QTL MAPPING FOR CROP IMPROVEMENT AGAINST ABIOTIC STRESSES IN CEREALS.
Quantitative trait loci (QTL) approach is the source for plant breeders to scrutinize the complex traits like drought, temperature, salinity stress tolerance, etc. into their components as they largely affect the crop productivity. Agricultural production can be enhanced by incorporating the tolerance against these stresses. In this review article, we will discuss the progress made by various researchers in the area of QTL mapping for the improvement of abiotic stress tolerance in cereals. The knowledge may be helpful for plant breeders to accelerate the release of abiotic stress resistant cultivars by reducing selection time of desirable traits and to incorporate the precise and accurate alleles in breeding programs.
Keywords: QTL mapping, abiotic stress, drought stress, salinity stress, heavy metal stress, heat stress.
Development of resistant cultivars against environmental stress is the main task for plant breeders. As in nature, plants have to face various environmental stresses simultaneously that cause the reduction in the productivity and significant economic loss to cereal crops (Sanghera et al., 2011; Ramegowda and Kumar, 2015). Abiotic stresses like salinity, drought, heavy metals, water lodging, submergence, shattering, high and low temperature largely affect the crop productivity by reducing the fertility near flowering and seed growth stages. It has been estimated that abiotic stresses causes to reduce 70% yield in agricultural crops throughout the world. Due to abiotic stresses, a series of molecular and biochemical modifications take place in plants which lead to generate morphological and physiological changes (Sahoo et al., 2014).
Many functional and regulatory genes have been identified by plant breeders (Mizoi and Yamaguchi-Shinozaki, 2013) which are up or down-regulated in response to abiotic stresses. Generally, plants response to abiotic stresses varies depending upon plant species, genotypes, age, stress intensity and timing of stress application (Gall et al., 2015).
QTL mapping: importance and procedure: QTL mapping is statistical procedure used to distinguish the complex plant traits into their components (Ahmad et al., 2015; Yao et al., 2016). It controls the heritable variations in crop plants (Collins et al., 2008). It is also helpful to learn the genetic architecture of plants to improve them for desirable traits during the course of their evaluation (Bo et al., 2015). This approach also dissect the physiological and genetic elements affecting source sink relationship under abiotic stress (Welcker et al., 2007). In the field of agriculture, evolutionary biology and medicines, QTL mapping is being intensively used to find the precise location of the interested regions/genes. Functional genomics is an important tool to find the correlation between phenotype and genome of an organism subjected to diverse environmental conditions (Soda et al., 2015).
To identify the abiotic stresses resistant QTLs, a lot of work has been done by plant biologists but identified QTLs proved unstable across different environmental conditions due to their complex inheritance mechanism of abiotic stress tolerance. In this review, we will illustrate the various major and minor QTLs identified in different crops for different abiotic stresses that may help the researchers to find the desirable QTLs according the abiotic stress they are dealing with. To carry out the QTL analysis, two basic things are required, (i) two or more strains of genetically different organism and (ii) availability of molecular markers (SSRs, SNPs, RFLPs, etc.) to distinguish between these strains. By crossing the strains, heterozygous (F1) individual is obtained and then these heterozygous individuals are crossed in different ways to obtain maximum diversity.
QTLs can also be performed by using hybrid population, pedigree and sibships (Svishcheva, 2007) domesticated and wild relatives of crops (Pandey et al., 2008). Molecular markers genetically linked to the interested traits will segregate more frequently with trait value on the other hand unlinked markers will not show considerable association (Figure-1). Major objective of this analysis is to find the fact that phenotypic differences are due to few loci with large effects or many loci with smaller effects. Most of the phenotypic variations in quantitative traits are due to few loci with great effect and less due to large loci with lower effect (Maki-Tanila and Hill, 2014). In mapping QTLs, sample size is also an important factor because low sample size may fail to detect the exact effects of QTLs (Svishcheva et al., 2012; Belonogova et al., 2013).
QTLs for drought stress: A condition in which the requirement of water increases than the supply in plants is termed as drought. Among abiotic stresses, drought stress is a major source that has limited the crop productivity worldwide in arid and semi-arid environments (Bita and Gerats, 2013; Noreen et al., 2017). Drought is a major threat to crop productivity (Hussain et al., 2015). At cellular level drought causes physiological and biochemical disorders i.e. electrolytic leakage through cell membrane. So the cell membrane stability in the presence of stresses like drought or high temperature may indirectly indicate the ability of plant to withstand periods of stress (Ahmad et al., 2015). It is difficult to characterize the drought related physiological and phenotypic traits due to inadequate understanding about the mechanism of drought tolerance.
However crop yield can be enhanced in water limited environments by optimizing the root architecture because large root system is less affected by water deficient conditions (Manschadi et al., 2010). Improving water use efficiency, uptakes of available water, reduces water loss from plant surfaces by reducing leaf area or control of stomatal conductance (Abbate et al., 2004), abscisic acid content (Reynolds and Tuberosa, 2008), relative water content, adjusting osmotic, penicale and leaf water potential (Jongdee et al., 2002) and altering the flowering time may enhance the yield under drought conditions (Reynolds and Tuberosa, 2008). A large variation in timing and severity of drought stress has made it complex to screen for the improvement of crop production under drought conditions. That's why traits based approaches to enhance the crop performance under water limited environments remained low due to complexity in genetic makeup of plants.
Evidence shows that some QTLs have pleotropic effects on multi stress tolerance because in some cases drought related QTLs also influenced the plant growth under salt stress (Sharma et al., 2011). So it is important to identify specific genes from new germplasm resources that are tolerant to multiple stresses. By exploiting the functions of genes that are responsible for drought will enable the plant biologists to use them in plant breeding programs to get drought resistant cultivars (Price et al., 2002; Ahmed et al., 2011). Root architecture plays a vital role to avoid terrestrial plants from drought because, for deep rooted plants, it is easy to absorb more water from deep soil layers (Rich and Watt, 2013; Uga et al., 2015). Investigation that mechanism of drought tolerance is quantitatively inherited and controlled by various genetic loci has lead the plant breeders to the development of several drought related QTLs (Sayed et al., 2012; Kalladan et al., 2013).
QTLs for water deficient conditions have been studied in almost all cereals, regarding the factors controlling drought stress. QTLs for water-use-efficiency and deep root ratio in wheat were investigated by (Spielmeyer et al., 2007; Hamada et al., 2012). Three QTLs for root angle (QRA.qgw-3D, QRA.qgw-2A and qRA.qgw-5D), and a QTL for number of roots (qRN.qgw-1B) were mapped in wheat (Christopher et al., 2013). QTLs for root length (QRl.ccsu-2B.1), dry weight of roots (QRdw.ccsu-2A.2 and QRdw.ccsu-2A.1) on chromosome 2B and 2A respectively in wheat were studied (Bharti et al., 2014). QTLs related to drought tolerance in wheat were investigated in wheat which were associated with net photosynthetic rate (QPn2AC), cell membrane stability (QCMSa2AC), relative water content (QCMSa2AC) (Malik et al., 2015).
QTLs for water use efficiency have been investigated in maize (Landi et al., 2007), sorghum (Harris et al., 2006), brassica (Hall et al., 2005), barley (Teulat et al., 2002), rice (Steele et al., 2007; Laza et al., 2010) and pearl millet (Yadav et al., 2004; Bidinger et al., 2007). QTLs for seedling root growth in rice were also reported (Atkinson et al., 2015). Three QTLs (qtlRWC1, qtlWC-2, qtlELWL) were studied in cotton that were associated to excised loss of water and relative water content under drought stress (Saleem et al. 2015). Mace et al. (2012) mapped a QTL (qTLA3-8) for leaf area in sorghum. Adaptation of drought stress after flowering is correlated with stay green phenotypes. Post-flowering drought tolerance QTLs linked with stay green traits have been mapped by (Haussmann et al., 2002; Thomas and Ougham, 2014) in different crops. Four major QTLs having characteristics of stay green Stg1, Stg2, Stg3 and Stg4 were mapped in sorghum.
Stg1 and Stg2 were found on chromosome 3 of sorghum (Harris et al., 2006; Xu et al., 2012). Stg3 was located on chromosome 2 while Stg4 was mapped on chromosome 5 (Harris et al., 2006).
QTLs for high and low temperature stresses: Extreme temperature situations either high or low, both severely damage the plant structure and physiology. Due to global warming, elevated temperature has become the major abiotic stress and plants are needed to adjust with these stresses to survive (Hall, 2010). In many areas of the world, heat stress is an important cause to reduce the economical yield of agricultural crops (Wahid et al., 2007). High temperature disturbs many cellular and developmental processes in plants. It reduces fertility rate, lower the grain production and quality in agricultural crops (Barnabas et al., 2008). Goulas et al., (2006) reported that heat stress has also lead to change in plant metabolism and gene expression. High temperature causes to early abortion in tapetal cells leading the pollen mother cells toward mitotic phase and finally goes to PCD yield to pollen sterility (Parish et al., 2012).
To overcome the heat related stresses, plant biologists have mapped many QTLs in various important crops. In order to improve the crop productivity of rice under high temperature, Vijayalakshmi et al., (2010) reported many QTLs that were related to senescence under high temperature stress. Similarly, Zhao et al., (2016) reported an important QTL on chromosome 9 in Indian rice cultivar that explained up-to 50% phenotypic variation which was related to heat tolerance and amylose content. In bread wheat, QTLs for heat tolerance were mapped by using different heat related traits such as decrease in canopy temperature (Pinto et al., 2010), senescence (Vijayalakshmi et al., 2010) and discrimination of carbon isotopes (Rebetzke et al., 2008). Four QTLs (Qhr1; qhr3-1qhr4-3qhr8-1) associated to heat tolerance at flowering stage in rice were reported by Ye et al., (2012 and 2015).
Two minor QTLs (qHTSF1.1 and qHTSF4.1) controlling the spikelet fertility under high temperature conditions were mapped in rice by (Ye et al., 2012 and 2015). High temperature decreases spikelet fertility and disturb membrane stability so keeping these traits in mind, Talukder et al., (2014) investigated QTLs (qHTSF1.1, qHTSF4.1) related to spikelet fertility in high temperature. Like high temperature stress, cold stress also imparts many physiological, biochemical events in agricultural crops (Thomashow, 1999). It also delay seed germination and seedling growth, and non uniform maturity causes to disturb plants biological functions by chilling and freezing injuries (Andaya, 2003; Manangkil et al., 2013). Due to chilling stress plant traits such, as stay green leaf, leaf area, shoot weight and nitrogen contents are affected (Jompuk et al., 2005).
Root conductivity (RC) is an important trait that can be used as an indicator to map quantitative trait loci (QTLs) of cold tolerance in cereals (Xiao et al., 2014). Cold tolerance can be controlled by controlling biological mechanism taking place in plants, i.e cold sensing, transcriptional regulations and post-transcriptional modifications (Zhang et al., 2008; Sanghera et al., 2011). Different researchers have explained the molecular and cellular mechanism of chilling temperature however its genetic mechanism is not understood yet (Pandey et al., 2009). Among cereals rice is most affected cereal by cold stress so by broadening the rice gene pool through introducing cold tolerance traits and its related genes in wild rice, its production can be enhanced (Xie et al., 2012). QTLs for cold tolerance at seedling and booting stage have been reported in rice (Lou et al., 2007; Shinada et al., 2014).
Mamun et al., (2006) reported nineteen QTLs for cold tolerance in rice that were present on chromosome No.3 and 8. Reinheimer et al., (2004) mapped a frost tolerance QTL (QFr-H1) in barley. Low temperature related QTLs have mapped in lentil (Kahraman et al., 2004), maize (Hund et al., 2005; Presterl et al., 2007; Revilla et al., 2016), ryegrass (Zhang et al., 2009) and Sorghum (Knoll and Ejeta, 2008) faba bean (Sallam et al., 2016). QTLs (qCTB7) and (qCTB8) related to low temperature tolerance at booting stage in rice were mapped (Zhou et al., 2010; Kuroki et al., 2007). Most of the cold tolerant QTLs identified in cereals were linked with avoidance mechanism.
However two QTLs related to cold tolerance at seedling stage (qCTS11-1) and (qSCT-11) were mapped and associated to cold induced wilting tolerance and recovery of growth after cold stress in rice (Andaya, 2003), (qSCT-3-1) (Kuroki et al., 2007) and a QTL (qCTS8.1) by (Wang et al., 2011) qCTS12, qCTS4 by (Andaya and Tai, 2006). Germination in low temperature conditions is one of the most important traits in seedling development under direct sowing of rice (Satoh et al., 2016). Many QTLs have been identified and evaluated for low temperature germination ability i.e qLTG3-1, (qLTG11.1) (Wang et al., 2011) and qCtss11. Four QTLs related to germination factors were evaluated by (Satoh et al., 2016) and reported that qLTG3-1; qLTG3-2 and qLTG11-1 enhanced the germination under cold stress while the QTL qLTG1-1 delayed germination under cold climatic conditions.
In maize, six QTLs on chromosome 4,5,6,7, and 9 were mapped that were associated to germination at low temperature and primary root length in this crop (Hu et al., 2016). Due to low temperature male sterility is induced at reproductive stage and badly affects the production of important crops. So two QTLs (qCTR5 and qCTR12) were detected on chromosome 5 and 12 of maize genome which control this trait at reproductive stage (Koumoto et al., 2016). The major QTLs detected, named 4A-1, was located on the long arm of chromosome 4 (Barrero et al., 2015).
QTLs for salinity stress: Soil Salinity is one of the major abiotic stresses that reduce the yield of cereal crops. Excess amount of soluble salts present in soil solution adversely affect plant metabolic activities (Lutts et al., 1995). About 830 million hectares area is affected by salinity worldwide (Rengasamy, 2006). Salinity tolerance is a complicated trait having various components. They can be understood by genome wide association studies (Kumar et al., 2015). To improve the crop production under saline conditions QTL mapping is an important approach to enhance the productivity (Gimhani et al., 2016). It provide good understanding about the understanding of genetic control of salinity tolerance (Turki et al., 2015). Newly developed high yielding cultivars are more sensible to slat stress (Tiwari et al., 2016).
Salinity effects agricultural crops in many ways i.e reduces germination rate (Kamyar, 2011), eliminate seedling survival (Lutts et al., 1995), damages chloroplast structure (Yamane et al., 2008), diminish photosynthesis rate and reduces grain yield (Asch et al., 2000). Adverse effect of salinity can be minimized by improving antioxidant machinery and photosynthesis rate (Tuteja et al. 2013), eliminating Na+ ion contents of tissues (Xue et al., 2009) and water soluble carbohydrates and chlorophyll contents (Siahsar and Narouei 2010). Salinity is controlled by various genes which are expressed at different developmental stages of plants. So by understanding the mechanism of salinity tolerance crop loss due to salinity stress can be minimized (Tuteja et al., 2013). Significant variability has been studied in yield and other yield related traits in different plants under salt stress.
Hence the productivity of agricultural crops can be improved by discovering and incorporating salt tolerant genes in genetic makeup (Kumar et al., 2015). QTL mapping approach may be helpful to improve the salt tolerant traits in agricultural crops (Hossain et al., 2015). Many major and minor salt tolerant QTLs have been identified by plant biologists which are being used in breeding programs (Flowers et al., 2000; Shavrukov et al., 2010; Sbei et al., 2014). However the efficiency of these QTLs is low due to relative lower heritability and factors effecting gene expression. QTLs for salinity tolerance in rice were reported (Hossain et al., 2015; Kumar et al., 2015; Tiwari et al., 2016) Similarly, salt resistant QTLs in barley cultivars were also reported (Xue et al., 2009; Sbei et al., 2014). Sodium (Na+) and Potassium (K+) related QTLs in various cereals have been reported (Koyama, 2001).
Various salt ion concentration i.e Na+,K+, Cl-QTLs on reproductive stage of leaf tissues in rice genotypes were also reported (Ammar et al., 2009; Pandit et al., 2010). Low Na+ tolerance QTLs were investigated in barley (Mickelson, 2003), in rice (Lian et al., 2005) and in seedling stage of maize (Wang et al., 2012). Dubcovsky et al., (1996) reported that a locus "kna1" present on chromosome 4D of wheat controls K+/Na+ ratio in leaves and is associated with high salt tolerance. A QTL for Na+ exclusion have been mapped by (Lindsay et al., 2004; James et al., 2013) on chromosome 2A in wheat. Ren et al., (2005) and James et al., (2006) reported sodium tolerant QTLs (OsHKT1;5 and TmHKT1;5-A) in rye and wheat (TaHKT1;5-D). Pandit et al., (2010) find the QTL (qNaSH8.1) that was controlling sodium content under high salinity conditions in rice. Ahmadi and Fotokian (2011) reported a QTL (qNar/Kr5) associated to sodium and potassium ratio in rice roots.
Potassium and sodium concentration in plant tissues is also an important factor regarding the salinity of plants, keeping this in view Xu et al., (2012) investigated two QTLs qSKC9; qSNC9) on chromosome 9 of rice that were controlling K+ and Na+ concentration. Recently two salinity tolerance QTLs (QST.TxFr.7H and QS1wd, YG.4H) were investigated (Ma et al., 2015). A QTL with the characteristic of Na+ exclusion in wheat organs was reported by (Genc et al., 2010). Cl-uptake and its accumulation is a polygenic trait and a QTL(5A; barc56/gwm186) controlling the concentration of Cl-was mapped by (Genc et al., 2014) in wheat. During their work on wheat salinity tolerance, James et al., (2006) find two QTLs (TaHKT1;5-D and TmHKT7-A2) that were linked with sodium tolerance and its accumulation in shoots of wheat respectively. Quantitative trait loci (QTLs) for P deficiency tolerance had been identified in rice (Wissuwa et al., 1998; Wang et al., 2014).
Ten salinity tolerance QTLs (qSFW-1a-CK, qSFW-1b-CK, qRK-1-CK, qSN-1-CK, qSDW-3-CK, qSTR-4-CK, qSNK-6-CK, qSDW-7-CK, qSNK-9-CK, qRNK-9-CK) were detected by (Anh et al., 2014) on rice chromosome 1,3,4,6,7 and 9. A salt tolerance QTL (Saltol QTL) was reported in rice by (Ganie et al., 2016). QTLs for heavy metal stress: Presence of heavy metals adversely affect the crop productivity along with health risks to animals and human beings which are fed by these crops (Stitt and Hurry, 2002; Goulas et al., 2006). The excess amount of heavy metals exert harmful effects on plant cells due to their toxicity, reduces crop yield and increases health problems for the consumers of these crops (Appenroth, 2010). So the harmful effects of these metals can be minimized by understanding the mechanism of metal tolerance and their accumulation in contaminated soils.
Identification of heavy metal stress tolerant genotypes will make the researchers enable to enhance the crop yield in contaminated soils and reduce the soil pollution as well. Here are some examples of QTLs detected for metal tolerance in cereals. Zhang et al., (2008) detected arsenic tolerance QTLs in rice cultivars with characteristics of low straw and high grain yield. Similarly Aluminum stress related QTLs have been detected in wheat (Raman et al., 2005), oat (Wight et al., 2006), rye (Matos et al., 2005), barley (Wang et al., 2007), rice and sorghum (Magalhaes, 2004; Magalhaes et al., 2007; Caniato et al., 2007), soybean (Xue et al., 2007) and maize (Ninamango-Cardenas et al., 2003). An Al tolerance QTL (SbMATE) was reported by (Magalhaes et al., 2007) in sorghum genotypes. QTLs for nickel tolerance were reported in S. vulgaris (Bratteler et al., 2006) and wild cabbage (Burrell et al., 2012).
QTLs resistant to boron accumulation were observed in wheat (Schnurbusch et al. 2007), rice (Ochiai et al., 2008; de Abreu Neto et al., 2017) and maize (Ducrocq et al., 2008). QTLs for zinc content has been reported by (Wahid et al., 2007), in seeds of been. Filatov et al., (2007) and (Frerot et al., 2010) analyzed the cross between C. perraea and C. halleri that were grown in zinc polluted soils and identified five QTL regions related to zinc concentration. QTLs related to heavy metal tolerance through phytoremediation in cereals were reported by Verbruggen et al., (2013). Ding et al., (2011) reported ten QTLs (qLAC1, qSAC1a, qSAC1b, qLAC5, qSAC5, qKAC5, qKAC7, qLAC8, qBAC9a, qBAC9b) in maize plants that were present on different chromosomes and showing resistance to arsenic concentration. Cadmium (Cd) is a toxic element, and rice is known to be a leading source of dietary Cd for people who consume rice as their main caloric resource (Sun et al., 2016).
To overcome the Cd toxicity in rice crop, Yan et al., (2013) found a shoot cadmium accumulation resistant QTL (scc10) and three grain cadmium accumulation resistant QTLs (gcc3, gcc9 and gcc11). Three QTLs (HvMATE, HvAACT1 and Bot1) controlling boron toxicity in barley have been reported by (Furukawa et al., 2007; Sutton et al., 2007; Wang et al., 2007) respectively.
QTLs for water lodging and submergence stress: Water lodging is a condition when excess amount of available water covers only root system of plants instead of all plant or stem. Lodging causes to displace the plant stem from upright position permanently. Lodging has been very serious problem in cereals (Verma et al., 2005) and due to sever lodging whole crop field is compressed causing huge loss to the crop production (Crook and Ennos, 1995). Lodging stress causes to reduce the production and quality of cereals by breaking or bending of stem (Zhang et al., 2016). Lodging can be divided into two categories; (1). Stem lodging: This is caused by bending or breakage of lower culm internodes. This type of lodging is dependent to tensile strength of inter node, diameter of stem wall and its thickness. (2) Root lodging; this loading is caused by disturbance of plant root system and culms of crown (Berry et al., 2003; Pinera-Chavez et al., 2016).
Lodging can be minimized by selecting proper variety, suitable sowing time, deep drilling, sustaining soil fertility (Sterling et al., 2003). High nitrogen also causes to increase plant height, increase lower internode length, increases fresh weight of areal parts which may lead to lodging so optimal use of nitrogen may reduce this stress (Pinthus, 1967). Lodging stress can be minimized by reducing plant height and producing semi dwarf cultivars but this trait may also affect the biomass production and have negative effects on yield related components (Keller et al., 1999). Deficiency of GA decreases self weight of above plant parts which results in lodging resistance cultivars (Okuno et al., 2014).
Culm and stem strength is very important feature for improving cereal cultivars against lodging stress (Okuno et al., 2014) so for the improvement of lodging resistance major focus should be given for the improvement of this trait (Zhang et al., 2013) because stem, culm thickness, length, and strength are directly correlated with lodging resistance (Zhang et al., 2013). Basal internodes with short length and hard basal culm may also be lodging resistant. QTLs for lodging resistance were reported in many cereal crops like barley (Backes et al., 1995; Hayes et al., 1993), rice, oat (De Koeyer et al., 2004) and maize (Flint-Garcia et al., 2003). A water lodging resistant QTL (lrt5) was mapped in rice under typhon conditions by Ishimaru et al., (2008). Despite other abiotic stresses traits, only a little work has done for the improvement of lodging resistance in cereals. So by characterizing the resistant genes this trait can be improved and yield can be enhanced in cereals.
A stress condition where the water covers areal parts along with root system of plants is termed as submergence. Submergence stress is more dangerous as compared to lodging because too much water at any developmental stage may cause crop injury and yield loss. First effect of this stress is deprived seedling growth, less germination percentage with poor crop establishment when rainfall occurs few days following seedling (Ismail et al., 2012; de Melo et al., 2015). Previously more emphasize was given to the lodging resistance while this stress remained uncharacterized (Abiko et al., 2012; Thirunavukkarasu et al., 2013). Toojinda (2003) reported submergence QTL (SUB1) in rice. From the fine mapping of this QTL a cluster of submergence genes (SUB1A, SUB1B, and SUB1C) were reported (Xu et al., 2006).
Further experiment confirmed that high degree of submergence tolerance in rice can be achieved by SUB1A (Septiningsih et al., 2008; Sarkar and Bhattacharjee, 2011; Singh et al., 2014). Campbell et al., (2015) recently reported a submergence related QTL (Subtol6) in maize on chromosome 6.
QTLs for shattering stress: Like other environmental factors, shattering also causes huge yield loss in cereal crops. Main factors involved in shattering are high or low temperature, excess or lower availability of water, pest pathogen attack (Zhou et al., 2010). The other source of grain shattering is abscission layer (Ji, 2006). The degree of shattering depend upon the morphology of abscission layer. In wheat abscission layer associated genes (sh2, sh4, and sh-h) have been mapped on chromosome 1,3 and 7 respectively (Oba et al., 1995; Fukuta and Yagi, 1998). Kernel shattering is the important trait which can affect shattering in wheat. It has been reported that chromosome 2B, 3B and 7A have agronomic regions which are associated with kernel shattering (Zhang and Mergoum, 2007). Konishi et al., (2006) mapped a QTL (qSH1) in rice that was resistant to seed shattering at maturity.
A pod shattering resistant QTL (qPDH1) was mapped in soybean by (Funatsuki et al., 2012). Lin et al., (2012) reported that a gene (sh1) is a universal shattering resistant gene present in sorghum, rice and maize crops. In past years shattering resistant genes sh4 (Li, 2006; Lin et al., 2007), qSH1 (Konishi et al., 2006), OsCPL1 (Cheng et al., 2016), SHAT1 (Hofmann, 2012 and Cheng et al., 2016), and SH5 (Yoon et al., 2014) have been characterized in rice. Lee et al., (2016) detected QTLs (qSh1 and qSh6) pertaining to breaking tensile strength and abscission layer, on chromosome 1 and 6 of rice. Kwon et al.,(2015) in his study confirmed the roll of these genes (qSH-4 and sh-h) in rice for shattering resistance. Recently, Cheng et al., (2016) reported four important QTLs (qSH1JCQ, qSH3JCQ, qSH6JCQ, and qSH11JCQ) they also confirmed that QTL qSH1JCQ may cause significant decrease in expression of shattering related genes (qSH1, OsCPL1, Sh4, SH5, and SHAT1).
Two shattering resistant QTLs (qSRI.A06 and qSRI.A09) were mapped in rapeseed (Liu et al., 2016). In addition to rice shattering genes, the wheat Q gene was studied to have effects on the compaction wheat ears (Simons et al., 2006).
Table.1 Important QTLs identified for abiotic stress tolerance in cereals.
Sr.###Crop###Abiotic stress/ Plant Trait###QTL/Gene###Linkage###group/###References
1###Wheat###Root angle###QRA.qgw-2A###2A1###Christopher et al., 2013
2###Sorghum###Leaf area###qTLA3-8###SBI-08-II###Mace et al., 2012
3###Wheat###Root angle###QRA.qgw-3D,###3D###Christopher et al., 2013
4###Wheat###Number of roots###qRN.qgw-1B###1B###Christopher et al., 2013
5###Wheat###Root length###QRl.ccsu-2B.1###Chromosome-2B###Bharti et al., 2014
6###Wheat###Root dry weight###QRdw.ccsu-2A.1,###Chromosome-2A,###Bharti et al., 2014
7###Wheat###Net photosynthetic rate###QPn2AC###2A###Malik et al., 2015
8###Wheat###Cell membrane stability###QCMSa2AC###2A###Malik et al., 2015
9###Wheat###Relative water content###QCMSa2AC###2A###Malik et al., 2015
10###Rice###Heat tolerance at flowering###Qhr1###Chromosome-1###Cao et al., 2003
11###Rice###Low temperature tolerance###qCTB8###Chromosome-8###Kuroki et al., 2007
12###Rice###Heat tolerance at flowering###qhr3-1###Chromosome-3###Ye et al., 2012
13###Rice###Cold tolerance###qSCT-3-1###Chromosome-3###Zhang and Xie, 2014
14###Rice###Spikelet fertility under high###qHTSF1.2,###Chromosome-1###Ye et al., 2015
15###Wheat###Spikelet fertility under high###qHttmd.ksu-6A###Chromosome-6A###Talukder et al., 2014
16###Wheat###Plasma membrane damage###QHtpmd.ksu.2B###Chromosome-2B###Talukder et al., 2014
###under high temperature
17###Barley###Frost tolerance###QFr-H1###Chromosome arm###Reinheimer et al., 2004
18###Rye###Na Tolerance###OsHKT1;5,###Skc1,###James et al., 2006; Ren et
19###Wheat###Na Tolerance###TaHKT1;5-D###Kna1###James et al., 2006; Ren et
20###Rice###Na content under high salinity###qNaSH8.1###Chromosome-8###Pandit et al., 2010
21###Rice###Na+/K+ ratio in roots###qNar/Kr5###Chromosome-5###Ahmadi and Fotokian,
22###Rice###K+concentration###qSKC9,###Chromosome-9###Wang et al., 2012
23###Wheat###Na Tolerance###TaHKT1;5-D,###Kna1,###James et al., 2006
###Shoot Na accumulation###TmHKT7-A2###Nax1
24###Wheat###Na+ exclusion###HKT1###Chromosome-2A###Genc et al., 2010
25###Barley###Salinity tolerance###QS1wd,YG.4H,###4H,###Ma et al., 2015
26###Maize###Arsenic concentration###qLAC1,###Chromosome-1###Ding et al., 2011
27###Rice###Shoot cadmium accumulation###scc10###Chromosome-10###Yan et al., 2013
28###Rice###Grain cadmium accumulation###gcc3,###Chromosome-3###Yan et al., 2013
29###Barley###Boron toxicity tolerance###Bot1###N/A###Sutton et al., 2007
30###Barley###Aluminum toxicity tolerance###HvMATE###Alp###Furukawa et al., 2007
31###Barley###Aluminum toxicity tolerance###HvAACT1###Alp###Wang et al., 2007
32###Sorghum###Al Tolerance###SbMATE###AltSB###Magalhaes et al., 2007
33###Rice###Cold tolerance###qSRS1,###1###Cheng et al., 2012
34###Rice###Heat Tolerance###qSF4,###4###Cheng et al., 2012
35###Barley###chlorophyll fluorescence###QFv2H###2H###Siahsar and Narouei,
36###Barley###Chlorophyl content under###QCh7Ha###7H###Siahsar and Narouei,
37###Barley###water soluble carbohydrate###QWSC2H###2H###Siahsar and Narouei,
38###Rice###Break tensile strength###qSh1###Chromosome1###Lee et al., 2016
Conclusions: A lot of work has been done in the field of quantitative traits loci (QTL) mapping by plant breeders but it is needed to link this work to achieve the fruitful results. Main problem in QTL approach is that its effect varies during the change in environment. Recently, QTLs detected on the bases of high density genetics maps has increased the understanding about the genetic control of abiotic stresses in cereals and other agricultural crops. This approach helps to discover the resistant genes and to understand the mechanism of plant adaptation under stress conditions.
Acknowledgements: The authors acknowledge the support from Higher Education Commission, Government of Pakistan.
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