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Identification of QTLs for drought tolerance traits on wheat chromosome 2A using association mapping.

Byline: Muhammad Qadir Ahmad Sultan Habibullah Khan Abdus Salam Khan Abdul-Mujeeb Kazi and S.M.A. Basra

Abstract

Association mapping analysis was performed on a set of 108 diverse wheat accessions to dissect the genetic background of drought adaptive traits. These genotypes were characterized with 28 SSR loci on chromosome 2A. Fifteen agronomic traits were evaluated under well irrigated and drought conditions. Population structure and Kinship were inferred on the basis of 30 unlinked SSR loci covering all the 21 chromosomes which enhanced the mapping strength by eliminating spurious associations. A total of 11 marker trait associations were detected by mixed linear model approach (MLM). The phenotypic variation explained by each marker ranged 4.26% to 13.6%. Out of 11 eight marker trait associations were found under drought and 3 marker trait associations were found under normal conditions. SSR locus Xgwm312 showed association with relative water content (RWC) under normal conditions and detected in both years. Similarly awn length showed association with Xcfa2099 under drought conditions. SSR locus Xwmc455 showed association with thousand grain weight (TGW) under well irrigated conditions. Xbarc124 was associated with coleoptiles length shoot length and extrusion length. This study has highlighted a novel set of loci which are associated with drought adaptive traits and can be used for marker assisted breeding to enhance the wheat performance under unfavorable conditions.Copyright 2014 Friends Science Publishers

Keywords: Wheat; Drought; Marker trait association; Linkage disequilibrium; Mixed linear model; Association mapping

Introduction

Climate change consequences like drought salinity and extreme temperature demands unprecedented efforts to sustain food production (Semenov and Halford 2009). Understanding the genetic bases of morphological and physiological traits responsible for maintaining yield under stressful conditions is the key towards a more targeted breeding approach (Araus et al. 2002; Salekdeh et al.2009). Recent developments in the field of genomics enable the selection and identification of the genomic regions harboring QTLs controlling important agronomic and yield traits in crops (Tuberosa and Salvi 2006; Collins et al.2008; Cooper et al. 2009). Family based QTL mapping has helped in identification of many genomic regions influencing complex traits in different plant species (Mauricio 2001; Holland 2007; Hall et al. 2010). However there are several limitations which lower the efficiency of linkage mapping. Firstly as only two parents are used to establish mapping population the allelic variation within mapping population is limited (Hall et al.2010). Secondly only those experimental events can be exploited that have been taken place during theestablishment of the mapping population and recombination do not have sufficient time to shuffle genome into small fragments (Myles et al. 2009; Mir et al. 2012).Association mapping approach utilizes linkage disequilibrium (LD) in natural populations to identify markers with significant allelic differences (Ochieng et al.2007). Resolution of association mapping depends on LD levels if LD decays rapidly resolution of association mapping will be high and vice versa (Rafalski 2002). Thereare two common ways to visualize the extent of LDbetween pairs of loci. LD decay plots are used to visualize the rate at which LD declines with genetic or physical distance. Scatter plots of r2 values versus genetic/physical distance among all pairs of alleles within a gene along a chromosome or across the genome are constructed. So a graphical view of LD can be presented either as a LD decay plot of D' or r2 over physical or genetic distances or as a linear arrangement of LD between polymorphic sites within a gene or loci along a chromosome (Flint-Garcia et al.2003). Alternatively disequilibrium matrices are also effective for visualizing the linear arrangement of LD between polymorphic sites within a gene or along chromosome.Association mapping can be classified into two categories (i) candidate-gene association mapping which dissect polymorphism in selected candidate genes that play role in controlling phenotypic variations for specific traits and (ii) genome-wide association mapping which trace genetic variation in the whole genome to find association for various complex traits (Risch and Merikangas 1996). In the past association mapping has been extensively used in medical genetics to dissect diseases most notably Alzheimer's disease (Lander and Schork 1994; Risch2000) and its application is gradually moving to other fields such as plant genetics (Ochieng et al. 2007).Application of association mapping methods in plants has been limited by the fear of spurious associations that may result from population structure (Pritchard and Przeworski 2001). Given the geographical origins local adaptation and breeding history of assembled genotypes in an association mapping panel the non-independent samples usually contain both population structure and familial relatedness (Yu and Buckler 2006). Association tests that don't attempt to account for the effect of population structure must be viewed with skepticism however recent developments in statistical methodologies make it possible to properly interpret the results of association tests. To account for population structure many statistical methodologies have been designed such as structured association (SA) (Falush et al. 2003) genomic control (GC) (Devlin et al. 2004) mixed model approach (Yu and Buckler 2006) and principal component approach (Price et al. 2006). With these methods the issues of false positives generated by population structure may be solved (Yu et al.2005; Price et al. 2006).Most of the association mapping studies so for have been reported under favorable conditions (Breseghello and Sorrells 2006; Ravel et al. 2006; Crossa et al. 2007; Yao et al. 2009). In the previous studies a number of QTLs for agronomic traits (spike length spikelets spike-1 grains spike-1 plant height spikelet density and thousand kernel weight) have been identified on different chromosomes (Yao et al. 2009; Liu et al. 2010; Mir et al. 2012). For complex traits like drought tolerance and yield the knowledge of interactions between marker/QTLs and environment are necessary for efficient utilization of marker assisted selection in plant breeding (Francia et al. 2005; Gupta et al. 2008; Zoric et al. 2012). Comparing the performance of same genotypes across the environments the best suited environment for the expression of a particular QTL can be identified but the hundreds of markers are required for sufficient mapping resolution covering the whole genome of wheat therefore targeting an individual linkage group is a quite reasonable strategy as adopted by (Yao et al. 2009) and (Liu et al. 2010). So the following study was carried out. a) To identify thepopulation structure in a collection of 108 germplasm lines. b) To identify the extent of LD decay on chromosome 2A.c) To identify the association of SSR markers with drought tolerance traits on chromosome 2A.

Materials and Methods

Plant Materials and Phenotyping

A core collection of 108 genotypes was selected after phenotyping a set of 200 genotypes on the basis of diversity found in 15 morphological traits. The germplasm was maintained at the department of Plant Breeding and Genetics University of Agriculture Faisalabad (Latitude =31 26' N Longitude = 73 06' E Altitude =184.4 m) during years 2009-2010 and 2010-2011 according to randomized complete block design with two replicates. One row of each germplasm accession was sown and evaluated and was kept 5m in length. Plants were kept 12cm apart and row to row distance was 30cm. Pre-anthesis water stress was created by withholding water after the 1st irrigation at tillering stage (Dhanda and Sethi 2002) followed by a second irrigation after anthesis. The control treatment received four irrigations during the whole season. Ten plants were selected randomly and tagged for data recording.All the genotypes were extensively phenotyped for two years like days to heading (DH) relative water content (RWC) proline content ( g/g) leaf rolling (LR) glaucousness/waxiness (Gla/wax) peduncle length (PL) peduncle extrusion length (EXL) awn length (AL) plant height (PH) thousand grain weight (TGW) grains per spike (GR/S) and yield/plant (Y). Software STATISTICA v7 was used to estimate the summary statistics like ANOVA and correlation analysis for all the traits. For seedling traits a separate experiment was conducted under laboratory conditions. For creating drought stress genotypes were grown on PEG-6000 solution and the traits like root length (RL) shoot length (SL) and coleoptiles length (CL) were recorded 10 days after sowing.

Genotypic Analysis

Fresh wheat leaf tissues were used to extract DNAaccording to the method described by (Rogowsky et al.1991). A set of 30 SSR markers covering whole genome were chosen from GrainGenes data base (http://wheat.pw.usda.gov) were used to detect population structure. For analysis of linkage disequilibrium (LD) and marker trait associations (MTA) 28 SSR markers flanking on chromosome 2A were scanned. High resolution agarose (2.5%) was used to analyze PCR products. The PCR profile for each SSR primer pair was the same as reported in Grain Genes (http://wheat.pw.usda.gov).

Polymorphism and Population Structure

The summary statistics of polymorphism and allelic information was determined using Power-Marker v3.25(http://www.powermarker.net). Population structure was estimated with unlinked markers using STRUCTURE v2.3-a model based (Bayesian) cluster software (Pritchard et al.2000). The number of sub-populations (K) was set from 2-20 based on admixture and correlated allele frequencies models. For each K 10 runs were performed separately. Each run was carried out with 30000 iterations and 30000 burn-in period. A value of K was selected where the graph of InPr (X/K) peaked in the range of 2-20 sub-populations. For selected K again 10 runs were performed each with100000 iterations and 100000 burn-in period.

Linkage Disequilibrium

Linkage disequilibrium between all pairs of loci was estimated using LD parameter r2 (the squared correlation coefficient between all bi-allelic combinations at two loci and summarizes both recombination and mutational history. Both unlinked and syntenic r2 were evaluated using the software TASSEL v3 (http://www.maizegenetics.net) by setting 1000 permutation. The LD decay with genetic distance (cM) between markers on chromosome 2A was plotted by software Power-Marker v3.25. The 95% percentile of unlinked r2 was used to derive a critical value for r2 as evidence of linkage (Breseghello and Sorrells2006). The loci having Pless than 0.001 were considered to be in significant LD. If all pairs of flanking loci within a chromosomal region were in significant LD the region was designated as LD block (Stich et al. 2005).

Marker-trait Association

Mixed linear model was used to calculate association between SSR marker alleles and traits by software TASSEL v3 (Yu et al. 2005). The advantage of mixed linear model is that it reduces both type I and type II errors. The population structure Q matrix derived from STRUCTURE v2.2 and the relative kinship matrix K obtained from unlinked markers by TASSEL v3 was combined and co-variated in association test to minimize false positive rate. The P less than 0.01 was used to declare the significance of SSR marker and trait associations and the magnitude of QTL effects were calculated by r2-marker.

Results

Molecular Diversity

From the scoring of 58 SSR primers on a set of 108 genotypes 361 alleles were detected with an average of 6.2 alleles per locus. The range of alleles varied from 2 to18. Polymorphic markers were used to identify molecular diversity population structure linkage disequilibrium and marker trait associations (MTAs). Major allele frequency ranged from 0.30-0.84. Average PIC values of primers ranged from 0.25-0.93 with an average of 0.65.Population Structure

The admixture model-based analysis using software STRUCTURE was employed to investigate the nature of genetic relationships among genotypes. To select an optimal number of sub-populations the STRUCTURE was run in the range of two to twenty sub-populations which revealed the peak of likelihood at 3 (Fig. 1). Therefore K value was set to 3 to identify the sub-populations. The number of genotypes assigned to different sub-population was 33 37 and 38 (Fig. 2). The FST values between sub-populations were significant (Pless than 0.001) confirming the prevalence of genetic structure.

Linkage Disequilibrium

Background LD was determined by unlinked markers which were also used to detect population structure. This back ground LD caused by population structure was also used to set critical value of LD for markers on chromosome 2A. The unlinked r2 value ranged from 0.000 to 0.06 for all unlinked loci pairs with an average of 0.002.The 95th percentile of the distribution of unlinked r2(0.015) was used as population specific threshold for this parameter as an evidence of LD because of linkage. 28SSRs on chromosome 2A were used to obtain syntenic r2. The pairwise syntenic r2 ranged from 0.0000 to 0.1194 with an average of 0.0189 which is higher than the average of unlinked r2. The scatter plot showing LD decay of thesyntenic LD values of r2 in population is shown in Fig. 3.LD extent on chromosome 2A was ~ 8 cM with the critical value of 0.02. The chromosomal region having all the pairs of loci in LD was referred as LD block as shown in Fig. 4.

Marker-trait Associations

Mixed linear model approach was used to determine marker trait associations for 15 phenotypic traits and 28 SSRs loci on chromosome 2A of hexaploid wheat. Sub-populations were used as covariates for association mapping analysis. A total of 8 marker trait associations were identified for 6 traits under drought stress conditions at the probability level of0.001 with the range of 6.56 to 14.4% of the phenotypic variation. On the other hand well irrigated conditions generated only 3 marker trait associations with r2 ranging from 10 to 17.8. Traits showed significant associations under drought include relative water contents (RWC) thousand grain weight (TGW) awn length (AL) plant height (PH) shoot length (SL) and coleoptile length (CL). The traits which showed significant marker trait associations under well irrigated conditions include relative water contents (RWC) peduncle extrusion length (EXL) and coleoptile length (CL). Of a total of 7 SSR markers showing significant marker traits association four of them (wmc181 wmc407 gwm312 gwm558) were associated with only one trait and therefore can be considered as trait specific MTAs.

Table 1: Association (r2) of the SSR markers with drought related traits in wheat

Traits###QTL###Marker###Position cM###1st year N###1st year D###2nd year N###2nd year D###R2###P(Q+K)

RWC###QRWC.uaf.2A.2###GWM 312###74###0.1526###0.0076

###0.1782###0.0009

RWC###QRWC.uaf.2A.3###WMC 181###103###0.0656###0.007

RWC###QRWC.uaf.2A.1###WMC 407###15###0.1225###0.0004

TGW###QSZ.uaf.2A.1###WMC 455###59###0.0763###0.0046

AL###QAl.uaf.2A.1###CFA 2099###66.4###0.0809###0.0073

###0.1463###0.0005

PH###QPH.uaf.2A.1###GWM 558###54###0.0946###0.0035

PH###QPH.uaf.2A.2###WMC 455###59###0.0734###0.0069

EXT###QEXT.uaf.2A.1###BARC 124###8###0.1121###0.0094

SL###QSL.uaf.2A.1###BARC 124###8###0.144###0.0026

CL###QCL.uaf.2A.1###BARC 124###8###0.1008###0.0079

CL###QCL.uaf.2A.3###CFA 2099###66.4###0.0863###0.0035

Two markers cfa2099 and wmc455 were associated with two traits whearase barc124 was found to be associated with three traits.Under drought stress conditions wmc181 and wmc407 showed significant association with relative water contents. Awn length was found to be associated with cfa2099. This association for awn length was found across two years under drought environment. Marker trait associations for plant height were detected with primer wmc455 and gwm558 under drought conditions during 1st and 2nd year respectively. Marker wmc455 showed overlapping association with two traits (thousand grain weight and plant height) during 2nd year under drought stress. Shoot length showed associations with barc124. Coleoptile length and awn length showed an overlapping association with cfa2099 under 2nd year drought. Only one MTA was found for thousand grain weight with wmc455 under drought conditions in the 2nd year. Under favorable conditions only three marker trait associations were found. For relative water content gwm312 was detected across two years under normal conditions. Barc124 showed significant association with coleoptile length and extrusion length under well irrigated condition. Barc124 showed association with three different traits under three different environments (Table 1).

Discussion

It was noted that 58 SSR loci were detected in this study on2.5% high resolution agarose gel. More genetic diversity could have been observed if analysis performed using capillary electrophoresis and more diverse germplasm used. However in previous study (Sanchez-PACopyrightrez et al. 2006) observed no significant difference in results obtained bycapillary agarose polyacrylamide electrophoresis. Theextent of observed genetic diversity in the current study 0.68 was higher than that of (Zoric et al. 2012) having 0.64 genetic diversity and by (Dreisigacker et al. 2005) showing0.57 gene diversity and (Prasad et al. 2009) showing 0.62 gene diversity however our extent of genetic diversity wasvery close to (Chao et al. 2007) 0.66 genetic diversity.Huang et al. (2002) detected highest level of geneticdiversity (0.77) from a set of 998 accessions collected from68 countries of 5 continents across the globe.Gene diversity values and average number of alleles per locus (6.2) is an evidence of sufficient genetic diversity present in the germplasm lines used in this study. Previously Liu et al. (2010) found 6.1 numbers of alleles per locus in a core collection of Chinese wheat accessions. Yao et al. (2009) studied 108 Chinese wheat accessions and found 5.7 alleles per locus. 60 European wheat accessions showed 4.8 alleles per locus (Stachel et al. 2000). Breseghello and Sorrells (2006) also found 4.8 alleles per locus in 95 soft winter cultivars of USA. High number of alleles per locus (18.1) was observed in IPK gene bank hexaploid wheat accessions (Huang et al. 2002). Allelic diversity found in the present study is very close to the allelic diversity observed in the Chinese germplasm but higher than the European and USA wheat population as discussed above.The admixture model based analysis grouped the accessions into 3 sub-populations. Presence of familial relationship and sub-population in cultivars and modern breeding materials is a common feature (Flint-Garcia et al.2003; Maccaferri et al. 2005; Camus-Kulandaivelu et al.2006; Cockram et al. 2008). Fig. 2 explains the nature of population structure obtained from structure analysis. First sub-population consisted of 33 genotypes and primarily contain genotypes originating from CIMMYT (CIMMYT=23 ICARDA=3 Pakistan=5 and India=2). The second sub-population consisted of accessions primarily from Pakistan (Pakistan=22 CIMMYT=9 ICARDA=5 and India=1). Similarly third sub-population also contained genotypes mostly originating from Pakistan (Pakistan=26 CIMMYT=8 and Miscellaneous=2). Although a general division rule can be seen in the first sub-population containing mostly Mexican originated accession (also in the2nd and 3rd with majority of Pakistani accessions) the sub- populations cannot be explained geographically due tooverlapping of several accession from the same region in all groups. This is possibly due to regional breeding objectives and adaptation preferences. Presence of population structure results type I and type II errors in association mappingstudies and must be accounted during marker trait association studies (Pritchard and Rosenberg 1999; Malosetti et al. 2007). The identification of sub-populations within wheat genotypes to reduce spurious association for association mapping studies has been highlighted by many researchers (Breseghello and Sorrells 2006; Crossa et al.2007; Yao et al. 2009). Breseghello and Sorrells (2006) used the same statistical tool and identified four sub- populations in diverse group of soft winter wheat accessions. Yao et al. (2009) identified nine sub-populations within a set of 108 Chinese wheat accessions. To avoid spurious associations matrix of population structure was co- variated with 2A data and the kinship matrix (Breseghello and Sorrells 2006; Tommasini et al. 2007; Yao et al.2009).To establish the critical value of linkage disequilibrium the 95th percentile of the distribution of unlinked r2 was used (Yao et al. 2009) instead of r2 as used in most of the previous studies (Remington et al. 2001; Tenaillon et al. 2001; Palaisa et al. 2003; Andersen et al.2007). The LD value defines the extent of LD attributable to linkage and the approach was first mentioned by(Breseghello and Sorrells 2006). In this approach unlinked LD distribution incorporates the effects of selection and population structure in the experiment (Breseghello and Sorrells 2006; Yao et al. 2009). LD decay in the targetedgenomic region is essential for association mapping studies.Levels of LD or linkage disequilibrium are different in different populations or species due to many genetic and breeding factors such as genetic drift recombination's and mating system (Yao et al. 2009; Al-Maskri et al. 2012). Mating system is a major factor that affects LD extent. Self- pollinated crops showed higher levels of LD as compared to out-crossing crop species (Gupta et al. 2005; Abdurakhmonov and Abdukarimov 2008). Wheat being an auto-gamous has LD extent which is greater by three orders than the LD extent in maize which is an out-crossing species (Breseghello and Sorrells 2006). Strong LD extending upto20cM with r2greater than 0.6 was found by (RhonACopyright et al. 2007). Crossa et al. (2007) found r2 within ~40cM. In a study using a collection of 95 soft winter wheat accessions analyzed with 33 and 20 SSRs on chromosome 2D and 5A respectively Breseghello and Sorrells (2006) reported astrong LD within less than 5cM and ~ 1cM for centromeric regionof chromosome 5A and chromosome 2D respectively. Higher level of LD in the centromeric region may be due to low frequency of recombination around centromere (Jones et al. 2002) and loss of variability during domestication. Domestication related QTLs for plant height yield plant-1 and days to heading have been detected in centromeric region of chromosome 2A by (Peng et al. 2003). These levels observed in wheat were higher than the LD level observed in maize which decayed at about 1 kb distance (Remington et al. 2001; Palaisa et al. 2003). Remington et al. (2001) reported LD decay within 1500 bp in maize and (Jung et al. 2004) surveyed adhI locus andfound extent of LD within 500 kb. In a survey of 32European maize lines LD decay (3.7 kb) was persisted over the length of PAL gene locus (Andersen et al. 2007). Mating system is the main cause for this difference of this deviation in LD in maize and wheat (Yao et al. 2009).

Presence of rare alleles can also lead towards imprecise estimates of LD (Abdurakhmonov and Abdukarimov2008). Accordingly rare alleles with less than 5% were pooled as missing data for linkage disequilibrium analysis in our study. Accurate estimates of LD are very important for marker trait association analysis. Criterion which establishes the critical values to test the significance syntenic LD as statistical factor may also affect the analysis. We observed LD extent upto ~ 8cM (r2 = 0.02) on chromosome 2A in 108 spring wheat genotypes mostly Pakistani landraces cultivars and elite lines surveyed with 28 polymorphic SSRs. Extent of LD varies in various wheat populations (Sajjad et al. 2013).Various patterns of LD extent depict the selection pressure on respective genomic regions. Previously Yao et al. (2009) observed very low LD extent (2.3cM) on chromosome 2A in a collection of 137 wheat accessions assayed with 37 SSR primers covering whole chromosome with an average marker interval of 3.75. They found two LD block on chromosome 2A one in the centromeric region having eight loci within 6.3cM and other on the short arm within 3.3cM having three loci. It was also found that some markers which are very close in the map but was not as on LD (Yao et al. 2009). It demonstrates thehigh resolution of LD mapping. Chao et al. (2010) observed higher level of LD extent in D-genome (20.8-27.1cM)followed by B-genome (20.4-21.5cM) and lowest level in A-genome (16.7-19.8cM) in a set of 478 winter and spring wheat accessions assayed by 394 SNPs. A set of 205 US winter lines genotyped with 254 SSRs showed a highly variable extent of LD throughout genome. The mean genome LD decay observed 10cM while the highest LD block was (greater than 40cM) (Zhang et al. 2011). Contrary Liu et al. (2010) observed very low level of LD (3cM) in a set of 103Chinese wheat accession assayed with 31 SSR markers. In a survey of 96 diverse wheat accessions assayed with 874DArT highly variable results were observed. Chromosome7A showed very high values of LD about 45cM while few very closely linked markers showed no significant LD. Tenaillon et al. (2001) reported higher extent of LD in nine U.S. inbred lines than sixteen exotic lines. It is obvious that different populations of wheat selected from diverse germplasm exhibit different levels of resolution for association mapping studies. Therefore LD patterns in wheat may vary with genomic regions marker types and populations.We used a mixed linear model (MLM) approach to determine marker-trait associations for fifteen traits and28 2A specific SSRs markers. The mixed linear model (MLM) is a powerful approach as compared to general linear model (GLM) or any other model developed so far. Its power over GLM (that takes only population structureinto account) lies in taking into accounts both populationstructure and kinship for accurate marker traits associations. Theoretically kinship creates LD between genetically linked loci but it can also create LD between genetically unlinked loci when predominant parents are included in the population. In maize kinship equally generated LD between genetically linked and unlinked loci (Stich et al. 2005). In the present study QTLs detected under drought environment were higher in numbers than under normal conditions. Due to differences in structure in population environmental conditions and methods of QTLs detection it can be difficult to compare QTLs identified in this study with those of previously reported (Lakew et al. 2013).In our study TGW was found associated with locus Xwmc455 which is at 59cM distance on chromosome 2A. Yao et al. (2009) found marker trait association for TGW with wmc819 located at 60cM distance. QTLs controlling TGW on the same chromosome has also been reported previously in the marker interval gwm275 (56cM) gwm312 (79cM) and gwm372 (80cM) (Wang et al. 2012) whereas wmc455 is also located in this interval. Many researchers have also found genomic regions associated with TGW on chromosome 2A (Huang et al. 2004; Gupta et al. 2006; Snape et al. 2007; Tsilo et al. 2010). Peng et al. (2003) also found QTLs for TGW on chromosome 2A.Peduncle extrusion length showed association with locus Xbarc124 (8cM) in 2nd year under normal conditions. Peduncle extrusion and peduncle length facilitate theemergence of spikes from boot. Inability of spikes to emerge out negatively effects seed setting and seed development due to reduced photosynthetic contribution (Rao et al. 2007). In this study it was also observed that genotypes having high peduncle length also have high peduncle extrusion length. Neumann et al. (2011) found six common QTLs for peduncle length and plant height across the genome in a study of 96 wheat core collections and showed that these traits are co-located at the same region. Yao et al. (2009) found significant marker trait association with plant height at7.7cM and 10.4cM distance on chromosome 2A. In our study Xbarc124 is located at 8cM distance on chromosome2A. So the results obtained by (Neumann et al. 2011) and (Yao et al. 2009) confirms the presence of QTLs for plant developmental trait like peduncle extrusion length as identified in this study. Many QTLs for peduncle length and peduncle extrusion has been identified in wheat and barley on different chromosomes. BAlrner et al. (2002) identified QTLs for peduncle length on chromosome 6A. Neumann et al. (2011) identified 13 QTLs in wheat for peduncle length across the genome. In barley Rao et al. (2007) identified QTLs for peduncle length on chromosome 1H. Similarly Tenaillon et al. (2001) identified QTLs for ear extrusion on chromosome 3H of barley.Relative water content (RWC) showed 3 marker trait associations under normal and drought conditions. Chromosomal region Xgwm312 showed association under normal conditions and detected under both years. Under drought condition two distant markers showed association with RWC. Chen et al. (2010) reported QTLs for RWC on 2H barley chromosome under drought conditions. However Teulat et al. (2003) also found QTLs for RWC on 6H chromosome of barley under dry environment. It shows influence of environment on this trait that's why; different QTLs were detected under different environments.In the present study markers cfa2099 showed association with awn length on chromosome 2A under drought stress conditions for two consecutive years. Awn length is controlled by 3 genes (Hd B1 and B2) which has been reported in previous studies (Sears 1954 1966). Sears (1954) also reported that some awn promoting genes are present on chromosome 2A. In a study of disomic addition lines of group 2 chromosome in Chinese spring (awnless) showed awned phenotype (Dvorak 1980; Friebe et al.1999). So awning seems to be a very complex trait in addition to three major inhibitor genes several minor genes also involved in awning which may be awning promoters or suppressors (Sood 2008) as found in the present study.Coleoptile length was found to be associated with two different genomic regions (Xbarc124 and Xcfa2099) at 8 and 66.6cM under normal and drought respectively. Landjeva et al. (2008) in a study of 114 recombinant inbred lines of ITMI population found 5 genomic regions located 1 on chromosome 1A 3 on chromosome 1B and 1 on 7Dassociated with coleoptile length. Rebetzke et al. (2001) found QTLs for coleoptile length on short and long arm of chromosome 4B near Rht-B locus. Qian et al. (2011) scanned 168 F2 and 57 DH populations with 323 SSR markers and found many associations between coleoptiles length and genomic regions on different chromosomes (4B 3B and 6D).Xbarc124 also showed association with shoot length under drought condition during first year. In a previous study Landjeva et al. (2008) found four QTLs for shoot length on chromosome 2D under drought conditions and three QTLs on chromosome 2B under normal conditions both of these chromosomal regions are homologous to 2A. Genomic region Xbarc124 showed association with three height related traits as peduncle extrusion length shoot length and coleoptile length. Positive significant association between extrusion length and plant height was also observed in this study. Similarly positive significant association between shoot length and coleoptile length was observed during phenotyping under laboratory conditions. In previous studies positive association between plant height and coleoptile length has been reported by many researchers (Allan 1980; Rebetzke et al. 1999) which indicates that QTLs affecting developmental traits might be clustered around this locus.In conclusion the study successfully identified many marker trait associations under normal and drought condition. Marker traits associations detected for RWC will help to improve our understanding about the water relations in plant. Marker wmc455 which is associated with thousand grain weight under drought conditions can be used to determine the yield potential of wheat genotypes under drought conditions using marker assisted selection. Similarly marker trait associations for awn length extrusion length and relative water content which are found to be associated with yield in this study or in previous studies could also improve our understanding about the performance of genotypes under drought. This study also identified some important QTLs for seedling traits under normal and drought stress conditions which improved our understanding of complex genetic nature of shoot length and coleoptile length.

Acknowledgement

We acknowledge Higher Education Commission of Pakistan for providing funds for this research under the scheme of 5000-Indeginous Scholarships.

References

Abdurakhmonov I.Y. and A. Abdukarimov 2008. Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int. J. Plant Genomics 2008Al-Maskri A. M. Sajjad and S.H. Khan 2012. Association mapping: a step forward to discovering new alleles for crop improvement. Int. J. Agric. Biol. 14: 153160Allan R. 1980. Influence of semidwarfism and genetic background on stand establishment of wheat. Crop Sci. 20: 634638Andersen J.R. I. Zein G. Wenzel B. KrA1/4tzfeldt J. Eder M. Ouzunova and T. LA1/4bberstedt 2007. High levels of linkage disequilibrium and associations with forage quality at a Phenylalanine AmmoniaLyaselocus in European maize (Zea mays L.) inbreds. Theor. Appl. Genet.114: 307319Araus J. G. Slafer M. Reynolds and C. Royo 2002. Plant breeding and drought in C3 cereals: what should we breed for Ann. Bot. 89: 925940BAlrner A. E. Schumann A. FA1/4rste H. CAlster B. Leithold M. RAlder and W. Weber 2002. Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat (Triticum aestivum L.). Theor. Appl. Genet. 105: 921936Breseghello F. and M.E. Sorrells 2006. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetic. 172: 11651177Camus-Kulandaivelu L. J.B. Veyrieras D. Madur V. Combes M.Fourmann S. Barraud P. Dubreuil B. Gouesnard D. Manicacci and A. Charcosset 2006. Maize adaptation to temperate climate: relationship between population structure and polymorphism in the Dwarf8 gene. Genetics 172: 24492463Chao S. W. Zhang J. Dubcovsky and M. Sorrells 2007. Evaluation of Genetic Diversity and Genomewide Linkage Disequilibrium among US Wheat (L.) Germplasm Representing Different Market Classes. Crop Sci. 47: 10181030Chao S. J. Dubcovsky J. Dvorak M.C. Luo S.P. Baenziger R.Matnyazov D.R. Clark L.E. Talbert J.A. Anderson and S. Dreisigacker 2010. Population and genome specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genomics 11: 727Chen G. T. Krugman T. Fahima K. Chen Y. Hu M. RAlder E. Nevo and A. Korol 2010. Chromosomal regions controlling seedling drought resistance in Israeli wild barley Hordeum spontaneum C. Koch.Genet. Resour. Crop Ev. 57: 8599Cockram J. J. White F.J. Leigh V.J. Lea E. Chiapparino D.A. Laurie I.J.Mackay W. Powell and D.M. O'Sullivan 2008. Association mapping of partitioning loci in barley. BMC Genetics 9: 16Collins N.C. F. Tardieu and R. Tuberosa 2008. Quantitative trait loci and crop performance under abiotic stress: where do we stand PlantPhysiol. 147: 469486Cooper M. F.A. van Eeuwijk G.L. Hammer D.W. Podlich and C.Messina 2009. Modeling QTL for complex traits: detection and context for plant breeding. Curr. Opin. Plant Biol. 12: 231Crossa J. J. Burgueno S. Dreisigacker M. Vargas S.A. HerreraFoessel M. Lillemo R.P. Singh R. Trethowan M. Warburton and J. Franco2007. Association analysis of historical bread wheat germplasmusing additive genetic covariance of relatives and population structure. Genetics 177: 18891913Devlin B. S.A. Bacanu and K. Roeder 2004. Genomic control to the extreme. Nat. Genet. 36: 11291130Dhanda S. and G. Sethi 2002. Tolerance to drought stress among selectedIndian wheat cultivars. J. Agric. Sci. 139: 319326Dreisigacker S. P. Zhang M. Warburton B. Skovmand D. Hoisington and A. Melchinger 2005. Genetic diversity among and within CIMMYT wheat landrace accessions investigated with SSRs and implications for plant genetic resources management. Crop Sci.45: 653661Dvorak J. 1980. Homoeology between Agropyron elongatum chromosomes and Triticum aestivum chromosomes. Can. J. Genet. Cytol. 22: 237259Falush D. M. Stephens and J.K. Pritchard 2003. Inference of populationstructure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 15671587Flint-Garcia S.A. J.M. Thornsberry and B. IV 2003. Structure of LinkageDisequilibrium in Plants. Annu. Rev. Plant Biol. 54: 357374Francia E. G. Tacconi C. Crosatti D. Barabaschi D. Bulgarelli E.Dall'Aglio and G. Vale 2005. Marker assisted selection in crop plants. Plant Cell Tiss. Org. 82: 317342Friebe B.R. N.A. Tuleen and B.S. Gill 1999. Development and identification of a complete set of Triticum aestivumAegilops geniculata chromosome addition lines. Genome 42: 374380Gupta P.K. H.S. Balyan P.L. Kulwal N. Kumar A. Kumar R.R. Mir A.Mohan J. Kumar. 2008. QTL analysis for some quantitative traits in bread wheat. J Zhejiang Uni. Sci. 8: 807814Gupta P.K. S. Rustgi and P.L. Kulwal 2005. Linkage disequilibrium and association studies in higher plants: present status and futureprospects. Plant Mol. Biol. 57: 461485Gupta P.K. S. Rustgi and N. Kumar 2006. Genetic and molecular basis of grain size and grain number and its relevance to grain productivity in higher plants. Genome 49: 565571Hall D. C. TegstrAlm and P.K. Ingvarsson 2010. Using association mapping to dissect the genetic basis of complex traits in plants. Brief Funct. Genomics 9: 157165Holland J.B. 2007. Genetic architecture of complex traits in plants. Curr.Opin. Plant Biol. 10: 156161Huang X. A. BAlrner M. RAlder and M. Ganal 2002. Assessing genetic diversity of wheat (Triticum aestivum L.) germplasm using microsatellite markers. Theor. Appl. Genet. 105: 699707Huang X. H. Kempf M. Ganal and M. RAlder 2004. Advanced backcrossQTL analysis in progenies derived from a cross between a German elite winter wheat variety and a synthetic wheat (Triticum aestivum L.). Theor. Appl. Genet. 109: 933943Jones L. K. Rybka and A. Lukaszewski 2002. The effect of a deficiency and a deletion on recombination in chromosome 1BL in wheat. Theor. Appl. Genet. 104: 12041208Jung M. A. Ching D. Bhattramakki M. Dolan S. Tingey M. Morgante and A. Rafalski 2004. Linkage disequilibrium and sequence diversity in a 500kbp region around the adh1 locus in elite maize germplasm. Theor. Appl. Genet. 109: 681689Lakew B. R.J. Henry S. Ceccarelli S. Grando J. Eglinton and M. Baum2013. Genetic analysis and phenotypic associations for drought tolerance in Hordeum spontaneum introgression lines using SSR and SNP markers. Euphytica 189: 929Lander E.S. and N.J. Schork 1994. Genetic dissection of complex traits.Sci. New York Washington 20372037Landjeva S. K. Neumann U. Lohwasser and A. BAlrner 2008. Molecular mapping of genomic regions associated with wheat seedling growth under osmotic stress. Biol. Plant. 52: 259266Liu L. L. Wang J. Yao Y. Zheng and C. Zhao 2010. Associationmapping of six agronomic traits on chromosome 4A of wheat(Triticum aestivum L.). Mol. Plant Breed. 2: 00426Maccaferri M. M.C. Sanguineti E. Noli and R. Tuberosa 2005.Population structure and longrange linkage disequilibrium in a durum wheat elite collection. Mol. Breed. 15: 271290Malosetti M. C.G. van der Linden B. Vosman and F.A. van Eeuwijk2007. A mixedmodel approach to association mapping using pedigree information with an illustration of resistance to Phytophthora infestans in potato. Genetics 175: 879889Mauricio R. 2001. Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nat. Rev. Genet. 2: 370381Mir R.R. N. Sreenivasulu R.K. Varshney M. ZamanAllahand R.Trethowan 2012. Integrated genomics physiology and breeding approaches for improving drought tolerancein crops. Theor. Appl. Genet. 125: 625645Myles S. J. Peiffer P.J. Brown E.S. Ersoz Z. Zhang D.E. Costich and E.S. Buckler 2009. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell Online 21:21942202Neumann K. B. Kobiljski S. Dencic R. Varshney and A. BAlrner 2011.Genomewide association mapping: a case study in bread wheat(Triticum aestivum L.). Mol. Breed. 27: 3758Ochieng J.W. A.W. Muigai and G.N. Ude 2007. Localizing genes using linkage disequilibrium in plants: integrating lessons from the medical genetics. Afr. J. Biotechnol. 6: 650657Palaisa K.A. M. Morgante M. Williams and A. Rafalski 2003.Contrasting effects of selection on sequence diversity and linkage disequilibrium at two phytoene synthase loci. Plant Cell Online 15:17951806Peng J. Y. Ronin T. Fahima M.S. RAlder Y. Li E. Nevo and A. Korol2003. Domestication quantitative trait loci in Triticum dicoccoides the progenitor of wheat. Proc. Natl. Acad. Sci. USA 100: 24892494Prasad B. M. Babar X. Xu G. Bai and A. Klatt 2009. Genetic diversity in the US hard red winter wheat cultivars as revealed by microsatellite markers. Crop Past. Sci. 60: 1624Price A.L. N.J. Patterson R.M. Plenge M.E. Weinblatt N.A. Shadick and D.Reich 2006. Principal components analysis corrects for stratification in genomewide association studies. Nat. Genet. 38: 904909QianY. L. ZhuoKun T. JiChun and H. ShuXiao. 2011. QTL mapping for coleoptile length and radicle length in wheat under different simulated moisture stresses. Acta. Agron. Sin. 37: 294-301Pritchard J.K. and N.A. Rosenberg 1999. Use of unlinked genetic markers to detect population stratification in association studies. Amer. J. Human Genet. 65: 220228Pritchard J.K. and M. Przeworski 2001. Linkage disequilibrium in humans:models and data. Amer. J. Human Genet. 69: 114Pritchard J.K.M. Stephens and P. Donnelly 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945959Rafalski A. 2002. Applications of single nucleotide polymorphisms in crop genetics. Curr. Opin. Plant Biol. 5: 94Rao H. O. Basha N. Singh K. Sato and H. Dhaliwal 2007. Frequency distributions and composite interval mapping for QTL analysis inSteptoe'x Morex'barley mapping population. Barley Genet. Newslett. 37: 520Ravel C. S. Praud A. Murigneux L. Linossier M. Dardevet F. Balfourier P. Dufour D. Brunel and G. Charmet 2006. Identification of Glu B11 as a candidate gene for the quantity of high molecular weight glutenin in bread wheat (Triticum aestivum L.) by means of an association study. Theor. Appl. Genet. 112: 738743Rebetzke G. R. Richards V. Fischer and B. Mickelson 1999. Breeding long coleoptile reduced height wheats. Euphytica 106: 159168Rebetzke G. R. Appels A. Morrison R. Richards G. McDonald M. Ellis W. Spielmeyer and D. Bonnett 2001. Quantitative trait loci on chromosome 4B for coleoptile length and early vigour in wheat (Triticum aestivum L.). Crop Past. Sci. 52: 12211234Remington D.L. J.M. Thornsberry Y. Matsuoka L.M. Wilson S.R. Whitt J. Doebley S. Kresovich M.M. Goodman and E.S. Buckler 2001. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. USA 98: 1147911484RhonACopyright B. A.L. Raquin and I. Goldringer 2007. Strong linkage disequilibrium near the selected Yr17 resistance gene in a wheat experimental population. Theor. Appl. Genet. 114: 787802Risch N. and K. Merikangas 1996. The future of genetic studies of complex human diseases. Sci. AAAS Weekly Paper Ed. 273: 15161517Risch N.J. 2000. Searching for genetic determinants in the new millennium. Nature 405: 847856Rogowsky P. F. Guidet P. Langridge K. Shepherd and R. Koebner 1991.Isolation and characterization of wheatrye recombinants involving chromosome arm 1DS of wheat. Theor. Appl. Genet. 82: 537544Sajjad M. S.H. khan M.Q. Ahmad A. Rasheed A. MujeebKazi and I.A.Khan 2013. Association Mapping Identifies QTLS on WheatChromosome 3A for Yield Related Traits.CRC.2013.0061Salekdeh G.H. M. Reynolds J. Bennett and J. Boyer 2009. Conceptual framework for drought phenotyping during molecular breeding. Trends Plant sci. 14: 488496Sanchez-PACopyrightrez R. J. Ballester F. Dicenta P. ArAs and P. Martinez Gomez 2006. Comparison of SSR polymorphisms using automated capillary sequencers and polyacrylamide and agarose gel electrophoresis: Implications for the assessment of genetic diversity and relatedness in almond. Sci. Hortic. 108: 310316Sears E. 1966. Chromosome mapping with the aid of telocentrics. Proc2nd Int Wheat Genet Symp. Hereditas Suppl 370381Sears E.R. 1954: The aneuploids of common wheat. Uni of Missouri College of Agric. Agric. Experiment Station Missouri USASemenov M.A. and N.G. Halford 2009. Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. J. Exp. Bot. 60: 27912804Snape J.W. M.J. Foulkes J. Simmonds M. Leverington L.J. Fish Y.Wang and M. Ciavarrella 2007. Dissecting geneA- environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154: 401408Somers D.J. P. Isaac and K. Edwards 2004. A highdensity microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor. Appl. Genet. 109: 11051114Sood S. 2008: Molecular Characterization of Threshability Genes inWheat ProQuest.Stachel M. T. Lelley H. Grausgruber and J. Vollmann 2000. Application of microsatellites in wheat (Triticum aestivum L.) for studying genetic differentiation caused by selection for adaptation and use. Theor. Appl. Genet. 100: 242248Stich B. A.E. Melchinger M. Frisch H.P. Maurer M. Heckenberger and J.C.Reif 2005. Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor. Appl. Genet. 111: 723730Tenaillon M.I. M.C. Sawkins A.D. Long R.L. Gaut J.F. Doebley and B.S. Gaut 2001. Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays spp. mays L.). Proc. Natl. Acad. Sci. USA 98: 91619166Teulat B. N. ZoumarouWallis B. Rotter M.B. Salem H. Bahri and D.This 2003. QTL for relative water content in fieldgrown barley and their stability across Mediterranean environments. Theor. Appl. Genet. 108: 181188Tommasini L. T. Schnurbusch D. Fossati F. Mascher and B. Keller 2007.Association mapping of Stagonospora nodorum blotch resistance in modern European winter wheat varieties. Theor. Appl. Genet. 115:697708Tsilo T.J. G.A. Hareland S. Simsek S. Chao and J.A. Anderson 2010.Genome mapping of kernel characteristics in hard red spring wheat breeding lines. Theor. Appl. Genet. 121: 717730Tuberosa R. and S. Salvi 2006. Genomicsbased approaches to improve drought tolerance of crops. Trends Plant Sci. 11: 405412Wang M. J. Yan J. Zhao W. Song X. Zhang Y. Xiao and Y. Zheng2012. Genomewide association study (GWAS) of resistance to head smut in maize. Plant Sci. 196: 125131Yao J. L. Wang L. Liu C. Zhao and Y. Zheng 2009. Association mapping of agronomic traits on chromosome 2A of wheat. Genetica137: 6775Yu J. and E.S. Buckler 2006. Genetic association mapping and genome organization of maize. Curr. Opin. Biotechnol. 17: 155160Yu J. G. Pressoir W.H. Briggs I.V. Bi M. Yamasaki J.F. Doebley M.D.McMullen B.S. Gaut D.M. Nielsen and J.B. Holland 2005. A unified mixedmodel method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38: 203208Zhang J. C. Hao Q. Ren X. Chang G. Liu and R. Jing 2011. Association mapping of dynamic developmental plant height in common wheat. Planta 234: 891902Zoric M. D. Dodig B. Kobiljski S. Quarrie and J. Barnes 2012.Population structure in a wheat core collection and genomic loci associated with yield under contrasting environments. Genetica 140:259275
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Author:Ahmad, Muhammad Qadir; Khan, Sultan Habibullah; Khan, A. Salam; Kazi, Abdul-Mujeeb; Basra, S.M.A.
Publication:International Journal of Agriculture and Biology
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Date:Oct 31, 2014
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