Software

Information about the versions of the informatics software described here and further details can be found in table 2.2.

Chapter 2 General materials and methods

34 Sequence editing and alignments were performed in Geneious 8 using three different aligners available as plugins; MUSCLE (Edgar 2004), ClustalW (Thompson et al. 1994) and MAFFT (Katoh et al. 2002). Phylogenetic trees were performed using either PAUP* (when maximum parsimony used to build the tree) MEGA 6 (maximum likelihood). Alignments for figures were graphed with GeneDoc software.

All statistical analyses were conducted using either GraphPad Prism or IBM SPSS statistics version 22, with a significance level of 0.05 for all analyses unless otherwise specified.

Genetic maps were performed using Join Map 4 and graphed using either this software or MapChart. QTL analysis were done with MapQTL 6.0.

Gene expression and phenotype charts were made using GraphPad Prism. If necessary, pictures, graphs and phylogenetic trees were edited to make the final figures in Adobe Illustrator.

Table 2.2 Information about software packages used

Software Version Developer and Website Reference

GraphPad Prism 6.01 GraphPad Software

(www.graphpad.com) -

Geneious 8.1.9 Biomatters

(www.geneious.com) (Kearse et al. 2012a)

MEGA 7.0.20 MEGA Software

(www.megasoftware.net) (Kumar et al. 2016) MapChart 2.30 Wageningen Plant Research

(www.wur.nl/en/show/Mapchart-2.30) (Voorrips 2002)

JoinMap 4 Kyazma B.V.

(www.kyazma.nl) (Stam 1993)

MapQTL 6 Kyazma B.V. (

www.kyazma.nl) (Van Ooijen and Maliepaard 1996)

Adobe Illustrator cs5 Adobe Systems

www.adobe.com/Illustrator -

GeneDoc 2.7.0 www.psc.edu/biomed/genedoc (Nicholas and Nicholas 1997) IBM SPSS Statistics 22.0

IBM Corporation (www.ibm.com/analytics/us/en/

technology/spss)

-

PAUP* 4.0b10 Smithsonian Institution

36

Chapter 3. Conservation of Flowering Genes in chickpea

3.1 Introduction

Legume crops are tremendously important on a global scale, providing food for humans and livestock, and enhancing soil fertility through symbiotic nitrogen fixation, but until recently were considered to be genomic “orphans” due to a lack of genetic and genomic tools. As result, crop improvement in these species has largely occurred through traditional breeding approaches, with limited or no impact from molecular technologies (Varshney et al. 2009a). In the last two decades, however, important genomic advances have been made in a great number of legume species, opening up the possibility of accelerating legume crop improvement through application of genomics-assisted breeding.

In the particular case of chickpea, the transition to the genomic era is now well-established. As outlined in Chapter 1 (see section 1.6), the development of genetic and physical maps began in the nineties and progressed with the incorporation of new types of markers that allowed researchers to compare different maps, unify linkage groups nomenclature, and facilitated the study of chickpea synteny with other legumes. (Zhang et al. 2010; Pfaff and Kahl 2003; Hiremath et al. 2012; Nayak et al. 2010; Palomino et al. 2009; Winter et al. 1999; Winter et al. 2000; Thudi et al. 2011; Cobos et al. 2005; Millan et al. 2010; Deokar et al. 2014; Tekeoglu et al. 2002). It is now apparent that despite significant rearrangements, genomic structure is well conserved across legume species (Nayak et al. 2010; Lee et al. 2017; Gujaria-Verma et al. 2014), which has great significance since it allows the transfer of knowledge obtained in other legumes (e.g. genome structure, position of potential genes, potential identity of genetic loci) for comparative studies to chickpea. In this respect, the legume model species Medicago truncatula is of particular relevance because among all the sequenced legume genomes it is taxonomically the closest to chickpea and its synteny with other legume species is well-documented, enabling cross-species comparison (Tang et al. 2014; Choi et al. 2004). Coupled to the development of molecular markers comes the possibility of linking them to valuable traits, as reflected by the considerable number of Quantitative Trait Loci (QTL) analyses published for chickpea in the last two decades, linking different regions of chickpea genome with the control of agronomically interesting traits(Millan et al. 2014).

Chapter 3 Conservation of flowering genes in chickpea

37 Without any doubt, the definitive genomic tool for any species is the sequencing of its whole genome, since it enables the identification of genes and functional elements responsible for expression of phenotype and provides a platform for gene mapping, gene isolation and molecular breeding (Varshney et al. 2013a). The sequencing of the chickpea genome from representative

desi and kabuli types was reported in 2013 (Jain et al. 2013; Varshney et al. 2013c), providing a

reference that has facilitated the re-sequencing of a wider range of varieties (Thudi et al. 2016; Li et al. 2017; Das et al. 2015a).

Another consequence of the genome sequence availability is that it enables sequence-based markers from older studies to be located on the physical map. This allows the chromosomal location of previously reported QTLs to be physically defined, and makes it possible to identify potential (candidates) genes governing a trait. Since molecular research in chickpea is restricted to recent years, it is necessary to look for available information in other species with advanced molecular knowledge in order to find these candidates genes. In the particular case of flowering control, much of our current understanding is derived from studies using the model species A.

thaliana. Hundreds of genes have been described affecting this trait as response to external and

endogenous signals, and the general model described in this species is well conserved among plants, as reflected by the fact that many orthologs of these genes maintain their capacity to alter time to flower in even far related species. Although still far from the level reached in Arabidopsis, the molecular physiology of flowering time is increasingly well studied in the legume species pea, soybean and Medicago, as reviewed in (Jung et al. 2012; Hecht et al. 2005; Weller and Ortega 2015; Kim et al. 2013b; Kim et al. 2012).

In chickpea more than 50 QTLs have been reported for flowering time, distributed across all eight chickpea linkage groups (LG) (table 1.2). Multiple reports of QTL on chromosomes 3 and 4 indicate the existence of two genomic regions of particular importance for chickpea flowering time variation. Of special relevance is the central portion of LG3 where reports from several different inter and intraspecific populations suggest the presence of a major gene (Cobos et al. 2009; Mallikarjuna et al. 2017; Hossain et al. 2010; Rehman et al. 2011; Aryamanesh et al. 2010). Moreover, the syntenic genomic region has been associated with flowering control in several other legumes including Medicago truncatula (Pierre et al. 2008), Vicia faba (Cruz-Izquierdo et al. 2012), Lupinus angustifolius (Nelson et al. 2006), Lotus japonicus (Gondo et al. 2007), and