Open access peer-reviewed chapter

Insights into Marker Assisted Selection and Its Applications in Plant Breeding

Written By

Gayatri Kumawat, Chander Kanta Kumawat, Kailash Chandra, Saurabh Pandey, Subhash Chand, Udit Nandan Mishra, Devidutta Lenka and Rohit Sharma

Submitted: 09 July 2020 Reviewed: 12 November 2020 Published: 30 November 2020

DOI: 10.5772/intechopen.95004

From the Edited Volume

Plant Breeding - Current and Future Views

Edited by Ibrokhim Y. Abdurakhmonov

Chapter metrics overview

1,611 Chapter Downloads

View Full Metrics


Burgeoning the human population with its required food demand created a burden on ever-decreasing cultivated land and our food production systems. This situation prompted plant scientists to breed crops in a short duration with specific traits. Marker-assisted selection (MAS) has emerged as a potential tool to achieve desirable results in plants with the help of molecular markers and improves the traits of interest in a short duration. The MAS has comprehensively been used in plant breeding to characterize germplasm, diversity analysis, trait stacking, gene pyramiding, multi-trait introgression, and genetic purity of different cereals, pulses, oilseeds, and fiber crops, etc. Mapping studies pointed out several marker-trait associations from different crop species, which specifies the potential application of MAS in accelerating crop improvement. This chapter presents an overview of molecular markers, their genesis, and potential use in plant breeding.


  • marker-assisted selection
  • plant breeding
  • molecular markers
  • QTLs
  • indirect selection

1. Introduction

It was estimated that the global population would touch 9 billion individuals, and the annual growth rate will be 0.75 percent by 2050. To feed this burgeoning human population only, it is required to produce a surplus of one billion tons of cereals by the end of 2050 [1]. It is well known that to achieve these targets new integrated approaches must be practiced with the conventional breeding programmes to accelerate the breeding cycle by reducing net time and cost per unit production [2, 3].

The primary objective of plant breeding is to increase crop yield [4], and the secondary objectives are quality improvement, development of photo & thermo-insensitive cultivars, tolerance to biotic and abiotic stresses, synchronous maturity, water and nutrient use efficiency, elimination of toxic substances, and different crop maturity groups [5, 6] for high agricultural output and sustainable development. The advanced understanding and developments in molecular genetics have significantly enhanced the efficiency of plant breeding to achieve the desired objectives in crop plants [7]. The efficient and effective application of molecular markers in crop improvement programmes improves the selection efficiency, degree of precision, and accelerates the breeding cycle to develop a new cultivar with a trait of interest [5].

Marker-assisted selection (MAS) can be defined as the manipulation of genomic regions that are involved in the desirable trait of interest through DNA markers [7], and their potential use in crop improvement begins a new era of molecular breeding [8]. The MAS has an edge over the visual phenotypic selection because the trait of interest is linked with a molecular marker which increases the selection efficiency of the targeted trait [9].

The fundamental aim of any crop improvement programme is the selection of effective plants with a trait of interest. In conventional plant breeding, there are more chances to skip the trait of interest and delays the time to develop new cultivars with desirable traits. Whereas, MAS has shown its utility in crop plants for improvement of various traits by reducing the environmental effect and by increasing selection efficiency for a trait of interest [10]. However, the efficacy of MAS on selection may be impeded by genetic background [11], reliability and accuracy of QTLs [12], the insufficient linkage between the gene of interest (QTLs) and marker [13], relative high input cost, [14, 15] limited molecular markers and their narrow range of polymorphism and knowledge gap between plant breeders and molecular biologist [5].

Various markers such as morphological (trait-specific), proteinaceous (isoenzyme), cytological (chromosome-specific), and DNA markers have been utilized in plant breeding: however, DNA based markers are used extensively in MAS for various traits and crops by the plant breeders [16]. The basic requirements for effective MAS in plant breeding are- reliability of DNA marker, qualitative and quantitative assurance of genetic material (DNA), marker analysis procedures, genomic coverage of marker, level of polymorphism, genetic nature of marker such as co-dominance [5, 17, 18, 19].

Recent advances in molecular breeding such as the use of PCR based techniques [simple sequence repeats (SSRs), and insertion/deletion mutations (Indels)]; single nucleotide repeats (SNPs); Genomic sequencing (GS) and genotype by sequencing (GBS), etc. have extensively been used in crop improvement programme throughout the world [3, 19].


2. Molecular markers: road for easy and reliable selection

Any fixed property of an individual showing the heritable variations is termed as a character or trait, whereas marker can be defined as any mark which inherits together with the trait of interest throughout generation [20, 21]. Markers are categorized into four main groups- morphological, biochemical, cytological and molecular (DNA based) markers [22].

Morphological markers are also known as naked eye marker or phenotypic marker, used for quality traits such as flower shape, size, color, seed structure, growth habit, and other agronomic traits in plants. These markers are eco-friendly; easy to use, and need not require any specific instrument; however, their number is limited in crop species and highly influenced by prevailing environmental conditions [22, 23, 24].

Biochemical markers, mostly isozymes, are the results of variation in enzymes (protein and amino acid sequences) encoded by various genes, but functionally they are the same [25]. They are the result end product of allelic variation of enzymes. They are co-dominance in inheritance, cost-effective, and easy to use. They have been widely used in plant breeding for the study of gene flow, population structure, and genetic diversity [26]. However, they are limited in number, show less polymorphism, and predominantly affected by plant tissue being used, growth stage, and method of their extraction [27].

Cytological markers are based on prevailing variation in number, shape, size, the position of chromosomes, and their banding pattern. Cytological analysis reveals the unique characteristics of chromosomes such as knob and satellite, and the number of nucleoli in the nucleus, etc. This variation shows a different pattern of euchromatin and heterochromatin in the chromosome [22], such as Giemsa stain recognizes G bands. They have been extensively utilized in plant breeding for the identification of linkage groups and physical mapping [9]. In contrast, molecular markers are defined as nucleotides polymorphism present between individuals as a result of deletion, duplication, insertion, substitution, point mutation and translocation, etc. [27] but do not affect the function of the gene.

Molecular markers do not inevitably target genes, instead, inherit as a ‘flag’ with the gene of interest during transmission of a trait from one generation to the next generation [28]. Molecular markers associated with the close proximity of genes of interest are known as gene tags i.e. linked with target gene [9]. The essential characteristic features of an ideal marker are co-dominance inheritance, high level of polymorphism, high reproducibility, whole-genome coverage, easy and fast to detect, neutral to environmental conditions, high resolution, low cost, and whole-genome coverage [22, 27, 29]. Different types of molecular markers have been developed, and are used in various crops. These molecular markers are mainly categorized into the following classes based on their method of detection.

2.1 Hybridization-based markers

DNA bands are captured where labeled probe i.e. DNA fragment of known sequence hybridizes with DNA fragment digested by restriction endonuclease enzyme. The restriction fragment length polymorphism (RFLP) was the first and last marker which was only based on the hybridization method [22].

2.2 PCR-based markers

The idea of polymerase chain reaction (PCR) was conceived by Kary Mullis in 1983, and invented the process in 1985 which is based on denaturation, annealing, and extension [30]. The PCR based markers use primer dependent PCR amplification and/or DNA hybridization followed by electrophoresis. Polymorphism is detected based on the presence or absence of an amplicon or based on the band size and mobility. The most commonly used PCR based markers are Randomly amplified polymorphic DNA (RAPD) [31], Amplified fragment length polymorphism (AFLP) [32], microsatellites or simple sequence repeats (SSRs) [33], sequence-related amplified polymorphism (SRAP) [34], inter simple sequence repeat (ISSR) [35], cleaved amplified polymorphic sequences [36], sequence characterized amplified region (SCAR) [37].

2.3 Sequence-based markers

Sequencing technique is characterized by the identification of nucleotide sequences and their order along with the DNA strand [38]. Sequence-based markers are designed as per a specific sequence of DNA in a pool of unknown DNA. The modern sequencing techniques are genotyping by sequencing (GBS) and next-generation sequencing (NGS), which help to develop a large array of polymorphism at the nucleotide level; however, the most commonly used marker are single nucleotide polymorphisms (SNPs) [39] and diversity array technology (DArT Seq), which are known to be more accurate and reliable [22, 40].

The historical development of molecular markers is also represented in the Table 1, which is adapted and modified from: Singh and Singh [41].

1923Sax reported a linkage map between quantitative (seed size) and qualitative trait (seed coat color) in common bean for the first time.
1961Thodey described QTLs mapping in Drosophila melanogaster
1980Linkage mapping in humans using RFLP (Restriction Fragment Length Polymorphism) was described for the first time by Botstein et al.
1985Kary Mullis discovered the Polymerized Chain Reaction (PCR) which led to the designing of PCR based markers
1989Olson et al. reported Sequence-tagged site (STS) markers
1990Williams JGK et al. developed ‘RAPD’ (Random Amplified Polymorphic DNA)
1991Williams MNV et al. reported ‘CAPs’ (Cleaved Amplified Polymorphic sequence)
1993Development of Marker-assisted techniques: Paran and Michelmore developed ‘SCAR’ (Sequence Characterized Amplified Regions) and Zabeau and Vos developed ‘AFLP’ (Amplified Fragment Length Polymorphism) technique
2001Li and Quiros developed ‘SRAP’ (Sequence Related Amplified Polymorphism) technique
2009Collard and Mackill reported ‘SCoT’ (Start Codon Targeted Polymorphism)
2014Singh AK et al described ‘CAAT box-derived polymorphism marker’

Table 1.

A chronology of the historical steps in molecular breeding.

We have discussed several molecular marker systems; however, the most commonly used markers in plant breeding are RFLP, SSR, RAPD, AFLP, SCAR, and SNP [42]. The single-locus markers are RFLP, VNTR, SSLP, STMS, SSR, STS, SNP, CAPS, and SCAR whereas; multi-locus markers are RAPD, AP-PCR, ISSR, AFLP, M-AFLP, and S-SAP marker [43]. All these markers are used in plant breeding for germplasm characterization and protection, gene tagging, genome mapping, linkage map construction and analysis, evolution studies, parental selection, F1 hybrid testing, genetic purity test of seeds, genes or QTLs mapping etc. [44, 45].


3. Marker assisted selection (MAS)

The direct phenotypic selection in plant breeding for crop improvement is labor-intensive, costly, and time-taking. This selection is also affected by target gene expression, their specific biological or environmental condition, and heritability of a trait. Phenotypic selection is less efficient for the quantitative traits that are frequently under the selection [46].

In MAS, the phenotypic selection is made with the help of genotypic markers. This technique helps to avoid difficulties and challenges that are occurred during the conventional crop breeding [47]. It is mostly used by plant breeders in their breeding programmes for the identification of desired dominant or recessive alleles throughout generations, also it helps to identify best genotypes from segregating generations [48]. The prerequisite for an efficient MAS program is reliable markers, quality of DNA extraction method, genetic maps, knowledge of marker-trait association, quick and efficient data processing, and availability of high throughput marker detection system [49]. Marker development pipeline adapted from [5] Collard and Mackill, 2008, in Figure 1 explain that how marker assisted selection imposed from development of population through various steps.

Figure 1.

Marker development flow chart.


4. Variations of MAS

There are different molecular approaches used under the umbrella of MAS, such as marker-assisted backcrossing (MABC), gene pyramiding, marker-assisted recurrent selection (MARS) and genomic selection (GS). These approaches have been utilized in plant breeding for the characterization of genetic material and selection of individuals in the early segregating generation, which fastens the breeding cycle with more accuracy [22].

4.1 Marker-assisted backcrossing (MABC)

Convention backcrossing is an age-old practice and is a very useful technique for the transfer of oligogenic traits from donor parents to recipient parents by recovering the whole genome of recipient parents except trait of interest after 6–7 generations of backcrossing. The MABC is a backcrossing technique and is assisted by molecular markers [50] to speed up the selection process and genome recovery of recipient parents. The MABC technique has been extensively used to remove the undesirable traits such as insect and disease susceptibility, and anti-nutritional factors etc. from high yielding popular varieties by introducing gene of interest or quantitative trait loci (QTLs) from donor parent [51].

The fundamental basis of MABC is the close association of marker with gene/s or QTLs. Recovery of recurrent parent genome is specified by using formula- 1-(1/2)m+1 (m is the number of generation of selfing or backcrossing). This technique has been used in different crops such as rice [52], wheat [53], barley [54], soybean [55], cotton [56], tomato [57], and pea [58], etc. There are three basic steps in the MABC technique viz. foreground selection, recombinant selection, and background selection.

Foreground selection is the first step of MABC, where the gene of interest from the donor parent is the primary target which is linked with the marker. The efficiency of foreground selection depends on marker-trait association, the physical distance between marker and gene of interest, genetic load or linkage drag, number of genes/QTLs/loci targeted to selection, etc. [59]. Linkage drag is undesirable for selection due to the negative effect of associated genes on targeted traits.

Recombinant selection is the second step of MABC, where selection is made for target gene in backcross progeny, and the recombination process is done between the gene of interest and linked flanking marker for reducing the effect of linkage drag [22].

Background selection is the third step of MABC, where the major target is the recovery of a large amount of recipient parental genome from backcross progeny by using molecular markers that are unlinked with the gene of interest [5]. The efficiency of background selection is determined by various factors such as the size of the population, the number of markers and targeted genes, and linkage drag, etc. It helps to speed up the recovery of the recipient parent genome with the trait of interest and also termed as ‘complete line conversion’ [60].

4.2 Marker-assisted gene pyramiding (MAGP)

Current breeding programs mainly focus on the development of lines governing complex traits such as biotic and abiotic stress. Modern MAS methods involve pyramiding of different genes to accomplish such goals referred to as MAGP. In MAGP, two or more than two genes at a time are selected for pyramiding. Different approaches have been utilized for pyramiding multiple genes/QTLs from donor parent to recipient parent. Some of them are recurrent selection, backcrossing, and multiple-parent crossing or complex crossing. The 3-4 desirable genes from other lines would be incorporated by convergent or stepwise backcrossing. The incorporation of more genes is usually carried through multiple crossing or recurrent selection. If we want to pyramid multiple genes/QTLs, marker-assisted convergent crossing (MACC) can be used [8, 61].

4.3 Marker-assisted recurrent selection (MARS)

Recurrent selection is an efficient technique used in plant breeding for the improvement of quantitative traits by continuous crossing and selection process. However, its efficiency of selection is adversely affected by environmental fluctuations which leads to delays breeding cycle. In MARS, molecular markers are used at each generation level for the targeted traits. Here, the selective crossing is done in selected individual plants at every crossing and selection cycle. The selection is made based on phenotypic data with marker scores. Thus, it increases the efficiency of recurrent selection and accelerates the breeding or selection cycle. The MARS has been extensively used for polygenic traits such as crop yield, biotic and abiotic stress tolerance, and considered as a forward breeding tool for augmenting multiple genes or QTLs [62].

4.4 Genomic selection (GS)

The genomic selection was developed by Hayes and Goddard [63] and is known as an advanced version of MAS. It can predict the genetic values of selected individuals which depend on genome estimated breeding values (GEBVs) by using high-density markers that are distributed throughout the genome. The GEBV prediction model combines genotypic data with phenotypic data with their pedigree and increases the prediction accuracy. The GS is mostly dependent on all the molecular markers which have both major and minor marker effect. Molecular markers are selected based on their whole genome coverage and all the QTLs should be in linkage disequilibrium with at least a single marker [23, 62, 63]. Two different types of populations are used in GS, such as training and testing population. The training population is related to the breeding population, and used to estimate the genomic selection model parameter. A testing population is a group of individuals in which genomic selection is carried out. The GEBV value is calculated by using molecular markers. Selection is based on GEBVs values, and no direct phenotypic selection is required [22, 64, 65, 66].


5. Innovative breeding schemes of MAS

Utilizing molecular markers, MAS has a broad spectrum application in plant breeding. Molecular markers can genotype all the accession present in germplasm. This potentiality permits the categorization of germplasm as well as reducing duplication. Here some of the innovative applications of MAS have been presented.

5.1 Combined marker-assisted selection

The MAS, along with phenotypic selection, increases genetic gain to unravel unidentified QTLs through QTL mapping compared to phenotypic screening or MAS alone [67]. The term ‘combined MAS’ was coined by Moreau et al., 2004 [68]. This approach not only reduces the population size but also increase selection efficiency. The combination of phenotypic selection and MAS also helps select traits where markers genotyping is economical compared to phenotypic screening [69]. With this view, this scheme explain that always a confirmation of MAS is necessary through phenotypic screening like in the case of QTL identified for Fusarium head blight resistance [70].

5.2 Marker-directed phenotyping

In most cases, there is a low level of recombination between QTL and marker is observed [13] which means we cannot believe 100% on markers for selecting desirable phenotypes. However, it will reduce the number of plants that are about to evaluate. This approach is mainly used for quality traits [71]; where phenotypic screening is costlier than marker genotyping [72]. The method is also known as tandem selection [71] and stepwise selection by [73]. One of the successful examples to explain this scheme is that rice primary QTL sub 1controls submergence tolerance, which assisted in breeding for the same [74].

5.3 Inbred or pureline enhancement and QTL mapping

This approach's main features are constructing the introgression library, evaluating the line for QTL detection, mapping, and further superior line used in the breeding program [41]. This scheme starts with hybridizing the two inbred line. One is the recurrent parent (agronomically superior having defects for one trait), and the other is the donor parent (have the desirable target gene). Further, the F1 obtained from this cross is backcrossed again to the recurrent parent, and genome-wide markers have been utilized to select the genetic segment from the donor parent. To generate a set of NILs, F1 is repeatedly backcrossed to the recurrent parent, and this set of NILs is known as the introgression line library. Therefore, this scheme seeks to introduce QTL from a suitable donor parent and simultaneously maps the QTL [75].

5.4 Advance backcross QTL analysis

It is designed to facilitate QTL introgression from unadapted germplasm like landraces and wild species into elite lines, simultaneously mapped for introgressed QTL [76]. This scheme is somewhat similar to the introgression line library, as discussed in section 5.3. However, the differences in the incorporation of phenotypic selection are in contrast to the introgression line library. Apart from this, several advantages like simplicity of mapping population in phenotype to the recurrent parent and reducing deleterious allele from donor parent, possibility of epistasis, andlinkage drag. After QTL mapping, only one or two generationsare needed for identifying QTL-NILs. In several crops like maize, tomato, soybean, cotton, rice, barley, and wheat, this approach is effectively used [9].

5.5 Single large scale MAS: a strategy applied at early generation

Single large scale MASwas proposed by Ribaut and Betran, 1999 [77], where marker-assisted selection is utilized at first segregating generation (F2 or F3). As the name describes, a single means one; large scale means up to three QTLs, explaining the most considerable phenotypic variance. The shortening of crop duration by reducing the breeding cycle prompted the idea of early generation MAS. Further plants having targeted gene/QTLs are selected whereas undesirable gene combination was discarded. Further, selected alleles were fixed in homozygous condition, and individual plants with undesirable genes would be discarded in early segregating generations. Thus, emphasis can be given on a few selected lines in the later stage, which reduces the wastage of resources and increases the selection efficiency [78].

5.6 Breeding by design

MAS's most ambitious objective is to improve plant type having the anticipated alleles at each locus participating in the control of all the traits [79]. Plant breeders will exploit known allelic variation to frame elite lines by accumulating multiple favorable alleles through this approach [80]. Therefore, the breeder can pre-plan the combination of genes he is looking for, and consequently, he can select the plant with the desired characteristics that will save expensive field testing.

5.7 Mapping As You Go (MAYG)

This method revised assessments of QTL allele effects for remapping new elite germplasm produced continuously over the selection cycle. In this approach, initial breeding crosses are utilized to estimate the QTL location and its impact. The information revealed from this estimation will be used in the mapping. This updated QTL information will be used in a new set of breeding cycles as the name suggest, mapping as you go, which means that the breeding cycle can be continued as long as desired. Overall an enhanced response has been achieved with frequent re-estimation of QTL compared to single QTL estimation at the initial level of this approach [41]. Hence, this method's advantage is that it ensures that the QTL estimate remains significant for the germplasm currently used in the breeding program [81].

5.8 Characterization of breeding material

Well-documented and characterized breeding material is a prerequisite for improving crop yield in plant breeding programs. The MAS could help to select desirable traits and have been exploited to identify cultivars/purity assessment, evaluate genetic diversity and selection of suitable donor parent, heterotic grouping, and identification of genomic regions for effective utilization in breeding programs [82, 83, 84].


6. Achievements made through MAS

Several examples illustrate the achievement, made through marker-assisted selection; however, in Table 2, few paradigm crop-wise and trait wise have been presented.

YieldSix QTLSNPCross between CLM495 and LPSC7F64[85]
Maize earliness and yieldQTLRFLPQTLs on chromosomes number 5, 8 and 10[86]
Sugarcane mosaic virus(SMV)Scm1 and scm2SCAR and CAPSFine mapping show present on chromosome number 6 (scm1) and 3 (scm2) in maize[87]
Maize rough dwarf disease (MRDD)QTL qMrddSSRConventional method coupled with MAS is used to introgress qMrdd8 from X178 into elite germplasm[88]
Nothern corn leaf blight(NCLB)Ht1,Ht2,Ht3, Ht4, HtM, HtP,HtNB, Htn1RFLPHt 1 located on chromosome 2 and Ht2 located on chromosome 8.[89]
European corn borer, sugarcane borer and southwestern corn borerLIR4, 17, and 22 MQTLSNPLIR MQTL present on chromosome number 1 and contain QTL for cell wall acidic constituents, fiber components and diferulates.[90]
European corn borer and Mediterranean corn borer42 SIR MQTLSNPHighest SIR MQTL present on chromosome number 2 and 5. cross-linking between fiber and hydroxycinnamate against mechanical damage by insects.[90]
maize weevil and the Mediterranean corn borerKIR MQTL, KIR3, 15, and 16SNPHighest KIR MQTL presents on chromosome 4 and 10. Provide resistance to kernel damage and associated post-harvest loss and contaminations.[90]
Drought resistanceMajor QTLMajor QTLs on chromosome number 1, 2, 8 and 10[91]
QPMo2 alleleSSRQPM hybrids accumulate essential amino acids (lysine, tryptophan) in the endosperm.[92]
YieldYld1.1 and yld2.1SSRFeasibility of SSR marker associated genes (yld1.1 and yld2.1) in screening rice HYVs.[93]
Bacterial blightXa21RFLPSeedling and adult stage resistance against blight[94]
Bacterial blight, Rice BlastPi9, Xa23PCR based primerRice blast and bacterial blight resistance.[95]
Rice BlastPi9, Pi2PCR based primerHybrid of Hui 316 (restorer line) and Pi9, Pi2 respectively impart blast resistance.[96]
Brown plant hopper (BPH)Bph3, bph4, Bph13(t), bph19(t), and Qbph-9SSRPhenotypic variations associated with BPH infestation varies from 17 to 20% concerning BPH biotypes.[97]
Submergence tolerance beyond SUB15 QTLSSR5 QTL were found on Chromosome 1, 4, 8, 9, and 10[98]
YieldQTLPCR based markerQTL present on Baronesse chromosome 2HL and 3HL fragments[99]
Malting quality in barleyQTL1, QTl2PCR based markerQTL1 is located on chromosome number 1 and QTL2 located on chromosome number 4.[71]
Fusarium head blight (FHB) along with agronomic traitsAdditive and Epistatic QTLsSSR and DArT markersMulti-QTL analysis for the improvement of FHB resistance and agronomic traits using recombinant inbred population.[100]
Drought toleranceYield and biomass associated QTLsSSRQTL alleles introgression ensured yield potential and biomass stability under multiple environments.[101]
Drought toleranceQTLs for photosynthesis, water content, cell membrane stability)SSRQTL present on 2A chromosome[102]
Bacterial blight resistance (Common bean)QTLSTS, RAPD, SCAR, AFLPMulti-QTL loci analysis based on linkage maps can predict the phenotypic variation up to a large extent.[103]
Resistance to Fusarium wilt (Chickpea)QTLFoc02, QTLFoc5SSRGenetic distance is 10 cM[104]
Ascochyta blight resistanceQTLAR3SSRGenetic distance is 24 cM[105]
Powdery mildew resistance (Mungbean)QTLsRAPD, CAP, AFLPGenetic distance is 1.3[106]
Common bean (Drought resistant)QTLs (yield components, pod harvest index)SNPQTL present on chromosome 1, 3, 4, 7, 8, and 9[107]
Chickpea (salinity)48 QTLs (days to 50% flowering and maturity and days after sowing)SSR and SNPQTL present on CaLG05 and CaLG07 Chromosome loci[108]
Drought (Chickpea)93 QTLs (plant height, days to flowering and days to maturitySSRQTL present on LG3 and LG4 Chromosome loci[109]
Salinity (Cowpea)1 QTL (pod length and seed size)SSRQTL present on LG1 Chromosome loci[110]
Al tolerance (Soybean)2 QTLs (root extension)RFLP and SSRQTL present on Gm08 and Gm16 Chromosome loci[111]
Salinity (Soybean)1 QTL (salt-tolerant)SSR and SNPQTL present on 3 Chromosome[112]
Drought (Soybean)7 QTLs (canopy wilting trait)SSRQTL present on Gm12 Chromosome loci[113]
Pea (Frost)161 QTLsSSR and SNPQTL present on 1, 2, 3, 4, 5, 6, and 7 Chromosome[114]
Pea (Drought)10 QTLsSSRQTL present on LGI, LGIII, and LGIV Chromosome loci[115]

Table 2.

The paradigm of MAS in crops.

Apart from the improvement in specific traits through an indirect selection via MAS, there are varieties that are released through MAS also presented in Table 3.

Pusa Basmati 1 (IPB1) varietyQTL (xa13)-Chromosome 8 and QTL (xa21)-Chromosome 11Bacterial leaf blight resistance from IRBB55
Improved Sambha Mahsuri (Improved BPT 5204)xa5, xa13 and xa21Bacterial leaf blight resistance
Vivek QPM9opaque-2 from Vivek Hybrid MaizeHigh tryptophan, lysine and iron content
Improved Pusa RH10xa13, xa21, pi54 and piz5Bacterial leaf blight resistance and blast resistance

Table 3.

Varieties developed through Marker Assisted Selection [41].


7. Conclusion and future perspectives

Molecular marker technology has traveled more than 30 years since the identification of the first marker i.e. RFLP, and reached its peak by using SNP or DArT. Molecular marker can assist in the selection process with phenotypic selection and speed up the pace of the breeding cycle. In recent times modern technologies such as NGS i.e. low cost with high throughput, GS, and GBS have been used in plant breeding but could not achieve the desired goal. The most probable reason is inaccurate phenotyping, and this problem can be alleviated by using modern throughputs phenotyping techniques such as camera or computer or sensor-based techniques in phenomics. Edge cutting technologies such as CRISPR/Cas and genome editing can be used for precise modification in the genome as per the need of human beings for their welfare.



AFLPAmplified fragment length polymorphism
AP-PCRArbitrarily primed polymerase chain reaction
CAPSCleaved amplified polymorphic sequence
DArTDiversity array technology
DNADeoxyribonucleic acid
GBSGenotyping by sequencing
GSGenomic selection
InDelsInsertions and Deletions
ISSRInter-simple sequence repeat
MABCMarker-assisted backcrossing
MASMarker assisted selection
NGSNext generation sequencing
NILsNear isogenic line(s)
PCRPolymerase chain reaction
QTLQuantitative trait locus
RAPDRandom amplified polymorphic DNA
RFLPRestriction fragment length polymorphism
SCARSequence characterized amplified region
SCoTStart codon-targeted
SNPSingle nucleotide polymorphism
SRAPSequence related amplified polymorphism
SSAPSequence-specific amplification polymorphism
S-SAPSequence-specific amplification polymorphism
SSLPSimple sequence length polymorphism
SSRSimple sequence repeats
STMSSequence-tagged microsatellite site
STSSequence-tagged site
VNTRVariable number of tandem repeats


  1. 1. Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision
  2. 2. Kage U, Kumar A, Dhokane D, Karre S, Kushalappa AC. Functional molecular markers for crop improvement. Critical reviews in biotechnology. 2016 Sep 2;36(5):917-30
  3. 3. Platten JD, Cobb JN, Zantua RE. Criteria for evaluating molecular markers: comprehensive quality metrics to improve marker-assisted selection. PloS one. 2019 Jan 15;14(1):e0210529
  4. 4. Chand S, Chandra K, Khatik CL. Varietal Release, Notification and Denotification System in India. InPlant Breeding-Current and Future Views 2020 Oct 19. IntechOpen
  5. 5. Collard BC, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences. 2008 Feb 12;363(1491):557-72
  6. 6. Bhargava A, Srivastava S. Toward Participatory Plant Breeding. InParticipatory Plant Breeding: Concept and Applications 2019 (pp. 69-86). Springer, Singapore
  7. 7. Ribaut JM, Hoisington D. Marker-assisted selection: new tools and strategies. Trends in Plant Science. 1998 Jun 1;3(6):236-9
  8. 8. Gupta PK, Langridge P, Mir RR. Marker-assisted wheat breeding: present status and future possibilities. Molecular Breeding. 2010 Aug 1;26(2):145-61
  9. 9. Jiang GL. Molecular markers and marker-assisted breeding in plants. Plant breeding from laboratories to fields. 2013 May 22:45-83
  10. 10. Tester M, Langridge P. Breeding technologies to increase crop production in a changing world. Science. 2010 Feb 12;327(5967):818-22
  11. 11. Charcosset A, Moreau L. Use of molecular markers for the development of new cultivars and the evaluation of genetic diversity. Euphytica. 2004 Jun 1;137(1):81-94
  12. 12. Melchinger AE, Utz HF, Schön CC. Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics. 1998 May 1;149(1):383-403
  13. 13. Sharp PJ, Johnston S, Brown G, McIntosh RA, Pallotta M, Carter M, Bariana HS, Khatkar S, Lagudah ES, Singh RP, Khairallah M. Validation of molecular markers for wheat breeding. Australian Journal of Agricultural Research. 2001;52(12):1357-66
  14. 14. Brennan JP, Rehman A, Raman H, Milgate AW, Pleming D, Martin PJ. An economic assessment of the value of molecular markers in plant breeding programs. 2005
  15. 15. Brumlop S, Finckh MR. Applications and potentials of marker assisted selection (MAS) in plant breeding. Final report of the F+ E project “Applications and Potentials of Smart Breeding”(FKZ 350 889 0020) On behalf of the Federal Agency for Nature Conservation December. 2010 Dec
  16. 16. Madina MH, Haque ME, Dutta AK, Islam MA, Deb AC, Sikdar B. Estimation of genetic diversity in six lentil (Lens culinaris Medik.) varieties using morphological and biochemical markers. International Journal of Scientific & Engineering Research. 2013;4:819-25
  17. 17. Mackill DJ, Ni J. Molecular mapping and marker-assisted selection for major-gene traits in rice. In Rice genetics IV 2001 (pp. 137-151)
  18. 18. Mohler V, Singrün C. General considerations: marker-assisted selection. In Molecular Marker Systems in Plant Breeding and Crop Improvement 2004 (pp. 305-317). Springer, Berlin, Heidelberg
  19. 19. Bohar R, Chitkineni A, Varshney RK. Genetic molecular markers to accelerate genetic gains in crops
  20. 20. Singh AK, Gopalakrishnan S, Singh VP, Prabhu KV, Mohapatra T, Singh NK, Sharma TR, Nagarajan M, Vinod KK, Singh D, Singh UD. Marker assisted selection: a paradigm shift in Basmati breeding. Indian Journal of Genetics and Plant Breeding. 2011 May 1;71(2):120
  21. 21. He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Frontiers in plant science. 2014 Sep 30;5:484
  22. 22. Nadeem MA, Nawaz MA, Shahid MQ , Doğan Y, Comertpay G, Yıldız M, Hatipoğlu R, Ahmad F, Alsaleh A, Labhane N, Özkan H. DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnology & Biotechnological Equipment. 2018 Mar 4;32(2):261-85
  23. 23. Eagles HA, Bariana HS, Ogbonnaya FC, Rebetzke GJ, Hollamby GJ, Henry RJ, Henschke PH, Carter M. Implementation of markers in Australian wheat breeding. Australian Journal of Agricultural Research. 2001;52(12):1349-56
  24. 24. Karaköy T, Baloch FS, Toklu F, Özkan H. Variation for selected morphological and quality-related traits among 178 faba bean landraces collected from Turkey. Plant Genetic Resources. 2014 Apr 1;12(1):5
  25. 25. Tanksley S, Orton T. Isozymic variation and plant breeders’ rights. Iso Plant Genet Breed. 1983;1:425
  26. 26. Mateu-Andres I, De Paco L. Allozymic differentiation of the Antirrhinum majus and A. siculum species groups. Annals of botany. 2005 Feb 1;95(3):465-73
  27. 27. Mondini L, Noorani A, Pagnotta MA. Assessing plant genetic diversity by molecular tools. Diversity. 2009 Sep;1(1):19-35
  28. 28. Nakaya A, Isobe SN. Will genomic selection be a practical method for plant breeding?. Annals of botany. 2012 Nov 1;110(6):1303-16
  29. 29. Collard BC, Jahufer MZ, Brouwer JB, Pang EC. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica. 2005 Jan 1;142(1-2):169-96
  30. 30. Mullis K, Faloona F, Scharf S, Saiki RK, Horn GT, Erlich H. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. InCold Spring Harbor symposia on quantitative biology 1986 Jan 1 (Vol. 51, pp. 263-273). Cold Spring Harbor Laboratory Press
  31. 31. Williams JG, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic acids research. 1990 Nov 25;18(22):6531-5
  32. 32. Vos P, Hogers R, Bleeker M, Reijans M, Lee TV, Hornes M, Friters A, Pot J, Paleman J, Kuiper M, Zabeau M. AFLP: a new technique for DNA fingerprinting. Nucleic acids research. 1995;23(21):4407-14
  33. 33. Zane L, Bargelloni L, Patarnello T. Strategies for microsatellite isolation: a review. Molecular ecology. 2002 Jan;11(1):1-6
  34. 34. Uzun AY, Yesiloglu T, Aka-Kacar Y, Tuzcu O, Gulsen O. Genetic diversity and relationships within Citrus and related genera based on sequence related amplified polymorphism markers (SRAPs). Scientia horticulturae. 2009 Jul 2;121(3):306-12
  35. 35. Semagn K, Bjørnstad Å, Ndjiondjop MN. An overview of molecular marker methods for plants. African journal of biotechnology. 2006;5(25)
  36. 36. Weiland JJ, Yu MH. A cleaved amplified polymorphic sequence (CAPS) marker associated with root-knot nematode resistance in sugarbeet. Crop science. 2003 Sep;43(5):1814-8
  37. 37. Yang L, Fu S, Khan MA, Zeng W, Fu J. Molecular cloning and development of RAPD-SCAR markers for Dimocarpus longan variety authentication. SpringerPlus. 2013 Dec 1;2(1):501
  38. 38. França LT, Carrilho E, Kist TB. A review of DNA sequencing techniques. Quarterly reviews of biophysics. 2002 May 1;35(2):169
  39. 39. Davey JW, Hohenlohe PA, Etter PD, Boone JQ , Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics. 2011 Jul;12(7):499-510
  40. 40. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A. Diversity Arrays Technology (DArT) for whole-genome profiling of barley. Proceedings of the National Academy of Sciences. 2004 Jun 29;101(26):9915-20
  41. 41. Singh BD, Singh AK. Marker-assisted plant breeding: principles and practices. New Delhi: Springer; 2015 Jun 26
  42. 42. Agarwal M, Shrivastava N, Padh H. Advances in molecular marker techniques and their applications in plant sciences. Plant cell reports. 2008 Apr 1;27(4):617-31
  43. 43. Barcaccia G. Molecular markers for characterizing and conserving crop plant germplasm. InMolecular techniques in crop improvement 2010 (pp. 231-254). Springer, Dordrecht
  44. 44. MirMohammadi Maibody SA, Golkar P. Application of DNA Molecular Markers in Plant Breeding. Journal of Plant Genetic Research. 2019 Sep 10;6(1):1-30
  45. 45. Ahmar S, Gill RA, Jung KH, Faheem A, Qasim MU, Mubeen M, Zhou W. Conventional and Molecular Techniques from Simple Breeding to Speed Breeding in Crop Plants: Recent Advances and Future Outlook. International Journal of Molecular Sciences. 2020 Jan;21(7):2590
  46. 46. Watson A, Hickey LT, Christopher J, Rutkoski J, Poland J, Hayes BJ. Multivariate Genomic Selection and Potential of Rapid Indirect Selection with Speed Breeding in Spring Wheat. Crop Science. 2019 Sep;59(5):1945-59
  47. 47. Tabor HK, Risch NJ, Myers RM. Candidate-gene approaches for studying complex genetic traits: practical considerations. Nature Reviews Genetics. 2002 May;3(5):391-7
  48. 48. Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall’Aglio E, Valè GI. Marker assisted selection in crop plants. Plant Cell, Tissue and Organ Culture. 2005 Sep 1;82(3):317-42
  49. 49. Dar AA, Mahajan R, Sharma S. Molecular markers for characterization and conservation of plant genetic resources. Indian J. Agric. Sci.. 2019 Nov 1;89(11):3-11
  50. 50. Holland JB. Implementation of molecular markers for quantitative traits in breeding programs—challenges and opportunities. InNew Directions for a Diverse Planet: Proceedings for the 4th International Crop Science Congress. Regional Institute, Gosford, Australia, 2004 Sep 26
  51. 51. Ribaut JM, Sawkins MC, Bänziger M, Vargas M, Huerta E, Martinez C, Moreno M. Marker-assisted selection in tropical maize based on consensus map, perspectives, and limitations. Resilient Crops for Water Limited Environments. 2004:267
  52. 52. Hasan MM, Rafii MY, Ismail MR, Mahmood M, Rahim HA, Alam MA, Ashkani S, Malek MA, Latif MA. Marker-assisted backcrossing: a useful method for rice improvement. Biotechnology & Biotechnological Equipment. 2015 Mar 4;29(2):237-54
  53. 53. Yadawad A, Gadpale A, Hanchinal RR, Nadaf HL, Desai SA, Biradar S, Naik VR. Pyramiding of leaf rust resistance genes in bread wheat variety DWR 162 through marker assisted backcrossing. Indian J Genet Pl Br. 2017 May 1;77:251-7
  54. 54. Xu Y, Zhang XQ , Harasymow S, Westcott S, Zhang W, Li C. Molecular marker-assisted backcrossing breeding: an example to transfer a thermostable β-amylase gene from wild barley. Molecular Breeding. 2018 May 1;38(5):63
  55. 55. Rawal R, Kumar V, Rani A, Gokhale SM. Genetic elimination of off-flavour generating lipoxygenase-2 gene of soybean through marker assisted backcrossing and its effect on seed longevity. Plant Breeding and Biotechnology. 2020 Jun 1;8(2):163-73
  56. 56. Wu J, Zhang M, Zhang X, Guo L, Qi T, Wang H, Tang H, Zhang J, Xing C. Development of InDel markers for the restorer gene Rf1 and assessment of their utility for marker-assisted selection in cotton. Euphytica. 2017 Nov 1;213(11):251
  57. 57. Osei MK, Prempeh R, Adjebeng-Danquah J, Opoku JA, Danquah A, Danquah E, Blay E, Adu-Dapaah H. Marker-Assisted Selection (MAS): A Fast-Track Tool in Tomato Breeding. InRecent Advances in Tomato Breeding and Production 2018 Nov 5. IntechOpen
  58. 58. Mannur DM, Babbar A, Thudi M, Sabbavarapu MM, Roorkiwal M, Sharanabasappa BY, Bansal VP, Jayalakshmi SK, Yadav SS, Rathore A, Chamarthi SK. Super Annigeri 1 and improved JG 74: two Fusarium wilt-resistant introgression lines developed using marker-assisted backcrossing approach in chickpea (Cicer arietinum L.). Molecular Breeding. 2019 Jan 1;39(1):2
  59. 59. Hospital F. Selection in backcross programmes. Philosophical Transactions of the Royal Society B: Biological Sciences. 2005 Jul 29;360(1459):1503-11
  60. 60. Ribaut JM, Jiang C, Hoisington D. Simulation experiments on efficiencies of gene introgression by backcrossing. Crop Science. 2002 Mar;42(2):557-65
  61. 61. Luo Y, Sangha JS, Wang S, Li Z, Yang J, Yin Z. Marker-assisted breeding of Xa4, Xa21 and Xa27 in the restorer lines of hybrid rice for broad-spectrum and enhanced disease resistance to bacterial blight. Molecular breeding. 2012 Dec 1;30(4):1601-10
  62. 62. Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK. Molecular markers in a commercial breeding program. Crop Science. 2007 Dec;47:S-154
  63. 63. Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001 Apr 1;157(4):1819-29
  64. 64. Ingvarsson PK, Street NR. Association genetics of complex traits in plants. New Phytologist. 2011 Mar;189(4):909-22
  65. 65. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S. Genomic selection in plant breeding: methods, models, and perspectives. Trends in plant science. 2017 Nov 1;22(11):961-75
  66. 66. Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection. Theoretical and Applied Genetics. 2019 Mar 1;132(3):669-86
  67. 67. Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics. 1990;124(3):743-56
  68. 68. Moreau L, Charcosset A, Gallais A. Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica. 2004; 137(1):111-8
  69. 69. Randhawa HS, Mutti JS, Kidwell K, Morris CF, Chen X, Gill KS. Rapid and targeted introgression of genes into popular wheat cultivars using marker-assisted background selection. PloS one. 2009; 4(6):e5752
  70. 70. Zhou WC, Kolb FL, Bai GH, Domier LL, Boze LK, Smith NJ. Validation of a major QTL for scab resistance with SSR markers and use of marker-assisted selection in wheat. Plant breeding. 2003 ;122(1):40-6
  71. 71. Han F, Romagosa I, Ullrich SE, Jones BL, Hayes PM, Wesenberg DM. Molecular marker-assisted selection for malting quality traits in barley. Molecular Breeding. 1997 Dec 1;3(6):427-37
  72. 72. Akhtar S, Bhat MA, Wani SA, Bhat KA, Chalkoo S, Mir MR, Wani SA. Marker assisted selection in rice. Journal of Phytology. 2010 Dec 19
  73. 73. Langridge P, Chalmers K. The principle: identification and application of molecular markers. InMolecular marker systems in plant breeding and crop improvement 2004 (pp. 3-22). Springer, Berlin, Heidelberg
  74. 74. Mackill DJ, Collard BC, Neeraja CN, Rodriguez RM, Heuer S, Ismail AM. QTLs in rice breeding: examples for abiotic stresses. InRice Genetics V 2007 (pp. 155-167)
  75. 75. Stuber CW, Polacco M, Lynn M. Synergy of empirical breeding, marker-assisted selection, and genomics to increase crop yield potential. Crop Science. 1999 Nov;39(6):1571-83
  76. 76. Pillen K, Zacharias A, Leon J. Advanced backcross QTL analysis in barley (Hordeum vulgare L.). Theoretical and Applied genetics. 2003 Jul 1;107(2):340-52
  77. 77. Ribaut JM, Betrán J. Single large-scale marker-assisted selection (SLS-MAS). Molecular Breeding. 1999 Dec 1;5(6):531-41
  78. 78. Eathington SR, Dudley JW, Rufener GK. Usefulness of Marker-QTAL ssociations in Early Generation Selection. Crop Science. 1997 Nov;37(6):1686-93
  79. 79. Peleman JD. Breeding by design: exploiting genetic maps and molecular markers through marker-assisted selection. The Handbook of Plant Genome Mapping: Genetic and Physical Mapping. 2005
  80. 80. Peleman JD and Van der Voort JR. Breeding by design. Trends in plant science. 2003 Jul 1;8(7):330-4
  81. 81. Podlich DW, Winkler CR, Cooper M. Mapping as you go: An effective approach for marker-assisted selection of complex traits. Crop Science. 2004 Sep;44(5):1560-71
  82. 82. Yashitola J, Thirumurugan T, Sundaram RM, Naseerullah MK, Ramesha MS, Sarma NP, Sonti RV. Assessment of purity of rice hybrids using microsatellite and STS markers. Crop Science. 2002 Jul;42(4):1369-73
  83. 83. Xu Y, Beachell H, McCOUCH SR. A marker-based approach to broadening the genetic base of rice in the USA. Crop science. 2004 Nov;44(6):1947-59
  84. 84. Reif JC, Melchinger AE, Xia XC, Warburton ML, Hoisington DA, Vasal SK, Beck D, Bohn M, Frisch M. Use of SSRs for establishing heterotic groups in subtropical maize. Theoretical and Applied genetics. 2003 Sep 1;107(5):947-57
  85. 85. Cerrudo D, Cao S, Yuan Y, Martinez C, Suarez EA, Babu R, Zhang X, Trachsel S. Genomic selection outperforms marker assisted selection for grain yield and physiological traits in a maize doubled haploid population across water treatments. Frontiers in plant science. 2018 Mar 20;9:366
  86. 86. Bouchez A, Causse M, Gallais A, Charcosset A. Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics. 2002 Dec 1;162(4):1945-59
  87. 87. Dussle C, Quint M, Xu M, Melchinger A, Lübberstedt T. Conversion of AFLP fragments tightly linked to SCMV resistance genes Scmv1 and Scmv2 into simple PCR-based markers. Theoretical and Applied Genetics. 2002 Dec 1;105(8):1190-5
  88. 88. Xu Z, Hua J, Wang F, Cheng Z, Meng Q , Chen Y, Han X, Tie S, Liu C, Li X, Wang Z. Marker-assisted selection of qMrdd8 to improve maize resistance to rough dwarf disease. Breeding Science. 2020:19110
  89. 89. Galiano-Carneiro AL, Miedaner T. Genetics of resistance and pathogenicity in the maize/Setosphaeria turcica pathosystem and implications for breeding. Frontiers in plant science. 2017 Aug 29;8:1490
  90. 90. Badji A, Otim M, Machida L, Odong T, Kwemoi DB, Okii D, Agbahoungba S, Mwila N, Kumi F, Ibanda A, Mugo S. Maize combined insect resistance genomic regions and their co-localization with Cell Wall constituents revealed by tissue-specific QTL meta-analyses. Frontiers in plant science. 2018 Jul 5;9:895
  91. 91. Prasanna BM. Molecular markers for maize improvement inAsia. InAsian Regional Maize Workshop, 10. Maize for Asia-Emerging Trends and; Technologies. Proceedings of The Asian Regional Maize Workshop; Makassar, Indonesia; 20-23 October, 2008 2009 (p. 202). CIMMYT
  92. 92. Hossain F, Muthusamy V, Pandey N, Vishwakarma AK, Baveja A, Zunjare RU, Thirunavukkarasu N, Saha S, Manjaiah KM, Prasanna BM, Gupta HS. Marker-assisted introgression of opaque2 allele for rapid conversion of elite hybrids into quality protein maize. Journal of genetics. 2018 Mar 1;97(1):287-98
  93. 93. Liang F, Deng Q , Wang Y, Xiong Y, Jin D, Li J, Wang B. Molecular marker-assisted selection for yield-enhancing genes in the progeny of “9311× O. rufipogon” using SSR. Euphytica. 2004 Jan 1;139(2):159-65
  94. 94. Chen H, Wang S, Zhang Q. New gene for bacterial blight resistance in rice located on chromosome 12 identified from Minghui 63, an elite restorer line. Phytopathology. 2002 Jul;92(7):750-4
  95. 95. Ni D, Song F, Ni J, Zhang A, Wang C, Zhao K, Yang Y, Wei P, Yang J, Li L. Marker-assisted selection of two-line hybrid rice for disease resistance to rice blast and bacterial blight. Field Crops Research. 2015 Dec 1;184:1-8
  96. 96. Tian D, Guo X, Zhang Z, Wang M, Wang F. Improving blast resistance of the rice restorer line, Hui 316, by introducing Pi9 or Pi2 with marker-assisted selection. Biotechnology & Biotechnological Equipment. 2019 Jan 1;33(1):1195-203
  97. 97. Shabanimofrad M, Yusop MR, Ashkani S, Musa MH, Adam NA, Haifa I, Harun AR, Latif MA. Marker-assisted selection for rice brown planthopper (Nilaparvata lugens) resistance using linked SSR markers. Turkish Journal of Biology. 2015 Sep 18;39(5):666-73
  98. 98. Gonzaga ZJ, Carandang J, Sanchez DL, Mackill DJ, Septiningsih EM. Mapping additional QTLs from FR13A to increase submergence tolerance in rice beyond SUB1. Euphytica. 2016 Jun 1;209(3):627-36
  99. 99. Schmierer DA, Kandemir N, Kudrna DA, Jones BL, Ullrich SE, Kleinhofs A. Molecular marker-assisted selection for enhanced yield in malting barley. Molecular Breeding. 2004 Dec 1;14(4):463-73
  100. 100. Lv C, Song Y, Gao L, Yao Q , Zhou R, Xu R, Jia J. Integration of QTL detection and marker assisted selection for improving resistance to Fusarium head blight and important agronomic traits in wheat. The Crop Journal. 2014 Feb 1;2(1):70-8
  101. 101. Merchuk-Ovnat L, Barak V, Fahima T, Ordon F, Lidzbarsky GA, Krugman T, Saranga Y. Ancestral QTL alleles from wild emmer wheat improve drought resistance and productivity in modern wheat cultivars. Frontiers in plant science. 2016 Apr 15;7:452
  102. 102. Malik S, Malik TA. Genetic mapping of potential QTLs associated with drought tolerance in wheat. JAPS: Journal of Animal & Plant Sciences. 2015 Aug 1;25(4)
  103. 103. Tar'An B, Michaels TE, Pauls KP. Mapping genetic factors affecting the reaction to Xanthomonas axonopodis pv. phaseoli in Phaseolus vulgaris L. under field conditions. Genome. 2001 Dec 1;44(6):1046-56
  104. 104. Cobos MJ, Winter P, Kharrat M, Cubero JI, Gil J, Millan T, Rubio J. Genetic analysis of agronomic traits in a wide cross of chickpea. Field Crops Research. 2009 Mar 15;111(1-2):130-6
  105. 105. Iruela M, Pistón F, Cubero JI, Millán T, Barro F, Gil J. The marker SCK13 603 associated with resistance to ascochyta blight in chickpea is located in a region of a putative retrotransposon. Plant cell reports. 2009 Jan 1;28(1):53-60
  106. 106. Chen HM, Liu CA, Kuo CG, Chien CM, Sun HC, Huang CC, Lin YC, Ku HM. Development of a molecular marker for a bruchid (Callosobruchus chinensis L.) resistance gene in mungbean. Euphytica. 2007 Sep 1;157(1-2):113-22
  107. 107. Mukeshimana G, Butare L, Cregan PB, Blair MW, Kelly JD. Quantitative trait loci associated with drought tolerance in common bean. Crop Science. 2014 May;54(3):923-38
  108. 108. Pushpavalli R, Krishnamurthy L, Thudi M, Gaur PM, Rao MV, Siddique KH, Colmer TD, Turner NC, Varshney RK, Vadez V. Two key genomic regions harbour QTLs for salinity tolerance in ICCV 2× JG 11 derived chickpea (Cicer arietinum L.) recombinant inbred lines. BMC plant biology. 2015 Dec 1;15(1):124
  109. 109. Hamwieh A, Imtiaz M, Malhotra RS. Multi-environment QTL analyses for drought-related traits in a recombinant inbred population of chickpea (Cicer arientinum L.). Theoretical and Applied Genetics. 2013 Apr 1;126(4):1025-38
  110. 110. Chankaew S, Isemura T, Naito K, Ogiso-Tanaka E, Tomooka N, Somta P, Kaga A, Vaughan DA, Srinives P. QTL mapping for salt tolerance and domestication-related traits in Vigna marina subsp. oblonga, a halophytic species. Theoretical and applied genetics. 2014 Mar 1;127(3):691-702
  111. 111. Abdel-Haleem H, Carter TE, Rufty TW, Boerma HR, Li Z. Quantitative trait loci controlling aluminum tolerance in soybean: candidate gene and single nucleotide polymorphism marker discovery. Molecular breeding. 2014 Apr 1;33(4):851-62
  112. 112. Ha BK, Vuong TD, Velusamy V, Nguyen HT, Shannon JG, Lee JD. Genetic mapping of quantitative trait loci conditioning salt tolerance in wild soybean (Glycine soja) PI 483463. Euphytica. 2013 Sep 1;193(1):79-88
  113. 113. Abdel-Haleem H, Carter TE, Purcell LC, King CA, Ries LL, Chen P, Schapaugh W, Sinclair TR, Boerma HR. Mapping of quantitative trait loci for canopy-wilting trait in soybean (Glycine max L. Merr). Theoretical and Applied Genetics. 2012 Sep 1;125(5):837-46
  114. 114. Klein A, Houtin H, Rond C, Marget P, Jacquin F, Boucherot K, Huart M, Rivière N, Boutet G, Lejeune-Hénaut I, Burstin J. QTL analysis of frost damage in pea suggests different mechanisms involved in frost tolerance. Theoretical and Applied Genetics. 2014 Jun 1;127(6):1319-30
  115. 115. Iglesias-García R, Prats E, Fondevilla S, Satovic Z, Rubiales D. Quantitative trait loci associated to drought adaptation in pea (Pisum sativum L.). Plant molecular biology reporter. 2015 Dec 1;33(6):1768-78

Written By

Gayatri Kumawat, Chander Kanta Kumawat, Kailash Chandra, Saurabh Pandey, Subhash Chand, Udit Nandan Mishra, Devidutta Lenka and Rohit Sharma

Submitted: 09 July 2020 Reviewed: 12 November 2020 Published: 30 November 2020