QTL Mapping in Crop Improvement: Present Progress and Future Perspectives

QTL Mapping in Crop Improvement: Present Progress and Future Perspectives presents advancements in QTL breeding for biotic and abiotic stresses and nutritional improvement in a range of crop plants. The book presents a roadmap for future breeding for resilience to various stresses and improvement in nutritional quality. Crops such as rice, wheat, maize, soybeans, common bean, and pigeon pea are the major staple crops consumed globally, hence fulfilling the nutritional requirements of global populations, particularly in the under-developed world, is extremely important. Sections cover the challenges facing maximized production of these crops, including diseases, insect damage, drought, heat, salinity and mineral toxicity.

Covering globally important crops including maize, wheat, rice, barley, soybean, common bean and pigeon pea, this book will be an important reference for those working in agriculture and crop improvement.

• Uses the latest molecular markers to identify QTLs/genes responsible for biotic and abiotic stress tolerance in plants
• Includes multiple core crops for efficient comparison and translational learning
• Provides a ready reference for improving quality traits through the use of the latest technologies

• Language: English
• Publisher: Academic Press
• Release date: Nov 19, 2022
• ISBN: 9780323902854

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Chapter 1: Recent advances in molecular marker technology for QTL mapping in plants

Kirti Rania; Mithlesh Kumarb; Ali Razzaqc; B.C. Ajaya; Praveen Konaa; Sandip Kumar Beraa; Shabir H. Wanid    a Department of Genetics, ICAR-Directorate of Groundnut Research, Junagadh, Gujarat, India

b Department of Genetics, College of Agriculture, Tharad, S.D. Agricultural University, Banaskantha, Gujarat, India

c Agronomy Department, University of Florida, Gainesville, FL, United States

d Department of Genetics and Plant Breeding, Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology, Anantnag, Srinagar, Jammu and Kashmir, India

Abstract

Discovering an association between a measurable phenotype and a genetic marker is what quantitative trait loci (QTL) mapping is all about. Researchers use statistical approaches to locate regions on chromosomes containing genes governing phenotypic variance in a population for a quantitative trait of interest. Because phenotypic performance largely reflects individual genetic values and in plant breeding most traits of interest have a quantitative inheritance that limits the breeding process. QTL mapping by linkage mapping and association analysis are the two prominent methods, and utilizing markers in plant breeding entails identifying a set of few markers linked with one or more QTLs that influence the complex trait expression. Till now, numerous QTL mapping projects have revealed QTLs that explain a considerable amount of phenotypic variance, leading to an optimistic appraisal of markers-assisted selection's prospects. So, the aim of this chapter is to decipher an overview of recent advances in QTL analysis such as advances in the development of mapping population, approaches of mapping QTLs, and advanced marker technologies commonly used to map QTLs. The high-throughput and cost-effective marker techniques have changed the entire scenario of marker applications. In the present chapter, we will also review available marker techniques, their strengths, and limitations. In the end, an overview of important or major QTLs recently identified in different crops for economically important traits will be discussed. The chapter also offers a review of the most recent genomic discoveries, methods, and techniques used, as well as their possible applications for crop improvement.

Keywords

QTL mapping; Linkage mapping; Association mapping; Markers and advanced marker technologies

1: Introduction

Plant breeding's primary objective is to enhance crop productivity, keeping sustained stability in yield, and quality improvement by crossing of elite or commercial cultivars with advanced lines that carry desirable novel traits. Traditional breeding involves hybridization of noble cultivar with the donor parent followed by the subsequent selection of better segregants or recombinants. Several crossings and advancements of generations are involved in the process, which also necessitates careful selection based on phenotypes. Furthermore, the whole process is tedious, laborious, and time consuming. Moreover, there is a risk of passing on undesirable gene blocks in addition to beneficial traits due to linkage drag. These flaws are important roadblocks to increasing agricultural production (Collard et al., 2005). Molecular markers used to tag the required genes or chromosome areas make the breeding process more competent and faster (Collard et al., 2005). Molecular tools are also used to select for favorable alleles, i.e., foreground selection and background selection against undesirable genomic regions that helps the breeder to work efficiently to improve populations.

Genetic markers have progressed from morphological to biochemical and now molecular markers with more recent advances. Morphological markers are limited in quantity and are heavily impacted by environment factors, which restricts their utility. Biochemical markers, on the other hand, while more numerous, are also impacted by the environment, are sometimes difficult to detect, and hence result in false positives in gene mapping. Nucleotide alterations such as duplication, deletion, transition, inversion, and transversion in genome are detected by molecular markers. Initially, molecular marker discovery was focused on proteins and isozymes, followed by rapid progress in the discovery of DNA-based markers such as restriction fragment length polymorphisms or RFLP, random amplified polymorphic DNA or RAPD, amplified fragment length polymorphisms or AFLP, simple sequence repeats or SSRs, sequence characterized amplified regions or SCAR, single-nucleotide polymorphisms or SNPs, STSs or sequence tagged sites, and expressed sequence tags or ESTs. SSR and SNP marker discovery frequently necessitates DNA sequence information, which is costly. Due to multitude characteristics of SSRs such as high reproducibility, polymorphism, multiallelic, genome distribution, codominance inheritance, simple assay, and transferability across species, SSRs are markers of choice for the molecular breeding (Weber, 1990). Recent developments in genomics, combined with the use of available genetic resources, have raised the status of the majority of crops to that of a genomic resource-rich crop. DNA markers are the most useful genomic tools for characterizing and harnessing usable genetic variability. Researchers are now moving faster toward traits and their genetic mapping studies.

The use of more robust techniques such as SNPs, kompetitive allele-specific polymerase chain reaction (PCR) or KASPar and genotyping by sequencing (GBS) approaches are required due to the lower genetic variation at a molecular level. There have been major developments over the last decade, with the discovery of massively parallel technology, next-generation sequencing technology (NGS). Sequencing cost has reduced dramatically since the introduction of NGS, and it is now widely utilized for the production of molecular markers for genotyping (Elshire et al., 2011; Wetterstrand, 2014). NGS data is used for large number of lines genotyping and to find polymorphisms at DNA level. These markers have a wide range of applications in plant breeding studies, including the construction of dense physical and genetic maps, in associating traits with markers via quantitative trait loci (QTL) mapping or genome-wide association mapping (GWAS), and subsequent use in molecular-assisted breeding (Koebner, 2005). Several multiple approaches to bioinformatics, whole-genome study using de novo assembly, and resequencing have enabled the development of large numbers of SNPs and SSRs (Bertioli et al., 2016).

2: Advances in marker developments

With their wide applicability, molecular markers have transformed the entire landscape of crop sciences, particularly in providing breeders with a simple yet effective tool for precise genotype selection. Traditional breeding programs have been successful in some areas but not significantly achieved in others due to a lack of improved and more efficient screening methods and techniques, as well as a lack of knowledge about the underlying mechanisms of phenotypic expression. Emerging molecular tools offer a way to improve the efficiency, effectiveness, and gain from traditional breeding programs, especially for complex polygenic traits. A comprehensive approach incorporating traditional and molecular breeding would offer solutions to the complex problems presently confronting crop improvement. With the development of genetic linkage maps followed by marker discovery and identification of QTL and genetic mapping of the target traits, the crop improvement program has accelerated during the last decade. However, the impacts of climate change can be seen all over the world, stressing the urgent need for designing climate-smart (CS) crops to be able to cope-up these unfavorable conditions and aid in sustaining agriculture in order to achieve food and nutritional security. For, improvement of two or more traits simultaneously, it is important to identify markers for important traits and use them in a breeding program.

DNA base sequence with an identified location on a linkage map or chromosome is called a molecular marker, and it is associated with phenotypic expression of a gene (Henry, 2013). In breeding, markers that are closely linked to the trait of interest can be used. Morphological, biochemical, or DNA-based markers can be genetically related markers to the trait of interest. However, morphological markers are less in number, have large pleiotropic, epistatic and environmental effects. Further, expression of morphological markers is stage specific. Biochemical markers or isozyme markers are the differences in definite banding patterns of the proteins separated in SDS/Native PAGE. These isozymes are noted for their near-genetic neutrality, making them ideal for building linkage maps (Tanksley and Orton, 1983). Biochemical markers have some limitations, namely, less abundant, limited polymorphisms, stage specific, and environment-specific expression that limit their use in crop improvement. The variations in the nucleotide sequence at corresponding sites of homologous chromosomes are known as DNA markers. These differences are due to events like point mutations (transitions and transversions), genome rearrangements (additions and deletions) or errors during replication processes of tandemly repeated DNA. They follow a simple Mendelian pattern of inheritance. These are being extensively utilized in crop improvement programs because unlike morphological and biochemical markers, these are abundant in nature, show high levels of polymorphism, and are not affected by developmental changes in the plant or environmental factors. The inheritance of molecular markers is either in a dominant or codominant fashion. Based on the method of their detection, molecular markers are broadly classified into four categories:

1.Hybridization-based DNA markers, e.g., RFLP.

2.PCR-based DNA markers.

2.1PCR-based markers that use short arbitrary defined sequences of nucleotides as primers, e.g., RAPD, SCARs, and AFLP.

2.2PCR-based markers that use specific primers synthesized based on their complementary with target DNA sequences, e.g., SCARs, SSRs, STSs, ESTs, etc.

3.Sequence-based markers, e.g., SNPs.

4.Next-generation molecular marker technologies.

Most of the markers are developed either by exploiting genomic libraries (e.g., RFLPs or SSRs) or arbitrary amplification using PCR (e.g., RAPDs) or a hybrid of both, i.e., restriction fragmentation using enzymes followed by selective amplification (e.g., AFLPs). RFLP markers are reproducible and reliable, but it is expensive, labor intensive, and time consuming. For, more practical usage, RFLP markers that are completely linked with the given trait are converted into PCR-based specific markers—STS or CAPS markers. Whereas assays of STS (PCR-based sequence-tagged site markers derived from closely linked RFLP markers) and SCAR (sequence-characterized amplified region originated from polymorphic RAPD bands) are more accurate, codominant in nature, and can be used for high-throughput genotyping (Paran and Michelmore, 1993). Further, dominant marker RAPD is distributed in whole genome but has less reproducibility. Due to the multitude characteristics of SSRs, such as reproducibility, polymorphism, multiallelic, genome distribution, codominance inheritance, simple assay, and transferability across species, SSRs are markers of choice for the molecular breeding (Weber, 1990). As a result, several novel SSRs have been found in different crops and utilized in breeding program. SSRs or microsatellites polymorphisms are identified by target locus PCR amplification using designed flanking regions synthetic oligoes. PCR-based genetic markers made up of 300–500 nucleotide sequences derived from partial sequencing of cDNA clone ends (3′ or 5′) are referred to as ESTs or expressed sequence tags (Adams et al., 1991). These markers are simple single-base changes in the genomic region sequence, so they are biallelic in nature, present throughout the genome, and are the most abundant marker (Edwards and Batley, 2009). In recent years, SSR markers have also been produced using methods such as the construction and subsequent sequencing of SSR-densed genomic DNA libraries, the sequencing and mining of bacterial artificial chromosome (BAC)-end sequences (BESs) for repeats motifs, and the mining of transcript sequences developed either by Sanger method of sequencing or more improved NGS approaches (Pandey et al., 2012).

2.1: Sequence-based markers

SNPs or single-nucleotide polymorphism marker, a most frequent and abundant marker, covers a broad range of variations in the genome at the molecular level. These markers originate from simple single-base modifications in the genome and are biallelic. At molecular level SNPs, makers are known to arise due to point mutations like transversions/transitions/substitutions/insertions deletions that are detected by aligning two sequences at the same chromosomal or genomic position from different genotypes (Weising et al., 2005). These markers are found in coding, noncoding, and intergenic areas of the genome at a varied frequency (Edwards and Batley, 2009).

When based on Sanger sequencing, genome-wide SNP discovery was too expensive, but, with the advent of the number of NGS methods, it has become more economical (Varshney et al., 2009a,b). The SNP markers are developed either by using overlapping genomic DNA sequences or by using available extensive information of unique genomic and EST sequences available in the public domain databases (Picoult-Newberg et al., 1999). Reduced representation shotgun is another approach in which a specific fragment of the genome representing several lines/genotypes is simultaneously sequenced for identifying SNPs in coding and regulatory regions of genomes. This technique has not been utilized in plant genomes to find SNPs till date.

2.2: Next-generation molecular marker technologies

Plant breeding has evolved with the advent of next-generation molecular marker technology. These new ultra-high-throughput methods, known as NGS technologies, have transformed traditional breeding strategies into genomics-based breeding. GBS, RAD sequencing, ddRAD sequencing, de novo sequencing, whole-genome resequencing (WGRS) strategies and SCWGS or single-cell-based whole-genome sequencing approaches are being widely used (Varshney et al., 2009a,b). Nonautomated markers like RFLP, CAPS, STS, or other markers linked to a phenotype of interest can now be converted to automatic marker systems using next-generation techniques. These markers rely on the presence of indels/substitutions in primer annealing sites or restriction enzyme recognition sites. The search of millions of polymorphisms in DNA such as SNPs and In-Dels by comparing the whole genome sequences of several genotypes of a species was possible by advances in bioinformatics in assembly, alignment, and short DNA reads based polymorphism calling created by NGS technologies. In addition, the WGS has been used in a range of crops for which reference genomes are accessible such as in Brassica napus (Huang et al., 2013; Bus et al., 2012), soybean (Kim et al., 2010), maize (Yan et al., 2010), and rice (Yamamoto et al., 2010).

3: Trait associations and QTL mapping

3.1: Mapping populations

The development of genetic mapping populations by crossing genetically divergent parents is the first step in developing linkage maps and the identifying QTLs/genes linked to the trait of interest (Fig. 1). Several genetic populations for mapping traits have been developed including F2 population, F2:3 populations, recombinant inbred lines (RILs), backcross introgression lines (BILs), near isogenic lines (NILs), and association mapping populations based on natural populations, and multiparent advanced generation intercross (MAGIC) populations. F2, backcross, or the conceptual near-isogenic lines developed following the bulk segregant analysis (BSA) approach are the short-term mapping populations found to be a good beginning point in molecular mapping. However, RILs, NILs, DHs, and CSSLs, or immortalized F2, NAM, or MAGIC, which are long-term mapping populations, could be developed for precision phenotyping of important traits and for populations sharing among different research personnel. As DHs, RILs, NILs, and CSSLs are homozygous, they are not appropriate for dominance and interaction effects studies, except for additive × additive interaction effects. In contrast, immortalized F2 populations combine the benefits of perpetual mapping populations and the opportunity of dominance and all interaction effects study estimable from F2 populations.

Fig. 1

Fig. 1 A schematic representation of various mapping populations.

Detecting an association between the desired trait and genotyping is the main principle of QTL analysis. Based on genotyping data at the marker locus mapping population is partitioned into different genotypic classes, and the correlative statistics is implied to determine the significant differences among genotypic classes with respect to the trait under study. If a significant difference in the phenotypic means of the two/more genotypic groups exists, it indicates that the marker locus being used to partition the mapping population is linked to the trait. Next is the construction of a linkage map, it is the location and relative genetic distance between markers along the chromosomal length. Genotyping refers to segregation patterns for each of the polymorphic markers analyzed by screening the mapping population. Higher levels of polymorphism greatly encourage the development of more saturated genetic linkage maps that form the basis for identifying markers of economically significant characters, i.e., closely linked to governing QTLs. Nearly all maps, however, constructed using low-throughput markers, including RFLPs, SSRs have produced comparative low-density map and are unable to provide reliable information on complex trait. Precise measuring of the target quantitative traits has to be done. The data is pooled over location and replication to obtain a single quantitative value. Further, phenotyping data from multiple locations or environments is also necessary to have a better picture of the QTL × environment interaction. The steps involved in QTL mapping are depicted in Fig. 2.

Fig. 2

Fig. 2 Steps in QTL mapping.

3.2: Statistical tools used in QTL mapping

For QTL mapping, or detecting associations between markers and phenotype or desired trait, a variety of data analysis approaches have been developed such as single-marker analysis (SMA), simple interval mapping (SIM), composite interval mapping (CIM), and multiple interval mapping (MIM).

1.Single-marker analysis or SMA/SF-ANOVA

The SMA is carried individually for each marker locus irrespective of information from any other loci. The statistics such as F-tests, t-tests, ANOVA, regression, maximum likelihood estimations, and log-likelihood ratios show whether or not differences between genotype classes for marker loci are significant.

The limitations of SMA

➢The farther a marker is from a QTL, the more difficult the QTL is to detect, due to recombination between the marker and QTL.

➢Due to recombination between the marker and QTL, the QTL effects may be underestimated.

➢The approach is incapable of determining whether or not the markers are linked to one or more QTLs.

➢Because chromosome maps are not generated, it is difficult to determine what fraction of an organism's genome is covered by a set of markers.

The major limitation is that the farther is QTL from a marker, there is less chance of its detection because of the marker and the QTL recombination. Confounded recombination frequencies limit the estimation of the effect of QTLs. These limitations may be overcome by using a large number of molecular markers spread throughout the genome.

2.Simple interval mapping or SIM

The SIM method uses linkage maps and simultaneously analysis intervals between adjacent pairs of linked markers along the chromosomes, instead of SMA. If the logarithm of odds ratios (LOD) exceeds a critical threshold which is more often fixed as ≥ 3, it means the presence of a putative QTL. It is considered statistically more powerful than SMA as it uses the linked markers for statistical analysis and thus compensates for recombination of marker and the trait governing the genomic region, if any. However, when multiple QTLs segregate in a cross, SIM fails to take into account genetic variance caused by other QTLs. In the single-factor ANOVA technique, the presence of a QTL is evaluated only at marker sites on the chromosome map that may be 20 cM or more apart, resulting in imprecise detection of QTL positions and effects. SIM is a step forward because it checks for QTL existence every 2 cM between each pair of neighboring markers. As a result, single-factor analysis underestimates the most likely site of a QTL and the extent of its impacts. At each test site, the SIM technique calculates a LOD score, which represents the chance of a QTL being present. The presence of a QTL in a chromosomal area is indicated when the LOD score projected along the chromosome map exceeds a threshold significance level. The peak LOD score is most likely interpreted as the QTL position.

Limitations of simple interval mapping

➢It requires a linkage map to be constructed first.

➢Specialized QTL analysis software is required.

➢The positions of QTLs are sometimes ambiguous or influenced by other QTLs.

➢Difficulty in separating effects of linked QTLs.

3Composite interval mapping or CIM

CIM was developed in order to overcome some of the shortcomings of SIM. Interval test that is used to separate and isolate individual QTL effects by combining interval mapping with multiple regressions is the basis of this method. Genetic variation in other regions of the genome is regulated by this, thus reducing background noise that can affect QTL detection. To control background variation, the analysis software incorporates into the model cofactors, a set of markers that are significantly associated with the trait and may be located anywhere in the genome. They are usually identified by forward or backward stepwise regression, with user input to resolve the number of cofactors and other characteristics of the analysis. The CIM approach increases precision of QTL mapping because it uses linked markers as cofactors, so the test is not affected by background QTLs.

Disadvantages of composite interval mapping

➢Requires linkage map to be constructed first.

➢Specialized QTL analysis software is required.

➢CIM can be slow, as it may need an hour or more for genome-wide analysis because of the intensive computations involved.

➢CIM is highly dependent on background markers and permutation is slow.

4Multiple interval mapping

Interval mapping is extended to multiple QTLs in the same way that multiple regressions are extended to analysis of variance. It fits multiple putative QTL directly in the model for mapping QTL using multiple marker intervals at the same time. With the MIM approach, the precision and power of QTL mapping is improved. Also, interaction among QTLs, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed.

3.3: Bulk segregant analysis: Rapid approach for quantitative trait mapping

The development of a mapping population, phenotyping for the traits of interest, as well as genotyping of each individual plant of a population with genome-wide markers, is often required for quantitative trait analysis. However, genotyping of a big mapping population is a cumbersome process, time consuming, and comparatively costly. With the advent of high-throughput sequencing technologies, different methods have been established to genotype the mapping population such as restriction site-associated sequencing (RAD-seq) double digest RAD-seq, GBS, and high-density SNPs or insertion/deletions (InDel). These dense genetic maps would have a greater effect on genetic studies and marker-assisted selection programs to improve traits. So, instead of examining each plant DNA separately in a population, the genotyping process is reduced to only two samples in BSA by combining plants from two end tails or two extreme phenotypic classes obtained for quantitative trait and bulking DNA from these two contrasting bulks. Depending on whether population is generated by crossing two contrasting parental lines or from a natural population of plants with various genetic backgrounds (e.g., composite populations or variety mixes), two variants of the BSA are available. Michelmore et al. (1991), first described this technique for tagging disease resistance gene using contrasting bulks genotyped with RAPDs. Tagging of different traits using various markers is also described over other genetical techniques for gene tagging, such as use of near-isogenic lines produced by repeated back crossing, which is a tedious and cumbersome process. Most economically significant traits are complex and are influenced by other genes, environments, and their interactions. However, assaying all the individuals from a sample population for the target traits is required by conventional analysis methods. As a result, it is often more expensive and takes longer time (Zou et al., 2016). Selective genotyping, in which only extreme features governing individuals (usually the two tails selected from a sample population) are tested, has been proposed to maintain the robustness of data by reducing costs and simplifying the analytical procedure (Sun et al., 2010).

BSA has recently been updated to locate the target genes using huge populations, enlarged tail sizes, and high-density markers, so that validation of suspected positive markers through genotyping of the entire population is no longer required (Sun et al., 2010). As a result, the cost of genotyping for selective samples is considerably lowered, and statistical power in QTL mapping is comparable to that of the total population analysis (Sun et al., 2010). BSA will cost only 0.4% (= 2/500) of the overall cost of complete population analysis, which will involve selecting 25 extreme individuals from a population of 500 to generate two different bulks (Zou et al., 2016). In recent years with the advent of molecular technologies, many improvements have been witnessed by BSA. Xu and Crouch (2008) and Sun et al. (2010) also suggested that the bulked DNA analysis can be employed for two contrasting groups of individuals from any population apart from biparental segregating populations. BSA was primarily designed to target the major traits with large genotypic effects and less interaction with the environment (Zou et al., 2016). However, recent advancements in BSA have increased the power of BSA in identifying small causative alleles.

3.4: Advanced approaches for QTL mapping

Trait mapping can be done by various approaches including linkage mapping, linkage disequilibrium (LD)-based association mapping and joint use of linkage and LD-based linkage-cum-association mapping (JLAM). In linkage mapping, biparental populations (RILs, NILs, BILs, and F2:3) are commonly used; however, recent advances in the area of marker trait association, linkage disequilibrium-based association mapping like candidate gene-based association (CGAS) and GWAS were also used in natural populations (Zhu et al., 2008). Biparental populations have high trait mapping ability, but have disadvantages in being able to have few traits and low resolution with allelic variation. In contrast, association mapping has the advantages of use of large number of germplasm to cover a huge amount of allelic variation in nature, which can provide high-resolution mapping; however, QTL detection power is very low. Further, multiparent populations, namely, MAGIC population, training population and RIAIL (recombinant inbred advanced intercross line) populations (Morrell et al., 2012) are being exploited. MAGIC populations involve the recombination of alleles from multiple parents and provide a high mapping resolution and high power of detecting QTL (Cavanagh et al., 2008). By choosing different founder parents and creating a wide collection of interrelated RILs populations, nested association mapping or NAM population captures genetic diversity, which allows achieving high-resolution mapping by using power of ancestral meiotic recombination. Recently, a combined technique of GWAS and QTL mapping was utilized to uncover genetic regions that control soybean oil and protein content (Sonah et al., 2014). In addition to that, whole-genome average interval mapping (WGAIM) along with the joint association mapping approaches has been developed to analyze QTL accurately (Verbyla et al., 2014).

In addition, advanced-backcross QTL (AB-QTL) is proposed by Tanksley et al. (1996) to save time and increase the precision of identifying associated markers and simultaneous ingression of desirable traits from wild species and wild forms to cultivated genotypes. Further higher resolution toward mapping efforts can be gained with NGS methods and mapping by sequencing approaches (Huang et al., 2009; Schneeberger and Weigel, 2011). Furthermore, QTL-seq, MutMap, and BSR-seq are three new trait mapping methods that have demonstrated rapid recognition of candidate regions of genome and diagnostic markers for the targeted traits. The DNA samples pooled from F2 segregating progeny derived from a cross between a mutant type and corresponding wild type are used in the MutMap method to conduct WGRS (WT). The SNP index is used to identify new SNPs, and then the sequence of bulk DNA is compared to the reference sequence. The SNPs that have sequence reads containing only the mutant sequences (SNP index = 1) are assumed to be related to the causal SNP responsible for the mutant phenotype. The MutMap strategy was conceptually integrated to the standard F2 and RIL populations in the QTL-seq technique (Takagi et al., 2013). For accelerated detection of agronomically significant QTLs, a combination of BSA and whole-genome resequencing is used. BSR-Seq uses RNA-Seq reads for mapping traits effectively, even though in populations where no molecular polymorphic survey has been previously conducted (Liu et al., 2012).

3.5: QTL mapping using high-throughput marker genotyping

High-throughput genotyping has greatly boosted the capability of QTL mapping and has also introduced new challenges for analysis. MAPMAKER/QTL, MapQTL, QGene, MQTL, PLABQTL, QTL Cartographer, MapManager, QTLNetwork, QTLMapper, etc., are some commonly used software to detect QTLs with phenotyping and genotyping data of a mapping population. Most of these softwares for QTL mapping have number of constraints; hence, there is a need for advanced analytical techniques. Although high-throughput marker genotyping has enhanced the QTL mapping power several times, it also has few challenges in effective analysis of data. Because closely positioned markers may cosegregate and there may be no crossing-over among hundreds of marker pairs, redundancy in QTL mapping is a general problem with high-throughput SNP data. A bin mapping strategy can be used to eliminate such marker pairs. Further, genotyping data errors are the main difficult to interpret correctly in a results analysis. To address these constraints, several softwares like IciMapping (Meng et al., 2015), SEG-Map (Zhao et al., 2010), MSTMAP (http://alumni.cs.ucr.edu), Lep-MAP (http://sourceforge.net/projects/lep-map), AntMap (http://lbm.ab.a.u-tokyo.ac.jp/), CARTHAGENE (www7.inra.fr/mia/T/CarthaGene/) etc., have been developed. Recently, genome sequencing of mapping populations has been successful in mapping the nematode resistance genes in soybeans (Xu et al., 2013). Table 1 provides a list of major QTLs in some major crops for important traits mapped more recently.

Table 1

4: Conclusion

There are still a number of important gaps related to QTL analysis. Merely, the QTLs with large effects and closest to a marker locus will have statistically reliable associations. If the QTLs interact strongly in their effects, it may be difficult to estimate even its presence. Also, the regions to which a QTL is localized can be quite large (several cM). Such regions may contain many genes, and there is no guarantee that a QTL will correspond to only a single gene. Thus to truly dissect quantitative variation at the mechanistic level, much further work is necessary with QTLs in hand. Particularly, important is fine mapping or high-resolution mapping of the QTL; in comparative genomics, functional genomics and evolutionary studies QTL can serve as useful tools once fine mapped.

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Chapter 2: A statistical perspective of gene set analysis with trait-specific QTL in molecular crop breeding

Samarendra Dasa,b,c,d; Shesh N. Raib,c,e,f,g    a Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, India

b Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, Louisville, KY, United States

c School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, United States

d ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar, India

e University of Louisville Alcohol Research Center, University of Louisville, Louisville, KY, United States

f Department of Hepatobiology and Toxicology, University of Louisville, Louisville, KY, United States

g Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, United States

Abstract

Over the last decade, gene set analysis (GSA) has become the first choice for gaining insights into underlying complex biology of stresses in plants through gene expression (GE) studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait-specific phenotype. In plant biology and breeding, analysis of gene sets with trait-specific quantitative trait loci (QTLs) data are considered as great source for biological knowledge discovery. Therefore we discuss various aspects of the GSA with QTLs, such as null hypothesis, sampling model, and nature of the test statistic, for interpreting high-throughput GE data in context of gene sets with the traits. Here, we also presented the key biological and statistical challenges in current GSA, which will be addressed by statisticians and biologists collectively in order to develop the next generation of GSA