s at 72 °C for extension. An additional final extension of the PCR products was performed for 5 min at 72 °C. PCR products were separated by 6 % polyacrylamide gel electrophoresis and visualized after silver staining. In addition to the 107 SSR markers, 869 DArT markers were generated by Triticarte (Australia; http://www.triticarte.com.au/) to profile the whole genome of the entire population. DArT markers are referred to using the prefix “wPt”, followed by numbers. Construction of a Genetic Map From the 976 markers, only 640 polymorphic markers (30 SSR and 610 DArT markers) were detected. Map Manager QTX (Manly et al. 2001) was used to construct the linkage
map using the Kosambi mapping function (Kosambi 1944) and LOD score of 3.0 as a threshold for considering a linkage to be significant. Segregation distortion (deviation from a Mendelian ratio) at each locus was tested using the chisquare test (P
