Biology Plant And Science Exercise

3 Natural Variation and Association Mapping Extensive genotypic and phenotypic variations have been documented in natural accessions of A. thaliana . Natural variation is the basis for traditional linkage mapping/quantitative trait loci (QTL) mapping aimed at identifying genes governing a trait of interest. F 2 populations and recombinant inbred lines (RILs) have been used as experimental populations for QTL mapping. RILs allow higher mapping resolution as compared to F 2 populations. Over 60 RIL populations have been developed and are available to the research community through stock centers. The wide range of intraspecifi c diversity (wild accessions) available in A. thaliana makes it well suited for association mapping (Fig. 1.3 ). Association mapping is based on linkage disequilibrium (LD) and offers very high resolution in comparison to traditional linkage mapping, since it takes advantage of historic recombination events accumulated over several generations. Linkage disequilibrium (LD) in Arabidopsis on an average extends over 5–10 kb, thus offering nearly single-gene resolution (Kim et al. 2007b ). Array-based re-sequencing of 20 maximally diverse natural accessions of A. thaliana has led to development of a genotyping array (AtSNPtile1) containing probe sets for 2,48,584 SNPs (Kim et al. 2007b ). Given the small size of the genome (~125 Mb), this array provides, on average, 1 SNP for every 500 bp, suffi cient enough for genome-wide association mapping. There have been several reports of genome-wide association studies (GWAS) in A. thaliana (Aranzana et al. 2005 ; Atwell et al. 2010 ; Brachi et al. 2010 ; Chan et al. 2011 ; Nemri et al. 2010 ). The SNP chip has been used for genotyping around 1,307 accessions, and the data is available to the public (Horton et al. 2012 ). Several software and web-based platforms have been developed for GWAS in plants, viz., TASSEL (Bradbury et al. 2007 ), GAPIT (Lipka et al. 2012 ), GEMMA (Zhou and Stephens 2012 ), Matapax (Childs et al. 2012 ), and the GWAportal (Seren et al. 2012 ). A major drawback of association mapping is the confounding caused due to population structure and the consequent increase in number of false positives. In addition, identifi cation of epistatic loci continues to be a major challenge in GWAS. A new mapping design that combines advantages of classical QTL mapping and association mapping, known as nested association mapping (NAM), has been pioneered in maize wherein experimental populations derived from crosses of several founder lines are used (McMullen et al. 2009 ; Yu et al. R. Sivasubramanian et al. 9 2008 ). In Arabidopsis , two such mapping populations have been developed, viz., AMPRIL (Arabidopsis Multiparent RIL) populations (Huang et al. 2011 ) and MAGIC (Multiple Advanced Generation Intercross) populations (Kover et al. 2009 ) (Fig. 1.3 ). The MAGIC population is derived from a heterogeneous stock of 19 inter-mated accessions which have been completely sequenced, and tools required for QTL/ association mapping in these populations are freely available (Table 1.1 ). Alternatively, association mapping can be combined with QTL mapping in several independent RIL populations to retain statistical power and, yet, not compromise on resolution of mapping. Though the arraybased re- sequencing effort led to identifi cation of around 250K SNPs, it also revealed that the reference accession Col-0 lacks a substantial portion of genes present in other accessions. A 1001 genome project for A. thaliana was announced in 2007 to sequence genomes of other accessions which would contain sequences not present in the reference genome (Weigel and Mott 2009 ) (Table 1.1 ). This multinational effort would not only shed light on local polymorphism patterns and chromosomal- scale differences but be directly useful in QTL and association mapping as well. Since many of the accessions sequenced are parents of RIL populations, availability of the genome sequence may identify polymorphisms responsible for various QTLs detected so far. Complete-genome sequences will not only help identify the causal allele directly in GWAS but also assist in predicting activity differences between causal alleles and tackling problems of allelic heterogeneity and rare variants.

https://www.google.me/url?q=https://zsactivatorskey.com/

https://images.google.al/url?q=https://zsactivatorskey.com/

https://www.google.com.qa/url?q=https://zsactivatorskey.com/

https://maps.google.bt/url?q=https://zsactivatorskey.com/

https://www.google.co.zm/url?q=https://zsactivatorskey.com/

https://www.google.ht/url?q=https://zsactivatorskey.com/

https://www.google.cg/url?q=https://zsactivatorskey.com/

https://cse.google.iq/url?q=https://zsactivatorskey.com/

https://maps.google.sn/url?q=https://zsactivatorskey.com/

http://www.google.mg/url?q=https://zsactivatorskey.com/

https://images.google.mw/url?q=https://zsactivatorskey.com/

https://images.google.bj/url?q=https://zsactivatorskey.com/

https://maps.google.ml/url?q=https://zsactivatorskey.com/

https://www.google.com.ai/url?q=https://zsactivatorskey.com/

http://cse.google.com.om/url?q=https://zsactivatorskey.com/

https://www.google.co.uz/url?q=https://zsactivatorskey.com/

https://maps.google.com.ly/url?q=https://zsactivatorskey.com/

https://cse.google.rw/url?q=https://zsactivatorskey.com/

https://www.google.tm/url?q=https://zsactivatorskey.com/

https://images.google.com.bn/url?q=https://zsactivatorskey.com/

http://www.google.com.jm/url?q=https://zsactivatorskey.com/

http://www.google.md/url?q=https://zsactivatorskey.com/

https://www.google.ms/url?q=https://zsactivatorskey.com/

RNA Interference (RNAi) for Targeted Mutagenesis Although insertional mutagenesis is an effective method for generating loss-of-function mutants, the method falters when dealing with lethal genes or those genes which are functionally redundant. Targeted gene silencing via antisense, co- suppression, posttranscriptional gene silencing, and most recently RNA interference (RNAi) has emerged as a powerful alternative reverse genetic approach for attenuating the gene expression. In this approach, stable transformants expressing double-stranded RNA (under a constitutive or inducible promoter) against target genes are generated, and the effect of knockdown of the gene expression is analyzed in terms of the phenotype. In comparison to T-DNA mutagenesis, RNAi lines typically show a wide spectrum of gene expression, from no reduction to complete shutdown (Waterhouse and Helliwell 2003 ). To date, experimental proof of function for only 10 % of the predicted genes is available. The AGRIKOLA project (Arabidopsis Genomic RNAi Knockout Line Analysis) aims to create targeted gene knockdown lines via RNAi for all the predicted genes in Arabidopsis (Hilson et al. 2004 ). In this project, about 150–600 bp long gene-specifi c tags (GSTs) have been designed for approx. 25,000 genes which were used to construct dsRNA-expressing vectors. These RNAi constructs have been transformed into wild-type Arabidopsis plants to generate a library of knockdown transformants. These knockdown lines will be an invaluable source for determining the function of individual Arabidopsis genes and, by extrapolation, function of orthologous genes in other crop plants as well. 1.3.2.4 Gain-of-Function Systems for Functional Analysis Activation tagging is another addition in the arsenal of gene identifi cation tools available to the plant scientifi c community. This is a popular gain-of-function approach where the overexpression of the gene results in a novel phenotype. Gene activation tagging systems have been established in Arabidopsis using either T-DNA vectors or transposon-based vectors carrying multimers of the 35S CaMV enhancers (Walden et al. 1994 ; Weigel et al. 2000 ; Nakazawa et al. 2003 ). Agrobacterium - mediated genome-wide random integration of the activation construct results in upregulation of the gene present in the vicinity of the integration site. Ectopic upregulation of the gene can result in an observable phenotype. Since the gene is tagged, identifi cation by inverse PCR or TAIL-PCR can be carried out rapidly. Activation tagging approach has been instrumental in deciphering the function of a number of genes including ADR1 in defense response (Grant et al. 2003 ; Aboul-Soud et al. 2009 ), BAK1 in brassinosteroid signaling (Li et al. 2002 ), and FT in fl oral transition (Kardailsky et al. 1999 ). FOX hunting system (full-length overexpression of cDNA for gene hunting) is an alternative approach to activation tagging where the ease of transformation of Arabidopsis and the availability of the full-length cDNA sequences have been exploited. As opposed to activation tagging, genes responsible for the overexpression phenotype can be easily identifi ed. Using a normalized full-length cDNA library, Ichikawa et al. 2006 have developed 30,000 independent Arabidopsis R. Sivasubramanian et al. 11 FOX lines which are available from RIKEN. As a further extension of this, full-length rice cDNA clones have been transformed into Arabidopsis with an aim of screening for rice functional genes in a high-throughput manner (Sakurai et al. 2010 ). 1.4 Omics: A High-Throughput Tool for Deciphering Gene Function Functional genomics is a genome-wide approach that attempts to use the ever expanding wealth of data produced through various highthroughput analysis for defi ning the gene function and its interactions in a given biological process. 1.4.1 Transcriptomic Resources for A. thaliana Transcriptomics, a comprehensive study of the whole-genome expression, is an informative approach towards functional gene analysis. The spatial and temporal expression of a gene to a certain extent is refl ective of its activity within the cell. This section discusses the various publically available resources for gene expression analysis and their application in functional genomics. 1.4.1.1 Expression Profi ling Provides Insights into Gene Function To get a glimpse of the transcriptional activity within the cell, a large-scale expressed sequence tag (EST) sequencing project was undertaken in Arabidopsis . The data generated in the EST sequencing was useful in gene discovery as it helped in annotating the expressed regions in the genome besides providing information about the gene expression. Serial analysis of gene expression (SAGE) and its various modifi cations like micro SAGE and mini SAGE were commonly used by various research groups to identify large number of differentially expressed transcripts present in different tissues/conditions. More recently, the NGS-based massively parallel signature sequencing (MPSS) has gained the edge. In this approach, short sequence tags are generated for a cDNA library by sequencing approx. 20–25 bp from the 3′ side of cDNA. Besides discovering novel transcripts, MPSS also provides a robust method for assessing the transcript abundance. Additionally, the MPSS platform has been used to identify a large number of small RNAs. The hybridization-based approaches for acquiring large-scale gene expression profi les have also been established for Arabidopsis and are being continuously improved. The traditional microarrays used for analyzing the transcriptional profi les were biased towards the known and predicted genes. With the whole genome sequence available, it became possible to develop tiling arrays (TA) and whole genome arrays (WGA). These arrays cover the entire genome with probes either at regular interval (TA) or probes that are overlapping along the entire length of the genome (WGA). These arrays have not only been used for estimating the transcript levels but also play a signifi cant role in identifying novel transcripts, various alternate transcripts, and polymorphisms (Mockler et al. 2005 ). Using the WGA, Zeller et al. ( 2009 ) studied the stressinduced changes in the A. thaliana transcripts in response to various abiotic stresses like salt, osmotic, cold, and heat. They identifi ed several novel stress-induced genes which were missed in the earlier classical microarray experiments. Thousands of microarray experiments that have been conducted in different laboratories with A. thaliana now form a part of large quantitative data on gene expression in different tissue and in response to different treatments and experimental conditions. Similarly, a comprehensive expression atlas for A. thaliana has been developed based on WGA and is available at A. thaliana Tiling Array Express (At-TAX) (Laubinger et al. 2008 ). The utility of the tiling array has been further extended by combining this platform with immunoprecipitation methods for detecting chromosomal locations at which protein-DNA interaction occurs across the genome (Wang and Perry 2013 ). Further, Zhang et al. ( 2006 ) generated an 1 Arabidopsis thaliana: A Model for Plant Research 12 extensive Arabidopsis methylome data by coupling tiling arrays with methylcytosine immunoprecipitation methods. Most of the microarray data has been deposited in the public databases such as NASC and TAIR, and this data can be accessed for analysis either directly from these sites or through the various available links/ tools like Genevestigator (Zimmermann et al. 2004 ) and MAPMAN (Thimm et al.

https://www.google.sh/url?q=https://zsactivatorskey.com/

https://www.google.com.fj/url?q=https://zsactivatorskey.com/

https://images.google.co.tz/url?q=https://zsactivatorskey.com/

https://maps.google.bf/url?q=https://zsactivatorskey.com/

http://www.google.sm/url?q=https://zsactivatorskey.com/

https://maps.google.co.ck/url?q=https://zsactivatorskey.com/

https://images.google.kg/url?q=https://zsactivatorskey.com/

https://maps.google.sc/url?q=https://zsactivatorskey.com/

https://www.google.cf/url?q=https://zsactivatorskey.com/

http://www.google.cv/url?q=https://zsactivatorskey.com/

https://www.google.la/url?q=https://zsactivatorskey.com/

https://cse.google.im/url?q=https://zsactivatorskey.com/

https://maps.google.mv/url?q=https://zsactivatorskey.com/

https://www.google.gg/url?q=https://zsactivatorskey.com/

https://maps.google.co.vi/url?q=https://zsactivatorskey.com/

https://maps.google.com.mm/url?q=https://zsactivatorskey.com/

https://www.google.co.zw/url?q=https://zsactivatorskey.com/

https://images.google.sr/url?q=https://zsactivatorskey.com/

https://images.google.co.ls/url?q=https://zsactivatorskey.com/

https://images.google.ps/url?q=https://zsactivatorskey.com/

https://www.google.gm/url?q=https://zsactivatorskey.com/

https://www.google.ws/url?q=https://zsactivatorskey.com/

https://www.google.tg/url?q=https://zsactivatorskey.com/

https://www.google.nr/url?q=https://zsactivatorskey.com/

https://images.google.com.nf/url?q=https://zsactivatorskey.com/

https://www.google.com.tj/url?q=https://zsactivatorskey.com/

https://cse.google.co.mz/url?q=https://zsactivatorskey.com/

https://www.google.pn/url?q=https://zsactivatorskey.com/

https://cse.google.nu/url?q=https://zsactivatorskey.com/

https://www.google.com.vc/url?q=https://zsactivatorskey.com/

Small RNA Database and Tools for Functional Genomics The regulation of gene expression occurs at multiple levels including mRNA stability. It has become evident that small RNAs are one of the major players in regulating gene expression during growth, development, and stress responses in plants. The cost-effective next generation sequencing has made it possible to discover hundreds of small RNA from Arabidopsis and other plants. This extensive data is available through the web interface –the Arabidopsis Small RNA Project (ASRP) which also integrates the community- wide resources related to small RNA and various bioinformatic tools for mi- and siRNA identifi cation (Backman et al. 2008 ). The Arabidopsis model system has been useful in dissecting the small RNA component of genetic and epigenetic regulation in plant development, growth, and disease resistance. Palatnik et al. ( 2003 ) showed the involvement of microRNA in controlling leaf morphogenesis. Auxin signaling responses were also shown to be regulated via small RNA. Several auxin response factors (ARFs) were predicted to be targets for miRNA. ARF10 and ARF17 contain potential sites for miR160 (Jover-Gil et al. 2005 ). Overexpression of miR160 resistant version of ARF17 led to higher accumulation of ARF17. These changes in expression correlate with the pleotropic morphological abnormalities and reduced fertility observed in the transgenic suggesting the regulation of ARF17 by miR160 (Mallory et al. 2005 ). Similarly, another miR393 also plays a signifi cant role in integrating the environmental cues to auxin signaling pathway (Windels and Vazquez 2011 ). Additionally, using the basic principle of small RNA-directed gene silencing, virus induced gene silencing (VIGS), hairpin-based RNA interference (RNAi), and artifi cial microRNA (amiRNA) tools have been developed to regulate targeted gene expression. The use of these strategies has been further extended for selectively regulating gene expression in crop plants as well. R. Sivasubramanian et al. 13 1.4.1.4 Tools for Regulatory Sequence Analysis The control of gene expression is pivotal to all cellular processes, and one of the major challenges in biology is to unravel the mechanisms that regulate gene expression. The gene function is directly linked to its spatial and temporal expression which is regulated by a network of transcription factors, the key regulatory proteins. The cues for gene regulation are hard wired into the promoter region which is formed by cis-regulatory elements. These cis-regulatory elements are recognized by specifi c transcription factors. Therefore, in order to understand the gene expression and thereby the gene function, basic information on the transcription factors and their binding sites is important. A. thaliana encodes more than 1,500 transcription factors which are classifi ed into 40–50 families based on the sequence similarity (Riechmann et al. 2000 ). Arabidopsis Gene Regulatory Information Server (AGRIS) is the interface that hosts AtcisDB, AtTFDB, AtRegNet, and ReIN databases (Table 1.1 ), which provide a catalogue of cis ad trans factors involved in gene regulation. 1.4.2 Epigenomic Resources Epigenetics is the study of changes in the regulation of gene expression that do not involve a change in the DNA sequence. Epigenetic changes/ modifi cations include methylation of the DNA, chemical modifi cation of the histones, and alternative histone variants. These modifi cations are known to play important roles during development and in responses to different environmental cues. An integrated epigenome map of Arabidopsis was published in 2008, which describes interactions between the methylome, transcriptome and the small RNA transcriptome, and their effect on gene regulation (Lister et al. 2008 ). Further, the epigenetic variation between the different accessions of Arabidopsis and its dependence on the genetic variation between the accessions has been reported (Schmitz et al. 2013 ). DNA methylations and their effect on the transcriptional activity have been studied in response to biotic stress (Dowen et al. 2012 ). The Epigenomics of Plants International Consortium (EPIC) provides a list of epigenetic resources in the form of databases and tools, which are available to the research community (Table 1.1). Also, The EPIC-CoGe Epigenomics Webbrowser provides a userfriendly interface to scan and search for epigenetic marks in the genome (Table 1.1 ).

https://cse.google.gl/url?q=https://zsactivatorskey.com/

https://www.google.vu/url?q=https://zsactivatorskey.com/

https://www.google.tk/url?q=https://zsactivatorskey.com/

https://www.google.to/url?q=https://zsactivatorskey.com/

https://maps.google.st/url?q=https://zsactivatorskey.com/

https://images.google.ga/url?q=https://zsactivatorskey.com/

http://www.google.ne/url?q=https://zsactivatorskey.com/

https://www.google.gy/url?q=https://zsactivatorskey.com/

https://images.google.so/url?q=https://zsactivatorskey.com/

https://maps.google.com.sl/url?q=https://zsactivatorskey.com/

https://maps.google.com.pg/url?q=https://zsactivatorskey.com/

https://www.google.tl/url?q=https://zsactivatorskey.com/