PhD Research Proposal
Genetic study of faba bean (Vicia faba) for resistance to chocolate spot (Botrytis fabae) disease
Table of content Page
- Introduction 1
- Hypothesis 2
- Objective 2
- Literature review 3
4.1 Economic importance and Origin of Faba bean (Vicia faba) 3
4.2 Production constraints of faba bean 3
4.3 Taxonomy and epidemiology of chocolate spot 4
4.4 Pathogenic Variability of Botrytis fabae 5
4.5 Management of Chocolate spot 6
4.6 Genetics of chocolate spot resistance 7
- Linkage Maps 8
- RNA sequencing and candidate gene analysis 11
- Materials and Methods 14
- Developing screening method for evaluation of faba bean genotypes 14
against chocolate spot (Botrytis Fabae)
5.1.1 Whole plant assay 14
5.1.2 Detached leaf assay 14
5.1.3 Field Screening 15
5.1.4 Disease Assessment and statistical analysis 15
5.2 Pathogenic diversity analysis of the chocolate spot isolates 16
from Western Canada
- Transcriptome Analysis in Faba Bean (Vicia faba L.) under
chocolate spot infection 16
5.3.1 Plant material and Botrytis fabae inoculation 16
5.3.2 Isolation of RNA, cDNA library construction and sequencing 17
5.3.3 Data Analysis and Annotation 17
5.3.4 Quantitative RT-qPCR Assay 18
5.4 QTL mapping of resistance to chocolate spot of faba bean 18
5.4.1 Plant Material and Disease Assessment 18
5.4.2 Genotyping and linkage map construction 19
5.4.2 QTL analysis 19
6. Work Plan 20
7. Courses 23
8. Reference 24
Faba bean (Vicia faba L.), also known as broad bean is one of the oldest legume crop, used as source of protein for human diet, animal feed (Sillero et al., 2010) and for available nitrogen in the biosphere (Villegas-Fernández et al., 2011). The presence of high contents of digestible proteins, fibers, particular vitamins and minerals contributes to its extensive use as food as well as health benefits (Crépona et al., 2010). According to the United Nation’s Food and Agriculture Organization report, the world’s area of faba bean production was 2.5 million hectares in 2014 (FAO, 2014). China, Ethiopia, Australia, France and the United Kingdom are world’s leading producers of faba bean (FAOSTAT, 2014). The crop ranks fourth in terms of cultivation after key food legumes such as bean, chickpea, pea and lentil (Toker, 2004). Global faba bean production in 2013 was 34 million metric ton (FAOSTAT, 2014). Canada’s contribution for world faba bean production was very small as compared to major producing countries. Approximately 50,000 acres of faba bean were planted in 2016 in Saskatchewan, Canada (Phelps, 2017).
Despite its importance, numerous biological constraints such as diseases, insect pests and weeds contribute to significant yield losses of faba bean in many countries, including Canada (Torres et al., 2006; Pe´rez-de-Luque et al., 2010). A number of diseases such as chocolate spot (Botrytis fabae), rust (Uromyes fabae), black rot (Fusarium solani), aschochyta blight (Ascochyta fabae) and faba bean necrotic yellow virus (FBNYV) adversely affect and cause great damage to the crop (Sahile et al., 2010). Among these, chocolate spot caused by Botrytis fabae is the most prevalent and important disease throughout the world where faba bean is grown (Akem and Bellar, 1999; Bouhassan et al., 2004; Tivoli et al., 2006). Yield loss due to chocolate spot as high as 67.5 was recorded in Ethiopia (Sahile et al., 2010). Experience over the past 30 years indicates that chocolate spot is a major biotic factor expected to affect faba bean in western Canada. Although different management practices can be applied in order to manage the disease the use of resistant genotypes would be the most effective and sustainable method. The level of resistance of these genotypes are evaluated by applying appropriate pathogenic isolates and screening methodologies conducted under controlled conditions or field conditions). Natural infection and artificial inoculation of test plots under field conditions considered to be the most effective and reliable methods for screening of large number of germplasm (Guzman, 1964). However, this requires the constant occurrence of disease epidemics year after year caused by virulent isolates of the target pathogen. This in turn will pause a challenge for both plant pathologists and breeders. Indoor screening by making use of high tunnels which are aided with the required humidity and temperature, are inexpensive to construct and facilitates rapid, accurate as well as continuous assessment of resistance and plays a major role during the study of genetics of disease resistance. Therefore, generating and making use of information on genetic analysis and characterization of the resistance is the basis for devising a strategy towards faba bean breeding for the improvement of the genetic resistance to chocolate spot.
- Low cost indoor screening methods help in selection of faba bean genotypes which are resistant to faba bean chocolate spot (Botrytis fabae).
- Pathogenic variability exists among Botrytis fabae isolates collected from different locations in western Canada.
- A recombinant inbred population (RIL; F8) from a cross between ILB938-1(R)/Disco (S) possess QTL regions associated with the resistance against faba bean chocolate spot.
- Candidates genes and pathways which are responsible for chocolate spot resistance will be used for the development of marker assisted selection
- To Develop a low cost high-throughput indoor screening system for faba bean chocolate spot
- To study the pathogenic variability of Botrytis fabae isolates collected from western Canada.
- To identify chromosomal regions containing QTLs associated with chocolate spot resistance.
- To identify potential candidate genes which are involved in chocolate spot resistance .
- Literature Review
4.1 Economic importance and Origin of Faba bean (Vicia faba)
Faba bean (2n 12) also referred to as the broad bean, fava bean, horse bean or field bean, is a major food and feed legume from fabacae family. It is an annual upright plant with a height ranging from 1.0 to 1.5 meters. Erect hollow stems, strong tap root and compound leaves with two to six oval shaped leaflets are some of the morphological characteristics that describe the plant. It is a crucial source of protein, especially in Mediterranean countries and China, its seeds have high nutritional value (Crépona et al., 2010). Additionally, it serves as a break crop in the cereal-based rotations, thus, playing a significant role in improving the productivity of the soil by fixing atmospheric nitrogen. The most important center of diversity for faba is Mediterranean basin (Maxted and Kell, 2009). Countries like China, Afghanistan and Ethiopia are also considered as secondary centers of diversity for the crop (Zong et al., 2009). China, Ethiopia, Egypt and the United Kingdom are the major faba bean producing countries (FAOSTAT, 2014). The average productivity of faba bean since 1999 to 2008 is 1.7 metric t ha-1 and 1.2 metric t ha-1 in China and Ethiopia respectively (FAOSTAT, 2014). Saskatchewan, Alberta and Manitoba are the western provinces that produce faba bean in Canada. The combined production areas in the three provinces during 2016 growing season was above 114,500 acres (Phelps, 2017).
4.2 Production constraints of faba bean
Several factors contribute for the decrease in production of faba bean. The low productivity mainly attributed to a number of yield-limiting factors including the inherent biological limitations i.e low grain yielding potentials of the indigenous cultivars, biotic and abiotic constraints. Of the biotic category, diseases are important factors limiting the production of food-legume crops as a whole (Nigussie et al., 2008 and Berhanu et al., 2003). Faba bean is affected by important diseases including chocolate spot (Botrytis fabae), rust (Uromyces viciae–fabae, black root rot (Fusarium solani). Abiotic stresses like waterlogging, moisture stress and low soil pH/acidity also reduce crop productivity in some areas. Chocolate spot, caused by Botrytis fabae Sard., is one of the economically important diseases of faba bean (Vicia faba L.) that causes leaf defoliation as well as death of the whole plants with a global distribution (Harrison 1988; Bouhassan et al. 2003; Tivoli et al., 2006; Stoddard et al., 2010). A yield loss of over 90% in Australia, 59% in the UK, over 50% in China and 61% in Ethiopia has been reported due chocolate spot caused by Botrytis fabae (Elad et al., 2004). It is a potential concern for faba bean growers in Western Canada. Despite its importance, studies regarding distribution and biology of the pathogen are yet to be carried out in the region (Fleury, 2016)
4.3 Taxonomy and epidemiology of chocolate spot
Botrytis fabae is imperfect fungi and it belongs to Kingdom Fungi, phylum Ascomycota, class Leotiomycotina, order Helotiales, family Sclerotiniceae, and genus Botrytinia (CPC, 2005). In addition to Botrytis fabae Sardina, Botrytis cinerea Pers (asexual stage of the teleomorph, Botryotinia fuckeliana) is the other necrotic fungi responsible for causing chocolate spot in faba bean. However, it is limited to restricted lesions on the leaf surface unlike Botrytis fabae which happens to cause severe crop failure during epidemics (Harrison, 1988). Botrytis species have restricted host range and usually affect single or a limited number of hosts (Elad et al., 2004). Lentil (Lens culinaris Medik.), vetch (Vicia sativa L.), chickpea, and pea are some of the minor hostsforBotrytis fabae in addition to faba bean (You et al., 2008). The disease can be introduced to a given area by wind dispersal or infected seeds (RHS, 2010; Harrison, 1988). Cool weather, a temperature range of 20°C to 23°C and high relative humidity (RH) (>80%) creates conducive conditions for initial infection (Harrison, 1988). During dry weather, the fungus tends to stay dormant, unlike humid conditions where there will be rapid growth and development of the disease. Dead tissues of leaves and flowers which are dropped to the ground facilitate the production of spores. Consequently, new leaf tissue will be infected, later giving rise to more conidiophores (Stoddard et al., 2009). Botrytis fabae can overwinter as sclerotia on crop debris and residues in the soil, in infected seed, or on self-sown volunteer plants for more than one season whenever appropriate host is absent (Richardson and Horsham, 2008). The fungus becomes aggressive at flowering time and causes significant damage to the plant; this, in turn, will lead to yield reductions (MacLeod and Sweetingham, 2006; Stoddard et al., 2010). Symptoms of chocolate spot are varied and range from non-aggressive, small red-brown spots on leaves, stems and flowers (RHS, 2010) to aggressive coalesced lesions causing necrosis and death. Leaves are the main part of the plant affected, but under favorable conditions for the disease; the stems, pods, seeds and even flowers are also infected (Richardson and Horsham, 2008)
Figure 1 Disease cycle of Chocolate Spot (Botrytis fabae) on Faba Beans
4.4 Pathogenic Variability of Botrytis fabae
One of the most dynamic and significant aspects of fungal pathogens is that the characteristics of individuals within a species are not fixed due to two mechanisms of variability, mutation and recombination (Agrios, 2005). Knowledge on pathogenic and genetic diversity of a pathogen population plays a key role for reliable screening, identification and deployment of locally adapted chocolate spot resistant plants. Variability among chocolate spot isolates has been reported from countries like China and Ethiopia. Xun-Yi (1989) classified chocolate spot isolates from china into three groups: those that produce abundant sclerotia with minimal growth of mycelium; those that produce few sclerotia with profuse mycelial growth; and those that showed moderate production of sclerotia and growth of mycelium. In Ethiopia, a study on pathogenic variability among 76 B. fabae isolates showed that 32 (42%) were virulent (V), 31 (41%) were moderately virulent (MV) and 13 (17%) were avirulent or weakly virulent (AV) (Sahile 2012). There is little information regarding population diversity studies of Botrytis fabae in Canada, thus, such kind of studies are needed to evaluate their pathogenic variability.
4.5 Management of Chocolate spot
Several methods can be used for the control of chocolate spot of faba bean. Host plant resistance, cultural practices, chemical control, and integrated pest management are some of the methods which are widely used. The use of resistant varieties is the least expensive, easiest, safest, and one of the most effective means of controlling plant diseases in crops (Agrios, 2005). Cultivars resistant to chocolate spot developed in collaboration with International Centre for Agriculture Research in Dry Areas (ICARDA) have been introduced to Australia, Egypt and Ethiopia (Stoddard et al., 2010). Additionally, source of resistance for chocolate spot (BPL-1179-A-1, BPL-1802-1 and BPL-1179-2) have been identified from introductions of ICARDA to Ethiopia (Mussa et al., 2008). However, varieties available in Western Canada are not currently rated for susceptibility to chocolate spot and are all considered equal in susceptibility (Saskatchewan Pulse Growers, 2016).
Cultural practices like crop rotation, removal of stubble and volunteer seedlings can minimize an epidemic of faba bean chocolate spot (Harrison, 1979). Rotation of faba bean with other non-hostplants for at least 4 years has been found to be effective in minimizing yield reduction caused by the disease (Hawthorne et al., 2004; Richardson, 2008). Reduction of relative humidity by facilitating aeration throughout the crop also effectively hinders the infection as well as the progress of the disease (Stoddard et al., 2010). Intercropping of faba bean with cereals significantly lessens the incidence of chocolate spot due to biomass reduction, altered microclimate and physical barriers to spore dispersal (Ferna´ndez-Aparicio and Rubiales, 2007; Sahile et al., 2008)
Chemical method is one of the most common means of controlling chocolate spot in faba bean (Khaled et al., 1995). Both systemic and contact fungicides are widely used to prevent the progress of chocolate spot and eventually minimize yield losses. The compounds responsible for the effective management of chocolate spot are: benzimidazoles (benomyl, carbendazim), dicarboximides (procymidone,iprodione, vinclozolin), dithiocarbamates (mancozeb), aromatics (chlorothalonil), conazoles (tebuconazole, cyproconazole, metconazole) and strobilurins (azoxystrobin, pyraclastrobin) (Stoddard et al., 2010). Due attention must be given for crop monitoring, accurate disease diagnosis and timeliness of spraying to make fungicidal application successful. Numerous fungicides were registered for use on faba bean disease in western Canada, however, only a limited number of them are available for the control of chocolate spot. Although fungicides like Priaxor® and Vertisan® registered for the control of B. cinerea, so far, no products are registered specifically for control of B. fabae(Fleury, 2016).
4.6 Genetics of chocolate spot resistance
Identification of source of resistance and understanding the mode of inheritance play a vital role in devising a strategy for breeding for resistant cultivars. There are two different genetic mechanisms for disease resistance; monogenic resistance based on singe genes whereas quantitative resistance is based on two or more genes. When resistance is monogenic, selection will be easier and the classes of disease reaction become more discrete. Polygenic resistance would be expressed quantitatively and selection may need to be based on relatively small differences between plants, but it is often viewed as durable and effective against all races of a pathogen (Kophina et al.,2000). Partial quantitative resistance was often exhibited by faba bean towards chocolate spot (Gnanasambandam et al., 2012). Most of the germplasms resistant to chocolate spot were originating from the Andean region of Ecuador and Colombia (Gnanasambandam et al., 2012). Additionally, limited number of accessions having a good level of resistance originate from Morocco, Tunisia and Algeria of the Maghreb region (Gnanasambandam et al., 2012). Application of molecular tools has huge potential for improving cultivar development. Genetic linkage maps are beneficial tools for several genetic and breeding applications like the identification of markers linked to relevant agronomic traits. Linkage maps of faba bean have been constructed by using various kinds of markers such as, RAPDs (Random Amplified Polymorphic DNA), RFLPs (Restriction Fragment Length Polymorphisms), ITAPs (Intron-Targeted Amplified Polymorphisms) and SSRs (Simple Sequence repeats) (Romamn et al., 2002), and various QTLs (Quantitative Trait Loci) have been identified (Torres et al., 2010). Likewise, markers linked to growth habit and nutritional value of faba bean seeds have been identified (Avila et al., 2006). Single nucleotide polymorphisms (SNPs) which are next-generation sequencing (NGS)-based high-throughput (HTP) DNA markers, replacing previously used traditional DNA markers (Bohra et al., 2014). The advent of RNA-Seq datasets fueled the next wave of SNP development utilized for faba bean breeding. Kaur et al. (2014) used 768 SNPs for high-density genetic mapping of a biparental faba bean mapping population (Icarus × Ascot) that segregates for resistance to ascochyta blight. More recently, Webb et al. (2016) reported design of individual KASP assays for 845 SNPs mined from alignment of assembled transcriptomes of “Albus” and “BPL10” inbred lines; of these, 653 were successfully mapped.
4.7 Linkage Maps
A linkage map can be considered as a ‘road map’ of the chromosomes and it mainly focuses on identifying the loci that are responsible for the expression of particular phenotypes and their variations (Myles et al., 2009). The process associated with construction of linkage maps and performing QTL analysis so as to identify genomic regions responsible for traits of interest is known as QTL mapping (also ‘genetic,’ ‘gene’ or ‘genome’ mapping) (McCouch & Doerge, 1995; Mohan et al., 1997). It indicates location and distance between markers along chromosomes. The main principle behind ‘QTL mapping’ is that, genes and markers segregate via crossing-over during meiosis, this in turn will lead to their analysis in the progeny (Paterson, 1996a). Tightly-linked or closely located genes/markers has high probability of being transmitted from parent to progeny as compared to genes or markers that are located further apart. Identifying QTLs by making use of DNA-markers aid the introgression genes into improved cultivars via marker-assisted selection (MAS) and map-based cloning of the tagged genes (Sehgal et al., 2016). Three major steps are needed for the construction of linkage maps. These are; production of a mapping population, identification of polymorphism and linkage analysis of DNA markers.
Mapping population is derived from sexual reproduction of two divergent parents which differ for one or more traits of interest. Double haploid lines (DHLs), backcross (BC) population, F2:3/F2:4 lines, F2 population and recombinant inbred lines (RILs) are the several types of mapping population can be produced from the heterozygous F1hybrids (Sehgal et al., 2016). The population size can vary from 50 to 200 individuals (Mohan et al., 1997), however larger populations are required for high-resolution mapping. Phenotypical evaluation of mapping populations must be carried out before subsequent QTL mapping on every QTL Study. Identification of DNA markers that indicate the difference between the two parents is the sequential stage to be conducted during QTL mapping. It is critical that sufficient polymorphism exists between parents to construct a linkage map (Young, 1994). The level of DNA polymorphism possessed by cross pollinating species is higher than inbreeding species and this indicates the need to select highly convergent parents while conducting mapping for inbreeding species. Availability of characterized markers or the appropriateness of particular markers for a particular species determines choice of DNA markers used for mapping. After the identification of polymorphic markers, screening them across the entire population including the parents will follow and the entire process is known as genotyping (Collard et al., 2005). Prior to genotyping, DNA extraction will be performed from each individual across the population. Data coding for each DNA marker on each individual of a population and carrying out linkage analysis by using appropriate computer software is the final step of linkage map construction (Collard et al., 2005). Linkage between markers is usually calculated with an odds ratio (i.e., the ratio of linkage versus no linkage). This ratio is more conveniently expressed as the logarithm of the ratio and is called a logarithm of odds (LOD) value or LOD score (Sehgal et al., 2016). LOD values of >3 are typically used to construct linkage maps. LOD values may be lowered in order to detect linkage over a greater distance or to place additional markers within maps constructed at higher LOD values (Collard et al., 2005). Mapmaker/EXP (Lander et al., 1987; Lincoln et al., 1993), MapManager QTX (Manly et al., 2001), and THREaD Mapper Studio (Cheema et al., 2010) are regularly used software programs while constructing linkage maps. JoinMap is another commonly used program for constructing linkage maps (Stam, 1993).
Molecular breeding strategies that utilize the latest developments in genetics and genomics coupled with conventional breeding methods have wider applications for future faba bean cultivar development. Improvement of efficiency of selection, enhancement of favorable gene action and expansion of useful genetic diversity are some of the benefits of obtained by applying molecular approaches (Gnanasambandam et al., 2012). Early genetic studies used morphological and isozyme variation, but the availability of DNA based markers has facilitated the construction of linkage maps of faba bean. Ellwood et al., (2008) developed the first gene based genetic map in fab bean by using gene-based orthologous markers and an F6 recombinant inbred line population. Linkage analysis by applying ITAP markers indicated the existence of seven major and five small linkage groups (LGs) with evidence of a simple and direct macrosyntenic relationship between faba bean and Medicago truncatula. In conclusion, the authors indicated that composite map anchored with orthologous markers mapped in M. truncatula provides central reference map for future use of genomic and genetic information in faba bean genetic analysis and breeding. Resistance to several major pathogens or pests of V. faba has been identified by QTL analysis. Early studies allowed the identification of eight QTLs (Af1–Af8) for resistance to ascochyta blight in faba bean (Román et al. 2003; Avila et al. 2004) by using two different F2 populations sharing the susceptible parental line Vf136. Kaur et al. (2014) confirmed the presence of four QTLs in the recombinant inbred line (RIL) population derivedfrom the cross between Icarus and Ascot, three of which may be novel and one may be identical to previous QTLs. Recently, progenies from a cross between 29H and Vf136 have been used to identify two QTLs (Af2 and Af1) for resistance to ascochyta blight in faba bean (Atienza et al., 2016). QTL identification for broomrape resistance has been carried out in faba bean during the last decade. The first study identified three QTLs (Oc1, Oc2 and Oc3) by using F2 population derived from the cross between Vf6 9 and Vf136 (Roma´n et al. 2002b). High percentage of phenotypic variation of the trait (74%) were explained by the QTLs mainly because one of the QTL Oc1, which explained more than 37% of the character. Two additional QTLs for resistance against Orobanche foetida were identified using the same RIL, but both instable across environments and explained very little phenotypic variation (7–9%) (Díaz-Ruiz et al. 2009). A study conducted by Gutiérrez et al. (2013) by utilizing the cross between 29H 9 and Vf136 detected seven QTLs for O. crenata and three QTLs for O. foetida. The information available so far for resistance to Ascochyta and crenate broomrape, reveals the potential use of molecular markers for indirect selection for resistance in faba bean.
Additionally, several QTLs were identified for various agronomically important faba bean traits in addition to those related with resistance to economically important pests (Table 1).
|Trait||Name of population||Marker associated with QTL(s)||PV explained by the QTLs (%)*||Reference|
|Floral characters||29 H × Vf 136||RAPD||20||Avila et al. (2005)|
|Days to flowering||Vf 6 × Vf 27||SSR||28||Cruz-Izquierdo et al., (2012)|
|Flowering length||Vf 6 × Vf 27||EST-derived marker||31||Cruz-Izquierdo et al., (2012)|
|Pod length||Vf 6 × Vf 27||SSR||25||Cruz-Izquierdo et al., (2012)|
|Number of ovules per pod||Vf 6 × Vf 27||EST-derived marker||27||Cruz-Izquierdo et al., (2012)|
|Number of seeds per pod||Vf 6 × Vf 27||RAPD||26||Cruz-Izquierdo et al., (2012)|
|Seed weight||–||RAPD||30||Vaz Patto et al., (1999)|
|Yield characters||29 H × Vf 136||RAPD||58||Avila et al., (2005)|
|Frost tolerance||Coted’Or 1 × BPL 4628||RAPD||40||Arbaoui et al., (2008)|
|Fatty acid content||Coted’Or 1 × BPL 4628||RAPD||63||Arbaoui et al., (2008)|
|Vicine–convicine seed concentration||Me´lodie/2 X ILB 938/2||SNP||–||Khazaei et al. (2015)|
Table 1 Trait mapping in faba bean (Bohra et al., 2014)
Genome sequencing is fundamental to understand the genomic composition and gene repertoire of a crop. Initially, only the genomes of model legumes were available due the prohibitive costs associated with sequencing. However, the ever-decreasing costs related with sequencing made DNA and RNA high-throughput sequencing, a very crucial molecular tool for plant genetics, genomics and biochemistry (Chia and Ware, 2011; Ozsolak and Milos, 2011). Whole transcriptome sequencing using NGS technologies (RNA-seq) is a more convenient and rapid method to study gene expression at whole-genome level and can be used to predict putative gene function (Wang et al., 2009; Ozsolak and Milos, 2011; Jain, 2012). RNA-seq can also be used for gene discovery by de novo constructing the complete transcriptome of several organisms including non-model species (Jain, 2011; Martin and Wang, 2011). Using RNA-seq, it is possible to gain insights into the gene expression without prior knowledge of sequence. Many studies have already demonstrated the power of RNA-seq in various biological contexts (Li et al., 2010; Zenoni et al., 2010; Garg et al., 2011; Yang et al., 2011; Singh et al., 2013). Likewise, RNA sequencing provides atlases of gene expression, defining QTL introgressions, characterizing small RNAs, demonstrating alternative splicing events, and identifying genes and pathways involved in acclimation to biotic and abiotic stresses (Wang et al., 2009; Li et al., 2010; Libault et al., 2010; Severin et al., 2010a, b; Zenoni et al., 2010; Matas et al., 2011; Yang et al., 2011; Meyer et al., 2012; O’Rourke et al., 2013b; Atwood et al., 2014). So far, the genomes of five legume plants namely, soybean, Lotus japonicus, Medicago truncatula, pigeonpea, and chickpea have been sequenced and available (Sato et al., 2008; Schmutz et al., 2010; Young et al., 2011; Varshney et al., 2012, 2013; Jain et al., 2013). In many crop legumes, efforts focused on the development of cDNA libraries, gene expression analysis, generation of expressed sequence tags, and mining of functional information from EST datasets. Sequence depositories in EST databases assist gene discovery and comparative mapping. Additionally, they facilitate the identification of candidate genes for various useful agronomic traits (Young and Bharti, 2012). Large number of ESTs, from a various kind of tissues, including from plants challenged by stress were generated in legume species. More than three million legume ESTs are available, predominantly from soybean (1.5 million, Vodkin et al., 2004) followed by the model legumes L. japonicus (242 000, Asamizu et al., 2004) and M. truncatula (270 000, Cheung et al., 2006). Among crop legumes, cowpea and common bean contributed around 200 000 ESTs (Muchero et al., 2009), and 114 139 (Blair et al., 2011) respectively. Regarding chickpea, cDNA libraries have been constructed from plants under drought and salinity stress (Varshney et al., 2009a). In the case of pigeonpea, Fusarium wilt and sterility mosaic disease (SMD), responsive ESTs were generated (Raju et al., 2010). Faba bean genetic and genomic resources have only started to be developed in the last few years, due in part at least to its outcrossing habit and large (approx.13 Gb) genome size. This is reflected by the low number of faba bean ESTs present in the dbEST database at NCBI. Only 5,510 ESTs are available for this crop compared with 1.5 million for Arabidopsis and 270,000 for M. truncatula (http://www.ncbi.nlm.nih.gov/dbEST). Currently, transcriptomes made available from nine specifically identified single genotypes and a selection of tissues including whole seedling, root, shoot, leaf, seed coat, and embryo (O’Sullivan and Angra, 2016). (Table 2)
|Bio project||Genbank reference||Cultivar||Tissue||References|
|PRJNA225873 10-d||SRP033593||BPL10||seedling||Webb et al., 2016|
|PRJNA225881||SRP033121||Albus 10-d||seedling||Webb et al., 2016|
|PRJNA238140||SRX476199||CDC Fatima||6-d root||Ray et al., 2015|
|SRX476200||CDC Fatima||6-d root||Ray et al., 2015|
|SRX476493||CDC Fatima||Seed coat||Ray et al., 2015|
|SRX476217||SSNS-1||6-d root||Ray et al., 2015|
|SRX476220||SSNS-1||6-d shoot||Ray et al., 2015|
|SRX475907||A01155||6-d root||Ray et al., 2015|
|SRX475873||A01155||6-d shoot||Ray et al., 2015|
|SRX476566||A01155||Seed coat||Ray et al., 2015|
|PRJNA277609||SRP055969||Fiord||Mixed tissues||Arun-Chinnappa and McCurdy, 2015|
|PRJEB8906||ERP009949||Fiord||Cotyledon epidermis and parenchyma||Zhang et al., 2015|
|NA||JR964201- JR970413*||Icarus x Ascot||Mixed tissues||Kaur et al., 2012|
|PRJNA253768||SRP043650||NS||Leaves||Suresh et al., 2015|
|NA||GI:219212932 GI:219282595||Windsor||2-week-old embryo||Ray and Georges, 2010|
|NA||SRP045955||INRA-29H||Leaf||Ocaña et al., 2015|
|NA||SRP045955||Vf136||Leaf||Ocaña et al., 2015|
Table 2 Key Vf transcriptome datasets (O’Sullivan and Angra, 2016) NA, not applicable; NS, not stated.
*Only assembled contigs available as TSA.
- Materials and Methods
5.1 Developing screening method for evaluation of faba bean genotypes against chocolate
spot (Botrytis fabae)
Three different types of screening methods namely, whole plant assay, detached leaf assay and field screening will be carried out to develop a low cost high-throughput indoor screening system. Eventually faba bean genotypes from CDC program developed in the past ten year will be evaluated so as to identify resistant lines. Additionally, seventy Chinese germplasm introduced through the Australian gene bank will also be screened.
5.1.1 Whole plant assay
Faba bean genotypes from CDC program and seventy Chinese germplasms will be grown in a phytotron by using Sunshine Mix #3 media for four weeks. The light will be adjusted to 16hrs photoperiod and the temperature will be 22oC during day time and 18oC for the night two days before inoculation and will continue for the rest the growing cycle. Three seeds will be planted in one gallon pot and later they will be thinned to one. The trial will be conducted in a randomized complete block design with four replications and repeated twice.
Isolate of Botrytis fabae from the existing collection at the Pulse pathology laboratory will be cultured on faba bean dextrose agar. The isolate will be multiplied on faba bean dextrose agar and kept under 12/12 h light/dark regime at room temperature. Ten to twelve day old cultures will be blended and spore suspensions will be adjusted to 4–5 x 105 spores/ml and Tween-20 (0.03 % v/v) will be added to the suspension. One month old plants will be sprayed to run-off with about 1.5 ml spore suspension per plant. Pots will be covered by polyethylene plastic overnight in the dark at 22oc with a relative humidity ≥95%. Variety Kontu will be used as a susceptible check.
5.1.2 Detached leaf assay
The inoculum needed for conducting detached leaf assay will be prepared similarly as indicated in whole plant assay. After growing the seedlings of CDC and Chinese genotypes in a in a phytotron young, fully expanded leaflets of similar physiological age will be detached from four-week-old faba bean genotypes. For each genotype four leaves will be used per each replicate and they will be placed in Petri-dishes containing moist filter papers. Similarly, ten to twelve day old cultures will be blended and spore suspensions will be adjusted to 4–5 x 105 spores/ml and Tween-20 (0.03 % v/v) will be added to the suspension so as to inoculate the upper laminar surface of the leaves. Detached leaflets will be inoculated by placing a 5 μl drop of the spore suspension on each leaflet close to the central nerve (Villegas et al., 2011). A leaflet inoculated with distilled water served as control. The plates will be arranged in completely randomized design with four replications. The plates were placed side by side in one layer on a bench in dark room (exposing plates to an alternating cycle of 16 hrs light from a fluorescent and 8 hours’ darkness. In the meantime, tap water will be dropped on filter paper to maintain high relative humidity for infection.
5.1.3 Field Screening
Field screening of the genotypes will be laid out using randomized complete block design with three replications at three different locations near Saskatoon (Preston, Skarsgard/SPG, Kernen). Each plot will be planted in one row of 1 m length and 0.5 m distance between the plots. The inoculum needed to conduct field screening will be prepared similarly as indicated in whole plant assay. The suspension will be sprayed by using manual knapsack sprayer so as to ensure high disease pressure in addition to natural infection, the inoculation will be carried out four weeks after planting by adjusting the concentration of the suspension to 4–5 x 105 spores/ml. Approximately
800 ml of the mixture will be poured along each row of plants. Artificial inoculation will be repeated four weeks later to ensure the presence of inoculum during the most propitious period for the development of the disease. Variety Kontu will be used as a susceptible check.
5.1.4 Disease Assessment and statistical analysis
Disease severity will be evaluated using a 1–9 disease rating scale of Hanounik and Robertson (1988, 1989). For whole seedling assay and field screening assessment will be started seven days after inoculation when first symptoms of chocolate spot will be visible and will be repeated four times every seven days until pod development. For detached leaf assay lesion diameter, will be measured on the second, third and sixth day after inoculation. Data on incubation period will also be collected. The disease data from all the experiments will be subjected to statistical analysis using SAS 9.3 (SAS Institute, 2012), and the genotypes will be evaluated based on their reaction to the pathogen. Area under the disease progress curve (AUDPC) will be calculated by using the data collected from all the experiments.
AUDPC= ∑ 1/2 [(yi+1 + yi ) (xi+1 − xi)]
yi:-value of evaluated parameter at day 1
xi:- time (days)
n:-total number of observations
5.2 Pathogenic diversity analysis of the chocolate spot isolates from Western Canada
The diversity analysis will be carried out by using fifteen Botrytis fabae isolates, two collected from Alberta and thirteen collected from Saskatchewan, and diverse 20 lines from the NORFAB (NORthern FABa) project. Pots will be arranged in phytotron using randomized complete block design. Spore suspension containing 4–5 x 105 spores/ml will be applied to the foliage of four-week-old plants using atomizer sprayer until run off. Five plants from each genotype will be inoculated and covered immediately with polyethylene sheets to provide adequate humidity and a water film on the leaf surfaces for infection. The control plant will be inoculated with sterile distilled water. Isolate virulence will be evaluated after seven days on a modified 1–3 scale, where 1virulent (large coalesced lesions, 76–100% leaflet necrosis, with abundant sporulation), 2 moderately virulent (medium-sized necrotic flecks or coalesced lesions, 26–75% necrosis, with poor or intermediate sporulation), 3 avirulent or weakly virulent (no infection or very small flecks, 1–25% necrosis, no sporulation) (Hanounik, 1986). The disease data from the experiment will be subjected to statistical analysis using SAS 9.3 (SAS Institute, 2012). After identifying the most aggressive isolate, it (the most aggressive isolate) will be used to screen current breeding lines from the CDC faba bean breeding program, starting with the elite materials developed over the past 10 years.
- Transcriptome Analysis in Faba Bean (Vicia faba L.) under chocolate spot infection
5.3.1 Plant material and Botrytis fabae inoculation
Two faba bean genotypes, ILB938-1 (resistant) and Disco (susceptible) will be used for denovo transcriptome assembly. Seeds of each genotype will be planted in one gallon pot filled with Sunshine Mix #4 soil media with three replications. Inoculation of B. fabae will be carried out inside growth chamber by adjusting day/night temperature of 22oC/18oC, 95% RH and 16hr photoperiod. Non-inoculated replicated plants will be included in the assay as controls. To make sure the occurrence of infection, inoculated plants will be compared with controls after two weeks.
5.3.2 Isolation of RNA, cDNA library construction and sequencing
To proceed with the RNA extraction, entire leaf tissue will be collected at 4, 8 and 12 hours after inoculation since early defense response occurs shortly after contact with the pathogenic organism (Ocaña et al., 2015). Consequently, the sample will immediately be frozen in liquid nitrogen. Similarly, leaf samples will also be collected from non-inoculated plants. RNA extraction will be carried out from both inoculated and non-inoculated plants using TRIZOL reagent (Invitrogen, Carlsbad, CA, USA). Due to the large number of sequence reads that will be generated from the reactions, pooled time point RNAs of each faba bean line will be used for generation of two cDNA libraries (Ocaña et al., 2015). The library construction and sequencing will be contracted to reputable service provider (will be decided later).
5.3.3 Data Analysis and Annotation
Transcripts will be compared against the nucleotide sequence database TAIR 10 of Arabidopsis orthologue using BLASTN (Altschul et al., 1990) with a threshold E-value of 10−10 in addition to the annotations provided by Full-Lengther Next in the pipeline legume species that has close relation with faba bean (M. truncatula, and Cicer aritenum) will be used for nucleotide comparison by making use of BLASTN. Nucleotide sequence database of chickpea will be compared against protein sequences derived from the faba bean transcriptome using the tBLASTX (E-value10-10) to derive other putative annotations (Ocaña et al., 2015). SNPs and InDels identification will be carried out by separated mapping data from each genotype against the tentative transcripts using Bowtie v2.1.0 (Langmead, 2002).
Separate mapping of sequence reads acquired from resistant and susceptible genotypes against the reference transcriptome will be carried out followed by counting using Sam2counts.py (Ocaña et al., 2015). The count chart will be loaded into the RobiNa software (Lohse et al., 2012) and analysed with the statistical method edgeR (Robinson et al., 2010). Kobas 2.0 (http://kobas.cbi.pku.edu.cn/home.do) with the Fisher’s exact test will be used for to identify genes that cause phenotypic differences between the two genotypes.
Reference genes, actin1 (ACT1) and cyclophilin (CYP2) will be used to normalize data (Gutierrez et al., 2011). In accordance with manufacturer’s instructions, M-MLV reverse transcription enzyme (Invitrogen, Carlsbad, CA, USA) together with oligodT (dT12–18) will be used for reverse transcription of total RNA which is extracted from both genotypes and inoculated/non-inoculated plants. According to Madrid et al. 2013 cDNA synthesis and quality control will be carried out. The real-time quantitative PCR (qPCR) reactions used the iTaq Universal SYBR (Biorad) according to manufacturer’s instructions.
- QTL mapping of resistance to chocolate spot of faba bean
5.4.1 Plant Material and Disease Assessment
A recombinant inbred population (RIL; F8) consisting of 200 lines from a cross between ILB938-1(R)/Disco (S) will be used for QTL mapping. Evaluation of RILs will be conducted in phytotron to determine their reaction against chocolate spot of faba bean. Seeds will be grown using one gallon pot filled with Sunshine Mix #3 media for one month. To increase the amount of disease inoculum, susceptible variety Kontu will be planted alongside each RIL in the same pot. Inoculation will be carried out by using a spore suspension of Botrytis fabae adjusted to 4–5 x 105 spores/ml. Relative humidity will be adjusted to 95% in addition to day/night temperature of 22oC/18oC and a photoperiod of 16hrs. Field evaluation of the reaction to chocolate spot will be conducted across two locations (Preston and Skarsgard or SPG farm) in Saskatchewan for two years (2018 and 2019) by using randomized complete block design with three replications at each location and year. Ten seeds of each RIL genotype will be sown along a 1-m-long row with row spacing of 0.7 m. Manual knapsack sprayers will be used for the artificial inoculation of Botrytis fabae suspension four weeks after planting. Reaction to chocolate spot resistance will be recorded for both phytotron and field evaluation seven days after inoculation by using a 1–9 disease rating scale of Hanounik and Robertson (1988, 1989). Additionally, various agronomically important traits like days to flowering (number of days at which 50% of the plants flower) days to maturity (the number of days to 95% physiological maturity counting from day of sowing or emergence to day of physiological maturity), plant height, number of pod per plant and number of seeds per pod will be recorded. The data from all the experiments will be subjected to statistical analysis using SAS 9.3 (SAS Institute, 2012).
DNA will be extracted from the leaves of each genotype using Illustra Nucleon Phytopure Genomic DNA Extraction kits (GE Health UK Limited). The DNA will be SNP (50K Axiom Faba SNP chip)-genotyped using KASP (Kompetitive Allele Specific PCR) assay platform. The genetic linkage map will be constructed using MapDisto v. 184.108.40.206.1 (Lorieux, 2012) with a logarithm of odds (LOD) score of 3.0 and recombination fraction of 0.3.
5.4.3. QTL analysis
QTL will be analyzed by MapQTL 4.0 package by using mean data from each experiment (van Ooijen et al. 2000). To detect association between traits and markers individually, nonparametric Kruskal–Wallis test will be conducted initially, without taking into consideration of the map information. Consequently, analyses of interval-mapping will be performed (Lander and Botstein 1989; van Ooijen 1992). An initial set of cofactors will be selected from the Kruskal–Wallis test and interval mapping results and a backward elimination procedure will be used to select significant markers as implemented in MapQTL 4.0. Markers significant at P = 0.01 will only be used as cofactors in the multiple QTL analysis (MQM) (Jansen 1993, 1994; Jansen and Stam 1994).
The presence of a QTL will be accepted based on its P-value and co-location with QTLs for different traits. An estimation of the additive effect and the total variance explained at the position with the highest LOD (log of odds) score will be given by MapQTL 4.0. QTL positions will be determined as the position with the maximum LOD score. Uncertainty of the position will be indicated by a 2-LOD support interval (Conneally et al. 1985; van Ooijen 1992). MapChart software will be used to produce figures (Voorrips 2002).
Candidate gene mapping will be carried out based on the linkage map constructed for ILB938-1(R)/Disco (S). Separate mapping of sequence reads acquired from the two genotypes against the reference transcriptome will be carried out followed by counting using Sam2counts.py (Ocaña et al., 2015). The count chart will be loaded into the RobiNa software (Lohse et al., 2012) and analysed with the statistical method edgeR (Robinson et al., 2010). Kobas 2.0 (http://kobas.cbi.pku.edu.cn/home.do) with the Fisher’s exact test will be used for to identify genes that cause phenotypic differences between the two genotypes.
6. Work Plan
|June 2017 to December 2017||
|January 2018-December 2018||
|January 2019-December 2019||
genotypes from CDC and china
|CRN||Subject||Course||Course Title||Final Grades|
|24227||PLSC||814||Physiology and Yield Formation||79%|
|82226||GSR||960||Introduction to Ethics and Integrity||100%|
Akem, C., and M. Bellar. 1999. Survey of faba bean (Vicia faba L.) diseases in the main faba bean-growing regions of Syria. Arab Journal of Plant Protection 17:113-116.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990; 215(3):403–10. PMID: 2231712
Arbaoui M, Balko C, Link W (2008) Study of faba bean (Vicia faba L.) winter-hardiness and development of screening methods.Field Crop Res 106:60–67
Asins M (2002) Present and future of quantitative trait locus analysis in plant breeding. Plant
Atienza S. G., Palomino C., Gutiérrez N., Alfaro C. M., Rubiales D., Torres A. M., Ávila C. M. (2016) QTLs for ascochyta blight resistance in faba bean (Vicia faba L.): validation in field and controlled conditions. Crop and Pasture Science 67, 216-224.
Arun-Chinnappa,K.S. and McCurdy,D.W.(2015). Denovo assembly of a genome-wide transcriptome map of Vicia faba (L.)for transfer cell research. Front. PlantSci. 6:217.doi:10.3389/fpls.2015.00217
Atwood SE, O’Rourke JA, Peiffer GA, et al. 2014. Replication protein A subunit 3 and the iron efficiency response in soybean. Plant, Cell and Environment, 37: 213–234.
Asamizu E, Nakamura Y, Sato S, Tabata S (2004) Characteristics of the Lotus japonicus gene repertoire deduced from large-scale expressed sequencetag (EST) analysis. Plant Molecular Biology 54, 405–414. doi:10.1023/B:PLAN.0000036372.46942.b8
Avila, C.M.; Nadal, S.; Moreno, M.T.; Torres, A.M. Development of a simple PCR-based marker for the determination of growth habit in Vicia faba L. using a candidate gene approach. Mol. Breed. 2006, 17, 185–190.
Avila CM, Satovic Z, Sillero JC, Rubiales D, Moreno MT, Torres AM (2004) Isolate and organ-specific QTLs for ascochyta blight resistance in faba bean (Vicia faba L). Theoretical and Applied Genetics 108, 1071–1078. doi:10.1007/s00122-003-1514-7
Avila MC, Satovi Z, Sillero JC, Nadal S, Rubiales D, Moreno MT, Torres AM (2005) QTL detection for agronomic traits in faba bean (Vicia faba L.). Agric Conspec Sci 3:65–73
Bouhassan, A., M. Sadiki, and B. Tivoli. 2004. Evaluation of a collection of faba bean (Vicia faba L.) genotypes originating from the Maghreb for resistance to chocolate spot (Botrytis fabae) by assessment in the field and laboratory. Euphytica 135:55–62.
Chia JM, Ware D. 2011. Sequencing for the cream of the crop. Nature Biotechnology 29: 138 139
BlairMW,Fernandez AC, Ishitani M, Moreta D, Seki M, Ayling S, Shinozaki K (2011) Construction and EST sequencing of full-length, drought stress cDNA libraries for common beans (Phaseolus vulgaris L.). BMC Plant Biology 11, 171. doi:10.1186/1471-2229-11-171
Crépona, K.; Marget, P.; Peyronnet, C.; Carrouéea, B.; Arese, P.; Duc, G. Nutritional value of faba bean (Vicia faba L.) seeds for feed and food. Field Crop. Res. 2010, 115, 329–339.
Cruz-Izquierdo S, Avila CM, Satovic Z, Palomino C, Gutiérrez N, Ellwood SR, Phan HT, Cubero JI, Torres AM (2012) Comparative genomics to bridge Vicia faba with model and closely-related legume species: stability of QTLs for flowering and yieldrelated traits. Theor Appl Genet 125:1767–1782
Cheung F, Haas BJ, Goldberg SMD, May GD, Xiao Y, Town CD (2006) Sequencing Medicago truncatula expressed sequenced tags using 454 Life Sciences technology. BMC Genomics 7, 272. doi:10.1186/1471- 2164-7-272
Collard, B. C. Y., Jahufer, M. Z. Z., Brouwer, J. B. & Pang, E. C. K. 2005 An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142, 169–196. (doi:10.1007/s10681-005-1681-5)
Lander ES, Green P, Abrahamson J et al (1987) MAPMAKER: an interactive computer package
for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181
Elad, Y., B. Williamson, P. Tudzynski, and N. Delen. 2004. Botrytis: Biology pathology and control. Kluwer Academic Publishers, Dordrecht, The Netherlands.
Ellwood S, Phan H, Jordan M, Hane J, Torres A, Avila C, Cruz- Izquierdo S, Oliver R (2008) Construction of a comparative genetic map in faba bean (Vicia faba L.); conservation of genome structure with Lens culinaris. BMC Genom 9(1):380
FAOSTAT. 2014. Data base. Available at: http://faostat3.fao.org/faostat-gateway.
Fernandez-Aparicio, M., Rubiales, D., 2007. Beneficial effects of intercrops with oatin reduction of chocolate spot (Botrytis fabae) infection in faba bean in anorganic farming system. In: Østerga˚ rd, H., Backes, G., Kova´ cs, G. (Eds.), VarietalCharacteristics of Cereals in Different Growing Systems with Special Emphasison Below Ground Traits: Proceedings of the COST SUSVAR Workshop, 29 May–01 June, Velence, Hungary, pp. 45–46.
Fleury Donna. 2016. Chocolate Spot Disease in Faba Bean. 2016 International Year of Pulsese, Saskatchewan Pulse Growers
Gnanasambandam, A., Paull, J., Torres, A., Kaur, S., Leonforte, T., Li, H., Zong, X., Yang, T., and Materne, M. 2012. Impact of molecular technologies on Faba Bean (Vicia faba L.) breeding strategies. Agronomy 2: 132–166.
Gutierrez N, Giménez MJ, Palomino C, Avila CM. Assessment of candidate reference genes for
expression studies in Vicia faba L. by real-time quantitative PCR. Mol Breed. 2011; 28(1):13–24.
Garg, R. and M. Jain. 2011. Pyrosequencing data reveals tissue-specific expression of lineage-specific transcripts in chickpea. Plant Signal. Behav. 6:1868-1870.
Harrison, J. G. (1988): The biology of Botrytis spp. on Vicia beans and chocolate spotdisease- a review. Plant Pathology 37, 168-201.
Harrison, J.G. 1988. The biology of Botrytis spp. on viciabeans and chocolate spot disease – a review. Plant Pathology 37:168–201.
Harrison, J.G. 1979. Overwintering of Botrytis fabae. Transactions British Mycological Society 72:389-394.
Hanounik SB, Robertson LD (1988). New sources of resistance in Vicia faba to chocolate spot caused by Botrytis fabae. Plant Dis 72:696–698
Hanounik SB, Robertson LD (1989) Resistance in Vicia faba germplasm to blight caused by Ascochyta fabae. Plant Dis 73:202–205
Hawthorne, W.A., Bretag, T., Raynes, M., Davidson, J.A., Kimber, R.B.E., Nikandrow,A., Matthews, P., Paull, J.G., 2004. Faba bean disease management strategy forsouthern region GRDC 2004. Pulse Australia, http://www.pulseaus.com.au/crops/faba_beans/.
Jansen RC (1993) Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211.
Jain, M., G. Misra, R.K. Patel, P. Priya, S. Jhanwar, A.W. Khan, N. Shah, V.K. Singh, R. Garg, G. Jeena, M. Yadav, C. Kant, P. Sharma, G. Yadav, S. Bhatia, A.K. Tyagi, and D. Chattopadhyay. 2013. A draft genome sequence of the pulse crop chickpea (Cicer arietinum L.). Plant J. 74:715–729. doi:10.1111/tpj.12173
Jansen RC (1994) Controlling the type I and type II errors in mapping quantitative trait loci. Genetics 138, 871–881.
Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136, 1447–1455.
Jain, M. 2012. Next-generation sequencing technologies for gene expression profiling in plants. Brief. Funct. Genomics 11:63-70.
Jain, M. 2011. A next-generation approach to the characterization of a non-model plant transcriptome. Curr. Sci. 101:1435-1439.
Kaur S, Pembleton L, Cogan N, Savin K, Leonforte T, Paull J, et al. Transcriptome sequencing of field pea and faba bean for discovery and validation of SSR genetic markers. BMC Genomics. 2012; 13 (1):104.
Kohpina S., Knight R., Stoddard F.L. 2000. Genetics of resistance to ascochyta blight in two populations of faba bean. Euphytica 112: 101–107, 2000.
Khazaei H, O’Sullivan DM, Jones H, Pitts N, Sillanpää MJ, Pärssinen P, Manninen O, Stoddard FL (2015) Flanking SNP markers for vicine–convicine concentration in faba bean (Vicia faba L.) Mol Breed. doi:10.1007/s11032-015-0214-8
Langmead B. Aligning Short Sequencing Reads with Bowtie. Curr Protoc Bioinformatics: John Wiley & Sons, Inc.; 2002.
Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199.
Lincoln S, Daly M. Lander E (1993) Constructing genetic linkage maps with MAPMAKER/EXP.Version 3.0. Whitehead Institute for Biomedical Research Technical Report, 3rd edn
Li L, Liu C, Lian X. 2010. Gene expression profiles in rice roots under lowphosphorus stress. Plant Molecular Biology 72: 423–432.
Libault M, Farmer A, Joshi T, et al. 2010. An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. The Plant Journal 63: 86–99.
Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, et al. RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res. 2012; 40(W1): W622–W7.
Lorieux,M.(2012). MapDisto: fast and efficient computation of genetic linkage maps. Mol.Breed. 30,1231–1235.doi:10.1007/s11032-012-9706-y MacLeod, B. and M. Sweetingham, 2006. Faba bean: chocolate spot disease. Farmnote 115/96. Available at http://www.agric.wa.gov.au/PC_92103.html?s=100. [Reviewed, content current June 2010].
Madrid E, Palomino C, Plötner A, Horres R, Rotter B, Winter P, et al. DeepSuperSage analysis of the Vicia faba transcriptome in response to Ascochyta fabae infection. Phytopatol Mediterranea. 2013; 52 (1):166–82.
Manly KF, Cudmore RH Jr, Meer JM (2001) Map manager QTX, cross-platform software for genetic mapping. Mamm Genome 12:930–932
Mussa Jarso, Dereje Gorfu and Gemechu keneni, 2008. Procedures of Faba BeanImprovement through Hybridization. Technical Manual No.21, Ethiopian Institute ofAgricultural Research. 48p.
Maxted, N., and S.P. Kell. 2009. Establishment of a global network for the in situ conservation of crop wild relatives: Status and needs. FAO commission on genetic resources for FAO, Rome, Italy
McCouch, S.R. & R.W. Doerge, 1995. QTL mapping in rice. Trends Genet 11: 482–487.
Mohan, M., S. Nair, A. Bhagwat, T.G. Krishna, M.Yano, C.R. Bhatia & T. Sasaki, 1997. Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breed 3: 87–103.
Muchero W, Diop NN, Bhat PR, Fenton RD, Wanamaker S, Pottor M, Hearne S, Cisse N, Fatokun C, Ehlers JD, Roberts PA, Close TJ (2009) A consensus genetic map of cowpea (Vigna unguiculata (L) Walp.) and synteny based on EST-derived SNPs. Proceedings of the National Academy of Sciences of the United States of America 106(43), 18159–18164. doi:10.1073/pnas.0905886106
Matas AJ, Yeats TH, Buda GJ, et al. 2011. Tissue and cell-type specific transcriptome profiling of expanding tomato fruit provides insights into metabolic and regulatory specialization and cuticle formation The Plant Cell 23: 3893–3910.
Meyer K, Stecca KL, Ewell-Hicks K, Allen SM, Everard JD. 2012. Oil and protein accumulation in developing seeds is influenced by the expression of a cytosolic pyrophosphatase in Arabidopsis. Plant Physiology 159:1221–1234.
Martin, J.A., and Z. Wang. 2011. Next-generation transcriptome assembly. Nat. Rev. Genet. 12:671-682. Li, P., L. Ponnala, N. Gandotra, L. Wang, Y. Si, S.L. Tausta, T.H. Kebrom, N. Provart, R. Patel, C.R. Myers, E.J. Reidel, R. Turgeon, P. Liu, Q. Sun, T. Nelson, and T.P. Brutnell. 2010. The developmental dynamics of the maize leaf transcriptome. Nat. Genet. 42:1060-1067.
Ocaña S, Seoane P, Bautista R, Palomino C, Claros GM, Torres AM, et al. (2015) Large-Scale Transcriptome Analysis in Faba Bean (Vicia faba L.) under Ascochyta fabae Infection. PLoS ONE 10(8): e0135143. doi: 10.1371/journal.pone.0135143
Ozsolak F, Milos PM. 2011.RNAsequencing: advances, challenges and opportunities. Nature Reviews Genetics 12: 87–98.
O’Rourke JA, Yang SS, Miller SS, et al. 2013b. An RNA-Seq transcriptome analysis of orthophosphate-deficient white lupin reveals novel insights into phosphorus acclimation in plants. Plant Physiology 161: 705–724.
O’Sullivan, D. M., and Angra, D. (2016). Advances in faba bean genetics and genomics. Front. Genet. 7:150. doi: 10.3389/fgene.2016.00150
Paterson, A.H., 1996a. Making genetic maps. In: A.H. Paterson (Ed.), Genome Mapping in Plants, pp. 23–39. R. G. Landes Company, San Diego, California; Academic Press, Austin, Texas.
Pe´rez-de-Luque, A., H. Eizenberg, J.H. Grenz, J.C. Sillero, C. Avila, J. Sauerborn et al. 2010. Broomrape management in faba bean. Field Crops Research 115:319-328.
Phelps Sherilyn. 2017. Faba bean agronomy. Saskatchewan pulse growers
Richardson, H., 2008. Chocolate Spot of Faba Bean. Agriculture Notes. State of Victoria, Department of Primary Industries.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139–40. doi: 10.1093/ bioinformatics/btp616 PMID: 19910308
Roman, B.; Torres, A.M.; Rubiales, D.; Cubero, J.I.; Satovic, Z. Mapping of quantitative trait loci controlling broomrape (Orobanche crenata Forsk.) resistance in faba bean (Vicia faba L.). Genome 2002, 45, 1057–1063.
Román B, Satovic Z, Avila CM, Rubiales D, Moreno MT, Torres AM (2003) Locating genes associated with Ascochyta fabae resistance in Vicia faba. Australian Journal of Agricultural Research 54, 85–90. doi:10.1071/AR02034
Ray,H.,Bock,C.,and Georges,F.(2015). Faba Bean: Transcriptome Analysis from etiolated seedling and developing seed coat of key cultivars for synthesis of proanthocyanidins, phytate, raffinose family oligosaccharides, vicine, and convicine. Plant Genome 8:1.doi:10.3835/plantgenome2014.07.0028
Raju NL, Gnanesh BN, Lekha P, Jayashree B, Pande S, Hiremath PJ, Byregowda M, Singh NK, Varshney RK (2010) The first set of EST resource for gene discovery and marker development in pigeonpea (Cajanus cajan L.). BMC Plant Biology 10, 45. doi:10.1186/1471- 2229-10-45
Ray,H.,andGeorges,F.(2010).Agenomic approach to nutritional, pharmacological and genetic issues of faba bean (Viciafaba): prospects for genetic modifications. GMCrops 1,99–106.doi:10.4161/gmcr.1.2.11891
Sahile S., Abang, M.M., Fininsa C., Ahmed S., Sakhuja P.K., and Baum M. (2012). Pathogenic and genetic diversity of Botrytis fabaeSand. isolates from faba bean fields in different agro-ecological zones of Northern Ethiopia. Arch. Phytopathol. Plant Prot. 1:1-19.
Sahile, S., Fininsa, C., Sakhuja, P.K. and Ahmed, S. 2010. Yield loss of faba bean (Vicia faba) due to chocolate spot (Botrytis fabae) in sole and mixed cropping systems in Ethiopia. Archives of Phytopathology and Plant Protection. Vol. 43, No. 12, 1144–1159
Sehgal, D., Singh, R. and Rajpal, R.V. 2016. Quantitative Trait Loci Mapping in Plants: Concepts and Approaches. Molecular Breeding for Sustainable Crop Improvement, 11:31-59.
Sillero, J.C., Villegas-Ferna´ ndez, A.M., Thomas, J., Rojas-Molina, M.M., Emeran, A.A., Ferna´ ndez-Aparicio, M., Rubiales, D., 2010. Faba bean breeding for disease resistance. Field Crops Res. 115, 297–307.
Stam P (1993) Construction of integrated genetic linkage maps by means of a new computer package: join map. Plant J 3:739–744
Stoddard, F.L., A.H. Nicholas, D. Rubiales, J. Thomas, and A.M. Villegas-Fern’andez. 2010. Integrated pest management in faba bean. Field Crops Research 115:308–318.
Singh, V.K., R. Garg, and M. Jain. 2013. A global view of transcriptome dynamics during flower development in chickpea by deep sequencing. Plant Biotechnol. J. doi: 10.1111/pbi.12059.
Schmutz, J., S.B. Cannon, J. Schlueter, J. Ma, T. Mitros, W. Nelson, D.L. Hyten, Q. Song, J.J. Thelen, J. Cheng, D. Xu, U. Hellsten, G.D. May, Y. Yu, T. Sakurai, T. Umezawa, M.K. Bhattacharyya, D. Sandhu, B. Valliyodan, E. Lindquist, M. Peto, D. Grant, S. Shu, D. Goodstein, K. Barry, M. Futrell-Griggs, B. Abernathy, J. Du, Z. Tian, L. Zhu, N. Gill, T. Joshi, M. Libault, A. Sethuraman, X.C. Zhang, K. Shinozaki, H.T. Nguyen, R.A. Wing, P. Cregan, J. Specht, J. Grimwood, D. Rokhsar, G. Stacey, R.C. Shoemaker, and S.A. Jackson. 2010. Genome sequence of the palaeopolyploid soybean. Nature 463:178– 183. doi:10.1038/nature08670
Sato, S., Y. Nakamura, T. Kaneko, E. Asamizu, T. Kato, M. Nakao, S. Sasamoto, A. Watanabe, A. Ono, K. Kawashima, T. Fujishiro, M. Katoh, M. Kohara, Y. Kishida, C. Minami, S. Nakayama, N. Nakazaki, Y. Shimizu, S. Shinpo, C. Takahashi, T. Wada, M. Yamada, N. Ohmido, M. Hayashi, K. Fukui, T. Baba, T. Nakamichi, H. Mori, and S. Tabata. 2008. Genome structure of the legume, Lotus japonicus. DNA Res. 15:227–239. doi:10.1093/dnares/dsn008
Severin AJ, Peiffer GA, Xu WW, et al. 2010a. An integrative approach to genomic introgression mapping. Plant Physiology 154: 3–12.
Suresh,S., Kim,T.-S., Raveendar,S., Cho,J.-H., Yi,J.-Y., Lee,M.C.,etal.(2015).Transcriptome characterization and large-scale identification of SSR/SNP markers in symbiotic nitrogen fixation crop faba bean (Viciafaba L.). Turk. J.Agric.For. 39,459–469.doi:10.3906/tar-1409-3
Severin AJ,Woody JL, BolonY-T, et al. 2010b.RNA-Seq atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biology 10: 160.
Tivoli, B., A. Baranger, C.M. Avila, S. Banniza, M. Barbetti, W. Chen et al. 2006. Screening techniques and sources of resistance to foliar diseases caused by major necrotrophic fungi in grain legumes. Euphytica 147:223-253.
Toker, C., 2004. Estimates of broad –sense heritabilityfor seed yield and yield criteria in faba bean.Heredites, 140: 222-225.
Torres, A.M., B. Rom, C.M. Avila, Z. Satovic, D. Rubiales, J.C. Sillero et al. 2006. Faba bean breeding for resistance against biotic stresses: Towards application of marker technology. Euphytica 147:67-80.
Torres, A.M.; Avila, C.M.; Gutierrez, N.; Palomino, C.; Moreno, M.T.; Cubero, J.I. Marker-assisted selection in faba bean (Vicia faba L.). Field Crop. Res. 2010, 115, 243–252.
Van Ooijen JW (1992) Accuracy of mapping quantitative trait loci in autogamous species. Theoretical and Applied Genetics 84, 803–811. doi:10.1007/BF00227388
Vaz Patto MC, Torres AM, Koblizkova A, Macas J, Cubero JI (1999) Development of a genetic composite map of Vicia faba using F2 populations derived from trisomic plants. Theor Appl Genet 98:736–743
Varshney, R.K., W. Chen, Y. Li, A.K. Bharti, R.K. Saxena, J.A. Schlueter, M.T. Donoghue, S. Azam, G. Fan, A.M. Whaley, A.D. Farmer, J. Sheridan, A. Iwata, R. Tuteja, R.V. Penmetsa, W. Wu, H.D. Upadhyaya, S.P. Yang, T. Shah, K.B. Saxena, T. Michael, W.R. McCombie, B. Yang, G. Zhang, H. Yang, J. Wang, C. Spillane, D.R. Cook, G.D. May, X. Xu, and S.A. Jackson. 2012. Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nat. Biotechnol. 30:83–89. doi:10.1038/ nbt.2022
Varshney, R.K., C. Song, R.K. Saxena, S. Azam, S. Yu, A.G. Sharpe, S. Cannon, J. Baek, B.D. Rosen, B. Tar’an, T. Millan, X. Zhang, L.D. Ramsay, A. Iwata, Y. Wang, W. Nelson, A.D. Farmer, P.M. Gaur, C. Soderlund, R.V. Penmetsa, C. Xu, A.K. Bharti, W. He, P. Winter, S. Zhao, J.K. Hane, N. Carrasquilla-Garcia, J.A. Condie, H.D. Upadhyaya, M.C. Luo, M. Thudi, C.L. Gowda, N.P. Singh, J. Lichtenzveig, K.K. Gali, J. Rubio, N. Nadarajan, J. Dolezel, K.C. Bansal, X. Xu, D. Edwards, G. Zhang, G. Kahl, J. Gil, K.B. Singh, S.K. Datta, S.A. Jackson, J. Wang, and D.R. Cook. 2013. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat. Biotechnol. 31:240–246. doi:10.1038/ nbt.2491
Varshney RK, Hiremath PJ, Lekha P, Kashiwagi J, Balaji J, Deokar AA, Vadez V, Xiao Y, Srinivasan R, Gaur PM, Siddique KHM, Town CD, Hoisington DA (2009a) A comprehensive resource of drought- and salinity- responsive ESTs for gene discovery and marker development in chickpea (Cicer arietinum L.).BMCGenomics 10(1), 523. doi:10.1186/ 1471-2164-10-523
Villegas-Fernández, A.M., Sillero J.C., Emeran, A.A., Flores, F., Rubiales, D., 2011. Multiple disease resistance in Vicia faba: Multi-environment field testing for identification of combined resistance to rust and chocolate spot. Field Crops Res. 124, 259–65
Vodkin LO, Khanna A, Robin Shealy R, Steven J, Clough SJ, Gonzalez DO, Philip R, Gracia Zabala G, Thibaud-Nissen F, Sidarous M , et al. (2004) Microarrays for global expression constructed with a low redundancy set of 27 500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant. BMC Genomics 5, 73. doi:10.1186/1471-2164-5-73
Villegas-Fern´andez, A. M., Sillero, J. C., and Rubiales, D. 2012. Screening faba bean for chocolate spot resistance: evaluation methods and effects of age of host tissue and temperature. Eur. J. Plant Pathol. 132: 443–453.
Wang, Z., M. Gerstein, and M. Snyder. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:57-63.
Yang SS, Tu JZ, Foo C, et al. 2011. Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems. BMC Genomics 12: 199.
Yang, H., P. Lu, Y. Wang, and H. Ma. 2011. The transcriptome landscape of Arabidopsis male meiocytes from high-throughput sequencing: the complexity and evolution of the meiotic process. Plant J. 65:503-516.
Young, N.D., 1994. Constructing a plant genetic linkage map with DNA markers, p. 39–57, In: I. K.V. Ronald & L. Phillips (Eds.), DNA-based markers in plants. Kluwer, Dordrecht/ Boston/London.
You, M.P., H.A. Yang, K. Sivasithamparam and M.J. Barbetti, 2008. A new leaf blight disease of Trifolium dasyurumcaused by Botrytis fabae. European Journal of PlantPathology. DOI 10.1007/s10658-9338-6.
Young ND, BhartiAK(2012) Genome-enabled insights into legume biology. Annual Review of Plant Biology 63, 283–305. doi:10.1146/annurevar plant- 042110-103754
Young, N.D., F. Debellé, G.E. Oldroyd, R. Geurts, S.B. Cannon, M.K. Udvardi, V.A. Benedito, K.F. Mayer, J. Gouzy, H. Schoof, Y. Van de Peer, S. Proost, D.R. Cook, B.C. Meyers, M. Spannagl, F. Cheung, S. De Mita, V. Krishnakumar, H. Gundlach, S. Zhou, J. Mudge, A.K. Bharti, J.D. Murray, M.A. Naoumkina, B. Rosen, K.A. Silverstein, H. Tang, S. Rombauts, P.X. Zhao, P. Zhou, V. Barbe, P. Bardou, M. Bechner, A. Bellec, A. Berger, H. Bergès, S. Bidwell, T. Bisseling, N. Choisne, A. Couloux, R. Denny, S. Deshpande, X. Dai, J.J. Doyle, A.M. Dudez, A.D. Farmer, S. Fouteau, C. Franken, C. Gibelin, J. Gish, S. Goldstein, A.J. González, P.J. Green, A. Hallab, M. Hartog, A. Hua, S.J. Humphray, D.H. Jeong, Y. Jing, A. Jöcker, S.M. Kenton, D.J. Kim, K. Klee, H. Lai, C. Lang, S. Lin, S.L. Macmil, G. Magdelenat, L. Matthews, J. McCorrison, E.L. Monaghan, J.H. Mun, F.Z. Najar, C. Nicholson, C. Noirot, M. O’Bleness, C.R. Paule, J. Poulain, F. Prion, B. Qin, C. Qu, E.F. Retzel, C. Riddle, E. Sallet, S. Samain, N. Samson, I. Sanders, O. Saurat, C. Scarpelli, T. Schiex, B. Segurens, A.J. Severin, D.J. Sherrier, R. Shi, S. Sims, S.R. Singer, S. Sinharoy, L. Sterck, A. Viollet, B.B. Wang, K. Wang, M. Wang, X. Wang, J. Warfsmann, J. Weissenbach, D.D. White, J.D. White, G.B. Wiley, P. Wincker, Y. Xing, L. Yang, Z. Yao, F. Ying, J. Zhai, L. Zhou, A. Zuber, J. Dénarié, R.A. Dixon, G.D. May, D.C. Schwartz, J. Rogers, F. Quétier, C.D. Town, and B.A. Roe. 2011. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480:520–524. doi:10.1038/480162a
Zenoni S, Ferraini A, Giacomelli E, et al. 2010. Characterization of transcriptional complexity during berry development in Vitis vinifera using RNA-Seq. Plant Physiology 152: 1787–1795.
Zhang,H.M., Wheeler,S., Xia,X., Radchuk,R., Weber,H., Offler,C.E., et al.(2015). Differential transcriptional networks associated with key phases of in growth wall construction in trans-differentiating epidermal transfer cells of Viciafaba cotyledons. BMCPlantBiol. 15:103.doi:10.1186/s12870-015- 0486-5
Zong, X., X. Liu, J. Guan, S. Wang, Q. Liu, J.G. Paull et al. 2009. Molecular variation among Chinese and global winter faba bean Germplasm. Theoretical and Applied genetics 118:971-978.
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