Biparental Crossing and QTL Mapping for Validation of Genome-Wide Association Studies.

Association mapping (AM), also known as genome-wide association studies (GWAS), is increasingly being employed in crop plants for the identification of QTL/genes and marker-trait associations (MTAs) in natural populations. Large numbers of such associations have been identified for variety of traits in different crop plants. However, not many of these associations have been used practically in the crop improvement program due to lack of validation. Although there are different ways through which the results of AM/GWAS could be validated, the best approach is to develop a biparental population for the trait of interest. An overview of the steps involved in the validation of results of AM using biparental mapping population in plants is provided in this chapter.

Cytogenetics to functional genomics: six decades journey of Professor P.K. Gupta.

We had the fortune of starting our scientific/research careers in the Molecular Biology and Crop Biotechnology Laboratory of Professor P.K. Gupta at Ch. Charan Singh University, Meerut, UP, India. Here, we describe the most important scientific contributions of our beloved mentor in the area of cytotaxonomy, cytogenetics, mutation breeding, quantitative genetics, molecular biology, crop biotechnology and plant genomics, on his 85th birthday. Important contributions made in the development and use of genomics resources including the development and use of different kinds of molecular markers, genetic and physical mapping, quantitative trait locus (QTL) interval mapping, genome-wide association mapping and molecular breeding including marker-assisted selection have been briefly summarized. Efforts have been also made to give readers a glimpse of important contributions in the study of cytology/apomixis of grasses, cytotaxonomic studies in asteraceae/fabaceae, nuclear/repetitive DNA content in Lolium, interspecific/intergeneric relationships involving the genus Hordeum and re-examining taxonomy of the tribe Triticeae.

Validation of the markers linked with drought tolerance related traits for use in MAS programme in chickpea.

Chickpea is an important cool season legume crop. The breeding efforts in chickpea are often hampered due to the narrow genetic base. Availability of diverse germplasm is an essential requirement for any crop improvement programme. This can facilitate development of desirable gene combinations and subsequently the improved cultivars. In any marker-assisted selection (MAS) programme, study of parental polymorphism using QTL linked markers is a pre-requisite for screening of desired genotypes. Any such study involving use of markers chosen randomly can only tell the diversity of the parents, but does not guarantee success of the MAS. The present study was undertaken to study the suitability of the SSR markers from the QTL-hotspot region linked with drought tolerance related traits in different genetic background. The study of polymorphism of the QTL-hotspot linked SSR markers NCPGR127, NCPGR21, TAA170, ICCM0249, STMS11, TR11 and GA24 between drought tolerant genotype ICC-4958 and remaining 32 chickpea genotypes revealed that most of the genotypes had monomorphic alleles as that of ICC-4958, while only a few genotypes showed polymorphic alleles. The markers that are found polymorphic between ICC-4958 and other chickpea genotypes can be used directly for foreground selection in MAS as they are mapped in the QTL-hotspot region. However, in cases where these are monomorphic, additional markers from QTL-hotspot region need to be screened. Besides validating the suitability of these markers, we also validated SSR markers that can be used for the background selection. Of the 21 SSR markers, 15 were found polymorphic between ICC-4958 and other genotypes suggesting their usefulness in the background selection.

Association Mapping in Plants.

Quantitative trait loci mapping has become a common practice in crop plants and can be accomplished using either biparental populations following interval mapping or natural populations following the approach of association mapping. Because of its ability to use the natural diversity and to search for functional variants in a broader germplasm, association mapping is becoming popular among researchers. An overview of the different steps involved in association mapping in plants is provided in this chapter.

Cataloguing of blast resistance genes in landraces and breeding lines of rice from India.

The rice blast caused by the fungus Magnaporthe oryzae is one of the most devastating diseases of rice and can lead to complete failure of the crop under severe cases. The first step in breeding for blast resistance in rice is therefore to identify the novel sources of resistance and cataloguing different blast resistant genes in these genotypes. In the present study, a set of 37 rice genotypes comprising of landraces, advanced breeding lines and released varieties were first characterized for blast resistance under epiphytotic conditions and subsequently different blast resistant genes were catalogued with the help of markers tightly linked to these genes. A total of 22 different blast resistant genes were catalogued in these genotypes. Lot of diversity was found to be present for different genes in the rice genotypes studied. In addition, a set of 2-3 markers were identified which could distinguish genotypes of a particular geographic area from each other.The results are useful for identifying the right combination of genotypes in the resistance breeding programme.

Association mapping in plants in the post-GWAS genomics era.

With the availability of DNA-based molecular markers during early 1980s and that of sophisticated statistical tools in late 1980s and later, it became possible to identify genomic regions that control a quantitative trait. The two methods used for this purpose included quantitative trait loci (QTL) interval mapping and genome-wide association mapping/studies (GWAS). Both these methods have their own merits and demerits, so that newer approaches were developed in order to deal with the demerits. We have now entered a post-GWAS era, where either the original data on individual genotypes are being used again keeping in view the results of GWAS or else summary statistics obtained through GWAS is subjected to further analysis. The first half of this review briefly deals with the approaches that were used for GWAS, the GWAS results obtained in some major crops (maize, wheat, rice, sorghum and soybean), their utilization for crop improvement and the improvements made to address the limitations of original GWA studies (computational demand, multiple testing and false discovery, rare marker alleles, etc.). These improvements included the development of multi-locus and multi-trait analysis, joint linkage association mapping, etc. Since originally GWA studies were used for mere identification of marker-trait association for marker-assisted selection, the second half of the review is devoted to activities in post-GWAS era, which include different methods that are being used for identification of causal variants and their prioritization (meta-analysis, pathway-based analysis, methylation QTL), functional characterization of candidate signals, gene- and gene-set based association mapping, GWAS using high dimensional data through machine learning, etc. The last section deals with popular resources available for GWAS in plants in the post-GWAS era and the implications of the results of post-GWAS for crop improvement.

Trait Mapping Approaches Through Linkage Mapping in Plants.

Quantitative trait loci (QTL) mapping in crop plants has now become a common practice due to the advances made in the area of molecular markers as well as that of statistical genomics. Consequently, large numbers of QTLs have been identified in different crops for a variety of traits. Several computational tools are now available that suit the type of mapping population and the trait(s) to be studied for QTL analyses as well as the objective of the program. These methods are comprised of simpler approaches like single marker analysis and simple interval mapping to relatively exhaustive inclusive composite interval mapping and Bayesian interval mapping. The relative significance of each of these methods varies considerably. The progress made in the area of computational analysis involving the identification of QTLs either through interval mapping or association mapping is unprecedented, and it is expected that it will continue to evolve over the coming years. An overview of the different methods of linkage-based QTL analysis is provided in this chapter. Graphical Abstract.

Association mapping in crop plants: opportunities and challenges.

The research area of association mapping (AM) is currently receiving major attention for genetic studies of quantitative traits in all major crops. However, the level of success and utility of AM achieved for crop improvement is not comparable to that in the area of human health care for diagnosis of complex human diseases. These AM studies in plants, as in humans, became possible due to the availability of DNA-based molecular markers and a variety of sophisticated statistical tools that are evolving on a regular basis. In this chapter, we first briefly review the significance of a variety of populations that are used in AM studies, then briefly describe the molecular markers and high-throughput genotyping strategies, and finally describe the approaches used for AM studies. The major part of the chapter is, however, devoted to analysis of reasons why the results of AM have been underutilized in plant breeding. We also examine the opportunities available and challenges faced while using AM for crop improvement programs. This includes a detailed discussion of the issues that have plagued AM studies, and the solutions that have become available to deal with these issues, so that in future, the results of AM studies may prove increasingly fruitful for crop improvement programs.

Novel SSR markers from BAC-end sequences, DArT arrays and a comprehensive genetic map with 1,291 marker loci for chickpea (Cicer arietinum L.).

Chickpea (Cicer arietinum L.) is the third most important cool season food legume, cultivated in arid and semi-arid regions of the world. The goal of this study was to develop novel molecular markers such as microsatellite or simple sequence repeat (SSR) markers from bacterial artificial chromosome (BAC)-end sequences (BESs) and diversity arrays technology (DArT) markers, and to construct a high-density genetic map based on recombinant inbred line (RIL) population ICC 4958 (C. arietinum)×PI 489777 (C. reticulatum). A BAC-library comprising 55,680 clones was constructed and 46,270 BESs were generated. Mining of these BESs provided 6,845 SSRs, and primer pairs were designed for 1,344 SSRs. In parallel, DArT arrays with ca. 15,000 clones were developed, and 5,397 clones were found polymorphic among 94 genotypes tested. Screening of newly developed BES-SSR markers and DArT arrays on the parental genotypes of the RIL mapping population showed polymorphism with 253 BES-SSR markers and 675 DArT markers. Segregation data obtained for these polymorphic markers and 494 markers data compiled from published reports or collaborators were used for constructing the genetic map. As a result, a comprehensive genetic map comprising 1,291 markers on eight linkage groups (LGs) spanning a total of 845.56 cM distance was developed (http://cmap.icrisat.ac.in/cmap/sm/cp/thudi/). The number of markers per linkage group ranged from 68 (LG 8) to 218 (LG 3) with an average inter-marker distance of 0.65 cM. While the developed resource of molecular markers will be useful for genetic diversity, genetic mapping and molecular breeding applications, the comprehensive genetic map with integrated BES-SSR markers will facilitate its anchoring to the physical map (under construction) to accelerate map-based cloning of genes in chickpea and comparative genome evolution studies in legumes.

The first genetic map of pigeon pea based on diversity arrays technology (DArT) markers.

With an objective to develop a genetic map in pigeon pea (Cajanus spp.), a total of 554 diversity arrays technology (DArT) markers showed polymorphism in a pigeon pea F(2) mapping population of 72 progenies derived from an interspecific cross of ICP 28 (Cajanus cajan) and ICPW 94 (Cajanus scarabaeoides). Approximately 13% of markers did not conform to expected segregation ratio. The total number of DArT marker loci segregating in Mendelian manner was 405 with 73.1% (P > 0.001) of DArT markers having unique segregation patterns. Two groups of genetic maps were generated using DArT markers. While the maternal genetic linkage map had 122 unique DArT maternal marker loci, the paternal genetic linkage map has a total of 172 unique DArT paternal marker loci. The length of these two maps covered 270.0 cM and 451.6 cM, respectively. These are the first genetic linkage maps developed for pigeon pea, and this is the first report of genetic mapping in any grain legume using diversity arrays technology.

Linkage disequilibrium and association studies in higher plants: present status and future prospects.

During the last two decades, DNA-based molecular markers have been extensively utilized for a variety of studies in both plant and animal systems. One of the major uses of these markers is the construction of genome-wide molecular maps and the genetic analysis of simple and complex traits. However, these studies are generally based on linkage analysis in mapping populations, thus placing serious limitations in using molecular markers for genetic analysis in a variety of plant systems. Therefore, alternative approaches have been suggested, and one of these approaches makes use of linkage disequilibrium (LD)-based association analysis. Although this approach of association analysis has already been used for studies on genetics of complex traits (including different diseases) in humans, its use in plants has just started. In the present review, we first define and distinguish between LD and association mapping, and then briefly describe various measures of LD and the two methods of its depiction. We then give a list of different factors that affect LD without discussing them, and also discuss the current issues of LD research in plants. Later, we also describe the various uses of LD in plant genomics research and summarize the present status of LD research in different plant genomes. In the end, we discuss briefly the future prospects of LD research in plants, and give a list of softwares that are useful in LD research, which is available as electronic supplementary material (ESM).