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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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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Table1 Important events in the science of genetics.
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