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Quantitative trait loci from identification to exploitation for crop improvement Jitendra Kumara*, Debjyoti Sen Gupta a, Sunanda Guptaa, Sonali Dubeya, Priyanka Guptaa, Shiv Kumar b a Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kanpur India International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat-Institutes, B.P. 6299, Rabat, Morocco *Corresponding author E-mail address: [email protected]; [email protected] b 1 Quantitative trait loci from identification to exploitation for crop improvement ABSTRACT Rapid advances in the fields of genetics and genomics after the discovery of Mendel’s laws of inheritance have led to map many genes/QTL controlling qualitative and quantitative traits in plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine with the abundance of polymorphic molecular markers. Recently, the next generation sequencing methods have accelerated research on QTL mapping using both linkage and association mapping approaches. These efforts have led to identification of closely linked markers with gene/QTLs and identified markers even within the gene/QTL controlling the trait of interest. Some of the notable successes of utilizing major QTL in cultivar development include Fhb1 for Fusarium head vlight ressitance in wheat, Sub1 for submergence tolereance in rice and major QTL for resistance to cyst nematode in soybean. Efforts have also directed towards cloning of gene/QTLs for identification of potential candidate genes responsible for a trait. New concepts such as crop QTLome and QTL prioritization hold promise to accelerate the precise application of QTL for genetic improvement of complex traits in crop plants. In the present article, we reviewed QTL mapping approaches from identification to exploitation in plant breeding programs, and introgression of favourable QTL alleles through marker assisted breeding for improving productivity in major crops. Keywords: quantitative traits; genetic variation; quantitative trait loci; molecular markers; bi-and multiparental mapping; GWAS; candidate gene; QTL cloning; crop QTLome; marker assisted selection 2 Introduction With the discovery of Mendel’s laws of inheritance in the mid nineties, phenotypic studies using biparental segregating populations have identified a large number of major genes in crop species. Subsequently, linkage studies have helped to map genes encoding phenotypes on chromosomes and assigned them in linkage groups. This classical genetic approach has helped identify and map genes controlling qualitative traits especially disease resistance and domestication in several crops (Kessel and Rowe 1974; Magato and Yoshimura 1998). However, genetic improvement of crop plants relies on the manipulation of not only qualitative traits but also quantitatively inherited agronomic traits. Many genes control these complex traits and each gene has small and cumulative effect on the target trait. Hence, decoding the genetic architecture of important agronomic traits such as yield and adaptation was a challenge for breeders before the 1980s. Recent advances in gel-based molecular markers and next generation sequencing technolgies have made it possible to map genomic regions controlling quantitative traits (Martienssen et al. 2005; Johannes et al. 2008). Genomic studies have helped to map genes for thousands of traits leading to reconstruction of gene network at transcription level that explain relationships among traits (Jansen and Nap 2001; Li et al. 2008; Chaibub Neto et al. 2008). Now, genetic linkage analysis of quantitative trait loci (QTL) has become a common technique (Kearsey and Farquhar 1998; Holland 2007) to decipher the network of QTL underlying a complex trait (Doerge 2002; Jansen 2003). So far, about 2500 studies were published on QTL analysis including phenotypic QTL (phQTL), expression QTL (eQTL), protein QTL, and metabolic QTL (Gilad et al. 2008; Jansen et al. 2009; Ben-Ari and Lavi 2012). There are reports on the successful deployment of QTLs for genetic improvement in crop plants (Xu and Crouch 2008; Gupta et al. 2010; Kumar et al. 2011a; Hanson et al. 2016; Shamsudin et al. 2016). This review focuses on the recent developments in QTL analysis for agronomically important traits during the past two and a half decades and their exploitation in genetic improvement of crop plants. . Historical milestones in genetics Since the discovery of Mendel’s laws of inheritance in the mid nineties, several milestones have been achieved in the field of genetics (Table 1). These developments have employed different forward and reverse genetic approaches to accelerate the genetic gain in crop plants. Among them, the discovery of DNA structure (Watson and Crick 1953), DNA sequencing (Sanger and Coulson 1975) and polymerase chain reaction (PCR; Mullis et al. 1986) have been instrumental in changing the dimension of genetic research. These advancements have helped in developing various genomics tools and techniques, which have ultimately facilitated the whole genome sequencing of plant species (Table 2). The whole genome sequencing projects have generated huge amounts of sequence data and deciphered the vast gene sequences in many crop species. However, many functions of these gene sequences remain unknown. Assigning a function to these sequences will support identification and cloning of genes/QTL associated with agronomically important traits. Qualitative versus quantitative traits Plant phenotype is the sum of expression of a group of characters each of which has variable expression from one population to another. Traits are variable features of a character. For example, plant height is a character that can express as tall or dwarf trait in a population. Such traits can inherit qualitatively or quantitatively. Qualitative traits are the classical Mendelian traits, which 3 occur in discrete or distinct phenotypic classes and exhibit discontinuous variation in a population. Inheritance studies have shown that variation for each qualitative trait in a population is under the genetic control of two or more alleles of a single major gene with high heritability as environmental conditions have little or no effects to obscure the gene effects (Mather 1941). Quantitative traits, which are also known as metric traits, are economically important traits. These traits show continuous variation in a population and individuals can not be categorized into distinct classes. Nilsson-Ehle (1909) and East (1916) were the first to describe quantitative inheritance. They observed that several genes (perhaps 10 or more genes) are involved in the inheritance of a quantitative trait and each gene contributes small and cumulative effect to the total phenotype. These traits are significantly influenced by environmental factors and thus have lower heritability. Inheritance of quantitative traits is also called polygenic inheritance and can only be deciphered by statistical methods (Mather 1941). Sources of genetic variation Genetic variation for a trait in a population can either exist naturally or induce artificially through mutation. Therefore, both natural and induced variations are important for studying genetics of a trait and making genetic improvement in crop plants. In a natural population, genetic variation is generated through spontaneous mutation, recombination, and migration of genes. However, mutation is the ultimate source of all variation in a natural population. Recombination releases new variability by recombining already exiting variation, and migration changes gene frequency of a phenotype in natural population through migration of genes from one population to another. Induced mutation is an important discovery in the field of genetics (Muller 1930). It can generate rare variation and therefore, it is used preferably over the other advanced mutagenic tools such as transposon elements (Bingham et al. 1981). It creates a series of alleles at a locus and each allele can have large phenotypic effect. Therefore, induced mutation is used to generate mutant populations having variation for a number of qualitative traits. These mutant populations are useful as donors for desirable trait(s) and genetic resources for deciphering the gene function. Point mutations in a single nucleotide all over the genomes are responsible for creating variation in quantitative traits. Therefore, mutant populations have been generated in several model and crop species (Till et al. 2004; Krishnan et al. 2009; Tadege et al. 2009; Xin et al. 2009; Thompson et al. 2013; Henry et al. 2014; Xin et al. 2015). These mutant populations are now important genetic resources for establishing gene functions using TILLING approach in several crops including sorghum (Xin et al. 2008; Jiao et al. 2016), maize (Till et al. 2004), chickpea (Muehlbauer and Rajesh 2008), wheat (Slade et al. 2005), rice (Till et al. 2003; Till et al. 2007), soybean (Cooper et al. 2008), pea (Dalmais et al. 2008; Le Signor et al. 2009), and Medicago trancatula (Le Signor et al. 2009a). Quantitative trait loci (QTLs) Quantitative trait loci (QTLs) are phenotypically-defined chromosomal regions that contribute to allelic variation for a biological trait. In literature, quantitative trait locus has been mentioned as QTL and quantitative trait loci as QTLs. Most of the agronomically important traits follow quantitative inheritance and are the major breeding objectives for genetic improvement. Polygenic control with small additive or dominance effects of each individual genes on the expression of a quantitative trait suggests its complex inheritance. Consequently, the genetics of complex traits 4 was not known for a long time and biometrical assumptions that are known as ‘statistical fog’ were used to estimate the sum number of genes controlling a quantitative trait (Mauricio 2001).Thus, the knowledge of individual genes themselves was unknown. However, studies carried out during the first half of the 20th century helped to resolve complexity of quantitative traits. These studies suggested that genes with a major effect on quantitative traits do exist and can be experimentally mapped on chromosomes by establishing correlation between quantitative trait value and allelic states at linked genetic markers (Sax 1923; Thoday 1961). Sex (1923) studied for the first time the inheritance of a quantitative trait (seed weight) with the help of a major gene locus controlling a qualitative trait (seed color) in common bean (Phaseolus vulgaris). Segregation of seed color in a 1(PP): 2(Pp): 1(pp) ratio in F2 population indicated qualitative inheritance with a single dominant gene P/p while seed weight showed a continuous distribution. In F2 population of 166 plants, mean weight of three seed color classes was measured. The mean of individuals with ‘PP’ genotypes had heavier seeds compared to the mean of individuals with ‘pp’ genotypes while average seed weight of heterozygotes ‘Pp’ was exactly intermediate. In this way, for the first time, a quantitative trait was linked to a major gene locus. Using the same approach in subsequent studies, polygenes were located on chromosome (Thoday 1961). This has led to identification of a QTL or genetic locus at which functionally different alleles segregate and cause significant effects on phenotypic expression of a quantitative trait (Salvi and Tuberosa 2005). Type of QTLs Quantitative trait loci have been described in many types in literature on the basis of their degree of expression effects on phenotype and contribution to phenotypic variation. These are discussed below. Major versus minor QTL Marker-trait association analysis identifies genomic regions (i.e. QTLs) that control phenotypic variation of a quantitative trait segregating in a population. A number of QTLs contribute to the total phenotypic variation but each QTL explains only a part of the total variation. The amount of variation explained by a QTL is used to characterize it as major or minor QTL. A QTL that explains less than 10% of the total variation is considered as minor QTL and a QTL with more than 10% of total variation as major QTL. This criterion has been used in several studies conducted in rice, wheat, and other crops (Hartman et al. 2013; Uga et al. 2013; Gao et al. 2015). However, in some studies a criterion of 20% of phenotypic variation has been used for classifying major and minor QTLs (Davey et al. 2006). Therefore, the threshold level for classification of major and minor QTL has been variable from case to case studies. During QTL analysis, minor and environment specific QTLs are ignored while the recognized “stable” QTLs are few. As a result, genetic architecture of a complex trait is only partially deciphered. A concept of micro-real QTL (MR-QTLs) has been reported in rapeseed (Brassica napus) for recognizing and emphasizing the environment specific QTLs and protecting minor QTLs (Long et al. 2008). MR-QTLs refer to QTLs under the threshold (P ≤ 0.05) but above certain standard (P ≤ 0.5) in multi-environment trials. MR-QTLs account for 10-15% of the total examined QTLs in rapeseed (Long et al. 2008) Pleiotropic QTLs 5 QTL analysis sometimes locates more than one trait within the same genomic region. Clustering of QTLs for many traits within a small genomic region may be due to the presence of a single pleiotropic gene (Cai and Morishima 2002; Peng et al. 2003; Doebley 2004; Gyenis et al. 2007; Miller et al. 2014). Hence, identification of a QTL that affects more than one trait is called pleiotropic QTLs. However, it is required to separate such QTLs from those affecting more than one trait due to genetic linkage. Pleiotropic QTLs become advantageous in those cases where agronomically important traits are positively associated. In rice, the SCM2 QTL for culm strength is reported to be identical with the APO1 QTL for panicle structure, and is located on the same genomic region. Pleiotropic effect of the SCM2 QTL was established in a nearisogenic line (NIL) carrying this QTL, which showed enhanced culm strength and increased spikelet number (Ookawa et al. 2010). Epistatic QTL Epistasis involves non-allelic interactions between a pair of loci where an allele of one locus modifies the effect of another locus. Bateson (1909) described many phenotypes on the basis of epistatic interaction due to modified ratios in segregating populations (Eshed and Zamir 1996; Carlborg and Haley 2004). It is known that quantitative traits are under the control of several genes/QTLs and its phenotypic value is the sum effects of individual QTLs. However, sometimes due to epistatic interaction, phenotype of a given genotype may deviate from the sum of its component individual QTL effects. In several studies, an individual QTL does not have its own effects but when it comes together with other QTLs, it contributes to phenotype. Such QTLs are described as epistatic QTLs. QTL mapping approaches had initially focused on identification of the main genetic (i.e. additive and dominance) effects and ignored the epistatic QTLs underlying the complex traits and hence, no epistasis was assumed in various genetic models used for QTL mapping which could lead to a biased estimation of QTL parameters. In recent years, the focus has been directed on the identification of epistatic QTLs along with the main effect QTLs using different analytical tools (Xing et al. 2002; Bocianowski 2012; Krajewski et al. 2012; Lark et al. 1995; Eshed and Zamir 1996; Cockerham and Zeng 1996; Yu et al. 1997; Conti et al. 2011; ; Jiang et al. 2011; Li et al. 2011a; Mao et al. 2011; Upadhyaya et al. 2011; Bocianowski 2012abc). Expression QTLs (eQTLs) Quantitative expression of a gene can vary from one individual to another. This variation in the expression of a gene has been identified as heritable and the genomic region that governs the expression of such genes is defined as expression quantitative trait locus i.e., eQTL (Mangion et al. 2006; Wang et al. 2014c). Expression QTL helps to understand the regulation of gene expression. In the eQTL analysis, relationship between genome and transcriptome is established by quantification of transcript levels of genes underlying a complex trait (Hansen et al. 2008). When gene expression variation is corresponded with phenotypic variation for a trait among individuals of a mapping population, it helps to identify candidate genes for phenotypic QTLs (Holloway and Li 2010). The use of array analysis in e-QTL can help genetic mapping and phenotyping simultaneously (Rostoks et al. 2005; West et al. 2006; Luo et al. 2007). Expression QTLs are classified into cis- or trans-eQTLs based on their presence in the genome (Hansen et al. 2008). Cis-eQTL is physically located near the gene itself and differential 6 expression of the gene is occurred due to polymorphism in promoter region. A number of QTLs for glucosinolate biosynthesis and activation (Lambrix et al. 2001; Kliebenstein et al. 2001; Kroymann et al. 2003; Zhang et al. 2006;), phosphate sensing (Svistoonoff et al. 2007), and flowering time (Johanson et al. 2000; Caicedo et al. 2004; Werner et al. 2005; Clark et al. 2006; Salvi et al. 2007;) have been cloned through this approach. These QTLs are cis-eQTL because polymorphism in the promoter sequence has a direct influence on the gene expression, which leads to larger phenotypic effect on a trait. In contrast, trans-eQTLs are not physically located near the gene and hence trans-eQTL causes variation in the expression of genes due to polymorphism in a regulatory factor. These regulatory factors can be located elsewhere in the genome and many regulatory factors may be involved in controlling a gene. Therefore, change in one regulatory factor may cause small effect on the phenotypic expression of a gene. Many times, a regulatory factor controls multiple genes and hence trans-eQTL works as pleiotropic QTLs. It has been reported that large-effect mutations in pleiotropic genes are likely to be deleterious and there might be a constraint on the size of trans-eQTL effects (Wagner 2000; Jeong et al. 2001; Fraser et al. 2002; Yu et al. 2004; West et al. 2007). The expression value of a gene in e-QTL analysis is used as a quantitative trait and hence it is denoted as e-trait (Wang et al. 2014c). QTL analysis for e-traits could be a useful strategy to decipher regulatory relationships between genes (Gilad et al. 2008; Kliebenstein 2009) and identify the existing potential regulator in a genomic region for a gene (Hansen et al. 2008; Kliebenstein 2009). Further refinement in the resolution of e-QTL analysis has been made by combining eQTLwith co-expression analysis (Terpstra et al. 2010; Flassig et al. 2013). Using this strategy, regulatory network for many e-traits has been identified in rice. This has resulted in identification of 37.2% cis-eQTLs and 62.8% trans-eQTLs, which regulate the expression variation of many genes (Wang et al. 2014c). Protein or agronomic or phenotypic QTLs (pQTLs) and metabolic QTLs (mQTLs) Recent scientific advances have led to the development of new technologies in analysing protein profile of individuals. These protein profiles have been used as phenotypic trait in linkage or association mapping based QTL analysis. QTL that controls variation in protein profiles is called protein QTL (pQTL) (Jansen et al. 2009). Designation pQTL has also been used for agronomic QTL (Wen et al. 2015) and phenotypic QTL (Song et al. 2007; Sutton et al. 2007; Wang et al. 2014d). The pQTL analysis helps to understand the complexity of quantitative traits as pQTLs play an intermediate role between eQTL and mQTL. Several studies have been conducted to make inferences between eQTL and mQTL as well as among eQTL, pQTL, mQTL and phenotypic traits in Arabidopsis (Wentzell et al. 2007; Fu et al. 2009a) and relationship of mQTL with phenotypic traits in tomato (Schauer et al. 2008). QTL analysis for protein expression (pQTL) did not receive the same attention, although proteins are the major determinants for grain quality in crop plants. As protein quality is an important trait in barley, few studies have been conducted on identification of pQTL in this plant species (Witzel et al. 2011; Robison et al. 2015). Combining pQTL analysis with e-QTL and mQTL has resulted in better understanding of the regulatory network of genes (Wang et al. 2014d) and helped to dissect the genetics underlying the complex quantitative traits of agronomic importance. Metabolites are used as intermediary traits for cloning agronomic or phenotypic QTLs and as biomarkers for molecular breeding (Wen et al. 2015). It has been shown that the level of a particular metabolite at a particular plant stage is directly associated with phenotypic trait. For example, a QTL for ear leaf length in maize was co-localized with the QTL 7 that was identified for controlling the levels of fructose and xylulose in the leaf at reproductive and seedling stages, respectively (Wen et al. 2015). This co-localization of mQTL with phenotypic QTL indicates that both traits are genetically co-regulated (Wen et al. 2015). In Brassica, colocalization analysis of pQTLs and eQTLs has resulted in identification of four cis-regulated genes (BrFLC2_A02, BrKRP2_A03, BrER_A09, and BrLNG1_A10) and several trans-eQTLs. Colocalization of above cis-regulated genes with colocalized phenotypic QTLs (copQTLs) suggests pleiotropic regulation of leaf development in rapeseed (Xiao et al. 2014). Molecular markers development The use of morphological traits as genetic markers has paved the way for mapping genetic loci controlling both qualitative and quantitative traits. However, a population derived from a cross between two contrasting parents segregates for a limited number of loci. As a result, a large number of segregating populations are required for developing a comprehensive genetic linkage map of a target crop. To overcome this problem, efforts have been made to develop tester lines through wide hybridization for getting many markers and that was applied successfully only in a few extensively studied crops (Tanksley et al. 1982). Further, genetic stocks such as aneuploids, chromosome substitution lines and A/B translocation stocks have also been used in wheat, tomato, and maize to map genes on a specific chromosome (Roman and Ullstrup 1951; Sears 1954; Rick 1975). These efforts have led to the construction of detailed genetic linkage maps for some crop species (King 1975; O’Brien 1984; Vlaming et al. 1984; Weeden 1985) and could not be made available in other plant species due to the lack of genetic markers and difficulties in developing tester lines. Since the 1980s, restriction fragment length polymorphisms (RFLP) have been reported as DNA based markers (Botstien et al. 1980). Simple Mendelian inheritance and co-dominant nature of RFLP provided an opportunity to develop detailed genetic maps from a limited number of crosses and used extensively in maize and tomato for saturating the already existing genetic maps (Helentjaris et al.1986; Bernatzkyand Tanksley 1986). These markers were further used to develop linkage maps for quantitative traits (Lander and Botstein 1989). RFLP markers were very popular before the invention of PCR based markers. PCR technology has developed a number of DNA markers such as random amplified polymorphic DNA i.e. RAPD (Williams et al. 1990), simple sequence repeat i.e. SSR (Jacob et al. 1991) and amplified fragment length polymorphisms i.e. AFLP (Vos et al. 1995). These medium-throughput markers replaced the use of RFLP markers in crop plants and were categorized as the second generation markers. Among these markers, only SSRs have emerged as the “marker of choice” because of high reproducibility, high polymorphism and codominance nature. These markers have been used in crop plants for mapping and tagging of genes/QTL (Powell et al. 1996). On the other hand, RAPD and AFLP markers could not be used widely in molecular breeding program as they have poor reproducibility and lengthy and laborious detection method. In the recent past, high-throughput sequence-based SNP markers have shown their dominance over the SSR markers because of the fast and cost effective availability of genome sequence information (Wang et al. 1998). Since the 2005s, developments in the next-generation sequencing (NGS) technologies have revolutionized genome sequencing of crop plants. Solexa is the first NGS technology followed by the development of several sequencing methods. These methods can largely be grouped into three types of sequencing: sequencing by synthesis (Roche 454, pyrosequencing, Illumina, Ion Torrent), sequencing by ligation (SOLiD, Polonator) and single molecule sequencing (Helicos, Pacific BioSciences, Emerging technologies, Life Technologies, Nanopore sequencing) (Egan et al. 8 2012). These second and third-generation sequencing methods or NGS platforms can sequence an entire genome in one day. As a result, NGS technologies have become a powerful tool for rapid development of a large numbers of DNA markers. In all NGS methods, one or more restriction enzymes are used to digest multiple samples of genomic DNA. A final set of fragments, less than 1 kb in size, are selected for NGS owing to the read-length limits of current NGS platforms (Davey et al. 2011). Polymorphisms in the resulting sequenced fragments can be used as genetic markers. These methods include RRLs (reduced-representation libraries, Gore et al. 2009; Hyten et al. 2010); CRoPS (complexity reduction of polymorphic sequences, Mammadov et al. 2010); RADseq (restriction-site associated DNA sequencing, Miller et al. 2007; Pfender et al. 2011); low coverage genotyping (multiplexed shotgun genotyping, Andolfatto et al. 2011); GBS (genotyping by sequencing, Elshire et al. 2011); and SBP (sequence based polymorphism, Sahu et al. 2012). In RAD sequencing, dominant DNA markers are generated from the variation within the restriction sites and co-dominant markers from the sequence variation adjacent to the restriction sites (Pfender et al. 2011). These markers have been used for generating SNPs and developing linkage maps in crop plants (Pfender et al. 2011; Chutimanitsakun et al. 2011; Barchi et al. 2011; Wang et al. 2015; Marubodee et al. 2015; Zhang et al. 2015a). Similarly, GBS technology is also utilized for development of SNPs and their use in mapping genes/QTLs for agronomic traits in crop plants (Poland and Rife 2012; Peterson et al. 2014; Ariani et al. 2016). Mapping of Quantitative Trait Loci (QTL mapping) Quantitative traits have complex inheritance with a network of genes. In the past, majority of genes for quantitatively inherited domestication traits in staple food crops have been identified using classical Mendelian genetic approach. For example, initially, a single recessive gene sd1 was identified for semi dwarfism in rice, and now, as many as 61 genes (d1 to d61) are known for dwarfism (Singh et al. 1979; Cho et al. 1994; Ashikari and Matsuoka 2002; Neeraja et al. 2009). Similarly, in wheat, a number of genes controlling reduction in plant height (Rht1 to Rht 13) have been identified (Flingtham and Gale 1983; Borojevic and Borojevic 2005; Bachir and Yin-Gang 2014). Subsequently, linkage and association mapping approaches based on molecular markers have helped to locate these genes/QTLs on chromosomes and identify their functional role in controlling traits (Pearce et al. 2011). Moreover, molecular marker applications have efficiently addressed the issues related to genetic linkages and genotype x environment interactions for understanding the genetic basis of complex traits that could not resolve through the classical Mendelian approach (Comstock 1978; Vikram et al. 2015). Thus, gel based molecular marker and NGS technologies have significantly helped to decipher the genetic architecture of complex traits and develop the diagnostic markers for breeding programs. The present genotyping methods produce SNPs that provide high density of genome coverage compared to gel based markers. This information can be utilized in QTX mapping which involves the use of four-omics data bases including quantitative trait SNPs (QTLs), quantitative trait transcripts (QTT) (Mackay et al. 2009), quantitative trait proteins (QTP) and quantitative trait metabolites (QTM) in the QTL analysis for complex traits (Zhou et al. 2014). In the QTX mapping, G × G (epistasis) and G × E interactions can also be detected to explain a considerable proportion of the missing heritability associated with QTL based on individual molecular marker loci (Zuk et al. 2012). As a result, new knowledge on QTLs has significantly contributed to our understanding on the genetic basis of quantitative traits (Vikram et al. 2015). For example, in rice, fine molecular mapping has identified possible linkages 9 of grain yield QTLs under drought with co-locating flowering and/or plant height QTLs and its interaction effects with flowering QTLs (Vikram et al. 2016). Similarly, in wheat, QTL mapping has helped to understand the adult plant resistance to rust and powedery mildew (Singh et al. 2013; Asad et al. 2014; Zhou et al. 2014; Bajgain et al. 2015). Previously, different approaches have been used to map QTL involving family based mapping populations such as bi- and multi-parental populations or natural population (Xu et al. 2016). Identification of QTL using family based biparental mapping population In biparental mapping, genes/QTL for a target trait are mapped using a population developed from a cross between two parents with contrasting phenotypes of the trait of interest (i.e. wild × mutant or tall × dwarf or transgenic × non-transgenic). Linkage mapping of QTL based on biparental mapping population is useful for mapping rare variant in crop plants. However, the limited number of crossover events leads to large segments of chromosomes being passed intact from parents to offspring and therefore, a false marker-trait association may be detected for traits common in a germplasm collection. Therefore, the selection of parents from either natural or mutant populations for biparental population is very important. This conventional linkage mapping usually results in the identification of flanking markers harbouring the gene(s)/QTL within 2-20 cM. Primary analysis usually maps a QTL within a chromosome region (known as QTL supporting interval) of 10–30 cM. However, the total number of genes present within an interval of 10-cM chromosome have been estimated, on an average, 440 genes in Arabidopsis and 310 genes in maize genomes (Salvi and Tuberosa 2005). This indicates that many genes are underlying behind the identified QTLs for a target trait. It requires fine mapping of targeted genomic regions in order to identify more closely linked markers within the proximity of 1 cM distance to know the functional QTL/genes controlling the trait. These closely linked flanking markers could then be used to identify candidate genes. Thus, a genetic approach initiated with specific phenotype is eventually led to identify the genes encoding a target trait. In this way, a numbers of QTLs have been mapped for agronomic traits in several crops (Kumar et al. 2009; Mohan et al. 2009; Xu et al. 2016) and many of them have been characterized functionally. The NGS technologies have revolutionized the in-depth genetic dissection of major QTL(s)/gene(s) controlling important agronomic traits. QTL-seq is one of the NGS based strategies that provides a rapid high-resolution genome-wide mapping. It was used first time in chickpea to identify a major genomic region harboring a robust 100-seed weight QTL (CaqSW1.1) in a biparental intra-specific mapping population (ICC 7184 × ICC 15061). This QTL was further validated by SNP and SSR marker-based QTL mapping (47.6% r2 at higher LOD >19), which reflects the reliability and efficacy of QTL-seq strategy. The use of this strategy in combination with the classical QTL mapping has also narrowed down genomic region from 1.37 Mb (comprising 177 genes) to 35 kb genomic interval on desi chickpea chromosome 1 containing six genes (Das et al. 2015). More recently, two novel anthracnose resistance loci have been mapped in sorghum (Sorghum bicolor) based on SNPs generated by GBS method (Felderhoff et al. 2016). QTL analysis using family based multi-parent populations or diverse natural populations Biparental mapping populations are widely utilized in marker-trait association analysis for rare variants. As a result, it could detect thousands of markers associated with QTL for a variety of quantitative traits in many crops. However, these markers could only partly be used in marker 10 assisted breeding programs because the parents used in crossing programs might not differ for the targeted QTL (Bernardo 2008). The lack of tight association of marker with QTL and inefficient costly process of fine mapping have discouraged the use of biparental populations in identification of QTL for common traits (Parisseaux and Bernardo 2004). LD-based association mapping (AM) approach has been proposed to overcome the above problems of biparental mapping population. In this approach, a set of diverse genotypes derived from known/unknown ancestry are used to constitute an association panel for mapping genes/QTLs for a target trait. An association panel can comprise of natural germplasm lines, land races, breeding lines, individuals emanating from biand/or multi-parent populations (Steinhoff et al. 2011; Wurschum 2012; Gupta et al. 2014). As phenotypic data on breeding lines are routinely recorded, their use can make association mapping more cost-effective. Therefore, the use of diverse breeding lines as association panel is more useful compared to other kind of diverse panels (Gupta et al. 2014). Breeding populations are generally narrow in genetic base and therefore, the identified QTLs from such panel can be used directly in marker-assisted selection. In contrast, the QTLs identified from wide (elite × exotic) crosses are useful only for the introgression of traits from exotic material (Jannink et al. 2003). After several cycles of recombination, association panel covers most of the genetic variability available in a gene pool for the trait of interest as the germplasm used in association mapping may have been subjected to multiple historical recombinations (Mackay et al. 2009). This leads to a fine mosaic of chromosomal segments, thus leading to a potentially higher mapping resolution. In association mapping, multiple alleles at a locus are involved for QTL analysis in contrast to two alleles in the classical linkage mapping. As a result, the frequency of a particular allele in association panel provides an opportunity for identification of tightly linked makers for the trait of interest. However, marker-trait association is often spurious in association mapping because higher frequency of a particular allele may be due to population stratification and relatedness among individuals rather than due to tight linkage of markers with the trait of interest (Gupta et al. 2014). Now, statistical methods and softwares are available, which can analyze combined data derived from biparental populations and association panels and thus help to derive the benefit of both for genetic dissection of complex traits. Statistical methods are also available for the analysis of multiparental populations (Xu 1998; Rebai and Goffinet 2000; Jannink and Wu 2003). As a result, family-based mapping populations have been suggested to overcome the problem of spurious marker-trait associations (Stich et al. 2006; Rosyara et al. 2009). Different mating designs including diallel, partial diallel, random mating, three-way and four-way crosses have been used to develop family based mapping populations for QTL analysis in several crop plants including maize, wheat, and rice (Rosyara et al. 2009; Reif et al. 2010; Liu et al. 2011; Bandillo et al. 2013; Gupta et al. 2014). In recent years, multiparental advanced generation intercrosses (MAGIC; Cavanagh et al. 2008) and nested association-mapping (NAM; Yu et al. 2008) strategies are becoming popular for LD-based association mapping. In MAGIC population, 4, 8 or 16 parents are involved to cross in biparental fashion and followed by crossing between F1 hybrids in subsequent generations. These populations have been used as genetic resources for QTL mapping, and varietal development in several crops including wheat, rice, chickpea, and tomato (Huang et al. 2012; Mackay et al. 2014; Bandillo et al. 2013). Recently, a MAGIC population comprising of 397 MAGIC lines has been developed for identification of candidate genes in tomato (Pascual et al. 2015). This mapping population has been used to identify SNPs through re-sequencing related to QTL for quality traits (Pascual et al. 2016). MAGIC population can also be developed through intermating parental inbreds for four generations (Kover et al. 2009). In rice, MAGIC populations were developed using both indica and japonica ecotypes in order to capture wide genotypic 11 diversity. The MAGIC plus population has also been developed by inter-crossing for two more cycles within the recombinant lines of an indica MAGIC population for enhancing recombination. Bandillo et al. (2013) developed global MAGIC populations by inter-crossing recombinants of indica and japonica base populations in order to increase the overall diversity. More recently, three new MAGIC populations were used for QTL analysis, resulting in identification of three QTL for plant height and two QTL for heading date and among them, four QTL were very close to the known genes. Also, a novel QTL for plant height was detected in this association panel (Meng et al. 2016). The efforts made in the development of MAGIC populations, which are under progress and not published so far, have been reviewed recently (Huang et al. 2015). , In NAM population one common parental inbred line is crossed with a number of founder parental inbreds. The first NAM population was developed in maize consisting of 25 families each with 200 RILs and released as a genetic resource for identification of functional markers (FMs) , (YU et al. 2008). Similarly another NAM population comprising of 5000 members was developed to identify QTLs for important traits such as flowering time, disease resistance, plant architecture and 12 metabolites in maize (McMullen et al. 2009; Buckler et al. 2009; Kump et al. 2011; Poland , et al. 2011; Tian et al. 2011; Peiffer et al. 2013; Zhang et al. 2015b). More recently Nice et al. (2016) suggested advanced backcross-nested association mapping (AB-NAM) population developed from wild × cultivated barley to maximize the allelic diversity in the association panel. , Association analysis has resulted in mapping three qualitative traits: glossy spike glossy sheath and black hull color with high resolution to loci corresponding to the known barley mutants for , these traits. Besides 10 QTLs were identified for grain protein content. This AB-NAM population is considered as an important resource for high-resolution gene mapping and discovery of novel , allelic variation using wild barley germplasm (Nice et al. 2016). In another study natural variation in the exotic germplasm was exploited by taking a genome-wide association approach using NAM population in barley. This study dissected genetic architecture of flowering time under high salinity and identified putative genes affecting flowering time and salinity tolerance. Another locus was also identified under saline conditions, and lines homozygous for wild allele at this locus yielded 30% more yield than the lines homozygous for the Barke allele. Thus, introgression of wild allele into elite cultivars could markedly improve yield under saline conditions (Saade et al. 2016). NAM populations developed in Arabidopsis (Buckler and Gore 2007), barley (Schnaithmann et al. 2014), sorghum (Bouchet et al. 2015) and soybean (Guo et al. 2010) are now available publicly. The first barley NAM population HEB-25, developed by crossing 25 wild barleys with one elite cultivar, was used for dissection of genetic architecture of flowering time (Maurer et al. 2015). An important advantage of NAM population is that one can conduct joint linkage association mapping (JLAM), which is considered superior to QTLIM and AM for QTL detection. However, it is not necessary to develop NAM population in all crops. NAM populations using more than one common parent or fewer founder parents have also been developed in Arabidopsis (Bentsink et al. 2010; Brachi et al. 2010). 12 Although LD-based association mapping provides high resolution between marker and trait, there are several issues including population structure, cryptic familial relatedness, multi trait and multilocus analysis, markers with rare alleles and variants, and missing heritability that are required to consider in LD-based association mapping (Gupta et al. 2014). Diversity panels used in association mapping often have substantial sub-population genetic structure because they are mixture of geographically distinct lines with varying levels of pedigree relationships (Myles et al. 2009). As a result, subgroups within the diversity panel can differ for mean trait values and also for allele frequencies at many loci. This population substructure can lead to identify false-positive marker trait associations. Although advancement in statistical methods has helped to remove the confounding effects of population structure on association tests but it reduces the power of markertrait association (Holland 2015). LD-based association analysis also missed the heritability because it could not detect association of a marker with rare variation for the trait present within population. On the other hand, a rare marker allele present in a population is difficult to associate with a trait of interest due to low frequency of this allele in population (Gupta et al. 2014). Genomewide association scans/studies (GWAS) and candidate gene (CG) approaches are being utilized in crop plants for genetic dissection of complex traits. An overview of analysis procedure for these approaches has given in Figure 1 and discussed below. Genome wide associations scan (GWAS) This approach is often used to scan all those genomic regions that may control the trait of interest. In this case, however, we also often obtain statistically significant results with the loci, which are not actually associated with the targeted trait. As a result, it leads to identification of spurious marker-trait association. In spite of this, the approach has been used widely in several plant species in marker-trait analysis by using improved statistical methods, which minimize the risk of spurious marker-trait association (Table 2). This is a phenotype driven approach where we used phenotypic and molecular marker data for reaching the gene controlling the target trait. In barley, a tightly linked marker to the gene controlling β-glucan content was identified by using GWAS (Mezaka et al. 2011). Genome wide association mapping in tomato has not only validated previously identified several candidate genes and quantitative traits but also identified new associations for important traits among the accessions of Solanum lycopersicum var. cerasiforme (Ranc et al. 2012). In rice, elite genes within the landraces were mined for 12 agronomic traits through GWAS and verified the previous results of association mapping (Zhang et al. 2014). Candidate gene (CG) association mapping The CG approach is used when the genes controlling a trait are known in the related or model crop species. It has been used first in maize for flowering time (Remington et al. 2001; Thorsberry et al. 2001) and subsequently, in other crops for several agronomic traits (Ehrenreich et al. 2009; Li et al. 2011b; Table 3). It has been used separately and also in parallel with genome wide approach. In sorghum, 73 SNPs from 26 brassinosteroid candidate genes belonging to BR (brassinosteroid) pathway were found associated with the desired traits and also many SNPs showed their association with more than one traits (Mantilla Perez et al. 2014). Similarly, in potato, SNPs in eight genes controlling key functions in starch-sugar inter-conversion showed association with the natural variation of tuber starch and sugar content. An allele of the plastidic starch phosphorylase 13 ‘PHO1a’ responsible for increasing tuber starch content was also cloned in this study (Schreiber et al. 2014). The parallel use of CG approach with GWAS is more useful in those cases when no marker showed association with the trait of interest after applying the false discovery rate (FDR) correction in genome wide association analysis. For example, in maize, none of the 51741 SNPs showed any significant association with the trait after a multiple test for FDR (Cook et al. 2012). On the other hand, CG association mapping could detect a significant association of mutant gene with oil content. Earlier, this mutant gene was identified for increased oil content due to insertion of Phe in the DGAT1-2 gene (Zheng et al. 2008). Two additional SNPs located within the DGAT12 gene have been associated significantly with oil content. Use of this approach in parallel with GWAS has also increased the power and precision of QTL detection in maize (Lipka et al. 2013). In chickpea, candidate gene association mapping was taken along with genome wide association mapping, resulting in identification of SNPs in 5 drought responsive genes that were associated with several drought and heat related traits (Thudi et al. 2014). In carrot, GC approach has been used in an unstructured population for reducing the chance of detecting the false positive association. This study used an original population with a broad genetic base, developed through intercrossing 67 diverse cultivars, for identifying candidate genes responsible for carotenoid accumulation. Genotyping of 384 individuals with 109 SNPs located in 17 candidate genes belonging to carotenoid biosynthesis has identified genes for zea xanthin epoxidase (ZEP), phytoene desaturase (PDS) and carotenoid isomerase (CRTISO), which were significantly associated with total carotenoids and β-carotene contents. Moreover, association of the CRTISO and plastid terminal oxidase (PTOX) genes with α-carotene and ZEP gene with color components was also observed in this study (Jourdan et al. 2015). Functional characterization of agronomically important genes/QTL QTL mapping using different approaches for more than two decades has identified a large number of genes/QTL for yield, disease tolerance and seed quality traits in crop plants. Further efforts have been made to identify the functional role of these QTLs/genes. Initially, map-based cloning of genes was used for this purpose but progress remained slow because of relatively time-consuming approach. In the recent past, other methods have been developed to accelerate identification of the function of gene/QTL underlying the trait of interest. These methods followed both forward and reverse genetic approaches including insertional mutagenesis (loss of gene function cause change in phenotype) using tDNA and transposable elements (Meissner et al. 2000; Hayes 2003; Twyman and Kohli 2003 Acosta-Garcia et al. 2004; Tierney and Lamour 2005; Lazarow and Lütticke 2009; Chudalayandi 2011), homologous recombination mediated gene transfer (Hanin_ and Paszkowski 2003; Iida and Terada 2004; Tierney and Lamour 2005; Fu et al. 2012), gene silencing via RNAi (Waterhouse et al. 1998; Baulcombe 2004; Kusaba 2004; Vaucheret 2006; Eamens et al. 2008; Hebert et al. 2008; Ghildiyal and Zamore 2009; Fu et al. 2011; Mann et al. 2011;Duan et al. 2012; Ré et al. 2012; Rubinelli et al. 2013), virus induced gene silencing (Deng et al. 2012; Kachroo and Ghabrial 2012; Hosseini et al. 2012), TILLING (Ramos et al. 2009; Chen et al. 2012), and candidate gene association mapping (Wilson et al. 2004; Weber et al. 2007 2008; Kloosterman et al. 2010). These approaches could establish the function of genes/QTLs, which was not known previously. In the past, major QTLs have been cloned for flowering time (Salvi et al. 2007), carotenoid content (Huang et al. 2010) and aluminium tolerance (Famoso et al. 2011) in maize, yield components (Xing and Zhang 2010), submergence tolerance (Septiningsih et al. 2009), 14 aluminium tolerance (Famoso et al. 2011), phosphorus starvation and root architecture (Gamuyao et al. 2012; Steele et al. 2013) in rice, aluminium tolerance (Famoso et al. 2011) in sorghum and boron toxicity (Pallotta et al. 2014) in wheat (Table 4). Jadhav (2015) reviewed the cloning of functionally characterized genes/QTLs of agronomic importance in crop plants. More recently, the NGS facilities have further accelerated the identification of functional genes associated with important agronomic traits. For example, RNA-seq analysis of near isogenic lines differing for homozygous alleles at a QTL having low and high value of a trait has identified PM19A1 and A2 as candidate genes for a major dormancy QTL in wheat (Barrero et al. 2015). Novel chitinase genes associated with sheath blight resistance QTL (qSBR11-1) have been functionally characterized in rice line Tetep (Rich et al. 2016). Similarly, the PEBP family genes have been characterized functionally in upland cotton (Zhang et al. 2016). Prioritization of candidate genes within QTL regions QTL mapping establishes genotype-to-phenotype relationships in which variation at the trait level is linked to the variation at the genomic level. However, a QTL region identified for a trait typically contains tens to hundreds of genes and among them, only a few candidate genes actually control the trait (Bargsten et al. 2014). Methods have been developed to prioritize candidate genes in the QTL regions of the target trait on the basis of their over representation in the biological processes (gene functions). Prioritization method has been applied to rice QTL data (Bargsten et al. 2014). In this case, gene functions have been predicted based on sequence- and expression-information. This leads to, on an average, ten-fold reduction in the number of genes. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait (Bargsten et al. 2014). In chickpea, candidate genes in “QTL-hotspot” region for drought tolerance have been prioritized (Kale et al. 2015). The crop QTLome A more accurate and comprehensive characterization of QTLs based on recent progress made in genomics and phenomics is known as QTLome. In the QTLome, we have the knowledge of map position, allele identity and additive vs. dominant genetic effect and their magnitude for each QTL is targeted in breeding programs. It can also be restricted to specific QTL alleles that are accessible to breeders in a germplasm collection (Salvi and Tuberosa 2015). In the crop QTLome, information on all experimentally studied QTLs for all traits in a crop is included while only trait specific QTL information in one species is used in the trait QTLome. In a crop QTLome, generally the entire collection of genetically mapped loci and their allelic variation influencing any quantitative trait are used. As a result, we can have knowledge about the substitution effect of one haploytpe with other in any given genomic region on a trait (Salvi and Tuberosa 2015). For example, in maize, many QTLs that control yield and related traits are available in public database, which provides as the QTLome database. This maize QTLome database possesses 44 published studies including 32 independent mapping populations and 49 parental lines for assembling the yield QTLome for carrying out QTL meta-analysis. This has resulted in identification of 84 metaQTLs among 808 unique QTL on 10 maize chromosomes and 74% of QTLs explained only <10% proportion of phenotypic variance. This study concluded that high genetic complexity is involved in controlling grain yield in maize. This study also suggested that gene density is the main driver 15 for distribution of yield QTLs on chromosomes. It has also been observed that distribution of dominant and overdominant yield QTLs is not different from the additive effect QTLs (Martinez et al. 2016). In wheat, trait QTLome has been studied and dissected the QTLome governing root system architecture in durum wheat. Among three major QTL clusters identified for root length and number, five QTLs for RGA have been prioritized that particularly appeared suitable for a possible deployment in marker-assisted selection and positional cloning (Tuberosa 2016). QTLome information is helpful in marker assisted breeding for QTLs controlling the targeted traits because it also used information of environmental conditions under which the QTL alleles were detected or tested (Salvi and Tuberosa 2015). Productivity improvement using QTLs through marker assisted selection In the past, a number of major-effect QTLs have been mapped, validated and cloned as discussed above. In many cases, QTLs with major effects have also been successfully deployed through marker assisted selection for the development of improved cultivars for many traits including drought tolerance in chickpea (Varshney et al. 2013), stay green in sorghum (Borrell et al. 2014), cyst nematode resistance in soybean (Concibido et al. 2004) and Fusarium head blight, salt tolerance, grain protein content and preharvest sprouting in wheat (Anderson et al. 2007; Kumar et al. 2010; James et al. 2011; Kumar et al. 2011b), and submergence tolerance in rice (Septiningsih et al. 2009). Multi-environment evaluation of these lines showed significant yield advantage over check varieties and released for cultivation. The most significant progress has been made in rice, wheat and common bean. In these crops, the impact of QTL introgressed through marker assisted selection has been studied on productivity enhancement. In rice, drought tolerance has been improved in Indian upland variety, Kalinga III, by marker-assisted introgression of QTLs for root traits from Azucena, an upland japonica variety. In this marker assisted breeding program, four genomic segments carrying QTLs for root traits (root length and thickness) and one segment carrying a recessive QTL for aroma were introgressed. A set of lines pyramided with different numbers of target QTLs were selected and evaluated in eastern India over six years under field conditions (Steele et al. 2006). The lines carrying QTL 9 demonstrated an effect on increasing root length that had a significant effect of 0.2 t/hm2 on grain yield (Steele et al. 2007). Lines with two or more Azucena alleles at root QTLs showed a mean yield increase of 0.4 t/hm2 and QTL 7 was associated with a mean yield increase of 0.9 t/hm2 in combination with other QTLs (Steele et al. 2007). The combination of all four QTLs in one of the lines has resulted in an increased grain yield of 1.0 t/hm2 (Steele et al. 2007). This line was released for cultivation in Jharkhand state as Birsa Vikas Dhan 111. This is the first drought-tolerant rice cultivar developed through marker assisted selection for improved roots (Steele et al. 2013). In IRRI’s drought breeding program, four major drought QTLs (qDTY2.2, qDTY4.1, qDTY9.1 and qDTY10.1) were successfully pyramided in different lines having different combinations of these QTLs (Swamy and Kumar 2011). Those introgressed lines having three or two QTLs gave yield advantage of 1.2 to 2.0 t/hm2 in drought conditions. However, these lines had yield and other quality traits similar to improved background variety IR64 under normal irrigated conditions (Swamy and Kumar 2011). In addition to this, QTLs have also been introgressed in the background of improved lines for a number of traits including primary and secondary branches (Ando et al.2008), grain size and heading date (Wang et al. 2012b), yield related traits (Zong et al. 2012) and drought tolerance in rice (Li et al. 2005). A major QTL for grain number (Gn1) has also been combined with plant height QTL (Ph1) (Ashikari et al. 2005). The pyramided lines with these QTLs increased grain production (23%) and 16 reduced plant height (20%) compared with Koshihikaiin rice (Qian et al. 2007). In rice, near isogenic lines carrying QTLs for secondary (SBN1) and primary (PBN6) branch number individually or in combination (SBN1 + PBN6) produced more spikelets per panicle compared to the recurrent parent (Ohsumi et al. 2011). However, more number of spikelets per panicle did not increase yield significantly and pyramided lines had only 4% to 12% higher yield. In another study, marker assisted introgression of desirable allele at qHD8 locus increased grain yield per plant (>50%) and enlarged leaf size (~30%) in improved lines (Wang et al. 2012b). Phenotypic selection among the lines developed through pyramiding of eight QTLs for spikelet number per panicle and 1000-grain weight in the background of a single line by using four RILs allowed identification of few lines with increased panicle and spikelet size in rice (Zong et al. 2012). A gene (OsPPKL1) controlling grain length in rice was used in marker-assisted selection, resulting in the development of a NIL containing the desirable allele qgl3. Evaluation of this NIL under field conditions showed an increase in yield (16.20%), grain length (19.68%), grain width (1.15%), grain thickness (8.25%), filled-grain weight (37.03%) and panicle length (11.76% ) (Zhang et al. 2012). In rice, an improved line designated as TS4 was developed by introgression of semi-dwarf gene sd1, low amylase content gene Wxb and bacterial light resistance genes Xa4 using marker assisted selection. This line showed semi-dwarf phenotype with reduced growth duration and produced high yield along with good grain quality and broad-spectrum resistance to Xoo strains. This line achieved higher grain yield in field trials conducted in Indonesia and China and was found better in terms of degree of chalkiness and amylose content along with grain quality compared to recurrent parent (Luo et al. 2014). In sorghum, marker assisted selection was successfully used to release improved varieties for cultivation in sub-Saharan Africa. These lines have been developed through introgression of Striga resistance QTLs and selected with 180% to 298% yield superiority over the checks along with Striga resistant after multi-location evaluation (Mohamed et al. 2014). Concluding remarks Significant progress has been made in genomics and phenomics of crop plant species. This has led to the identification of QTL controlling complex agronomic traits. The next generation sequencing and other advanced genomic tools have helped to identify potential candidate genes underlying the complex agronomic traits. These developments have helped to use molecular marker technology in development of improved cultivars through marker-assisted selection for major QTLs. As a result, a number of traits were improved by exploiting QTLs through marker-assisted selection and these improved lines have shown better yield and tolerance to biotic and abiotic stresses. Evaluation of these lines at multilocations over the years has resulted in release of improved varieties for cultivation. However, the impact of these introgressed QTLs on the productivity could not be documented in a big way and it is still difficult to assess the impact of these QTLs on the overall productivity in crop species. Exploitation of QTLs in cultivar development is still a challenge in mainstream breeding programs due to genotype x environment interaction. Therefore, for actual potential of these QTLs, there is an urgent need to identify tightly linked molecular markers and network of genes/QTL that contribute to yield and yield traits. Complex traits are highly influenced by environmental conditions. Therefore, focus is required on identification of genes/QTLs that express in a particular environmental condition so that such QTLs can be exploited to develop environment specific cultivars to exploit the positive GE interaction. We also need to identify the interactive QTLs of which expression is governed by interaction between the 17 genotypes and environments. Now, it has become possible to sequence the genome of crop species more cost effectively and quickly. This has led to availability of genome sequences of several crop species and in coming years, more sequences would be available in more number of crop species. Therefore, in future, QTL analysis would be more precise and it would be possible to identify the actual network of genes governing the targeted traits. As a result, these approaches would be utilized routinely in breeding programs and it will boost the productivity of crop plant species. Acknowledgements Authors thank Department of Biotechnology (DBT) Government of India; ICAR-Indian Institute of Pulses Research Kanpur; Indian Council of Agricultural Research (ICAR) New Delhi; Department of Agricultural Cooperation and Farmers’ Welfare (DAC&FW) for funding the research. Confilct of Interest The authors declare that they have no conflict of interest. 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Year 1902 1905 1910 1913 1928 1931 Discovery Chromosome theory of inheritance Linkage theory Chromosome theory of inheritance First ever linkage map created X- Rays induced mutation Genetic recombination caused by physical exchange of chromosomal pieces shown in corn 43 Reference Sutton (1903) Bateson and Punnet (1905) Morgan (1910) Sturtevant (1913) Muller (1928) Creighton and McClintok (1931) 1941 1950 1961 1975 1980 1986 1990 1991 1991 1993 1993 1993 1993 1994 1994 1995 1996 2000 2000 2001 One gene encodes one protein Transposons Fine gene mapping DNA sequencing technology RFLP PCR RAPD DAF BSA (Bulk Segregation Analysis) Comparative linkage map AP-PCR CAPS QTL mappings SSR ISSR AFLP AB-QTL: advanced backcross QTL Comparative genomics Cloning of QTL Candidate gene association mapping 2003 2003 2005 2008 2014 2015 2015 T-DNA tagging Map based cloning QTL cloning Next Generation Sequencing (NGS) Prioritization of candidate genes The crop QTLome QTL-seq 44 Beadle and Tatum (1941) McClintock (1950) Benzer (1961) Sanger and Coulson (1975) Botstein(1980) Mullis(1986) William et al. (1990) Caetano- Anolles(1991) Michelmore et al. (1991) Ahn and Tanksley (1993) Bottema and Sommer(1993) Konieczny and Ausubel (1993) Tanksley (1993) Bell and Ecker (1994) Zietkiewicz et al. (1994) Vos et al. (1995) Tanksley and Nelson (1996) Paterson et al. (2000) Yano et al. (2000) Remingtonet al.(2001) Thoosberry et al. (2001) Memelink (2003) Peters et al. (2003) Salvi and Tuberosa(2005) Schuster (2008) Bargsten et al. (2014) Salvi and Tuberosa (2015) Das et al. (2015) Table 2 Genome wide association scan studies in crop species. Crop Markers Trait References Aegilops tauschii Barley Barley Brassica napus Brassica napus Foxtail millet Maize Maize Maize Maize Maize Rice Rice Rice Rice Rice Rice Rapeseed Sorghum Soybean Population size 322 175 ~500 523 248 916 384 999 368 25000 413 9 220 517 950 20 472 971 96 7185 SNPs 107 SSRs 1536 SNPs 41 SNPs 60k SNP s 0.8 m SNPs 681257 SNPs 56110 SNPs 1.03 m SNPs SNPs SNPs 44100 SNPs 71710 SNPs 6000 SNPs Liu et al. (2015) Nagel et al. (2015) Cockram et al. (2010) Xu et al. (2016) Hatzig et al. (2015) Jia et al. (2013) Pace et al. (2015) Ding et al. (2015) Li et al. (2013) Kump et al. (2011) Tian et al. (2011) Zhao et al. (2011) Begum et al. (2015) Kumar et al. (2015) Huang et al. (2010) Huang et al. (2012) Xu et al. (2014) Li et al. (2014) Morris et al. (2013) Kumar et al. (2014) Sugarcane Tomato Wheat 20 174 382 20 SSRs 182 SSRs SNPs morphological traits seed aging and longevity morphological trait flowering time seed germination and vigour agronomic traits seedling root architecture traits northern corn leaf blight resistance kernel oil concentration fatty acid composition southern leaf blight resistance leaf architecture agronomic traits agronomic traits salinity tolerance agronomic traits flowering time grain-related traits agronomic traits seed weight and quality plant height and architecture plant height pods/plant 100-seed weight plant growth habit seeds/pod days to 50% flowering and maturity cane weight tillers/plant flavor traits agronomic traits and carbon isotope ∼3.6 m SNP SNP 32655 SNP 60000 SNPs ~26500 SNP SSRs 45 Bilal et al. (2015) Zhang et al. (2015c) Mora et al. (2015) Table 3 Candidate gene association mapping studies in crop species Crop Trait Barley anthocyanin pigmentation total carotenoids and β-carotene content Carrot Chickpea Maize Oilseed rape Population Type of size marker used ~500 EST-SNP Candidate genes Function References HvbHLH1 - Cockram et al. (2012) 380 ZEP PDS CRTISO zeaxanthin epoxidase and phytoene desaturase and carotenoid isomerase plastid terminal oxidase zeaxanthin epoxidase - Jourdan et al. (2015) SNP α-carotene CRTISO PTOX color components ZEP drought and heat tolerance related traits root development with seedling and agronomic traits seed glucosinolate (GS) content Perennial Ryegrass tuber starch and sugar content drought tolerance traits Jourdan et al. (2015) 300 SNP ERECTA; ASRDREB; C AP2 promoter AMD 74 SNP Rtcl Rth3 Rum1 Rul1 - Narayana (2013) 520 SNP candidate genes from glucosinolatemetabolic pathway LpCBF1b - Qu et al. (2015) C-repeat binding factor - Qu et al. (2015) antioxidant metabolism Yu et al. (2013) winter survival Potato Jourdan et al. (2015) 208 SNP 8 genes 192 EST-SSR and SNP markers a set of candidate genes 46 Thudi et al. (2014) Schreiber et al. (2014) dehydration water movement across membranes and signal transduction Ryegrass Rapeseed Salix viminalis Sorghum Wheat green canopy cover chlorophyll index vegetation and chlorophyll index 192 SNP LpLEA3 LpCAT - - LpMnSOD LpChlCu-ZnSOD Drought tolerance bud burst leaf senescence number of shoots or shoot diameter phenotypic traits morphological physiological and agronomical traits 66 323 SNP SNP 79 candidate genes - 254 126 SSR SNP SNP AltSB DREB1AabERA1-B ERA1-D 1-FEH-A 1FEH-B 47 late embryogenesis and catalase magnesium superoxide dismutase and chlorophyll copper–zinc superoxide dismutase drought tolerance - Yu et al. (2015) drought tolerance response to abscisic acid and fructan 1exohydrolase Caniato et al. (2014) Edae et al. (2013) Zhang et al. (2015d) Hallingback et al. ( 2015) Table 4 List of QTLs cloned in plant species Species Arabidopsis Barley Chickpea Cucumber Maize Rice Trait flowering time flowering time glucosinolate structure root morphology insect resistance salt tolerance and ABA sensitivity stem rust resistance 100 seed weight fruit flesh thickness plant architecture kernel row number plant height heading time heading time heading time heading time heading date tiller number/Grain number grain shape and weight QTL ED1 FLW GS-elong BRX ESM1 RAS1 Rpg1 and Rpg5 CaqSW1.1 QTL fft2.1 Tb1 FASCIATED EAR2 ZmGA3ox2 Hd1 Hd3a Hd6 Ehd1 Ghd7 Moc1IPA1/WEP(OsSPL14) PROG1 EP3 GS3GW2GW5/qSW5GS5 SRS3 EP2/ DEP2/SRS1 48 Reference El-Assal. et al. (2001) Werner et al. (2005) Kroymann et al.(2003) Mouchel et al.(2004) Zhang et al. (2006) Ren et al. (2010) Brueggemann et al (2002, 2008) Das et al. (2015) Xu et al. (2015) Doebley et al. (1995, 1997) Bommert et al. (2013) Teng et al. (2013) Yano et al. (2000) Kojima et al. (2002) Takahashi et al.(2001) Doi et al. (2004) Xue et al. 2008 Li et al. (2003) Jiao et al. (2010) Miura et al.(2010) Jin et al.( 2008) Tan et al.( 2008) Piao et al. (2009) Fan et al.(2006) Shomura et al. (2008) Song et al. (2007) Weng et al.( 2008) Kitagawa et al. (2010) Zhu et al. (2010) Abe et al. (2010) Li et al. 2011c grain number/harvest index GN1a APO1 DEP 1 grain yield plant height heading date grain size and grain number adaptation to water trichome development phosphorus deficiency tolerance deeper roots (rice) grain protein content resistance to rice stripe virus (RSV) grain traits Ghd7 Ghd8 SP1 SNORKEL1 SNORKEL2 GLR1 Pstol1 DRO1 OsAAP6 STV11 Ashikari et al. (2005) Hung et al. (2009) Ikeda et al. (2007) Terao et al. (2010) Xue et al. (2008) Yan et al. (2011) Li et al. (2009) Hattori et al. (2009) Li et al. (2012) Gamuyao et al. (2012) Uga et al. (2013) Peng et al. 2014 Wang et al. (2014b) plant height GW2 GS3 GW5 (qSW5) Zhang et al. (2015e) qGL3 GS5 GW8 TGW6 sd1 Monna et al. 2002 Sorghum Tomato seed Shattering fruit sugar content Shattering1 (Sh1) Brix9-2-5 Wheat fruit shape fruit weight nonripening fruit yield heterosis LCN wheat stripe rust Ovate Fw2.2 Cnr Single Flower truss lcn2.1 Yr36 (WKS1) Yr 18 Lr21 Lr10 Lr34 Rht 1 Sr35 Sr33 TaB2 Fhb1 leaf rust resistance gene multiple rust resistance Reduce plant height stem rust resistance stem rust resistance heat stress tolerance B2 protein Fusarium head blight disease 49 Lin et al. (2012) Fridman et al. (2000) Friedman et al. (2004) Frary et al. (2000) Cong et al. (2002) Frary et al. (2000) Manning et al. (2006) Krieger et al. (2010) Munos et al. (2011) Fu et al. (2009b) Krattinger et al. (2009) Huang et al. (2003) Feuillet et al.(2003) Krattinger et al. (2009) Pearce et al. (2011) Saintenac et al. (2013) Periyannan et al. (2013) Singh and Khurana (2016) Rawat et al. 2016 50