In rice, Huang et al. (2009) first demonstrated a HTP GBS method by whole genome resequencing in a 150 recombinant inbred line (RIL) population, where 16 samples were multiplexed per lane, and a total of 112 samples per flow cell were sequenced using Illumina GA. In this case, the mapping population was derived from only a set of two parents vs. *indica* and *japonica* cultivars. Sequence alignment of the population data and validation of the SNPs using sliding window approach provided the genotype calls for the population. Another parent independent GBS approach was also implemented in rice by Xie et al. (2010), where an ultra-high density linkage map was constructed by low coverage sequencing of mapping population. In this study, genotype calls in the population were derived by maximum parsimonious inference of recombination assisted by Hidden Markov Model (HMM) and were validated with Bayesian inference. This approach can be

Whole genome re-sequencing is not always an option to implement GBS especially in crop species with large, complex, and repetitive genomes. Although GBS is fairly straightforward

especially in diverse natural populations, for association mapping.

populations and organisms (Elshire et al., 2011)**.**

flanking regions by verifying the haplotype information.

implemented in crops with no reference genome.

for small genomes, target enrichment or reduction of genome complexity must be employed to ensure sufficient overlap in sequence coverage for species with large genomes. Reducing genome complexity with restriction enzymes is relatively easy and reproducible compared to other target enrichment methods such as use of long range PCR, molecular inversion and capture probes etc. Elshire et al. (2011) applied complexity reduction approaches using *Ape*KI, a type II restriction endonuclease, to generate reduced representation libraries and then generated sequence data across these libraries from RIL mapping populations in both maize and barley. They analyzed the data to demonstrate GBS as a proof of concept for routine mapping and QTL identification studies. These studies illustrate and promise eventual application of GBS in introgression programs for traits of interest.

In human research, multiple cancer and other disease traits were investigated by GWAS using NGS technologies and that was possible due to the existence of narrow genetic variation in humans. Though GBS is an attractive option for populations derived from a set of parents, its application is very challenging for association mapping studies in plant species due to huge variations existing in natural populations. In GBS, variations are typically detected by aligning to the reference genome, but in natural populations, the variations are not limited to SNPs but also have PAVs. Detection of PAVs becomes exceptionally difficult unless comparative genome hybridization (CGH) approaches are applied along with NGS. Complex computational tools and deep sequencing data would help to overcome these problems.

### **<IP_ADDRESS> Targeted sequencing, Methylation profiling and DNA-protein interactions**

NGS technologies are often paired up with multiple accessory molecular biology methods to achieve the project-specific goals more efficiently and cost-effectively. Target enrichment is one of those accessory molecular techniques often used in NGS applications. Target enrichment methods mainly help to derive the NGS data from targeted regions such as candidate genes/exome regions and QTL regions and reduce the noise from unwanted regions in an experiment. Several commercial technologies are available for target enrichment and they include Roche/Nimblegen sequence capture arrays, Agilent SureSelectTM platform, RainDance technologies' RainStormTM microdroplet-based PCR technology and Fluidigm Access Array technologies. For a review of these technologies and applications in NGS refer to Mamanova et al. (2010). The NGS data derived from targeted regions could be used for variant detection, identification of gene analogs and paralogs, SNP discovery in QTL/exome regions (Nijman et al., 2010) and also for fine mapping efforts. Gene sequences are usually conserved and exome enrichment methods help to enrich the gene regions in libraries from genomic DNA by not only capturing exon regions but also the intron and other flanking regions next to the gene sequence. This helps to detect the mutations in the genes and intron-exon junctions to identify the splice variants (Ng et al., 2009).

Genome-wide sequence data should greatly facilitate our understanding of complex phenomena, such as heterosis and epigenetics, which have implications for crop genetics and breeding (Varshney et al., 2009). Expression of the genome is influenced by chromatin structure, which is governed by processes often associated with epigenetic regulation, namely histone variants, histone post-translational modifications, and DNA methylation. Developmental and environmental signals can induce epigenetic modifications in the

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 149

GLIMMER, SNAP are used to predict the coding regions (exons) in the genome and compare the translated protein to existing protein database for finding the gene function (Korf, 2004). Gene annotation tools such as BLAST2GO use the BLAST algorithm to find the similarity with the existing gene information to derive the gene annotation information (Conesa et al., 2005). Analysis of transcriptomic data using these bioinformatics tools helps

While structural genomics uses genetic variations to understand the phenotypic changes, functional genomics often uses gene expression differences to understand the same.  These methods frequently involve PCR-based, hybridization-based or sequencing-based approaches. Differential gene expression of known genes are usually characterized using quantitative PCR (qPCR), microarray technologies, serial analysis of gene expression (SAGE) and RNA-seq methods. Variations in gene expression data could be used to generate expression QTL (eQTL) information, similar to that of genetic markers. Different techniques and methods that are routinely used for

Measurement of gene expression (RNA) has been used extensively in monitoring biological responses to various environmental conditions. Quantitative gene analysis has been used for detecting the CNVs of a particular gene in the genome or in the transcriptome. PCR method has revolutionized many aspects of molecular biology including gene quantification. PCR protocols are modified and optimized by using either fluorescent probes or dyes in the reaction mixture to obtain accurate quantification of genes in the input DNA or RNA, and these procedures can be collectively called as quantitative PCR (qPCR). The qPCR approaches frequently use either fluorescent probes such as TaqMan® probes (Applied Biosystems) which detect a specific PCR product as it accumulates during PCR cycles, or fluorescent dyes such as SYBR Green which detects all double stranded DNA in a PCR

In TaqMan® assays, probes are designed specific to a target gene along with a pair of primer sequences in flanking regions of a probe. TaqMan® probes while hybridized to the target gene during PCR do not emit the fluorescence, but as the DNA extension is continued by the DNA polymerase using flanking primers in the PCR reaction, these hybridized probes are displaced and emit the fluorescence thereby facilitating the quantification of the target gene. TaqMan® probes have been routinely used for quantification of genes in the genome for allelic discrimination and zygosity studies and also in reverse transcribed mRNA/cDNA to study the gene expression. Using different reporter dyes, one can quantify two or more genes in the same PCR reaction. However, poor probe design could result in false positive signals. SYBR Green assays exploit the double stranded binding ability of the dye molecules during a PCR reaction. As the target gene product is accumulated in the PCR reaction, the fluorescence emitted by the dye increases and thus can quantify the gene. Though this procedure is simple to set up, there is no specificity to the target gene as it quantifies the entire double stranded DNA in the PCR reaction. There are several publications in the

gene discovery and associated pathways.

gene expression studies are briefly reviewed below.

literature that employed these techniques.

**2.4.1 Quantitative PCR**

reaction.

genome, and thus, the single genome in a plant cell gives rise to multiple epigenomes in response to developmental and environmental cues. N-terminal regions of nucleosome core complex histones undergo various post-translational modifications, namely acetylation, phosphorylation and ubiquitination that enhance transcription, while biotinylation and sumoylation repress genes (Chinnusamy and Zhu, 2009). Such modifications can be easily detected by combining the chromatin immunoprecipitation (ChiP) procedures with NGS technologies to analyze genome-wide histone modifications.

Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understand the influence of epigenetics (Laird, 2010). Determination of DNA methylation patterns usually requires the use of methylation-sensitive restriction endonucleases or affinity chromatography with methyl-binding proteins, or anti-mC antibodies. Reinders et al. (2008) have used bisulfite conversion for genome-wide DNA methylation profiling. In Arabidopsis, DNA methylation patterns and effects of methylation mutants were studied using Illumina GA sequencing technology by Cokus et al. (2008).

NGS technologies have been leading genome sequencing initiatives across many non-model and orphan crops in recent years to answer the complex biological questions. Newer NGS technologies are being developed and implemented to meet the ever increasing needs of research community and to solve the complex puzzles of nature. NGS methods are being extended to study population genetics, evolutionary biology, molecular ecology, hostpathogen interactions, organellar development, genotype-phenotype interactions and many others. NGS can also accelerate the development of better transformation technologies to modify genes and transform plants easily. In a nut shell, NGS technologies have already demonstrated significant impact on crop breeding and would certainly help to transform the practices and pace of molecular breeding of crops.

### **2.4 Functional genomics**

Functional genomics is the field of molecular biology that utilizes the vast wealth of data produced by genome sequencing projects to understand the gene functions, and their interactions. It is often referred to the study of the genes, their functions, interactions, and regulation to provide a biological function in an organism. Functional genomics mainly focuses on dynamic aspects such as gene transcription, translation, their interaction with other genes and proteins to define the gene function and their regulation. Functional genomics helps to understand the mechanism of a biological function and usually involves combination of both transcriptomics and proteomics.

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analyzing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues (Breyne and Zabeau, 2001). Transcriptional profiling studies routinely generate huge EST datasets using sequencing technologies to understand the biological significance of the genes. Availability of gene function information enables various applications of functional genomics. Assignment of gene function (annotation) in most instances is facilitated by comparing them with the genes of known function. Gene prediction modeling tools such as FGENESH, GENESCAN, GLIMMER, SNAP are used to predict the coding regions (exons) in the genome and compare the translated protein to existing protein database for finding the gene function (Korf, 2004). Gene annotation tools such as BLAST2GO use the BLAST algorithm to find the similarity with the existing gene information to derive the gene annotation information (Conesa et al., 2005). Analysis of transcriptomic data using these bioinformatics tools helps gene discovery and associated pathways.

While structural genomics uses genetic variations to understand the phenotypic changes, functional genomics often uses gene expression differences to understand the same.  These methods frequently involve PCR-based, hybridization-based or sequencing-based approaches. Differential gene expression of known genes are usually characterized using quantitative PCR (qPCR), microarray technologies, serial analysis of gene expression (SAGE) and RNA-seq methods. Variations in gene expression data could be used to generate expression QTL (eQTL) information, similar to that of genetic markers. Different techniques and methods that are routinely used for gene expression studies are briefly reviewed below.

### **2.4.1 Quantitative PCR**

148 Plant Breeding

genome, and thus, the single genome in a plant cell gives rise to multiple epigenomes in response to developmental and environmental cues. N-terminal regions of nucleosome core complex histones undergo various post-translational modifications, namely acetylation, phosphorylation and ubiquitination that enhance transcription, while biotinylation and sumoylation repress genes (Chinnusamy and Zhu, 2009). Such modifications can be easily detected by combining the chromatin immunoprecipitation (ChiP) procedures with NGS

Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understand the influence of epigenetics (Laird, 2010). Determination of DNA methylation patterns usually requires the use of methylation-sensitive restriction endonucleases or affinity chromatography with methyl-binding proteins, or anti-mC antibodies. Reinders et al. (2008) have used bisulfite conversion for genome-wide DNA methylation profiling. In Arabidopsis, DNA methylation patterns and effects of methylation mutants were studied using Illumina GA sequencing

NGS technologies have been leading genome sequencing initiatives across many non-model and orphan crops in recent years to answer the complex biological questions. Newer NGS technologies are being developed and implemented to meet the ever increasing needs of research community and to solve the complex puzzles of nature. NGS methods are being extended to study population genetics, evolutionary biology, molecular ecology, hostpathogen interactions, organellar development, genotype-phenotype interactions and many others. NGS can also accelerate the development of better transformation technologies to modify genes and transform plants easily. In a nut shell, NGS technologies have already demonstrated significant impact on crop breeding and would certainly help to transform the

Functional genomics is the field of molecular biology that utilizes the vast wealth of data produced by genome sequencing projects to understand the gene functions, and their interactions. It is often referred to the study of the genes, their functions, interactions, and regulation to provide a biological function in an organism. Functional genomics mainly focuses on dynamic aspects such as gene transcription, translation, their interaction with other genes and proteins to define the gene function and their regulation. Functional genomics helps to understand the mechanism of a biological function and usually involves

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analyzing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues (Breyne and Zabeau, 2001). Transcriptional profiling studies routinely generate huge EST datasets using sequencing technologies to understand the biological significance of the genes. Availability of gene function information enables various applications of functional genomics. Assignment of gene function (annotation) in most instances is facilitated by comparing them with the genes of known function. Gene prediction modeling tools such as FGENESH, GENESCAN,

technologies to analyze genome-wide histone modifications.

technology by Cokus et al. (2008).

**2.4 Functional genomics**

practices and pace of molecular breeding of crops.

combination of both transcriptomics and proteomics.

Measurement of gene expression (RNA) has been used extensively in monitoring biological responses to various environmental conditions. Quantitative gene analysis has been used for detecting the CNVs of a particular gene in the genome or in the transcriptome. PCR method has revolutionized many aspects of molecular biology including gene quantification. PCR protocols are modified and optimized by using either fluorescent probes or dyes in the reaction mixture to obtain accurate quantification of genes in the input DNA or RNA, and these procedures can be collectively called as quantitative PCR (qPCR). The qPCR approaches frequently use either fluorescent probes such as TaqMan® probes (Applied Biosystems) which detect a specific PCR product as it accumulates during PCR cycles, or fluorescent dyes such as SYBR Green which detects all double stranded DNA in a PCR reaction.

In TaqMan® assays, probes are designed specific to a target gene along with a pair of primer sequences in flanking regions of a probe. TaqMan® probes while hybridized to the target gene during PCR do not emit the fluorescence, but as the DNA extension is continued by the DNA polymerase using flanking primers in the PCR reaction, these hybridized probes are displaced and emit the fluorescence thereby facilitating the quantification of the target gene. TaqMan® probes have been routinely used for quantification of genes in the genome for allelic discrimination and zygosity studies and also in reverse transcribed mRNA/cDNA to study the gene expression. Using different reporter dyes, one can quantify two or more genes in the same PCR reaction. However, poor probe design could result in false positive signals. SYBR Green assays exploit the double stranded binding ability of the dye molecules during a PCR reaction. As the target gene product is accumulated in the PCR reaction, the fluorescence emitted by the dye increases and thus can quantify the gene. Though this procedure is simple to set up, there is no specificity to the target gene as it quantifies the entire double stranded DNA in the PCR reaction. There are several publications in the literature that employed these techniques.

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 151

Holloway et al. (2011) utilized expression data from 50,000 maize genes to identify both cisacting and trans-acting genetic elements that cooperate to regulate gene expression in maize crown roots and described the pitfalls of detecting false cis- or trans-acting eQTL in the absence of perfect genomic sequences from both parents. Multiple examples of eQTL analysis were reported in many plants and crop species (Druka et al., 2010). Despite many advantages, microarray technologies have their limitations, because expression profiling is conducted for only a limited set of genes that are currently known and we cannot detect the

The SAGE technique is based on counting sequence tags of 14–15 bases from cDNA libraries. These tags are generally derived from either 5' or 3' end of the expressed genes by restriction enzyme and can be up to 22 bases. Earlier, these tags used to be ligated to each other to create longer stretches of tags (Super-SAGE) and then sequenced by Sanger sequencing. But now, with the use of NGS short read technologies, these tags can be individually sequenced to generate the expression information. Each unique tag can represent a copy of the gene in the cDNA and thus by counting these tags, the gene expression can be quantified. The principal advantage of SAGE is that it gives an absolute measure of gene expression instead of measuring relative expression levels. Indeed, by counting the number of tags from each cDNA, one obtains an accurate measure of the number of transcripts present in the mRNA sample. This technology has been widely used to monitor gene expression in human cell cultures and tissue samples, but was used sporadically in plants. The principal limitation of SAGE is the need to sequence large numbers of tags in order to monitor scarcely expressed genes. Another drawback of SAGE is that the obtained tags are very short and hence not always unambiguous. Gene identification on the basis of short sequence tags relies on the availability of large databases

Recent developments in NGS technologies have transformed the way through which quantitative transcriptomics can be done. Due to their massively parallel sequencing abilities, these NGS technologies have been driving down the sequencing costs and time required to generate large amounts of sequencing data. Using these NGS technologies, RNA content of the cells can be directly sequenced without requiring any of the traditional cloning associated with EST sequencing. This approach, called ''RNA-seq", can generate quantitative expression scores that are comparable to microarrays, with the added benefit that the entire transcriptome is surveyed without the requirement of *a priori* knowledge of transcribed regions. One key advantage of this technique is that not only quantitative expression measures can be made, but transcript structures including alternatively spliced

RNA-seq procedures usually involve generation of multiplexed sequencing libraries from the mRNA/total RNA followed by high throughput sequencing. Short read sequencing instruments are more cost-effective for RNA-seq studies. Using the efficient bioinformatics tools, the sequence data is mapped to the reference genome to provide the multiple

influence of unknown genes for the phenotype.

**2.4.3 Serial analysis of gene expression (SAGE)**

of well-characterized ESTs (Breyne and Zabeau, 2001).

transcript isoforms, can also be identified (Wilhelm and Landry, 2009).

**2.4.4 RNA-Seq/digital gene expression**

#### **2.4.2 Microarray technology**

Parallel quantification of large numbers of mRNA transcripts for studying the regulation of gene expression was made possible by microarray technologies. The use of microarrays to analyze gene expression on a global level has recently received a great deal of attention. This should allow new understanding of gene signaling and regulatory networks that operate in various cell processes. The principle of a microarray experiment, as opposed to the classical northern-blotting analysis, is that mRNA from a given cell line or tissue is used to generate a labeled sample, sometimes termed the 'target', which is hybridized in parallel to a large number of DNA sequences, immobilized on a solid surface in an ordered array. Tens of thousands of transcript species can be detected and quantified simultaneously (Schulze and Downward, 2001).

The probes used in this technology could be cDNA fragments generated by PCR or synthetic oligonucleotides and these probes vary in their length based upon the instrumentation and technology available for synthesizing these arrays. Also, probes can be designed to represent the most unique part of a given transcript, making the detection of closely related genes or splice variants possible. To understand complex traits in crops, microarrays can be designed from existing sources of EST/functional gene information within the same crop or could leverage others with better resources. For example, Yang et al. (2007) utilized the microarrays designed from the model plant *Arabidopsis thaliana* to conduct transcriptional profiling experiments in canola for a disease condition.

Generally, in these experiments, mRNA from cells or tissue is extracted, converted to cDNA and labeled, hybridized to the DNA elements on the surface of the array, and detected by phospho-imaging or fluorescence scanning. The use of different fluorescent dyes (such as Cy3 and Cy5) allows mRNAs from two different cell populations or tissues to be labeled in different colors, mixed and hybridized to the same array, which results in competitive binding of the target to the arrayed sequences. After hybridization and washing, the slide is scanned at two different wavelengths corresponding to the dyes used, and the intensity of the same spot in both channels is compared. This results in the measurement of the ratio of transcript levels for each gene represented on the array. Microarrays can be used to investigate the changes in expression at single gene or across the whole genes to infer the changes in the phenotype. Analysis of expression differences at multigene level would facilitate understanding of gene regulatory pathways and gene-to-gene interactions besides providing the information of up or down regulation to a particular experimental condition. Several statistical and data analysis tools are available to interpret the microarray data (Schulze and Downward, 2001).

In traditional QTL analyses, linkage mapping leads to the detection of genomic regions which are associated with phenotypic variations within a population. Genetical genomics employs this same approach, except that the phenotypes are levels in gene expression resulting in the detection of expression QTL (eQTL). The eQTL do not necessarily result from sequence polymorphisms proximal to the gene being measured (cis-acting) but could result from differences in genes unlinked to the target. In these cases, the eQTL function in a trans-acting manner (Holloway et al., 2011). When the gene expression data is collected from a specialized tissue to understand the phenotype of the trait, we can use that data as a marker system to derive the eQTL information. In a recent genome wide eQTL study, Holloway et al. (2011) utilized expression data from 50,000 maize genes to identify both cisacting and trans-acting genetic elements that cooperate to regulate gene expression in maize crown roots and described the pitfalls of detecting false cis- or trans-acting eQTL in the absence of perfect genomic sequences from both parents. Multiple examples of eQTL analysis were reported in many plants and crop species (Druka et al., 2010). Despite many advantages, microarray technologies have their limitations, because expression profiling is conducted for only a limited set of genes that are currently known and we cannot detect the influence of unknown genes for the phenotype.

### **2.4.3 Serial analysis of gene expression (SAGE)**

150 Plant Breeding

Parallel quantification of large numbers of mRNA transcripts for studying the regulation of gene expression was made possible by microarray technologies. The use of microarrays to analyze gene expression on a global level has recently received a great deal of attention. This should allow new understanding of gene signaling and regulatory networks that operate in various cell processes. The principle of a microarray experiment, as opposed to the classical northern-blotting analysis, is that mRNA from a given cell line or tissue is used to generate a labeled sample, sometimes termed the 'target', which is hybridized in parallel to a large number of DNA sequences, immobilized on a solid surface in an ordered array. Tens of thousands of transcript species can be detected and quantified simultaneously (Schulze and

The probes used in this technology could be cDNA fragments generated by PCR or synthetic oligonucleotides and these probes vary in their length based upon the instrumentation and technology available for synthesizing these arrays. Also, probes can be designed to represent the most unique part of a given transcript, making the detection of closely related genes or splice variants possible. To understand complex traits in crops, microarrays can be designed from existing sources of EST/functional gene information within the same crop or could leverage others with better resources. For example, Yang et al. (2007) utilized the microarrays designed from the model plant *Arabidopsis thaliana* to

Generally, in these experiments, mRNA from cells or tissue is extracted, converted to cDNA and labeled, hybridized to the DNA elements on the surface of the array, and detected by phospho-imaging or fluorescence scanning. The use of different fluorescent dyes (such as Cy3 and Cy5) allows mRNAs from two different cell populations or tissues to be labeled in different colors, mixed and hybridized to the same array, which results in competitive binding of the target to the arrayed sequences. After hybridization and washing, the slide is scanned at two different wavelengths corresponding to the dyes used, and the intensity of the same spot in both channels is compared. This results in the measurement of the ratio of transcript levels for each gene represented on the array. Microarrays can be used to investigate the changes in expression at single gene or across the whole genes to infer the changes in the phenotype. Analysis of expression differences at multigene level would facilitate understanding of gene regulatory pathways and gene-to-gene interactions besides providing the information of up or down regulation to a particular experimental condition. Several statistical and data analysis tools are available to interpret the microarray data

In traditional QTL analyses, linkage mapping leads to the detection of genomic regions which are associated with phenotypic variations within a population. Genetical genomics employs this same approach, except that the phenotypes are levels in gene expression resulting in the detection of expression QTL (eQTL). The eQTL do not necessarily result from sequence polymorphisms proximal to the gene being measured (cis-acting) but could result from differences in genes unlinked to the target. In these cases, the eQTL function in a trans-acting manner (Holloway et al., 2011). When the gene expression data is collected from a specialized tissue to understand the phenotype of the trait, we can use that data as a marker system to derive the eQTL information. In a recent genome wide eQTL study,

conduct transcriptional profiling experiments in canola for a disease condition.

**2.4.2 Microarray technology**

Downward, 2001).

(Schulze and Downward, 2001).

The SAGE technique is based on counting sequence tags of 14–15 bases from cDNA libraries. These tags are generally derived from either 5' or 3' end of the expressed genes by restriction enzyme and can be up to 22 bases. Earlier, these tags used to be ligated to each other to create longer stretches of tags (Super-SAGE) and then sequenced by Sanger sequencing. But now, with the use of NGS short read technologies, these tags can be individually sequenced to generate the expression information. Each unique tag can represent a copy of the gene in the cDNA and thus by counting these tags, the gene expression can be quantified. The principal advantage of SAGE is that it gives an absolute measure of gene expression instead of measuring relative expression levels. Indeed, by counting the number of tags from each cDNA, one obtains an accurate measure of the number of transcripts present in the mRNA sample. This technology has been widely used to monitor gene expression in human cell cultures and tissue samples, but was used sporadically in plants. The principal limitation of SAGE is the need to sequence large numbers of tags in order to monitor scarcely expressed genes. Another drawback of SAGE is that the obtained tags are very short and hence not always unambiguous. Gene identification on the basis of short sequence tags relies on the availability of large databases of well-characterized ESTs (Breyne and Zabeau, 2001).

### **2.4.4 RNA-Seq/digital gene expression**

Recent developments in NGS technologies have transformed the way through which quantitative transcriptomics can be done. Due to their massively parallel sequencing abilities, these NGS technologies have been driving down the sequencing costs and time required to generate large amounts of sequencing data. Using these NGS technologies, RNA content of the cells can be directly sequenced without requiring any of the traditional cloning associated with EST sequencing. This approach, called ''RNA-seq", can generate quantitative expression scores that are comparable to microarrays, with the added benefit that the entire transcriptome is surveyed without the requirement of *a priori* knowledge of transcribed regions. One key advantage of this technique is that not only quantitative expression measures can be made, but transcript structures including alternatively spliced transcript isoforms, can also be identified (Wilhelm and Landry, 2009).

RNA-seq procedures usually involve generation of multiplexed sequencing libraries from the mRNA/total RNA followed by high throughput sequencing. Short read sequencing instruments are more cost-effective for RNA-seq studies. Using the efficient bioinformatics tools, the sequence data is mapped to the reference genome to provide the multiple

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 153

Owing to the collinearity between rice and other cereals and *Arabidopsis thaliana* and Brassicas, genomic resources of these model plants were leveraged to boost map-based cloning of genes from the genomes of other cereals with larger and complex genomes (Salse et al., 2008). Although the model plant, *A. thaliana*, and the field crop, rice, are the only plants with completely sequenced genomes, availability of high quality draft genome sequences of Brachypodium, sorghum and maize are believed to provide even more opportunities in gene and QTL discovery in orphan crops with zero to little genomic resources (Mayer et al., 2011). Molecular markers and EST collection of the above-mentioned crops, have been widely used to saturate genomic regions of other crops to narrow down the location of economically important QTL and genes. Using rice and Brachypodium ESTs, a powdery mildew resistance gene Ml3D232 was isolated from bread wheat (*Triticum aestivum*) (Zhang et al., 2010). Wheat tiller inhibition gene, tin3, was mapped using molecular markers developed based on ESTs from syntenic region of rice. Comparative analysis revealed collinear regions between perennial ryegrass (*Lolium perenne L.)*, *B. distachyon* and sorghum, which facilitated cloning of the self-incompatibility genes in the former (Shinozuka et al., 2010). Using synteny between rice chromosome 9 and Italian ryegrass LG5 and rice EST-based molecular markers, the location of LMPi1 gene conferring resistance to grey leaf spot was delimited to short chromosomal segment of the latter (Takahashi et al., 2010). Using homology between *B. rapa* and *A. thaliana*, the TRANSPARENT TESTA GLABRA 1 (TTG1) gene controlling both hairiness and seed coat color traits in Brassica species was isolated (Zhang et al., 2009). Sequences from Arabidopsis chromosome1 were used as RFLP probes to genetically map

Comparative genomics has been an indispensable tool to study evolution of genomes and gene families. *Brachypodium distachyon* has been widely used to study the evolution of important traits in barley and wheat, including flowering time pathways and (1,3;1,4)-b-Dglucans in plant cell walls (Higgins et al., 2010). Recently, the genomes of Brachypodium, rice and sorghum were used to assign 32,000 barley genes to the corresponding individual chromosomes (Mayer et al., 2011). Genome of Arabidopsis expanded its value to study xylem genomics of conifers (Xinguo et al., 2010). Two legume species, *Medicago truncatula* and *Lotus japonica,* are being sequenced to study genetic background of legume-specific phenomena such as symbiotic nitrogen fixation (Sato et al., 2008). Genome of *M. truncatula* should also facilitate the assembly of next generation sequence data in closely related taxa such as alfalfa (Young and Udvardi, 2009). Tremendous progress in sequencing technologies is opening new avenues for comparative genomics and enabling researchers to do genomewide comparisons. Current trends indicate that NGS technologies might change the focus of comparative studies by shifting them more towards evolutionary genomics rather than synteny-based gene-cloning or marker development. No matter what direction comparative genomics will take in the future, it is certain that it has the potential to broaden our current understanding of complex biological processes. An understanding of complex traits and processes such as adaptation of plants to biotic and abiotic stresses, yield, gene regulation, polyploidy and the influence of natural selection on gene and protein function can be

The concept and strategy of genetical genomics, outlined by Jansen and Nap (2001), aims to rapidly identify key gene targets by super-imposing gene expression data on that of genetic

translated into development of new strategies for crop improvement.

**2.6 Genetical genomics**

fertility restorer gene, *Rfp*, in *B. napus* genome.

sequence alignment. Removal of repetitive sequences from the dataset would improve the mapping process. Sequence data can be converted into expression data in different ways: i) by simply adding the number of reads which fall within the co-ordinates of each element (either exon or gene), and then normalizing the data for the length of the element; ii) by calculating a sequence score for each nucleotide in the genome based on the number of reads which cover each base position, and again normalizing for element lengths (Wilhelm and Landry, 2009); iii) by calculating RPKM (reads mapping to the genome per kilobase of transcript per million reads sequenced) values (Mortazavi et al., 2008) using a mathematical formula and use them as a measure of gene expression. These processes are often referred to as 'Digital Gene Expression'.

Compared to microarrays, the limits of the dynamic range measured in RNA-seq experiments are only determined by the amount of sequencing obtained. This means that through the continued sequencing of a given library, it should be possible to eventually measure the expression of every transcript present and so the ''dynamic range" only represents the actual biological diversity of the transcriptomes. To implement RNA-seq experiments effectively, availability of accurate annotation of the reference genome is necessary. This is particularly challenging for higher eukaryotes especially plant species with large genomes. However, RNA-Seq promises the gene quantification for studying complex phenotypic traits in cost-effective way in the near future for many crop species.

### **2.5 Comparative genomics**

Comparative genomics relates the structure and function of genomes of evolutionarily close species. It is a tool that helps researchers to study complex genomes of plants by leveraging sequence information of related species with smaller and less complicated genomes. However, in 1990s and at the beginning of 21st century when no reference sequences existed, comparative genomics was limited to comparative mapping. Both in dicot and monocot plants, collinearity at micro level, i.e. the same order of the molecular markers in genetic maps, were observed. In dicots, comparative sequencing approach revealed collinearity in gene order within several chromosomal segments of Arabidopsis, Capsella and Brassica genomes (Rossberg et al., 2001). Later Schranz et al. (2006) integrated all comparative genomics data in Brassicaceae and constructed a set of 24 genomics blocks, which represented the conserved segments of ancestral karyotype, *Arabidopsis thaliana*, and *Brassica rapa.* In monocots, comparative genetic mapping in oats, maize, rice, barley, wheat, sorghum, sugarcane and fox millet resulted in the construction of the "Crop Circle", which placed the small genome of rice in the center of the circle and aligned with maps of crops with larger genomes (Gale and Devos, 1998). Comparative mapping revealed 30 blocks of rice genome that could be found within genomes of other crops (Devos, 2005).

Sequencing of several model plants, including Arabidopsis, rice, *Medicago truncatula*, *Lotus japonicum* and Brachypodium, as well as crops such as maize, rice (both a model and a crop plant), sorghum and soybean confirmed, in general, the existence of synteny between genomes of related species at DNA level, which was, however, reported to be less obvious because of the unique patterns of distribution of repetitive sequences, duplications, insertions and deletion of genes (Dubcovsky et al., 2001). Nevertheless, comparative genomics has been a valuable tool for the development of molecular resources for crops and the identification of key genes for crop improvement.

Owing to the collinearity between rice and other cereals and *Arabidopsis thaliana* and Brassicas, genomic resources of these model plants were leveraged to boost map-based cloning of genes from the genomes of other cereals with larger and complex genomes (Salse et al., 2008). Although the model plant, *A. thaliana*, and the field crop, rice, are the only plants with completely sequenced genomes, availability of high quality draft genome sequences of Brachypodium, sorghum and maize are believed to provide even more opportunities in gene and QTL discovery in orphan crops with zero to little genomic resources (Mayer et al., 2011).

Molecular markers and EST collection of the above-mentioned crops, have been widely used to saturate genomic regions of other crops to narrow down the location of economically important QTL and genes. Using rice and Brachypodium ESTs, a powdery mildew resistance gene Ml3D232 was isolated from bread wheat (*Triticum aestivum*) (Zhang et al., 2010). Wheat tiller inhibition gene, tin3, was mapped using molecular markers developed based on ESTs from syntenic region of rice. Comparative analysis revealed collinear regions between perennial ryegrass (*Lolium perenne L.)*, *B. distachyon* and sorghum, which facilitated cloning of the self-incompatibility genes in the former (Shinozuka et al., 2010). Using synteny between rice chromosome 9 and Italian ryegrass LG5 and rice EST-based molecular markers, the location of LMPi1 gene conferring resistance to grey leaf spot was delimited to short chromosomal segment of the latter (Takahashi et al., 2010). Using homology between *B. rapa* and *A. thaliana*, the TRANSPARENT TESTA GLABRA 1 (TTG1) gene controlling both hairiness and seed coat color traits in Brassica species was isolated (Zhang et al., 2009). Sequences from Arabidopsis chromosome1 were used as RFLP probes to genetically map fertility restorer gene, *Rfp*, in *B. napus* genome.

Comparative genomics has been an indispensable tool to study evolution of genomes and gene families. *Brachypodium distachyon* has been widely used to study the evolution of important traits in barley and wheat, including flowering time pathways and (1,3;1,4)-b-Dglucans in plant cell walls (Higgins et al., 2010). Recently, the genomes of Brachypodium, rice and sorghum were used to assign 32,000 barley genes to the corresponding individual chromosomes (Mayer et al., 2011). Genome of Arabidopsis expanded its value to study xylem genomics of conifers (Xinguo et al., 2010). Two legume species, *Medicago truncatula* and *Lotus japonica,* are being sequenced to study genetic background of legume-specific phenomena such as symbiotic nitrogen fixation (Sato et al., 2008). Genome of *M. truncatula* should also facilitate the assembly of next generation sequence data in closely related taxa such as alfalfa (Young and Udvardi, 2009). Tremendous progress in sequencing technologies is opening new avenues for comparative genomics and enabling researchers to do genomewide comparisons. Current trends indicate that NGS technologies might change the focus of comparative studies by shifting them more towards evolutionary genomics rather than synteny-based gene-cloning or marker development. No matter what direction comparative genomics will take in the future, it is certain that it has the potential to broaden our current understanding of complex biological processes. An understanding of complex traits and processes such as adaptation of plants to biotic and abiotic stresses, yield, gene regulation, polyploidy and the influence of natural selection on gene and protein function can be translated into development of new strategies for crop improvement.

### **2.6 Genetical genomics**

152 Plant Breeding

sequence alignment. Removal of repetitive sequences from the dataset would improve the mapping process. Sequence data can be converted into expression data in different ways: i) by simply adding the number of reads which fall within the co-ordinates of each element (either exon or gene), and then normalizing the data for the length of the element; ii) by calculating a sequence score for each nucleotide in the genome based on the number of reads which cover each base position, and again normalizing for element lengths (Wilhelm and Landry, 2009); iii) by calculating RPKM (reads mapping to the genome per kilobase of transcript per million reads sequenced) values (Mortazavi et al., 2008) using a mathematical formula and use them as a measure of gene expression. These processes are often referred to

Compared to microarrays, the limits of the dynamic range measured in RNA-seq experiments are only determined by the amount of sequencing obtained. This means that through the continued sequencing of a given library, it should be possible to eventually measure the expression of every transcript present and so the ''dynamic range" only represents the actual biological diversity of the transcriptomes. To implement RNA-seq experiments effectively, availability of accurate annotation of the reference genome is necessary. This is particularly challenging for higher eukaryotes especially plant species with large genomes. However, RNA-Seq promises the gene quantification for studying complex phenotypic traits in cost-effective way in the near future for many crop species.

Comparative genomics relates the structure and function of genomes of evolutionarily close species. It is a tool that helps researchers to study complex genomes of plants by leveraging sequence information of related species with smaller and less complicated genomes. However, in 1990s and at the beginning of 21st century when no reference sequences existed, comparative genomics was limited to comparative mapping. Both in dicot and monocot plants, collinearity at micro level, i.e. the same order of the molecular markers in genetic maps, were observed. In dicots, comparative sequencing approach revealed collinearity in gene order within several chromosomal segments of Arabidopsis, Capsella and Brassica genomes (Rossberg et al., 2001). Later Schranz et al. (2006) integrated all comparative genomics data in Brassicaceae and constructed a set of 24 genomics blocks, which represented the conserved segments of ancestral karyotype, *Arabidopsis thaliana*, and *Brassica rapa.* In monocots, comparative genetic mapping in oats, maize, rice, barley, wheat, sorghum, sugarcane and fox millet resulted in the construction of the "Crop Circle", which placed the small genome of rice in the center of the circle and aligned with maps of crops with larger genomes (Gale and Devos, 1998). Comparative mapping revealed 30 blocks of

rice genome that could be found within genomes of other crops (Devos, 2005).

the identification of key genes for crop improvement.

Sequencing of several model plants, including Arabidopsis, rice, *Medicago truncatula*, *Lotus japonicum* and Brachypodium, as well as crops such as maize, rice (both a model and a crop plant), sorghum and soybean confirmed, in general, the existence of synteny between genomes of related species at DNA level, which was, however, reported to be less obvious because of the unique patterns of distribution of repetitive sequences, duplications, insertions and deletion of genes (Dubcovsky et al., 2001). Nevertheless, comparative genomics has been a valuable tool for the development of molecular resources for crops and

as 'Digital Gene Expression'.

**2.5 Comparative genomics**

The concept and strategy of genetical genomics, outlined by Jansen and Nap (2001), aims to rapidly identify key gene targets by super-imposing gene expression data on that of genetic

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 155

investigated in the study. While gene ontology system and other molecular function information resources are used for the functional classification of genes/proteins, pathway databases (Bauer-Mehren et al., 2009) such as KEGG (Kyoto Encyclopedia of Genes and Genomes; http://www.genome.jp/kegg/), Reactome (http://www.reactome.org), PANTHER (Protein ANalysis THrough Evolutionary Relationships; http://www. pantherdb.org/) and plant metabolic pathway databases ( http://plantcyc. org/ ) can be used for obtaining pathway information of the candidates. In step 3, the annotation information from step 2 is used for the generation of mathematical models and networks based on the associations and interactions observed in the datasets in comparison to known pathway information from the databases. Step 4 is the model development and validation where generated models are validated, and then variations introduced and models revised and validated again. Several software tools are used for this iterative cycle of model development – validation – perturbation – validation (Endler et al., 2009). In addition to the tools needed in these steps, availability of efficient platforms and infrastructure that can archive and support analysis of data from diverse sources and labs is a critical requirement for any systems biology project.

There can be multiple types of output expected from systems biology depending on the datasets used for integration. The output can either be a snap shot of a system with respect to a key gene or protein such as gene regulatory or biochemical networks or it can be based on the quantitative or qualitative dynamics of the system based on multiple perturbations. The most popular outcomes to date have been the construction of biological networks that include gene and transcriptional regulatory networks and interactome networks. A major promise of systems biology for crop improvement lies in the area of understanding quantitative traits and abiotic and biotic stresses and examples currently exist where systems biology approach has been explored in these areas. Cooper et al. (2003) developed a network of genes associated with developmental and stress responses in rice by measuring the gene expression changes with respect to environmental, biological and stress treatments related to interaction domains for 200 proteins from stressed and developing tissues. Data obtained from this study resulted in the identification of several stress response genes and were also found to be useful for the prediction of gene function in monocots and dicots. Use of systems biology in unraveling plant defense response has to deal with several dynamics related layers before key resistance gene candidates can be identified. In a study where mutant analysis and whole genome gene expression profiling was used for determining the regulatory roles of WRKY genes in systemic acquired resistance (SAR), a 'regulatory node' was identified in the transcriptional regulatory network controlling SAR (Wang et al., 2006). Although reports that utilized input from several omics for a detailed analysis of quantitative traits are yet to appear, approaches that utilized genome wide eQTL or metabolite QTL (mQTL) do exist that led to the construction of networks and identification of key metabolic QTLs that could be leveraged for crop improvement (Schauer et al., 2006). Current efforts in this area also include extending the impact of systems biology in understanding plant communities using a holistic approach by merging systems biology and systems ecology in order to improve agricultural productivity(Keurentjes et al., 2011).

The field of genomics has grown leaps and bounds during the recent decades. While the development and implementation of HTP genome sequencing and more than a dozen omics

**2.8 Bioinformatics**

mapping. Although molecular markers linked to quantitative trait loci (QTL), the genomic regions genetically determined to be associated with the observed phenotypic variation, can be used in marker-assisted breeding programs, in order to identify the gene(s) underlying the QTLs, comparison of gene expression differences between contrasting lines is very important. Genetical genomics approach involves expression profiling and molecular marker based genotyping of all individuals in a segregating population. It is followed by a comprehensive analysis using all statistical tools that are normally used in the analysis of quantitative trait loci. These analyses result in the identification of expression QTL (eQTL). Based on the nature of gene expression variation, there are two types of eQTL, cis-eQTL and trans-eQTL. If the variation or polymorphism is located near the gene, it is classified as a ciseQTL, whereas if the source of variation is located at a distant location in the genome then it is a trans-eQTL. Genetical genomics has been applied to a broad range of organisms and all studies have demonstrated the power of combining gene expression data with that of genetic analysis to fine tune pathways involved in complex phenotypes thereby enabling the identification of key genes (Joosen et al., 2009). Genetical genomics studies benefit greatly with the availability of reference genome sequence as well as with the use of large populations. In a genetical genomics study of the model plant Arabidopsis where a 162-line RIL population was used and data analyzed in conjunction with the genome sequence, researchers successfully predicted key regulators of flowering time and circadian rhythms through the construction of genetic regulatory network by combining eQTL mapping and regulator candidate gene selection (Keurentjes et al., 2007). Examples also exist where synteny of the target genome with other species can be used in genetical genomics. For example, in a wheat study, synteny with the sequenced rice genome was used to map the eQTL for seed quality parameters (Jordan et al., 2007). The use of genetical genomics is on the rise. Several technological advances such as expression profiling using next generation sequencing and the availability of several types of 'omics' data sets are enabling rapid construction of biological networks for not only identifying key genes for phenotypic variations but also for understanding the pathways and systems.

### **2.7 Systems biology**

Systems biology is the study of interactions among biological components using models and/or networks to integrate genes, metabolites, proteins, regulatory elements and other biological components (Yuan et al., 2008). Although integration of data from multiple areas of research is not a new concept in biological research, the availability of huge amounts of diverse sets of data obtained from modern, HTP 'omics' technologies has renewed interest in systems biology. In particular, next generation genome sequencing as well as HTP profiling of transcripts, proteins, metabolites, phenotypes etc. are providing the necessary raw material that can enable us to construct interaction networks of genes, their products and many associated players. It must be emphasized that the goal of systems biology exceeds that of the omics components in that it looks holistically at the biological systems with respect to the structure and dynamics.

Considering the complexity and diversity of datasets analyzed and integrated in systems biology, strategy for these projects can be broadly divided into 4 steps. Step 1 is the development of a sound experimental strategy and collection of reliable and reproducible data. Step 2 is the annotation of the data components, for example, genes and proteins investigated in the study. While gene ontology system and other molecular function information resources are used for the functional classification of genes/proteins, pathway databases (Bauer-Mehren et al., 2009) such as KEGG (Kyoto Encyclopedia of Genes and Genomes; http://www.genome.jp/kegg/), Reactome (http://www.reactome.org), PANTHER (Protein ANalysis THrough Evolutionary Relationships; http://www. pantherdb.org/) and plant metabolic pathway databases ( http://plantcyc. org/ ) can be used for obtaining pathway information of the candidates. In step 3, the annotation information from step 2 is used for the generation of mathematical models and networks based on the associations and interactions observed in the datasets in comparison to known pathway information from the databases. Step 4 is the model development and validation where generated models are validated, and then variations introduced and models revised and validated again. Several software tools are used for this iterative cycle of model development – validation – perturbation – validation (Endler et al., 2009). In addition to the tools needed in these steps, availability of efficient platforms and infrastructure that can archive and support analysis of data from diverse sources and labs is a critical requirement for any systems biology project.

There can be multiple types of output expected from systems biology depending on the datasets used for integration. The output can either be a snap shot of a system with respect to a key gene or protein such as gene regulatory or biochemical networks or it can be based on the quantitative or qualitative dynamics of the system based on multiple perturbations. The most popular outcomes to date have been the construction of biological networks that include gene and transcriptional regulatory networks and interactome networks. A major promise of systems biology for crop improvement lies in the area of understanding quantitative traits and abiotic and biotic stresses and examples currently exist where systems biology approach has been explored in these areas. Cooper et al. (2003) developed a network of genes associated with developmental and stress responses in rice by measuring the gene expression changes with respect to environmental, biological and stress treatments related to interaction domains for 200 proteins from stressed and developing tissues. Data obtained from this study resulted in the identification of several stress response genes and were also found to be useful for the prediction of gene function in monocots and dicots. Use of systems biology in unraveling plant defense response has to deal with several dynamics related layers before key resistance gene candidates can be identified. In a study where mutant analysis and whole genome gene expression profiling was used for determining the regulatory roles of WRKY genes in systemic acquired resistance (SAR), a 'regulatory node' was identified in the transcriptional regulatory network controlling SAR (Wang et al., 2006). Although reports that utilized input from several omics for a detailed analysis of quantitative traits are yet to appear, approaches that utilized genome wide eQTL or metabolite QTL (mQTL) do exist that led to the construction of networks and identification of key metabolic QTLs that could be leveraged for crop improvement (Schauer et al., 2006). Current efforts in this area also include extending the impact of systems biology in understanding plant communities using a holistic approach by merging systems biology and systems ecology in order to improve agricultural productivity(Keurentjes et al., 2011).

### **2.8 Bioinformatics**

154 Plant Breeding

mapping. Although molecular markers linked to quantitative trait loci (QTL), the genomic regions genetically determined to be associated with the observed phenotypic variation, can be used in marker-assisted breeding programs, in order to identify the gene(s) underlying the QTLs, comparison of gene expression differences between contrasting lines is very important. Genetical genomics approach involves expression profiling and molecular marker based genotyping of all individuals in a segregating population. It is followed by a comprehensive analysis using all statistical tools that are normally used in the analysis of quantitative trait loci. These analyses result in the identification of expression QTL (eQTL). Based on the nature of gene expression variation, there are two types of eQTL, cis-eQTL and trans-eQTL. If the variation or polymorphism is located near the gene, it is classified as a ciseQTL, whereas if the source of variation is located at a distant location in the genome then it is a trans-eQTL. Genetical genomics has been applied to a broad range of organisms and all studies have demonstrated the power of combining gene expression data with that of genetic analysis to fine tune pathways involved in complex phenotypes thereby enabling the identification of key genes (Joosen et al., 2009). Genetical genomics studies benefit greatly with the availability of reference genome sequence as well as with the use of large populations. In a genetical genomics study of the model plant Arabidopsis where a 162-line RIL population was used and data analyzed in conjunction with the genome sequence, researchers successfully predicted key regulators of flowering time and circadian rhythms through the construction of genetic regulatory network by combining eQTL mapping and regulator candidate gene selection (Keurentjes et al., 2007). Examples also exist where synteny of the target genome with other species can be used in genetical genomics. For example, in a wheat study, synteny with the sequenced rice genome was used to map the eQTL for seed quality parameters (Jordan et al., 2007). The use of genetical genomics is on the rise. Several technological advances such as expression profiling using next generation sequencing and the availability of several types of 'omics' data sets are enabling rapid construction of biological networks for not only identifying key genes for phenotypic

variations but also for understanding the pathways and systems.

Systems biology is the study of interactions among biological components using models and/or networks to integrate genes, metabolites, proteins, regulatory elements and other biological components (Yuan et al., 2008). Although integration of data from multiple areas of research is not a new concept in biological research, the availability of huge amounts of diverse sets of data obtained from modern, HTP 'omics' technologies has renewed interest in systems biology. In particular, next generation genome sequencing as well as HTP profiling of transcripts, proteins, metabolites, phenotypes etc. are providing the necessary raw material that can enable us to construct interaction networks of genes, their products and many associated players. It must be emphasized that the goal of systems biology exceeds that of the omics components in that it looks holistically at the biological systems

Considering the complexity and diversity of datasets analyzed and integrated in systems biology, strategy for these projects can be broadly divided into 4 steps. Step 1 is the development of a sound experimental strategy and collection of reliable and reproducible data. Step 2 is the annotation of the data components, for example, genes and proteins

**2.7 Systems biology**

with respect to the structure and dynamics.

The field of genomics has grown leaps and bounds during the recent decades. While the development and implementation of HTP genome sequencing and more than a dozen omics

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 157

maize are developed from B73 and Mo17 cultivars. Taking into account massive intraspecific variations among maize inbred lines (Eichten et al., 2011; Fu and Dooner, 2002; Springer et al., 2009), SNPs developed from a few lines will capture only a small portion of all allelic variations happening between parents of a cross designed for a QTL study. Consequently, there is a big chance that majority of allelic variations, including the causative mutation between parents of this cross will be missing. In order to avoid this situation, resequencing of genomes of both parents and discovery of allelic variations in low and single copy regions could be implemented using NGS technologies coupled with genome complexity reduction techniques. Discovered cross-specific polymorphisms can later be converted into any modern SNP genotyping assay (Mammadov et al., 2010; Trebbi et al., 2011). For instance, technologies such as complexity reduction of polymorphic sequences (CRoPS™) (Van Orsouw et al., 2007) or restriction site associated DNA (RAD) (Baird et al., 2008) can be successfully applied to generate cross-specific SNPs (Mammadov et al., 2010). Depending on the organism, this approach may result in the validation of about 1000 robust cross-specific markers, which can be later combined with public SNPs and used for mapping. NimbleGen Sequence Capture technology (Roche Applied Science) has also been used for the detection of cross-specific polymorphisms within low and single copy sequences (Springer et al., 2009). However, this technology can be applied only to crops for which reference genome sequences are available because of the necessity to design capture probes. The approach of development of cross-specific markers increases the precision of QTL mapping and consequently narrows down CI and can lead to the detection of causative

If the heritability and the effect of QTL governing the trait are high and CI is narrow enough, then the development of large mapping populations and the creation of highresolution or fine mapping can be sufficient (Jiao et al., 2010). However, in many cases there is a risk that the detected major QTL is in reality represented by several co-segregating loci with minor effects. In order to eliminate the effects of other co-segregating alleles affecting the phenotype, target locus is recommended to be Mendelized through the painfully long and expensive process of developing near isogenic lines (NILs) (Kearsey and Farquhar, 1998). In QTL-NIL the target QTL will behave as a single Mendelian gene. Development of NILs begins with the same population where all QTL were mapped, and can be carried out by marker-assisted backcross introgression. QTL Mendelization is believed to give an answer on the feasibility of cloning the target locus. In some cases, after Mendelization, the effect of target QTL may become so negligible that further cloning activities will not make any sense. However, if the high-resolution reveals that target QTL still explains the large portion of phenotypic variation, the physical mapping of the locus must be implemented

In order to physically anchor the QTL, bacterial artificial chromosome (BAC) library has to be developed, which is a very important prerequisite for map-based cloning. Researchers use markers flanking the gene of interest as probes to implement chromosome walking (Tanksley et al., 1995). When the price of sequencing was high, chromosome walking was a very tedious process, which included several rounds of marker-library hybridization, identification of new BAC clones spanning the region between the flanking markers, BAC fingerprinting to construct contigs, identification of minimum tiling path (MTP) and development of new markers from the extreme left and right BAC clones representing the

mutation(s).

(Saito et al., 2004).

technologies revolutionized our ability to study whole genomes and biological systems, one important capability that has also played a major role in this unprecedented growth is bioinformatics. The explosive growth of information from the biological and genomics research has accentuated the need for computational requirements. The development of computational biology tools enabled rapid and HTP analysis of large amounts of data generated by research community while the creation of comprehensive databases made the information available globally, further enhancing the pace of research. A recent review by Mochida and Shinozaki (2010) on various genomics and bioinformatics resources available and how they are helping in biological research provides a greater appreciation for the impactful contributions of bioinformatics over the years. Systems biology is one area where we need additional technological advances as well as improvements in our computational tools in order to efficiently analyze, integrate and interpret huge data sets resulting from dynamic biological systems and currently efforts are underway to this end.

### **3. Molecular breeding approaches**

### **3.1 Mapping and map-based cloning of QTL**

The availability of dense genetic maps with informative markers makes it possible to map genes and QTL. Numerous studies have been dedicated to mapping QTL governing major agronomically important traits. As of July 06, 2011, there were about 24,000 research articles in this direction in Google Scholar. However, majority of those studies ended at the mapping step and did not pursue the ultimate goal of cloning the gene(s) underlying the QTL due to many reasons including lack of funding, insufficient genomic resources, inadequate phenotypic data collection methodology, availability of experimental designs with limited detection power and finally complexity of the trait and the genome. Due to these factors, QTL cloning in crops remains a very challenging process. Researchers have to undergo multiple labor-, time- and cost-intensive steps prior to answering the question "to clone or not to clone plant QTL" (Salvi and Tuberosa, 2005, 2007). What are those steps and challenges?

The first step in every map-based cloning (MBC) project is genetic mapping of QTL using a small mapping population (200-300 individuals) and identifying flanking markers. Provided that phenotypic data collection methodology and experimental design are at adequate level, the main constraint that researchers face at this initial step is very large confidence interval (CI) of QTL, which can span 10-30 cM (Kearsey and Farquhar, 1998). However, even in cases where the CI is limited to a few centimorgans, the interpretation of this genetic distance is not a straight forward process. The reason is that the genetic distance largely depends on the rate of recombination frequency in the region and the size of the mapping population used to construct the linkage map. In many cases, markers that flank QTL are physically far from the target, and the interval between markers contains a large number of genes. Depending on the length of DNA segment spanning QTL, strategies to reduce the distance between markers and QTL have to be designed. One of the strategies to narrow down the CI is the right choice of molecular markers for QTL mapping. Majority of QTL mapping studies have been using a pool of publicly available markers, which are not always informative for a particular bi-parental cross under investigation. Another most important factor that can condition the success of QTL mapping and subsequent cloning is knowledge of all possible allelic variations existing between parents. For instance, most of the public SNP markers in

technologies revolutionized our ability to study whole genomes and biological systems, one important capability that has also played a major role in this unprecedented growth is bioinformatics. The explosive growth of information from the biological and genomics research has accentuated the need for computational requirements. The development of computational biology tools enabled rapid and HTP analysis of large amounts of data generated by research community while the creation of comprehensive databases made the information available globally, further enhancing the pace of research. A recent review by Mochida and Shinozaki (2010) on various genomics and bioinformatics resources available and how they are helping in biological research provides a greater appreciation for the impactful contributions of bioinformatics over the years. Systems biology is one area where we need additional technological advances as well as improvements in our computational tools in order to efficiently analyze, integrate and interpret huge data sets resulting from

The availability of dense genetic maps with informative markers makes it possible to map genes and QTL. Numerous studies have been dedicated to mapping QTL governing major agronomically important traits. As of July 06, 2011, there were about 24,000 research articles in this direction in Google Scholar. However, majority of those studies ended at the mapping step and did not pursue the ultimate goal of cloning the gene(s) underlying the QTL due to many reasons including lack of funding, insufficient genomic resources, inadequate phenotypic data collection methodology, availability of experimental designs with limited detection power and finally complexity of the trait and the genome. Due to these factors, QTL cloning in crops remains a very challenging process. Researchers have to undergo multiple labor-, time- and cost-intensive steps prior to answering the question "to clone or not to clone plant QTL" (Salvi and Tuberosa, 2005, 2007). What are those steps and

The first step in every map-based cloning (MBC) project is genetic mapping of QTL using a small mapping population (200-300 individuals) and identifying flanking markers. Provided that phenotypic data collection methodology and experimental design are at adequate level, the main constraint that researchers face at this initial step is very large confidence interval (CI) of QTL, which can span 10-30 cM (Kearsey and Farquhar, 1998). However, even in cases where the CI is limited to a few centimorgans, the interpretation of this genetic distance is not a straight forward process. The reason is that the genetic distance largely depends on the rate of recombination frequency in the region and the size of the mapping population used to construct the linkage map. In many cases, markers that flank QTL are physically far from the target, and the interval between markers contains a large number of genes. Depending on the length of DNA segment spanning QTL, strategies to reduce the distance between markers and QTL have to be designed. One of the strategies to narrow down the CI is the right choice of molecular markers for QTL mapping. Majority of QTL mapping studies have been using a pool of publicly available markers, which are not always informative for a particular bi-parental cross under investigation. Another most important factor that can condition the success of QTL mapping and subsequent cloning is knowledge of all possible allelic variations existing between parents. For instance, most of the public SNP markers in

dynamic biological systems and currently efforts are underway to this end.

**3. Molecular breeding approaches**

challenges?

**3.1 Mapping and map-based cloning of QTL**

maize are developed from B73 and Mo17 cultivars. Taking into account massive intraspecific variations among maize inbred lines (Eichten et al., 2011; Fu and Dooner, 2002; Springer et al., 2009), SNPs developed from a few lines will capture only a small portion of all allelic variations happening between parents of a cross designed for a QTL study. Consequently, there is a big chance that majority of allelic variations, including the causative mutation between parents of this cross will be missing. In order to avoid this situation, resequencing of genomes of both parents and discovery of allelic variations in low and single copy regions could be implemented using NGS technologies coupled with genome complexity reduction techniques. Discovered cross-specific polymorphisms can later be converted into any modern SNP genotyping assay (Mammadov et al., 2010; Trebbi et al., 2011). For instance, technologies such as complexity reduction of polymorphic sequences (CRoPS™) (Van Orsouw et al., 2007) or restriction site associated DNA (RAD) (Baird et al., 2008) can be successfully applied to generate cross-specific SNPs (Mammadov et al., 2010). Depending on the organism, this approach may result in the validation of about 1000 robust cross-specific markers, which can be later combined with public SNPs and used for mapping. NimbleGen Sequence Capture technology (Roche Applied Science) has also been used for the detection of cross-specific polymorphisms within low and single copy sequences (Springer et al., 2009). However, this technology can be applied only to crops for which reference genome sequences are available because of the necessity to design capture probes. The approach of development of cross-specific markers increases the precision of QTL mapping and consequently narrows down CI and can lead to the detection of causative mutation(s).

If the heritability and the effect of QTL governing the trait are high and CI is narrow enough, then the development of large mapping populations and the creation of highresolution or fine mapping can be sufficient (Jiao et al., 2010). However, in many cases there is a risk that the detected major QTL is in reality represented by several co-segregating loci with minor effects. In order to eliminate the effects of other co-segregating alleles affecting the phenotype, target locus is recommended to be Mendelized through the painfully long and expensive process of developing near isogenic lines (NILs) (Kearsey and Farquhar, 1998). In QTL-NIL the target QTL will behave as a single Mendelian gene. Development of NILs begins with the same population where all QTL were mapped, and can be carried out by marker-assisted backcross introgression. QTL Mendelization is believed to give an answer on the feasibility of cloning the target locus. In some cases, after Mendelization, the effect of target QTL may become so negligible that further cloning activities will not make any sense. However, if the high-resolution reveals that target QTL still explains the large portion of phenotypic variation, the physical mapping of the locus must be implemented (Saito et al., 2004).

In order to physically anchor the QTL, bacterial artificial chromosome (BAC) library has to be developed, which is a very important prerequisite for map-based cloning. Researchers use markers flanking the gene of interest as probes to implement chromosome walking (Tanksley et al., 1995). When the price of sequencing was high, chromosome walking was a very tedious process, which included several rounds of marker-library hybridization, identification of new BAC clones spanning the region between the flanking markers, BAC fingerprinting to construct contigs, identification of minimum tiling path (MTP) and development of new markers from the extreme left and right BAC clones representing the

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 159

variations between two parents within target BAC could be converted into KASPar assay and bulk segregant analysis (BSA) can be performed. If BSA does not reduce the number of candidate genes to one, then causative mutation can be identified through functional prediction. For example, if the target gene confers resistance to a disease and among the remaining candidate genes there is an NBS-LRR gene, which belongs to one of the classes of defense-related genes, then it might be considered as a gene of interest. However, if the researcher wants to confirm the gene candidate using classical methods, then either further increase in the resolution within a locus or functional characterization of each predicted protein is recommended. Both methods are very expensive and labor- and time-intensive. Functional characterization of gene candidates can be done using several methods including genetic complementation through transformation technique, down-regulation of a gene via RNAi, complementation of a known mutant or by marker-assisted trait introgression

One of the major limitations of QTL cloning using bi-parental mapping approach is insufficient amount of meiotic recombination in mapping populations such as F2, DH and RIL, which lead to a strong statistical association of QTL with the block of markers that physically span large chromosomal segments. QTL-mapping approach requires that specific mapping populations, usually consisting of several hundred F2 or RIL progeny, be developed from each germplasm accession to be examined for important genes effecting traits of interest. Each population must be genotyped using hundreds, perhaps thousands of molecular markers. This population development and marker screening is extremely timeconsuming, high-risk and expensive work - prohibitively expensive if dozens, let alone hundreds or thousands, of germplasm accessions are to be examined (Abdurakhmonov et al., 2008; Abdurakhmonov and Abdukarimov, 2008). However, geneticists mapping complex traits in the human genome have circumvented the need for large F2 or RI mapping populations (which are not available in humans) by making use of information contained within the genetic recombinations that have occurred in typical human populations during the course of recent evolution. Genetic loci linked to a specific disease will show historically reduced level of recombination (non equilibrium) with specific alleles at different loci controlling particular genetic variation in a population (Abdurakhmonov et al., 2008). This "linkage disequilibrium (LD)" can be detected statistically, and has been used to map and eventually clone a number of genes underlying complex genetic traits in humans (Schulze and McMahon, 2002; Weiss and Clark, 2002). LD-Mapping, referred also as Association mapping (AM), is an alternative approach to a now classical QTL-mapping in bi-parental mapping populations because it overcomes the problem related to the lack of recombination events. AM population is composed of genetically un-related individuals with unknown pedigrees and accumulates larger number of historical recombination events that occurred in the past (Nordborg and Tavaré, 2002). Multiple increases in number of recombination events will break the block of markers that is associated with QTL and increase the resolution of QTL region. Association Mapping is linkage disequilibrium (LD) based association while bi-parental approach is a genetic recombination-based mapping. AM attempts to reveal a significant association between a trait and a gene, or a molecular marker, or block of molecular markers, which are at LD. Marker and trait are in disequilibrium if they are truly linked to each other and historically have been passing from

(Borevitz and Chory 2004).

**3.2 QTL cloning through association mapping**

MTP. Chromosome walking had not been a straight forward process either. It was especially complicated in crops with complex genomes. In many cases, the isolation of BAC ends resulted in the dissection of repetitive sequences which were of no use in designing a new probe for the subsequent library hybridization. Currently, instead of doing BAC fingerprinting, NGS technology allows direct sequencing of all identified BACs and construction of BAC contigs based on sequence similarity (Schneeberger and Weigel, 2011). Availability of a reference sequence simplifies the assembly of BAC clone sequences and contig construction. Lack of reference sequence will force researchers to construct contigs *de novo*, which is not an easy task taking into account the complexity of a plant genome. From this point of view, MBC in crops with available reference genome sequence is supposed to be easier than in crops with no genome sequence available. However, this is not always the case because success of MBC depends not only on available structural genomics resources but also the complexity of a trait and availability of adequate phenotypic data collection methodology. Because QTL mapping results from the comparison of marker and phenotypic data, the accuracy of the latter is of great importance. Although accurate phenotypic data is equally important both for QTL and single gene cloning, the former is more sensitive to the robustness of phenotypic data due to their dependence on environmental conditions. Nowadays, precision phenotypic data collection remains the major bottleneck for the successful QTL mapping and cloning.

If the position of a QTL is delimited to one BAC clone, this large insert clone must be sequenced to identify the candidate genes. Ideally, homologous BACs from both parents should be sequenced, because this will facilitate the identification of gene candidates. Sequencing of a BAC clone using NGS technology is a very straightforward process if the target crop has reference genome sequence. For example, sequencing can be done using fairly cost-effective HiSeq instrument (Illumina, San Diego, CA), which generates small 70- 100 bp fragments which can later be mapped back to reference genome to facilitate the assembly. In orphan crops sequencing efforts are impeded due to the absence of reference sequence. Sequencing using traditional Sanger could be the only choice. However, it is very expensive and a long process. Another alternative is sequencing the same BAC clone with two NGS instruments in parallel such as GS20 Sequencer (454/Roche) and Illumina's HiSeq, which generate long (1 kb) and short (100 bp) sequences, respectively. A combination of short and long sequences may improve the assembly of a BAC clone from a crop with no reference sequence.

When a BAC clone is sequenced and assembled, next step is sequence analysis of the large insert clone. Sequence analysis of the BAC clone is necessary to reveal gene candidates. Normally, the entire BAC clone first gets scanned for the repetitive sequences using publicly available Repeat\_Masker software (http://www.repeatmasker.org/cgi-bin/WEBRepeat Masker). The Repeat\_Masker output can be used as an input template for any protein prediction software, including FGENESH (http://linux1.softberry.com/ berry.phtml?topic =fgenesh&group=programs&subgroup=gfind) and GenScan (http:// genes.mit.edu/ GENSCAN.html) and GenMark (http://exon.gatech.edu/). Finally, all predicted open reading frames (ORFs) must be BLASTed against protein database using 'blastp' algorithm to reveal their biological functions. Depending on the genome size and repetitive sequence content one BAC clone may have dozen or so genes. The number of candidate genes can be reduced by aligning homologous BAC clones from both parents. Further, any allelic variations between two parents within target BAC could be converted into KASPar assay and bulk segregant analysis (BSA) can be performed. If BSA does not reduce the number of candidate genes to one, then causative mutation can be identified through functional prediction. For example, if the target gene confers resistance to a disease and among the remaining candidate genes there is an NBS-LRR gene, which belongs to one of the classes of defense-related genes, then it might be considered as a gene of interest. However, if the researcher wants to confirm the gene candidate using classical methods, then either further increase in the resolution within a locus or functional characterization of each predicted protein is recommended. Both methods are very expensive and labor- and time-intensive. Functional characterization of gene candidates can be done using several methods including genetic complementation through transformation technique, down-regulation of a gene via RNAi, complementation of a known mutant or by marker-assisted trait introgression (Borevitz and Chory 2004).

### **3.2 QTL cloning through association mapping**

158 Plant Breeding

MTP. Chromosome walking had not been a straight forward process either. It was especially complicated in crops with complex genomes. In many cases, the isolation of BAC ends resulted in the dissection of repetitive sequences which were of no use in designing a new probe for the subsequent library hybridization. Currently, instead of doing BAC fingerprinting, NGS technology allows direct sequencing of all identified BACs and construction of BAC contigs based on sequence similarity (Schneeberger and Weigel, 2011). Availability of a reference sequence simplifies the assembly of BAC clone sequences and contig construction. Lack of reference sequence will force researchers to construct contigs *de novo*, which is not an easy task taking into account the complexity of a plant genome. From this point of view, MBC in crops with available reference genome sequence is supposed to be easier than in crops with no genome sequence available. However, this is not always the case because success of MBC depends not only on available structural genomics resources but also the complexity of a trait and availability of adequate phenotypic data collection methodology. Because QTL mapping results from the comparison of marker and phenotypic data, the accuracy of the latter is of great importance. Although accurate phenotypic data is equally important both for QTL and single gene cloning, the former is more sensitive to the robustness of phenotypic data due to their dependence on environmental conditions. Nowadays, precision phenotypic data collection remains the

If the position of a QTL is delimited to one BAC clone, this large insert clone must be sequenced to identify the candidate genes. Ideally, homologous BACs from both parents should be sequenced, because this will facilitate the identification of gene candidates. Sequencing of a BAC clone using NGS technology is a very straightforward process if the target crop has reference genome sequence. For example, sequencing can be done using fairly cost-effective HiSeq instrument (Illumina, San Diego, CA), which generates small 70- 100 bp fragments which can later be mapped back to reference genome to facilitate the assembly. In orphan crops sequencing efforts are impeded due to the absence of reference sequence. Sequencing using traditional Sanger could be the only choice. However, it is very expensive and a long process. Another alternative is sequencing the same BAC clone with two NGS instruments in parallel such as GS20 Sequencer (454/Roche) and Illumina's HiSeq, which generate long (1 kb) and short (100 bp) sequences, respectively. A combination of short and long sequences may improve the assembly of a BAC clone from a crop with no

When a BAC clone is sequenced and assembled, next step is sequence analysis of the large insert clone. Sequence analysis of the BAC clone is necessary to reveal gene candidates. Normally, the entire BAC clone first gets scanned for the repetitive sequences using publicly available Repeat\_Masker software (http://www.repeatmasker.org/cgi-bin/WEBRepeat Masker). The Repeat\_Masker output can be used as an input template for any protein prediction software, including FGENESH (http://linux1.softberry.com/ berry.phtml?topic =fgenesh&group=programs&subgroup=gfind) and GenScan (http:// genes.mit.edu/ GENSCAN.html) and GenMark (http://exon.gatech.edu/). Finally, all predicted open reading frames (ORFs) must be BLASTed against protein database using 'blastp' algorithm to reveal their biological functions. Depending on the genome size and repetitive sequence content one BAC clone may have dozen or so genes. The number of candidate genes can be reduced by aligning homologous BAC clones from both parents. Further, any allelic

major bottleneck for the successful QTL mapping and cloning.

reference sequence.

One of the major limitations of QTL cloning using bi-parental mapping approach is insufficient amount of meiotic recombination in mapping populations such as F2, DH and RIL, which lead to a strong statistical association of QTL with the block of markers that physically span large chromosomal segments. QTL-mapping approach requires that specific mapping populations, usually consisting of several hundred F2 or RIL progeny, be developed from each germplasm accession to be examined for important genes effecting traits of interest. Each population must be genotyped using hundreds, perhaps thousands of molecular markers. This population development and marker screening is extremely timeconsuming, high-risk and expensive work - prohibitively expensive if dozens, let alone hundreds or thousands, of germplasm accessions are to be examined (Abdurakhmonov et al., 2008; Abdurakhmonov and Abdukarimov, 2008). However, geneticists mapping complex traits in the human genome have circumvented the need for large F2 or RI mapping populations (which are not available in humans) by making use of information contained within the genetic recombinations that have occurred in typical human populations during the course of recent evolution. Genetic loci linked to a specific disease will show historically reduced level of recombination (non equilibrium) with specific alleles at different loci controlling particular genetic variation in a population (Abdurakhmonov et al., 2008). This "linkage disequilibrium (LD)" can be detected statistically, and has been used to map and eventually clone a number of genes underlying complex genetic traits in humans (Schulze and McMahon, 2002; Weiss and Clark, 2002). LD-Mapping, referred also as Association mapping (AM), is an alternative approach to a now classical QTL-mapping in bi-parental mapping populations because it overcomes the problem related to the lack of recombination events. AM population is composed of genetically un-related individuals with unknown pedigrees and accumulates larger number of historical recombination events that occurred in the past (Nordborg and Tavaré, 2002). Multiple increases in number of recombination events will break the block of markers that is associated with QTL and increase the resolution of QTL region. Association Mapping is linkage disequilibrium (LD) based association while bi-parental approach is a genetic recombination-based mapping. AM attempts to reveal a significant association between a trait and a gene, or a molecular marker, or block of molecular markers, which are at LD. Marker and trait are in disequilibrium if they are truly linked to each other and historically have been passing from

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 161

lines, LD decay may span more than 100-500 Kb (Jung et al., 2004; Tian et al., 2011). Recently developed first-generation haplotype map of maize presented the evidence for much longer haplotypes spanning several million bases (Gore et al., 2009), which was later supported by Mammadov et al. (2010). In cultivated barley, LD has been reported to span from 1 cM to 10 cM (Rostoks et al., 2006). Use of different molecular markers, can also significantly change the length of LD. In rice, it was indicated that LD decays within 1 cM or less using SNP markers (Agrama and Eizenga, 2008), whereas others reported 20-30 cM length of LD while using simple sequence repeat markers (SSRs) (Jin et al., 2010). Differences in the rate of LD decay within a crop could be explained by the nature of unit it was represented. Centimorgan is a unit of recombination, and the rate of recombination has proven to be not uniform across the genome. Consequently, 30 cM LD span in one region of rice genome physically may carry the same value as 1 cM LD span in another region of the genome. Physical distance seems to be more realistic way to designate LD decay. However, absence of reference sequence in some crops limits researcher to centimorgans only. Now-a-days researchers successfully use AM to study genetics of complex traits in all major crops, including rice, maize (Poland et al., 2011), barley (Massman et al., 2011), soybean (Wang et al., 2008) and canola (Honsdorf et al., 2010) and other crops (Abdurakhmonov and Abdukarimov, 2008; Stich and Melchinger, 2010). The common feature of these studies is the detection of large number of QTL with small effects. Whether or not the information on these QTL can be translated into real use in crop improvement is unclear and the answer is

To date there are only a few successful QTL cloning studies. Most of them have been reported in rice, which is not surprising because rice genome has been sequenced since 2004 (Table 3). Additionally rice genome is smaller and less complex. QTL cloning is in progress in maize and soybean, two crops that have reference genome sequence available. Undoubtedly, the progress in sequencing technologies, availability of reference genome in major crops, rapid evolution in high-throughput polymorphism detection platforms and bioinformatics tools and last, but not least, the development of accurate phenotypic data collection methodology will increase the precision of QTL mapping in major and orphan crops bringing us closer to the discovery of the Holy Grail of molecular geneticists, causative mutations, that will be subsequently translated into robust diagnostic tools to implement

Marker-assisted selection (MAS) as a process refers to the selection of superior genotypes using molecular markers. MAS is thought to have substantial advantages over conventional phenotypic selection because the latter could be (1) unreliable when the expression of the trait is environmentally dependent, (2) biologically deadline-sensitive, (3) expensive and difficult to screen and (4) subject to the mercy of weather. In contrast to phenotypic selection, MAS (1) does not rely on environmental conditions because it detects the structural polymorphisms at molecular level, (2) requires leaf tissue collected at seedling stage, which is very useful for traits that are expressed at later stages of development and which also helps to avoid adverse weather conditions that could kill the plant at adult stage, (3) could be cheaper and less labor intensive, (4) allows selection in off-season nurseries and

yet to come.

marker-assisted selection.

**3.3 Marker-assisted selection**

has a potential to accelerate breeding process.

generation to generation together. The theory of AM is based on the idea that LD tends to be preserved over many generations between loci, which are linked to one another and form haplotypes. The higher the LD, the tighter is the linkage between markers. However, in biparental mapping, because of lack of recombination event, several haplotypes could be grouped into one linkage and be statistically associated with QTL, which decreases the resolution of the map. To summarize, AM theoretically has several advantages over classical bi-parental approach: (1) increased mapping resolution; (2) availability of more genetic variability for marker-trait correlations and detection of multiple-alleles simultaneously; (3) elimination of the necessity to develop large populations for fine mapping which saves expenses and time; (4) one AM population can be leveraged for dissection of many traits, while bi-parental crosses are dead-end and in many cases can be used to study one trait only and (5) feasibility of employing historical phenotypic data collected over many years (Zhu et al., 2008).

However, not everything is that smooth and painless in the implementation of AM. This approach is often criticized for (1) detecting a large number of false-positive QTL due to population confounding effects and 2) influence of allele frequency distributions (rare and minor allele frequencies) of functional polymorphisms to the power of the detected associations (Abdurakhmonov and Abdukarimov, 2008; Aranzana et al., 2005; Stich and Melchinger, 2010). In order to avoid false positives, several factors have to be taken into consideration. Structure of the population must be carefully analyzed using various computational methods. Too structured population with too many sub-groups will detect pseudo LDs between loci that in reality are not linked, and cause false-positive association between a marker and a trait. In order to avoid this, prior to AM implementation, the population must be analyzed for the presence of hidden sub-structures. One of the popular software programs that researchers use to resolve this issue is publicly available software called STRUCTURE. Removal of rare alleles is a choice in AM to reduce false-positives (Abdurakhmonov and Abdukarimov, 2008), but studies showed that most phenotypic variations are due to rare alleles (Stich and Melchinger, 2010), suggesting importance of these rare alleles in tagging biologically meaningful associations. Both structured population and rare allele frequency issues can be greatly minimized by creating segregating populations and performing genetic crosses between several reference populations with known allele frequencies for functional polymorphisms. Such approach is referred to as nested association mapping (NAM) and NAM populations greatly enhance the power of association mapping in plants (Stich and Melchinger, 2010).

Because AM is LD-based, another consideration is the rate of LD decay in a crop under the study. The pattern of LD throughout the genome will determine the appropriate marker density for whole genome scanning (Yu et al., 2008). The longer the haplotype, the lesser is the marker density needed because all markers within a haplotype will behave similarly. In contrary, if the segment of a chromosome is characterized by the presence of short haplotypes then the density of markers has to be increased correspondingly. Additionally, the rate of LD decay with physical and genetic distances is important to determine the maximum resolution that can be achieved for association mapping. Length of haplotypes depends on a genome, the number of loci investigated, and the reproductive history of a population. It was previously reported that in maize, LD decay distance was on an average less that 2000bp (Remington et al., 2001). Later studies suggested that in commercial inbred lines, LD decay may span more than 100-500 Kb (Jung et al., 2004; Tian et al., 2011). Recently developed first-generation haplotype map of maize presented the evidence for much longer haplotypes spanning several million bases (Gore et al., 2009), which was later supported by Mammadov et al. (2010). In cultivated barley, LD has been reported to span from 1 cM to 10 cM (Rostoks et al., 2006). Use of different molecular markers, can also significantly change the length of LD. In rice, it was indicated that LD decays within 1 cM or less using SNP markers (Agrama and Eizenga, 2008), whereas others reported 20-30 cM length of LD while using simple sequence repeat markers (SSRs) (Jin et al., 2010). Differences in the rate of LD decay within a crop could be explained by the nature of unit it was represented. Centimorgan is a unit of recombination, and the rate of recombination has proven to be not uniform across the genome. Consequently, 30 cM LD span in one region of rice genome physically may carry the same value as 1 cM LD span in another region of the genome. Physical distance seems to be more realistic way to designate LD decay. However, absence of reference sequence in some crops limits researcher to centimorgans only. Now-a-days researchers successfully use AM to study genetics of complex traits in all major crops, including rice, maize (Poland et al., 2011), barley (Massman et al., 2011), soybean (Wang et al., 2008) and canola (Honsdorf et al., 2010) and other crops (Abdurakhmonov and Abdukarimov, 2008; Stich and Melchinger, 2010). The common feature of these studies is the detection of large number of QTL with small effects. Whether or not the information on these QTL can be translated into real use in crop improvement is unclear and the answer is yet to come.

To date there are only a few successful QTL cloning studies. Most of them have been reported in rice, which is not surprising because rice genome has been sequenced since 2004 (Table 3). Additionally rice genome is smaller and less complex. QTL cloning is in progress in maize and soybean, two crops that have reference genome sequence available. Undoubtedly, the progress in sequencing technologies, availability of reference genome in major crops, rapid evolution in high-throughput polymorphism detection platforms and bioinformatics tools and last, but not least, the development of accurate phenotypic data collection methodology will increase the precision of QTL mapping in major and orphan crops bringing us closer to the discovery of the Holy Grail of molecular geneticists, causative mutations, that will be subsequently translated into robust diagnostic tools to implement marker-assisted selection.

### **3.3 Marker-assisted selection**

160 Plant Breeding

generation to generation together. The theory of AM is based on the idea that LD tends to be preserved over many generations between loci, which are linked to one another and form haplotypes. The higher the LD, the tighter is the linkage between markers. However, in biparental mapping, because of lack of recombination event, several haplotypes could be grouped into one linkage and be statistically associated with QTL, which decreases the resolution of the map. To summarize, AM theoretically has several advantages over classical bi-parental approach: (1) increased mapping resolution; (2) availability of more genetic variability for marker-trait correlations and detection of multiple-alleles simultaneously; (3) elimination of the necessity to develop large populations for fine mapping which saves expenses and time; (4) one AM population can be leveraged for dissection of many traits, while bi-parental crosses are dead-end and in many cases can be used to study one trait only and (5) feasibility of employing historical phenotypic data collected over many years (Zhu et

However, not everything is that smooth and painless in the implementation of AM. This approach is often criticized for (1) detecting a large number of false-positive QTL due to population confounding effects and 2) influence of allele frequency distributions (rare and minor allele frequencies) of functional polymorphisms to the power of the detected associations (Abdurakhmonov and Abdukarimov, 2008; Aranzana et al., 2005; Stich and Melchinger, 2010). In order to avoid false positives, several factors have to be taken into consideration. Structure of the population must be carefully analyzed using various computational methods. Too structured population with too many sub-groups will detect pseudo LDs between loci that in reality are not linked, and cause false-positive association between a marker and a trait. In order to avoid this, prior to AM implementation, the population must be analyzed for the presence of hidden sub-structures. One of the popular software programs that researchers use to resolve this issue is publicly available software called STRUCTURE. Removal of rare alleles is a choice in AM to reduce false-positives (Abdurakhmonov and Abdukarimov, 2008), but studies showed that most phenotypic variations are due to rare alleles (Stich and Melchinger, 2010), suggesting importance of these rare alleles in tagging biologically meaningful associations. Both structured population and rare allele frequency issues can be greatly minimized by creating segregating populations and performing genetic crosses between several reference populations with known allele frequencies for functional polymorphisms. Such approach is referred to as nested association mapping (NAM) and NAM populations greatly enhance the power of

Because AM is LD-based, another consideration is the rate of LD decay in a crop under the study. The pattern of LD throughout the genome will determine the appropriate marker density for whole genome scanning (Yu et al., 2008). The longer the haplotype, the lesser is the marker density needed because all markers within a haplotype will behave similarly. In contrary, if the segment of a chromosome is characterized by the presence of short haplotypes then the density of markers has to be increased correspondingly. Additionally, the rate of LD decay with physical and genetic distances is important to determine the maximum resolution that can be achieved for association mapping. Length of haplotypes depends on a genome, the number of loci investigated, and the reproductive history of a population. It was previously reported that in maize, LD decay distance was on an average less that 2000bp (Remington et al., 2001). Later studies suggested that in commercial inbred

association mapping in plants (Stich and Melchinger, 2010).

al., 2008).

Marker-assisted selection (MAS) as a process refers to the selection of superior genotypes using molecular markers. MAS is thought to have substantial advantages over conventional phenotypic selection because the latter could be (1) unreliable when the expression of the trait is environmentally dependent, (2) biologically deadline-sensitive, (3) expensive and difficult to screen and (4) subject to the mercy of weather. In contrast to phenotypic selection, MAS (1) does not rely on environmental conditions because it detects the structural polymorphisms at molecular level, (2) requires leaf tissue collected at seedling stage, which is very useful for traits that are expressed at later stages of development and which also helps to avoid adverse weather conditions that could kill the plant at adult stage, (3) could be cheaper and less labor intensive, (4) allows selection in off-season nurseries and has a potential to accelerate breeding process.

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 163

Maize Vgt1 Flowering time Non-coding sequence Salvi et al.

Soybean *E3 locus* Flowering time Phytochrome A Watanabe et

In a review article, Xu and Crouch (2008) demonstrated an interesting chronology on the evolution of MAS as a technology. According to them, the term was coined in the mid – eighties by Beckmann and Soller (1986) as a technology that might have a potential use in plant breeding. Ten years later, MAS was already considered as a possible technology to tag genes (Concibido et al. 1996). Although as of June 27, 2011, according to Google Scholar, there were about 32,300 articles containing the keyword "marker-assisted selection", most of them still have been referring to potential application of MAS in plant breeding. A vast majority of those publications were from academia. Although private sector does not normally release the details of their breeding methodologies to public domain, several articles on successful application of MAS in the development of varieties of maize (Ragot et al., 2007) and soybean (Cahill and Schmidt, 2004; Crosbie et al., 2003) came mainly from industry. Fairly low impact of academic research in developing varieties using MAS can be explained by the lack of funding to complete the entire marker development pipeline (MDP), which can be long-term and cost-intensive task. MDP includes several steps such as (1) population development, (2) initial QTL mapping, (3) QTL validation (testing in several locations and years and implementing fine mapping) and (4) marker validation (development of inexpensive but high-throughput assays that are amenable to automation) (Collard and Mackill, 2008). Every step of the development of markers linked to QTL are associated with numerous constraints which may take several years and substantial funding to resolve. In the 1990s it was believed that molecular markers identified at step 2 were enough for successful MAS. In the 1990s it was observed that markers that were previously declared as tightly linked were failing to confirm the phenotype at advanced stages of MAS. One of the main reasons for the failure of a marker in MAS, which was identified at pre-fine mapping step, was the inconsistency in QTL mapping. Detection of QTL within one year and in one location was proved to be not enough to claim the robust QTL location because the expression of latter has been environmentally dependent. Thus, QTL validation and confirmation was required, which foresaw QTL mapping based on data collected within

*DGAT* High oil content acyl-CoA:diacylglycerol

architecture

Tga1 Glume

Table 3. QTL cloning studies in the literature

*Hd3a* Heading date Unknown protein Kojima et al.,

*Hd6* Heading date Protein kinase Takahashi et

*Hd1* Heading date Transcription factor Yano et al.,

*Tb1* Plant architecture Transcription factor Doebley et al

acyltransferase

2002

2000

2007

2005

1995, 1997

Zheng et al 2008

al 2009

Transcription factor Wang et al

al., 2001

### Table 3. QTL cloning studies in the literature

162 Plant Breeding

Transcription factor, a protein similar to Squamosa promoter

Gain-of-function mutation causing truncation of a phosphatidylethanolaminebinding protein-like domain

dioxygenase 8 (CCD8)

CCT (CO, CO-LIKE and TIMING OF CAB1) domain

RING-type protein with E3 ubiquitin ligase activity

VWFC membrane protein Fan et al.,

Transcription factor Xu et al., 2006

Cytokinin oxidase Ashikari et

development

*TAC1* Tiller angle Unknown Yu et al., 2007

*qSH1* Shattering BEL1-homeobox Konishi et al.,

*PSR1* Regenerability Nitrite reductase Nishimura et

*qUVR-10* UV resistance CDP photlyase Ueda et al.

*SKC1* Salt tolerance HKT transporter Ren et al.,

*Ehd1* Heading date B-type response regulator Doi et al.,

protein

*sh4* Shattering Transcription factor Li et al.,

novel nuclear protein likely acts in the ubiquitin-proteasome pathway to regulate cell division during seed

F-box protein Piao et al

Ethilene responsive factors Hattori et al.,

binding protein

protein

2010

2009

2009

2009

2008

2006

2006a

2006

al., 2005

al. 2005

2005

2005

2004

Zhoua et al 2009

Shomura et al., 2008; Weng, et al.,

Xue et al., 2008

Song et al., 2007

Jiao et al., 2010; Miura et al., 2010

Huang et al.,

**Crop Gene Trait Function Reference**  Rice *Ctb1* Cold tolerance F-box protein Saito et al

grain productivity

Erect Panicle architecture

grain number

tolerance

weight

weight

length

tolerance

productivity

*Sub1A* Submergence

Gn1a Grain

Mt1 Rice tillering carotenoid cleavage

plant height, heading date

OsSPL14 Panicle number,

DEP1 Panicle number,

*SK1/SK2* Deepwater

qSW5(GW5) Grain width and

Ghd7 Grain number,

GW2 Grain width and

GS3 Grain weight and

*ERECT PANICLE 3*

> In a review article, Xu and Crouch (2008) demonstrated an interesting chronology on the evolution of MAS as a technology. According to them, the term was coined in the mid – eighties by Beckmann and Soller (1986) as a technology that might have a potential use in plant breeding. Ten years later, MAS was already considered as a possible technology to tag genes (Concibido et al. 1996). Although as of June 27, 2011, according to Google Scholar, there were about 32,300 articles containing the keyword "marker-assisted selection", most of them still have been referring to potential application of MAS in plant breeding. A vast majority of those publications were from academia. Although private sector does not normally release the details of their breeding methodologies to public domain, several articles on successful application of MAS in the development of varieties of maize (Ragot et al., 2007) and soybean (Cahill and Schmidt, 2004; Crosbie et al., 2003) came mainly from industry. Fairly low impact of academic research in developing varieties using MAS can be explained by the lack of funding to complete the entire marker development pipeline (MDP), which can be long-term and cost-intensive task. MDP includes several steps such as (1) population development, (2) initial QTL mapping, (3) QTL validation (testing in several locations and years and implementing fine mapping) and (4) marker validation (development of inexpensive but high-throughput assays that are amenable to automation) (Collard and Mackill, 2008). Every step of the development of markers linked to QTL are associated with numerous constraints which may take several years and substantial funding to resolve. In the 1990s it was believed that molecular markers identified at step 2 were enough for successful MAS. In the 1990s it was observed that markers that were previously declared as tightly linked were failing to confirm the phenotype at advanced stages of MAS. One of the main reasons for the failure of a marker in MAS, which was identified at pre-fine mapping step, was the inconsistency in QTL mapping. Detection of QTL within one year and in one location was proved to be not enough to claim the robust QTL location because the expression of latter has been environmentally dependent. Thus, QTL validation and confirmation was required, which foresaw QTL mapping based on data collected within

Genomics-Assisted Plant Breeding in the 21st Century: Technological Advances and Progress 165

divided into two major groups with opposite tasks: (1) projects that deal with large sample volume (>10,000 plants) to be genotyped with a few markers (1-96 SNPs) and (2) projects that require genotyping a fewer samples (1-300) with large number of SNPs (384 to several millions of SNPs). The projects that fall into the first group are related to categories such as marker-assisted germplasm evaluation, marker-assisted trait introgression and markerassisted gene pyramiding. The categories of MAS-based projects such as genome wide selection (GWS) for complex traits that fall into the opposite category will require genotyping of several millions of SNPs in fairly small subset of samples. SNP genotyping platforms that would match the requirements of the project from group 1 could include OpenArray platform coupled with TaqMan chemistry and KBiosciences' Competitive Allele Specific PCR (KASPar) complemented with the SNP Line platform (SNP Line XL, Kbiosciences, Hoddesdon, England). The latter has proven to be more cost effective and flexible compared to TaqMan assay (Chen et al., 2010). The second group of projects can be implemented using Illumina's BeadArray technology coupled with GG and Infinium assays. Current throughput of Infinium assay is ~1.1 MM SNPs per iSelect. However, GWS might require several millions of SNPs depending on the complexity of genome and its LD decay rate. If this is the case, then genotyping-by-sequencing (Elshire et al., 2011)

Current MAS strategies fit the breeding programs for traits with high heritability and are governed by a single gene or one major QTL that explains large portion of the phenotypic variability. However, the application of MAS for breeding traits with complex genetics based on the interaction of multiple QTL with minor effects has been inefficient. Examples of complex traits are yield, drought tolerance, and nitrogen and water use efficiency. In classical MAS projects researchers use molecular markers that show statistically significant association with a phenotype and are linked to major QTL. Because minor QTL have small effects on phenotype, they have not been applicable in MAS. Meuwissen et al. (2001) described a new methodology in plant breeding, called genomic selection (GS) that was believed to solve problems related to MAS of complex traits. This methodology also applies to molecular markers but in different fashion. Unlike MAS, in GS markers are not used for tracking a trait. In GS high density marker coverage is needed to potentially have all QTL in LD with at least one marker. Then the comprehensive information on all possible loci, haplotypes and marker effects across the entire genome is used to calculate genomic

could be another alternative.

**3.4 Genomic selection approach towards breeding complex traits**

estimated breeding value (GEBV) of a particular line in the breeding population.

Genomic selection of superior lines can be carried out within any breeding population. In order to enable successful GS, the experimental population must be identified. The population should not be necessarily derived from bi-parental cross but must be representative of selection candidates in the breeding program to which GS will be applied (Heffner et al., 2009). Experimental population must be genotyped with large number of markers. Taking into account the low cost of sequencing, the best choice is the implementation of genotyping-by-sequencing which will yield maximum number of polymorphisms. The sequence of the two events, i.e. phenotypic and genotypic data collection, is arbitrary and can be done in parallel. When both phenotypic and genotypic data are ready, one can start "training" molecular markers (Zhong S. 2009). In order to train

several years and multiple locations. Molecular markers that were tightly linked to QTL and were consistent across several years and locations did have a potential in MAS. However, even after QTL validation, so called "tightly linked marker" hardly met the expectations because the confidence interval (CI) of QTL peak is so large that it is very difficult to predict the real distance between marker and QTL. Moreover, there are several hundreds of candidate genes within CI, and it is impossible to predict which gene explains the phenotypic variation. Genetic proximity of a marker to QTL depends on two factors such as the size of the population and the region of a genome where the marker and QTL are residing. If the size of the mapping population is small (100-200 individuals), then the claims of having a marker closely-linked to a gene are barely valid, because fine mapping will identify many crossing-over events happening within marker-gene complex. Occurrence of the recombination events between marker and QTL makes the marker unable to track a target. This type of situation is especially true for the regions of the genome with high rate of recombination frequency. However, certain regions of genome exhibit very low rate of recombination frequency. If QTL was mapped to low recombination frequency region, then it will be very difficult to prove that a marker tagging QTL is indeed physically close to the locus. In some cases, even fine mapping will not help to break the linkage between marker and QTL. Availability of reference genome sequence is helpful to define the physical proximity of the marker to a gene. However, even physical proximity may not insure successful MAS. There are examples showing that out of several mutations, which occurred within a target gene, only one of them, the causative, can be converted into viable assay to leverage in MAS (Zheng et al., 2008). In human molecular genetics, mostly causative mutations have been used to develop diagnostic tools to detect diseases. However, in plants development of gene-based diagnostic tools for MAS has been limited to major crops with available reference genome sequence, e.g. rice and maize (Chen et al., 2010; Zheng et al., 2008). With respect to orphan crops, including wheat and barley, it may require several years before gene-based molecular markers derived from QTL cloning can be used in MAS.

Tracking QTL using molecular markers is just one of the MAS applications in plant breeding. This application uses mostly one or a couple of markers ideally developed based on causative mutations. Applications of MAS in plant breeding were grouped into five broad categories (Collard and Mackill, 2008): (1) marker-assisted germplasm evaluation including pedigree verification, purity assessment, evaluation of genetic diversity, identification of heterotic patterns and event characterization; (2) marker-assisted trait introgression, (3) marker-assisted pyramiding of genes and (4) genomic selection (GS). The nature of the MAS-based molecular breeding projects determines the marker and sample throughput and consequently requires specific marker genotyping technologies. Most of the contemporary marker genotyping technologies are oriented towards SNP detection, because SNPs are amenable for high-throughput automation, and are preferred type of polymorphism in molecular genetics research projects because of their abundance and resolution (Chagné et al., 2007). Majority of SNP genotyping technologies that have been described in this chapter were originally developed for SNP detection in human genetics research. However, the rapid growth and expansion of agribusiness, challenges to 'increase the slope' and 'stay competitive' forced major seed companies to adapt those technologies to plant genome and use in high-throughput SNP genotyping for MAS projects. Although MAS projects are diverse, in terms of sample and marker throughput, they all can be