Department of Agricultural Sciences

Faculty of Agriculture and Forestry

University of Helsinki

Doctoral Program in Plant Science

Leaf traits associated with drought adaptation in

faba bean (Vicia faba L.)

Doctoral thesis

Hamid Khazaei

ACADEMIC DISSERTATION

To be presented, with the permission of the Faculty of Agriculture and Forestry, University of

Helsinki, for public examination in lecture room B4 at Viikki campus, Latokartanonkaari 7–9,

Helsinki, on 26 September 2014, at 12 o’clock noon.

Helsinki 2014

2

Supervisors:

Adjunct Professor Frederick L. Stoddard Department of Agricultural Sciences

University of Helsinki

Finland

Professor Mikko J. Sillanpää Department of Mathematical Sciences

Department of Biology

Biocenter Oulu

University of Oulu

Finland

Reviewers: Assistant Professor Petr Smýkal Department of Botany

Palacký University in Olomouc

Czech Republic

Adjunct Professor Simo Hovinen

Opponent:

Professor Wolfgang Link Department of Crop Sciences

Georg-August-University

Germany

Cover figure: Faba bean germplasm grown in the greenhouse. © Hamid Khazaei

ISBN 978-952-10-8905-3 (Print)

ISBN 978-952-10-8906-0 (Online)

ISSN 1798-7407 (Print)

ISSN 1798-744X (Online)

ISSN-L 1798-7407

Department of Agricultural Sciences | Publications | 38

Electronic publication at http://ethesis.helsinki.fi

Unigrafia

Helsinki 2014

3

CONTENTS

List of original publications ........................................................................................................... 5

Abbreviations ................................................................................................................................. 6

ABSTRACT .................................................................................................................................. 7

1 INTRODUCTION ...................................................................................................................... 8

1.1 Faba bean (Vicia faba L.) .................................................................................................... 8

1.2 Drought adaptation .............................................................................................................. 9

1.2.1 Response mechanisms ................................................................................................ 10

1.3 Breeding............................................................................................................................. 13

1.3.1 Conventional breeding ................................................................................................ 13

1.3.2 Genetic resources ........................................................................................................ 14

1.4 Genomics ........................................................................................................................... 16

1.4.1 Marker assisted selection (MAS) ................................................................................ 19

1.4.2 Markers based on DNA .............................................................................................. 19

1.5 Synteny .............................................................................................................................. 20

1.5.1 Medicago truncatula ................................................................................................... 21

1.6 Quantitative trait loci ......................................................................................................... 21

2 AIMS OF THIS STUDY .......................................................................................................... 24

3 MATERIALS AND METHODS ............................................................................................. 25

3.1 Germplasm survey ............................................................................................................. 26

3.1.1 Construction of the FIGS sets ..................................................................................... 26

3.1.2 Germplasm screening ................................................................................................. 26

3.2 Molecular breeding ............................................................................................................ 26

3.2.1 Mapping population development and selection ........................................................ 26

3.2.2 Genotyping ................................................................................................................. 26

3.3 Measurements .................................................................................................................... 27

3.4 Growth conditions and statistical analysis ......................................................................... 28

4 RESULTS AND DISCUSSION ............................................................................................... 31

4.1 Germplasm screening ........................................................................................................ 31

4.2 Molecular breeding ............................................................................................................ 34

4.2.1 Response of parental lines to water deficit ................................................................. 34

4.2.2 Linkage map and chromosome assignment ................................................................ 34

4.2.3 Putative QTLs ............................................................................................................. 36

4.2.4 Synteny ....................................................................................................................... 37

4

4.3 Mendelian traits ................................................................................................................. 38

5 CONCLUSION AND FUTURE PROSPECTS ....................................................................... 40

ACKNOWLEDGMENTS ........................................................................................................... 42

6 REFERENCES ......................................................................................................................... 43

5

List of original publications

This thesis is based on the following publications, which are referred in the text with their

roman numerals.

I. Khazaei H, Street K, Bari A, Mackay M, Stoddard FL (2013) The FIGS (Focused

Identification of Germplasm Strategy) approach identifies traits related to drought

adaptation in Vicia faba genetic resources. PLoS ONE 8:e63107

II. Khazaei H, Street K, Santanen A, Bari A, Stoddard FL (2013) Do faba bean (Vicia

faba L.) accessions from environments with contrasting seasonal moisture

availabilities differ in stomatal characteristics and related traits? Genetic Resources

and Crop Evolution 60:2343–2357

III. Khazaei H, O’Sullivan DM, Sillanpää MJ, Stoddard FL (2014) Use of synteny to

identify candidate genes underlying QTL controlling stomatal traits in faba bean

(Vicia faba L.). Theoretical and Applied Genetics (in press) doi:10.1007/s00122-

014-2383-y

IV. Khazaei H, O’Sullivan DM, Sillanpää MJ, Stoddard FL (2014) Genetic analysis

reveals a novel locus in Vicia faba decoupling pigmentation in the flower from that

in the extra-floral nectaries. Molecular Breeding (in press) doi:10.1007/s11032-014-

0100-9

Statement of contributions on these articles:

Author Contributions I II III IV

Conceived and designed the

experiments

HK KS FLS HK FLS HK FLS DOS HK FLS

Performed the experiments HK HK HK HK

Analysed the data HK AB HK HK DOS HK DOS MJS

Contributed

reagents/materials/analysis tools

HK KS AB

MM FLS

HK KS AB

AS FLS

HK DOS MJS

FLS

HK DOS MJS

FLS

Wrote the paper HK KS AB

FLS

HK KS AS

FLS

HK FLS MJS

DOS

HK FLS MJS

DOS

HK Hamid Khazaei, KS Kenneth Street, FLS Frederick L. Stoddard, AB Abdallah Bari, MM Michael

Mackay, AS Arja Santanen, DOS Donal M. O’Sullivan, MJS Mikko J. Sillanpää

The original publications (II, III and IV) are reprinted with the permission of Springer.

6

Abbreviations

AFLP Amplified fragment length polymorphism

BC Backcross

BSA Bulked sergeant analysis

CGIAR Consultative Group on International Agricultural Research

cM CentiMorgans

EST Expressed sequence tag

EUW Efficient use of water

FIGS Focused identification of germplasm strategy

gs Stomatal conductance

ICARDA International Centre for Agricultural Research in the Dry Areas

ISSR Inter-simple sequence repeats

ITAP Intron-targeted amplified polymorphism

KASP Kompetitive allele specific PCR

LG Linkage group

LOD Logarithm (base 10) of odds

MAS Marker assisted selection

PGR Plant genetic resources

QTL Quantitative trait loci

RAPD Randomly amplified polymorphic DNA

RFLP Restriction fragment length polymorphism

RGA Resistance gene analogue

RIL Recombinant inbred line

SCAR Sequence characterized amplified regions

SNP Single nucleotide polymorphism

SSR Simple sequence repeats

STS Sequence tagged sites

TRAP Target region amplification polymorphism

WUE Water use efficiency

7

ABSTRACT

The potential for use of faba bean (Vicia faba L.) is increasing worldwide due to its positive

environmental impact and nutritional interest, but there are many challenges for faba bean

breeding and cultivation. These include its mixed breeding system, its unknown origin and

wild progenitor, its large genome being the biggest diploid genome among field crops, and

its relative sensitivity to biotic and abiotic stresses (e.g., drought). Little is known about the

ecological adaptation of faba bean germplasm, or about the locations and effects of genes

that influence traits related to drought adaptation, especially stomatal morphology and

function as key characters for gas exchange between plant and atmosphere. Thus, the current

study had two main goals, a) to examine whether faba bean germplasm from drought-prone

and drought-free environments differed in leaf traits related to drought adaptation while

testing a novel genetic resources utilization tool, and b) to screen the genome for regions and

candidate genes controlling morpho-physiological traits related to drought adaptation.

Two sets of faba bean germplasm each containing 201 accessions from dry and wet

regions of the world were chosen according to the principles of FIGS (the Focused

Identification of Germplasm Strategy). Leaf morpho-physiological traits related to drought

response (e.g., stomatal characteristics and water status) were measured under well watered

conditions in a climate-controlled greenhouse. Thereafter, two subsets of 10 accessions each

were exposed to water deficit. The significance of the difference between the two sets

indicated the potential of FIGS to search for traits related to abiotic stress adaptation.

Machine-learning algorithms and multivariate statistics showed that the discrimination

between the two sets could be based on pertinent physiological traits, particularly leaf

temperature and relative water content. When the plants exposed to water deficit, leaf

temperature was the most responsive trait.

Four bi-parental populations were developed, of which Mélodie/2 × ILB938/2 showed

the highest number of polymorphic single nucleotide polymorphisms (SNPs) and was

advanced to the F5 generation, in which 211 individuals were tested for 222 SNPs derived

from Medicago truncatula sequence information. The population was phenotyped for several

morpho-physiological traits during 2013 and 2014. In total, 188 polymorphic SNPs were

assigned to nine linkage groups that covered ~ 928 cM with an average inter-marker distance

of 5.8 cM. The map showed a high degree of synteny with the genome of M. truncatula.

Most of the detected QTLs for stomatal morphology and function were in a single region of

faba bean chromosome II syntenic with a segment of M. truncatula chromosome IV that

harbours receptor-like protein kinase. Furthermore, a novel locus, ssp1, for stipule

pigmentation was mapped in a well conserved region of M. truncatula chromosome V

containing some candidate Myb and bHLH transcription factor genes.

The difference between the leaf temperatures of the two FIGS sets (the wet and dry set)

allowed us to find a reliable and cost-efficient phenotyping tool for screening drought

adaptation related traits in this species. Furthermore, using an appropriate mapping

population and using novel M. truncatula-derived SNPs all brought success to detect the

genetic regions and to indicate candidate genes. Furthermore, our results confirm the

genomic data from model plant species can easily be translated to faba bean. Finally,

breeding faba bean for drought adaptation can be made more straightforward by combining

the use of germplasm tools such as FIGS, rapidly assessed phenotypes such as canopy

temperature, and genomic tools for detecting candidate genes.

8

1 INTRODUCTION

1.1 Faba bean (Vicia faba L.)

Faba bean (Figure 1) also known in English as broad bean, field bean, fava bean or horse

bean is an annual herbaceous species. It is belong to the family Fabaceae, which is the third

largest angiosperm family, including 19,400 species and 740 genera (Lewis et al. 2005; Kew

2014). Faba bean is a member of subfamily Faboideae, tribe Vicieae and genus Vicia L. This

genus is unusual in having a huge variation in genome size, from ~ 1,880 Mbp in V.

monantha Retz. to 13,000 Mbp in V. faba (Raina and Rees 1983).

Figure 1 Faba bean. 1 flower, 2 stigma, 3 pod, 4 seed, 5 and 6 seed profiles, 7 pistil. Source:

Thomé (1885)

Faba bean is on the first domesticated food legumes, probably in the early Neolithic

period (8,000 B.C.). The word faba is originally from one of the roots of Greek verb, φάγέώ,

‘to eat’, indicating its basic role as food in ancient Greek and Rome (Muratova 1931). The

wild progenitor and place of origin of faba bean are still unknown (Cubero 1974; Maxted

1993), and no successful cross between faba bean and other Vicia species has been reported.

A Near or Middle East centre of origin has been proposed for the faba bean (Cubero 1974),

with radiation in four directions from the centre: Europe, along the North Africa coast to

Spain, along the Nile Valley to Ethiopia, and from Mesopotamia to India. In addition,

Afghanistan and Ethiopia have been proposed as secondary centres of diversity (Lawes et al.

1983), and subsequently China as well, since its faba bean gene pool is isolated from those

of Ethiopia and western Asia (Zong et al. 2009). Faba bean is classified into four groups

9

according to seed size: minor (tick bean, ellipsoidal seed; 0.4 – 0.6 g / seed), equina (horse

bean, field bean, flattened seed; 0.6 – 1.0 g / seed), major (broad bean, flattened seed; 1.0 –

3.0 g / seed) and paucijuga (a primitive form likely close to wild progenitor; 0.3 – 0.4 g /

seed) (Muratova 1931; Hanelt 1972; Cubero 1974). The major type apparently emerged

separately in the Mediterranean basin and China, the equina type mostly in North Africa and

Middle East, and the minor and paucijuga types mostly in Ethiopia, northern Europe, Nepal

and Bangladesh (Duc 1997).

In 2012, world production was 4.2 million tons (Tg) from nearly 2.5 million ha (M ha)

(FAOSTAT 2014). China was the major producer, with ~1 M ha (38% of the world planted

area) and 1.4 Tg (33% of world production), and Ethiopia was second (0.57 M ha). In the

European Union, its cultivated area was 0.34 M ha with production of 0.97 Tg (Eurostat

2014). Faba bean is the fourth most important cool-season food legume in the world after

chickpea (Cicer arietinum L.), pea (Pisum sativum L.) and lentil (Lens culinaris Medik.)

(11.6, 9.8 and 4.5 Tg, respectively), but all of these are produced on a much smaller scale

than soya bean (Glycine max [L.] Merr.) (241.8 Tg). Recently, interest in growing faba bean

has greatly increased in Europe, Australia and North America because of its adaptation to

cool climates and its high potential protein yield (Satovic et al. 2013).

Faba bean, like most other legumes, is symbiotic with nodule-forming bacteria with N2-

fixing ability, which provides major benefits to cropping systems and the environment, and

contributes to agricultural sustainability by soil improvement activities (Sprent 2009), so not

only is it free of the need for nitrogen fertilizer, it also contributes to the nitrogen nutrition of

the following crop. Faba bean is considered an excellent candidate to provide nitrogen input

into temperate agricultural systems on account of its wide adaptation and high biomass

productivity (Rispail et al. 2010).

Faba bean seeds are a good source of protein, fibre and nutritional value, and are widely

grown for food and feed (Duc 1997). The protein content in faba bean is 24 ~ 35% of the

seed dry matter and is very rich in lysine. It is one of the main sources of cheap protein for

people in the Middle East, Latin America and Africa (consumed as dry or canned), and for

livestock (mostly pigs and poultry) in many developed countries. Furthermore, the immature

beans are widely used as a vegetable. However, the quality-limiting factors, such as vicine,

convicin and tannins compromise the value of faba bean in many diets. Vicine and convicine

are stored in the cotyledons and are harmful for humans carrying a genetic defect (Glucose-

6-phosphate dehydrogenase deficiency, G6PD) and for both laying hen and broiler

production, while tannins in the seed coat reduce protein and energy digestibility in

monogastric animals (Crépon et al. 2010 and references in). Alleles conferring low tannin

content (zt1 and zt2) and low vicine-convicine content (zvc) have been identified and are

used in breeding programs (Crépon et al. 2010; Torres et al. 2010).

Though the agronomic and economic importance of faba bean is well documented, its

cultivation is still limited due to its susceptibility to several biotic and abiotic stresses such as

drought.

1.2 Drought adaptation

Dry lands cover over 40% of the world and are the home of around 2.1 billion poor people in

nearly 100 countries (Mortimore et al. 2009; United Nations 2011). On a global basis,

drought (temporary water deficit coupled with heat stress) is considered to be the most

10

important abiotic constraint to crop productivity (Fischer and Turner 1978; Huang et al.

2002). Climate change will lead to increased temperatures and changed precipitation patterns

in the world. Models predict that climate change attributed to the emission of greenhouse

gases will increase the frequency and intensity of droughts in agricultural areas worldwide

(Oldfield 2005; Gornall et al. 2010; Dai et al. 2013). In order to improve crop performance,

it is essential to understand how crop species respond and adapt to drought conditions. The

key to drought adaptation for plant breeders and crop physiologists is tailoring the

morphology, physiology and phenology of a crop to its environment in order to manage

water economy. Two key aspects of drought physiology are regulation of water use through

the stomata, and regulation of water uptake through the roots. This thesis focuses on leaf

attributes associated with drought adaptation.

Faba bean, as an important source of protein in those dry areas of developing countries

most likely to be impacted by climate change, is reputed to be relatively sensitive to water

deficit among legume species (McDonald and Paulsen 1997; Khan et al. 2010). It is highly

responsive to irrigation (Oweis et al. 2005), but in most production regions it is seldom if

ever irrigated and mostly depends on stored soil moisture and rainfall for its growth and

development. Thus, development of drought-adapted genotypes is an essential mission to

improve yield and its stability in this crop, but progress has been relatively slow compared to

other crops due to lack of efficient screening methods (Stoddard et al. 2006). Wide genotypic

variation in water stress response has been reported in this species (Heringa et al. 1984;

Grzesiak et al. 1997; Abdelmula et al. 1999; Amede et al. 1999; Link et al. 1999; Ricciardi et

al. 2001; Khan et al. 2007), indicating the potential for breeding of drought-prone

environments.

Field phenotyping for drought response is costly (when rain-exclusion shelters have to be

established and maintained) and time-consuming, and often produces unreliable output due

to seasonal variation, since periods of drought vary in length, timing and intensity (Khan et

al. 2007). In contrast, screening under controlled conditions provides the potential for rapid

response and uniform conditions. An ideal phenotyping method should be fast, low-cost,

non-destructive, accurate and working on several samples (Richards et al. 2010; Monneveux

et al. 2014). Then there should be a link to translate laboratory findings in controlled

conditions to the field, and a novel genotype should also perform well in field conditions in

order for the breeding program to be successful (Passioura 2012). Improvement in new

sensor technologies has allowed the development of automated, high throughput and precise

phenotyping platforms. Although costly to install, these platforms streamline and automate

data collection in large numbers of samples and thus practically reduce manual logistics and

some kinds of experimental errors. The LemnaTec (LemnaTec GmbH, Würselen, Germany;

http://www.lemnatec.com/plant-phenotyping.php), PHENOPSIS (Granier et al. 2006),

GROWSCREEN (Walter et al. 2006) and TraitMill (Reuzeau et al. 2006) are some of these

platforms. Some platforms are specialised for certain types of plants or organs, and GlyPh,

designed for high-throughput measurement of plant water use and growth in soya bean

(Pereyra-Irujo et al. 2012), maybe adaptable for use in other legumes such as faba bean.

1.2.1 Response mechanisms

Three types of plant strategies for coping with drought stress have been characterized,

namely drought avoidance, dehydration tolerance and escape (Ludlow and Muchow 1990;

11

Kramer and Boyer 1995). Each of these strategies has its own complex morphological and

physiological mechanisms that involve complex genetic control.

Drought avoidance refers to mechanisms related to keeping high plant water status under

water-limited conditions by minimizing water loss and / or maximizing water uptake through

root characteristics. Stomatal closure is the most important drought avoidance mechanism

and is affected by abscisic acid (ABA) transported from the roots (Schachtman and Goodger

2008). The guard cells are the structures and they open and close the stomata. Stomatal traits

such as morphology (e.g., density and size) and function (e.g., conductance, gs) are

considered key determinants of plant growth and water status (reviewed in publication I).

Faba bean accessions with higher stomatal density were shown to have lower yield and less

resistance to water stress, while lower stomatal density was associated with better drought

avoidance (Ricciardi 1989). Appropriate stomatal activity might be the key role for

improving drought adaptation in faba beans by reducing water loss and increasing

transpiration efficiency (Dawish and Fahmy 1997). The gs was found to be the most

important trait determining water use in relatively small sets of faba bean accessions (Nerkar

et al. 1981; Khan et al. 2007). Carbon isotope discrimination (Δ13

C) has been proposed as a

physiological criterion to select C3 crops, correlated with yield and water use efficiency, as a

result of the balance between photosynthetic activity and gs (e.g., Condon et al. 2004). Khan

et al. (2007) found wide genetic variation for Δ13

C in faba bean, and reported a negative

correlation between transpiration efficiency and Δ13

C under well watered conditions.

Nevertheless, Δ13

C is an expensive measurement, so it is suitable for important subsets of

samples rather than large germplasm surveys. Leaf temperature has been shown to be a rapid

and cost-efficient surrogate for measuring gs and water status in several plant species (Blum

2011a) including faba bean (Khan et al. 2010). Water use efficiency (WUE) has often been

proposed as one the most important determinant of crop productivity under water-limited

conditions and a target for plant drought adaptation. Blum (2009) argued, however, that

selection for high WUE under drought conditions leads to dramatic losses in yield and

drought adaptation. To overcome this, two layers of selection are needed: one for higher

Δ13

C and the other for shoot biomass (Krishnamurthy et al. 2013). Efficient use of water, i.e.,

maximizing soil moisture capture through transpiration, is the target for improvement of

crop yield under drought conditions (Blum 2009).

Cuticular wax protects leaves against non-transpiration water loss. Water stress improved

the deposition of cuticular wax and subsequently enhanced drought avoidance in transgenic

alfalfa (Medicago sativa L.) (Zhang et al. 2005). In the same way, accumulation of cuticular

waxes significantly increased in soya bean plants exposed to water deficit (Kim et al. 2007)

and cuticular wax accumulation is considered an important drought avoidance strategy in

many plants (Kosma et al. 2009; Zhou et al. 2013). However, abundant wax is not always

correlated with drought tolerance (Ristic and Jenks 2002). Variation in the wax content, and

in its synthesis in response to drought stress, have not yet been documented in faba bean.

Root characteristics have been shown to be an important contributor to drought

avoidance in many crop species (Blum 2011a; Monneveux et al. 2014), including legume

species such as groundnut (Arachis hypogaea L.), pigeon pea (Cajanus cajan (L.) Millsp.),

cowpea (Vigna unguiculata (L.) Walp.), soya bean, common bean (Phaseolus vulgaris L.)

(Purushothaman et al. 2013 and references in), chickpea (Kashiwagi et al. 2006; 2008a) and

lentil (Sarker et al. 2005; Kumar et al. 2012; Singh et al. 2013). The uptake of water can be

maximized and dehydration postponed by appropriate root characteristics. The first of these

12

is rooting depth, as deeper roots can continue to extract water from deep in the soil profile

when shallow roots have already exhausted the water available to them (Blum 2011b). Faba

bean is sometimes considered to be relatively shallow rooted, with maximum rooting depth

of 50 – 90 cm, depending on soil type (Manschadi et al. 1998). Extensive variation in rooting

depth exists in faba bean germplasm, for example, rooting depth of eight accessions at 35

days after sowing ranged from 32 to 45 cm (Khan and Stoddard, unpublished data, EU-Faba

project). Previous work on faba bean, which has been focused on traits related to stomatal

activity and other aspects of drought adaptation, has demonstrated the necessary need for

research on roots in this species (Khan et al. 2007; 2010).

Dehydration tolerance refers to maintaining metabolic activities at low tissue water

potential (surviving internal water deficits). Keeping high tissue turgor by osmotic

adjustment (OA), antioxidant defence systems and changed dynamics of plant hormones are

the major physiological stress-adaptive traits to minimize the detrimental effects of water

deficit (Morgan 1984). OA maintains cell water content by increasing the osmotic force that

can be exerted by cells on their surroundings and thus increasing water uptake (Blum

2011a). OA has not been deeply been investigated in faba bean, but it has been demonstrated

in many other legumes (Morgan et al. 1991; Ashraf and Iram 2005; Turner et al. 2007;

Silvente et al. 2012). In chickpea, OA was not related to changes in carbohydrate

composition or leaf gas exchange under drought conditions (Basu et al. 2007). A wide

variation was found in pea genotypes for OA when they were exposed to water stress, with

some producing considerable amounts of soluble sugars and proline (Sánchez et al. 1998).

Concerning faba bean, Amede et al. (1999) and Khan et al. (2007) reported a decline in

osmotic potential in 19 and six inbred lines, respectively, under water stress conditions, but

there was no evidence of osmotic adjustment. A wider spectrum of faba bean germplasm

needs to be surveyed for the presence of osmotic adjustment.

Drought escape is the ability of a crop to complete its life cycle before the onset of

drought or unfavourable conditions. The matching of crop growth to the pattern of rainfall is

one of the most important breeding goals for drought-prone environments, particularly those

prone to terminal drought. Early flowering is the most important phenological trait when

terminal drought is likely. Consequently, early sowing along with early seedling vigour and

better root establishment in dry environments allows rapid ground coverage and as a result

reduces evaporation from the soil (Loss et al. 1997). Terminal drought escape via early

production is a successful breeding strategy in Mediterranean environments (Araus et al.

2004 and references in). Although earliness allows crops to avoid terminal drought, it can

lead to a yield penalty under well watered conditions (Monneveux et al. 2012). There is

considerable variation in days to onset of flowering in faba bean accessions (Stoddard 1993;

Khazaei et al. 2012) and developmental responses to temperature and day length (Iannucci et

al. 2008; Patrick and Stoddard 2010). Earliness is also a desirable attribute for often short

season climates such as boreal zone (Stoddard and Hämäläinen 2011).

At the end it should be noticed that combinations of these traits, not a single trait, are

required to improve yield and its stability under drought conditions. For example, combined

selection for earliness, increased harvest index and phenotypic plasticity has been proposed

as a methodto stabilize legume yield under Mediterranean conditions (Siddique et al. 2003).

13

1.3 Breeding

The situation of crop production in the 21th century has been analysed by many researchers

and does not look bright (Pinstrup-Andersen et al. 1999). Furthermore many biotic and

abiotic stresses significantly reduce crop yields. Plant breeding plays an important role for

increasing crop yield and stress adaptation (Moose and Mumm 2008). Plant breeding

describes methods for the creation, selection, and fixation of superior plant phenotypes for

the development of improved cultivars. The primary goals of faba bean breeding are, those

of like any other crop, to improved yields, seed quality (e.g., low vicine-convicine and

tannin-free), adaptation to biotic and abiotic stresses, and other agronomic traits (Torres et al.

2006; 2010; 2012). Relatively little effort has been made to breed faba bean for adaptation to

abiotic stresses, especially using morpho-physiological traits related to drought adaptation.

1.3.1 Conventional breeding

Faba bean is partially cross-pollinated (allogamous) at frequencies ranging from 4-84%, with

the value determined by the interaction between the plant genotype, its environment, and the

population of pollinators (Bond and Poulsen 1983; Stoddard and Bond 1987). The mixed

mating system means that breeding methods for self-pollinated or cross-pollinated crops are

either not appropriate, or need adjustment. Two main breeding strategies are employed,

population development with recurrent selection, and line breeding with management of the

degree of cross pollination by isolation strategies (Gnanasambandam et al. 2012).

Conventional faba bean breeding may take around 1 decade, from crossing inbred lines,

population development, multi-environment evaluation of progenies for desired traits

(including yield and its stability) and then selecting the superior genotypes. Faba bean can

show high levels of heterosis (Bond 1989a; Link et al. 1996, 1997; Dieckmann and Link

2010; Meitzel et al. 2011). Part of this can be captured in a composite cultivar, where 2-3

inbreed lines are allowed to interbreed freely, and this was the main breeding strategy for

several decades (Bond 1989a). Cytoplasmic male sterility (CMS) systems were developed,

but suffered from instability, as an unacceptably high proportion of male-sterile individuals

reverted to fertility (Bond 1989b; Link et al. 1997). ICARDA maintains two different

categories of faba bean material, ILB (International Legume Bean) accessions from different

countries, and BPL (Bean Pure Line) accessions that are derived through selfing from each

chosen ILB line depending on the variation in it (Saxena and Varma 1985). There is

potential to use homozygous BPLs as parents in faba bean breeding programmes to ensure

the consistency of progeny.

Conventional breeding involves crossing whole genomes followed by selection of the

superior recombinants from among the many segregation products. This procedure is

laborious and time consuming, involving many crosses, several generations, and careful

phenotyping, and linkage drag (tight linkage of undesired traits with the desired loci) may

make it difficult to achieve the desired breeding outcome. DNA marker technologies, the

development of several types of molecular markers and molecular breeding strategies offer

possibilities to plant breeders and geneticists to overcome many of the problems faced

during conventional breeding. Drought adaptation is a complex quantitative trait (Khan et al.

2010) controlled by several genes or QTLs of small effects, so many authors hold that

breeding for it requires the combination of conventional and molecular strategies (Chaves et

al. 2003; Blum 2011a).

14

The total area occupied for faba bean production worldwide dropped by 57% (5.4 to 2.3

M ha) from 1961 to 2012, but average yield has increased around 2-fold (0.9 to 1.7 t/ha)

during the same time (Figure 2). The yield trend is still upward, showing how conventional

breeding efforts have been successful in this crop (Bond et al. 1994).

Figure 2 World faba bean area harvested and yield from 1961 to 2012 (FAOSTAT 2014)

Faba bean yields are, however, considered to be unstable, which has been attributed to

many causes, including the partial dependence on insect pollinators, and the general lack of

improvement in breeding for resistance to numerous environmental constraints (biotic and

abiotic stresses). Highly significant genotype × environment interaction for yield has been

reported in faba bean accessions (Annicchiarico and Iannucci 2008; Flores et al. 2013).

Breeding for adaptation to biotic and abiotic (particularly drought adaptation) stresses were

summarized by Sillero et al. (2010) and Khan et al. (2010).

1.3.2 Genetic resources

There is considerable variation in plant genetic resources (PGR) collections conserved ex

situ in agricultural genebanks, so screening genetic resources would be the first sound step of

any pre-breeding program. Plant breeders find useful genes within these collections that can

have impressive impacts on crop yields and adaptation to biotic and abiotic stresses. The

efficient use of PGR is essential to conserve biodiversity, respond to future challenges

including climate change, and thus pay big dividends to agriculture and environment

(Johnson 2008).

A wealth of germplasm is available, comprising over 7.4 million accessions conserved ex

situ in more than 1,700 national and international agricultural genebanks worldwide, and

about 18% of the accessions are food legumes. Concerning faba bean, 43,695 accessions

(accessed on 15 March 2013 from ICARDA genebank) are conserved mostly within 37

global genebanks. The ICARDA collection exceeds 9,000 accessions (21% of global

0

1

2

3

4

5

6

7

0.0

0.5

1.0

1.5

2.0

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

To

tal

are

a h

arv

este

d (

M h

a)

Yie

ld (t

/ha)

Year

15

collection), followed by the Institute of Crop Germplasm Resources of the Chinese Academy

of Agricultural Sciences, ICGR-CAAS (10%) and the Australian Temperate Field Crops

Collection, ATFCC (6%) (FAO 2010). Faba bean is fifth among grain legumes for number

of conserved ex situ accessions (after soya bean, ~ 230,000; chickpea, ~ 98,000; pea, ~

94,000 and lentil, ~ 58,000 accessions).

Faba bean is represented in the collections by only the cultivated forms. Both botanical

and molecular outcomes suggest that the wild ancestor of faba bean either has not yet been

discovered or has vanished (Duc et al. 2010). Modernization of agriculture also reduces

genetic diversity in crops, so ex situ collection saved in genebanks as well as in situ

conservation by farmers in their agro-ecosystems are all the more crucial for present and

future faba bean breeding programs. The next challenge concerning faba bean conservation

in genebanks, is that due to the partial allogamy status of this species, maintenance activities

are costly and difficult. Half of the keepers reported that their faba bean collections were

maintained with open pollination (Suso et al. 2005).

Wide genetic variation has already been reported in faba bean genetic resources for agro-

morphological traits such as morphology (Robertson and El-Sherbeeny 1991; Perrino et al.

1991; Suso et al. 1993; Lang et al. 1993; Terzopoulos et al. 2003, 2004; Karaköy et al.

2014), seed size (Nadal et al. 2003), seed composition (Gresta et al. 2010; Khamassi et al.

2013; Karaköy et al. 2014), and yield and its components (Polignano et al. 1993; Keneni et

al. 2005; Terzopoulos et al. 2008). Concerning abiotic stresses, high levels of adaptation

have been reported for winter hardiness (Arbaoui et al. 2008a; Link et al. 2010) and drought

(Amede et al. 1999; Khan et al. 2007). This enormous variability is expected in this ancient

domesticated crop with its wide adaptability to various latitudes and altitudes, its ability to

grow from spring and autumn sowings, and its various usages for food and feed.

In addition, molecular markers as a powerful and more reliable tool have been used for

characterising genetic diversity in faba bean genetic resources. Different marker types have

been used for this purpose such as RAPDs (Link et al. 2005), AFLPs (Zeid et al. 2001; 2003;

Zong et al. 2009; 2010), TRAPs (Kwon et al. 2010), ISSRs (Terzopoulos and Bebeli 2008;

Alghamdi et al. 2011; Wang et al. 2012) and SNPs (Kaur et al. 2014a). Exceptionally wide

genetic diversity was found in accessions from North China, while those from Central China

had less diversity and European and North African germplasm sets were genetically close to

each other. This high level of genetic diversity, confirmed by both phenotyping and

molecular markers, is a key to successful crop improvement.

Since agricultural genebanks conserve large numbers of accessions in overlapping

collections, identifying suitable material is like searching for the proverbial needle in a

haystack. Thus an efficient and rapid tool for mining genebanks is needed. Core collections

were introduced as a way to work with fewer accessions that would represent most of the

genetic variation of a large collection in a relatively small number (5–10 % of the total

collection) of chosen accessions (Frankel 1984). Nevertheless, core collections are not

constructed to highlight or identify materials that are most likely to contain the specific

adaptive traits desired in a particular search (see publication I and references in). In other

words, the core collection concept focuses on genetic diversity, not adaptive phenotype.

The concept of centres of diversity that established the association between diversity and

eco-geographic distribution was proposed in the early 1900s by Nicholas I. Vavilov (1887–

1943), who recognized the importance of locating genetic variation based on eco-climatic

16

conditions (Vavilov 1922). Much earlier, in the 1600s, farmers in eastern North America

were apparently aware of the need for ecological adaption and imported wheats from

western Europe with better resistance to cold and pests (Olmstead and Rhode 2011). The

Focused Identification of Germplasm Strategy (FIGS) was proposed by Mackay and Street

(2004) as a new approach to utilize PGR. FIGS uses available environmental data

(geographic and agro-climatic information) of collection sites to identify manageable sets of

accessions “most likely” and “least likely” to express the trait of interest (Figure 3). This

approach allows rapid reduction of the thousands of accessions to a manageable number with

a high probability of carrying desired traits for the breeder’s strategy (Mackay and Street

2004; Publication I). Odong et al. (2013) classified core collections into three categories,

namely those representing individuals (CC-I), extremes (CC-X) and distribution (CC-D).

Among them, CC-X is similar to FIGS in the sense of considering geographical selection,

but it still does not consider adaptive traits.

Figure 3 Distribution of core collection and FIGS sets in the overall spectrum of genetic

diversity

Novel statistical approaches are becoming available to screen plant genetic resources

more efficiently and precisely, such as machine learning algorithms (Bari et al. 2012; Bari et

al. 2014a, b) and Bayesian mathematics (Dayanandan et al. 2013). The work of this thesis

has run in conjunction with the testing of these methods for identifying potential sources of

abiotic stress in faba bean (Bari et al. 2014c). At the same time, genotyping is becoming

faster and cheaper. Next generation sequencing, NGS (i.e. high-throughput genotyping) is

providing new tools to analyse PGR, such as identifying patterns of genetic diversity,

mapping quantitative traits, and mining novel alleles that are not discovered yet (Kilian and

Graner 2012). This would be a suitable approach for a species such as faba bean with its

several breeding challenges.

1.4 Genomics

Faba bean is a diploid (2n = 12) with six remarkably large chromosomes, which makes it a

suitable tool for cytogenetic studies. Its genome is one of the largest of any diploid field

crops (~13,000 Mbp) and contains more than 95% of repetitive DNA. The faba bean genome

size is 2.6, 3.2, 17.6 and 26 times larger than those of pea, lentil, chickpea and Medicago

truncatula, respectively (Sato et al. 2010) and 4-fold larger than the human (Homo sapiens)

FIGS

Core collection

FIGS

17

genome (Figure 4). Hence, certain kinds of investigation are less advanced in faba bean than

in many other species. Sequence information is available on only 20,550 ESTs in the public

databases, in contrast to nearly 1.5 million for soya bean (GenBank®,

http://www.ncbi.nlm.nih.gov, 20.05.2014). Hence, functional genomic studies, identification

of important genes and development of a highly saturated map are relatively limited in this

species. Nevertheless, significant progress has been made to enrich its genetic and genomic

resources in the last two decades. The lack of genome compatibility of faba bean with other

Vicia species may explain why it is reproductively isolated from them. The mitochondrial

genome sequence of faba bean (cv. Broad Windsor) was estimated at 588,000 bp. It

comprised 52 genes, of which 32 encoded proteins, 3 rRNA, and 17 tRNA. Of the 114

unique open reading frames (ORFs) 36 lacked significant homology to known proteins

(Negruk 2013).

Moreover, advances in NGS combined with techniques for the quantification of gene

expression (e.g., SuperSAGE), are facilitating genome-wide transcriptome studies and target

gene identification in organisms such as faba bean, from which massive nucleotide sequence

information is not yet available. As a result, possible regulators revealed by SuperSAGE

analysis and associated with the ascochyta-faba bean and ascochyta-lentil interactions have

been genotyped and tentatively located on the crop genetic maps (Madrid et al. 2013) in

order to identify potential targets for molecular breeding for ascochyta resistance.

Furthermore, large-scale SSR marker development was successfully achieved using NGS on

the faba bean genome (Yang et al. 2012).

Figure 4 Genome size of faba bean compared to those of some other organisms

0

2000

4000

6000

8000

10000

12000

14000

Arabidopsis Medicago

truncatula

Chickpea Soybean Human Lentil Pea Faba bean

Gen

om

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, M

bp

)

18

Table 1 History of molecular marker application for constructing genetic linkage map in

faba bean

Marker type / total marker Mapping

population / type

Population

size

Total map

length

(cM)

Average inter-

marker

distance (cM)

No. of LGs

(Assigned to

chromosome)

Reference Application

7 RFLPs, 4 morpho, 3 iso, 3

RAPDs (17)

BC – 231.4 – 7 (–) van de Ven et

al. (1991)

M

43 RAPDs, 7 iso, 1 RFLP

(51)

2 F2 families 20+44 300-350 – 11 (1) Torres et al.

(1993)

M

8 Morpho, 7 RFLPs, 4 iso, 4

RAPDs (23)

BC – 300 – 7 (–) Ramsay et al.

(1995)

M, Q

147 RAPDs, 9 iso, 1 morpho

(157)

7 F2 families 813 850 – 48 (6) Satovic et al.

(1996)

M, T, C

105 RAPDs, 7 iso, 3 seed

protein genes, 1 morpho

(116)

3 F2 families 175 ~1,200 20 13 (7) Vaz Patto et

al. (1999)

M, T, Q, C

77 RAPDs (77) F7, RIL 57 973.2 14.66 14 (–) Surahman

(2001)

M

117 RAPDs, 2 iso, 2 seed

protein genes (121)

F2 196 1,445.5 13.72 16 (9) Román et al.

(2002; 2003)

M, Q

176 RAPDs, 6 iso, 4 SSRs, 3

seed protein genes, 2

morpho (191)

11 F2 families 654 1,559 8 14 (5) Román et al.

(2004)

M, T, C

94 RAPDs, 4 iso, 3 SSRs, 2

seed protein genes (103)

F2:3 families 159 1,308 – 18 (6) Avila et al.

(2004; 2005)

Q

135 ITAPs (135) F6, RIL 94 1,685.8 14.6 12 (–) Elwood et al.

(2008)

M, *

131 RAPDs (131) F6, RIL 101 1,635.39 14.73 21 (–) Arbaoui et al.

(2008b)

M, Q

238 RAPDs, 21 Intron-

spanning marker, 6 SSRs, 5

ESTs, 4 iso, 2 STSs, 1

SCAR (277)

F6, RIL 165 2,856.7 12.72 21 (9) Díaz-Ruiz et

al. (2009 a,b;

2010)

Q

167 ESTs, 71 RAPDs, 11

SSRs, 3 RGAs, 3 seed

protein genes, 2 iso, 1

morpho (258)

F6, RIL 124 1,875 7.26 16 (8) Cruz-

Izquierdo et

al. (2012)

M, Q, *

110 SSRs, 9 faba EST-SSRs,

5 grass pea EST-SSRs, 3 pea

EST-SSRs, 1 ITAP (128)

F2 129 1,587 12.5 15 (–) Ma et al.

(2013)

M

121 RAPDs, 38 ESTs, 6

SSRs, 5 RGAs, 1 defense-

related gene, 1 seed protein

gene (172)

F7:8, RIL 119 1,402.1 9.87 29 (15) Gutiérrez et

al. (2013)

Q, C

729 markers in total 3 RILs (RIL1 Cruz-

Izquierdo et al.

(2012), RIL2 Díaz-

Ruiz et al. (2009

a,b), RIL3

Gutiérrez et al.

(2013)

408 4,613 6 43 (13) Satovic et al.

(2013)

M, Q, C

465 SNPs, 57 EST-derived

SSRs (522)

F5:6, RIL 95 1,216.8 2.3 12 (12) Kaur et al.

(2014b)

M, Q, *

LG linkage group, morpho morphological marker, iso isozymes, T Assignation of linkage groups to

chromosomes by trisomic segregation, M Development of a map, Q QTL, C composite map, *

comparative map

19

1.4.1 Marker assisted selection (MAS)

The use of DNA markers in plant breeding is called marker-assisted selection (MAS) and

has opened a new gate in agricultural sciences, ‘Molecular breeding’, since the early 1980s.

Use of new biotechnological methods holds promise to provide tools that assist plant

breeders to develop new cultivars with greater reliability and cost-efficiency. There are three

major types of genetic markers, namely morphological, biochemical and DNA markers.

Three main advantages of selection based on DNA markers over conventional phenotyped-

based selection are the relative simplicity of MAS that may save time and resources, the

potential to use it at earlier stages of plant growth than phenotyping, and the lack of effect of

environment (Collard et al. 2005; Collard and Mackill 2008). MAS is based on the selection

of molecular markers in a segregating population and/or pyramiding desirable alleles at the

target loci. This approach was first used at faba bean breeding in the early 1990s and has had

numerous applications since, such as construction of linkage maps (Table 1), assessing the

level of genetic diversity within germplasm (see Alghamdi et al. 2012; Kaur et al. 2014a and

references in), quantitative trait loci (QTL) mapping, and detection of economically

important genes (Table 2). The current constraints of using MAS in faba bean breeding are

the early stages of its DNA marker technology development, the reliability and accuracy of

QTL mapping studies, and most importantly, the limited range of markers and limited

polymorphism of markers in breeding material, in contrast to some other species.

Sirks (1931) was the earliest to report on the faba bean genome, identifying 19 genetic

factors that formed four linkage groups. His material was lost during World War II. Four

decades later, Sjödin (1971) used translocation lines in the assignment of different loci

(morphological observations) to their respective chromosomes. Several morphological

markers and seven isozyme loci were subsequently added into the faba bean map (Suso et al.

1993; Torres et al. 1995 and references in). Primary trisomics were also used to assign genes

and linkage groups to specific chromosomes (Torres et al. 1998; Van Patto et al. 1999). The

first DNA-base linkage map in this species was constructed with just 17 markers, of which 10

were DNA-based (van de Ven et al. 1991). The faba bean genetic linkage map was initially

developed using morphological, isozyme, RFLP, RAPD and seed protein gene markers that

were limited in number, transferability or synteny with model plant species. The first SSR

markers were described in this species by Požárková et al. (2002) and soon enriched a

composite map (Román et al. 2004). The first gene-base map was reported in 2008 (Elwood

et al. 2008) and more recently enriched by using 511 genic markers (Kaur et al. 2014b) with

2.3 cM average inter-marker density (Table 1). Satovic et al. (2013) published a reference

consensus genetic map that covered 4,062 cM in six main linkage groups corresponding to the

six chromosomes of faba bean.

1.4.2 Markers based on DNA

DNA markers linked to desired traits are a valuable selection tool in modern plant breeding.

Molecular markers have been sought for both simple and complex traits (reviewed in Torres

et al. 2010; 2012). In the case of major genes, the bulked segregant analysis (BSA) method

has been used for the identification of markers that are tightly linked to the genes underlying

parameters such as tannin content (controlled by zt1 and zt2, two independent but

complementary genes), vicine-convicine content (Gutiérrez et al. 2006; 2007; 2008),

hypersensitive response to rust (Avila et al. 2003), and indeterminate growth habit (Vf-TFL1)

(Avila et al. 2006; 2007). The performance of these markers is still not completely

20

satisfactory because of their genetic distance from the trait and their lack of conservation in

all genetic backgrounds. Compared with BSA, the use of a candidate gene approach allows

the development of diagnostic or perfect markers that are completely linked to or within the

selected trait. This approach is possible only when the target gene is well characterized in the

target species or in a related species carrying orthologous genes.

Genic molecular markers are developed from the coding sequences such as ESTs or fully

characterized genes, frequently with known functions, in contrast to most of the previously

developed molecular markers (e.g., RAPDs and AFLPs) that are directly from the total

genomic DNA without any information on function (Varshney et al. 2007). The efficacy of

genic molecular markers may thus be superior to DNA-based markers in molecular breeding.

Gene-based SNPs are the most abundant class of polymorphic sites in any organism’s

genome, providing low genotyping cost per data point, locus specificity (accuracy and

reproducibility), co-dominance, simple documentation and potential for high-throughput

analysis. Thus they have become a powerful tool in genetic mapping, association studies,

assessing genetic diversity and positional cloning (Rafalski 2010; Semagn et al. 2014). The

discovery of SNPs in candidate genes or transcript sequences (ESTs) has been a recurrent

strategy in plant genomics, mainly because SNPs on which the markers are based could

themselves be causative for trait differences (Galeano et al. 2012). SNPs were recently used

to assess genetic diversity in faba bean germplasm (Cottage et al. 2012a; Kaur et al. 2014a).

Elwood et al. (2008) and Kaur et al. (2014b) have developed exclusively gene-based genetic

linkage maps in faba bean. Less progress has been made to develop and characterize EST-

derived SSRs in this species by screening the NCBI database (Gong et al. 2010; Ma et al.

2011; 2013; Akash and Myers 2012) than has been achieved in the other cool-season food

legumes, lentil (e.g., Kaur et al. 2011; Sharpe et al. 2013), chickpea (e.g., Garg et al. 2011;

Hiremath et al. 2011; Jhanwar et al. 2012; Stephens et al. 2014) and pea (e.g., Franssen et al.

2011; Kaur et al. 2012).

1.5 Synteny

Functional genomics in faba bean is more poorly developed than in many other field crops,

especially soya bean, common bean and chickpea among legumes, largely because of its

large genome. An indirect approach such as synteny with a model species may overcome

this shortcoming. Comprehensive comparative genomics to assess synteny can assist back-

and-forth use of genomic resources between different legume species, and also help to

decrease cost and increase the efficiency in genetics and crop breeding by speeding up gene

discovery (Ellwood et al. 2008). Comparative genomic analysis has established the

correspondence between faba bean chromosomes and those of the model legumes (Ellwood

et al. 2008; Cruz-Izquierdo et al. 2012).

Arabidopsis thaliana as a well known model species has played an important role in

illuminating the function of many genes, especially in monocotyledons (Meinke et al. 1998).

Nevertheless, the genomic information of A. thaliana may not be easily translated for use in

legumes, since it is susceptible to different pathogens, is incapable of biological nitrogen

fixation, and lacks many of the secondary pathways and quality traits that are widespread in

legumes (Handberg and Stougaard 1992). Hence, M. truncatula and Lotus japonicus have

been developed as model legumes (Sato et al. 2007; 2010), as both are diploid, with haploid

chromosome numbers of 8 and 6, respectively. The genomes are less than 4% of the size of

21

the faba bean genome (500 Mbp and 472 Mbp, respectively) (Sato et al. 2010). In addition,

they are small self-pollinating plants with short life cycles and able to be transformed by

such methods as using Agrobacterium tumefaciens or A. rhizogenes, making them ideal

model legume species (Rispail et al. 2010). Nearly 264 and 315.1 Mb of M. truncatula

(http://www.medicagohapmap.org/?genome) and L. japonicus

(http://www.kazusa.or.jp/lotus/) have been sequenced and there are more than 260,000 and

240,000 ESTs available in the public domain (Benson et al. 2013, GenBank®,

http://www.ncbi.nlm.nih.gov, last visit 15.05.2014).

Considerable synteny among legume genomes has been reported (e.g., Choi et al. 2004;

Kalo et al. 2004; Zhu et al. 2005; Phan et al. 2006; 2007; Elwood et al. 2008; Kalo et al.

2011; Cruz-Izquierdo et al. 2012). The recent soya bean (Schmutz et al. 2010) and chickpea

(Varshney et al. 2013) whole genome sequences are also being exploited in comparative

mapping studies, in order to identify orthologous genes and further develop efficient

molecular tools in other species such as faba bean.

1.5.1 Medicago truncatula

Comparative genomics studies have shown that M. truncatula (also known as: barrel medic,

barrel medick or barrel clover) has high collinearity with faba bean (Ellwood et al. 2008;

Cruz-Izquierdo et al. 2012; Kaur et al. 2012; 2014b). The high level of conservation in gene

order indicates that M. truncatula can be used to facilitate faba bean molecular breeding

(Rispail et al. 2010). An NCBI BLAST search showed that about 45% of faba bean

mitochondrial DNA sequence was homologous to M. truncatula nuclear sequences (Negruk

2013). The high synteny between M. truncatula and faba bean can be used in orthogonal

gene identification, MAS, and constructing highly saturated genetic linkage maps or physical

maps. Identification of QTLs and then candidate genes also may be used to produce

transgenic elite lines for specific breeding purpose by genetic engineering.

From use of synteny between M. truncatula and pea, 5,460 pea unigenes were identified

through comparative mapping and placed in the consensus map of pea (Bordat et al. 2011).

A high syntenic resolution map between M. truncatula and faba bean using 768 SNPs is in

progress to construct a consensus genetic map in faba bean (Webb et al. in preparation).

Both faba bean and M. truncatula are cool-season legumes. M. truncatula is considered

to be relatively drought-adapted, compared to other legumes (González et al. 1995) and pea

(Gálvez et al. 2005). The genetic similarity and synteny between M. truncatula and faba

bean may help in identifying genes responsible for drought adaptation in faba bean and

accelerate breeding for drought-prone environments. ESTs of genes involved in drought

adaptation in chickpea, pea and M. truncatula have been already identified (Jayashree et al.

2005; Buhariwalla et al. 2005).

1.6 Quantitative trait loci

Many agriculturally important traits (e.g., yield, quality factors and many forms of disease

resistance) are controlled by many genes and are called quantitative traits. Detecting the

major loci of these traits, QTLs, is one of the most important aspects of MAS application in

plant breeding. In QTL mapping, it is generally assumed that there is a small number of

major genes detectable in relatively small samples. Combining phenotypic data and allelic

marker segregation along a genome allows the detection of QTLs, which increases our

22

knowledge on their inheritance and gene action. The summary of detected QTLs in faba

bean is presented in Table 2.

The first QTL mapping in this species was reported by Ramsay et al. (1995), who

detected several loci for morphological and biochemical traits. Mapping QTL in faba bean

for biotic stresses, such as resistance to parasitic plants or pathogenic fungi, has been

attempted (Table 2). Two of the major disease constraints, namely ascochyta blight (caused

by Ascochyta fabae Speg.,) and crenate broomrape, have been widely subjected to QTL

studies on F2 and RIL populations. The QTLs accounting for significant proportions of these

resistances have been identified, mapped and validated in multi-environment trials (Roman

et al. 2003; Avila et al. 2004; Diaz-Ruiz et al. 2009a, b; 2010; Gutiérrez et al. 2013; Kaur et

al. 2014b). In addition, some efforts has been given to rust resistance (Avila et al. 2003), but

relatively little for chocolate spot (Botrytis fabae Sard.) in spite of the importance and

widespread nature of this disease. QTLs for these traits have been assigned to 5 of the 6

chromosomes (I, II, III, V and VI), but no linkage group had been assigned to chromosome

IV until recently, when Ruiz-Rodriguez et al. (2014) assigned linkage group I.B (previously

reported by Cruz-Izquierdo et al. 2012) to this chromosome.

A little progress has also been made in identifying QTLs for abiotic stresses such as frost

tolerance (Arbaoui et al. 2008b), yield components, and early flowering time associated with

drought avoidance during the reproductive stage (Vaz Patto et al. 1999; Avila et al. 2005;

Cruz-Izquierdo et al. 2012), but outcomes still are far behind those gained in biotic stresses.

Saturation of the genomic regions associated with target regions and QTL validation in

multiple environments and genetic backgrounds are prerequisites to uncover reliable marker-

trait associations. Nevertheless, marker density in faba bean remains rather low for the

identification of responsible QTLs / genes and use of MAS approaches. Considerably fewer

QTLs have been reported in this species than in other grain legumes (Kumar et al. 2011). At

present, linkage groups detected in faba bean (mostly by AM Torres’s research group, see

Ruiz-Rodriguez et al. 2014) have been anchored to all faba bean chromosomes, which

provides the source for integration of genetic and physical maps that will facilitate fine QTL

mapping and gene identification in this species.

23

Table 2 Summary of QTL / loci detected in faba bean for morphological traits, biotic and

abiotic stresses and quality traits

Trait No. of

QTL/loci

Chromosome Main output Reference

Morphology

Morphological trait – unknown The first QTL map in faba bean including

morphological and biochemical traits

Ramsay et al.

(1995)

Growth habit 1 V The first molecular marker (CAPs) for

growth habit (ti) by using candidate gene

approach (F2)

Avila et al.

(2006)

1 V Complete gene sequence of Vf-TFL1

using different genotypes

Avila et al.

(2007)

Seed coat colour Sc II Black seed coat colour on chromosome II Cruz-

Izquierdo et

al. (2012)

Biotic stress

Broomrape resistance

Orobanche crenata

Forsk.

3 I, II, VI QTLs Oc1, Oc2, Oc3 explained more than

70% of phenotypic variation

Román et al.

(2002)

4 I, II, VI QTLs Oc2, Oc3, Oc4, Oc5 in 2 years and

3 environments

Díaz-Ruiz et

al. (2010)

7 VI 2 new QTLs added, Oc7 and 8 Gutiérrez et

al. (2013)

Orobanche foetida

Poir.

2 I, III Mapping Of1 and Of2 Díaz-Ruiz et

al. (2009b)

3 V Co-localization of Oc8 and Of3 in

chromosome V confirms a common

resistance against O. crenata and O.

foetida.

Gutiérrez et

al. (2013)

Ascochyta fabae Speg., 2 II, III QTLs associated to ascochyta blight

resistance, Af1 and Af2 explained ~ 50%

of phenotypic variation

Román et al.

(2003)

Díaz-Ruiz et

al. (2009a)

6 III Isolate and organ-specific QTLs for

ascochyta blight resistance, Af3 to Af8

Avila et al.

(2004)

4 I, II, VI Detecting genes associated to ascochyta

blight resistance using SNPs

Kaur et al.

(2014b)

Uromyces viciae-fabae

(Pers.) J. Shört.

1 unknown Identifying the first molecular markers

linked to Uvf-1 gene conferring

hypersensitive resistance against rust (F2)

Avila et al.

(2003)

Abiotic stress

Frost tolerance 5 unknown Frost tolerance and physiologically related

traits

Arbaoui et al.

(2008b)

Yield and its

components

7 VI First putative QTLs for seed weight,

explained 30% of phenotypic variation

Vaz Patto et

al. (1999)

15 unknown Mapping floral characters, yield

distribution and yield components

Avila et al.

(2005)

12 II, V, VI QTLs for days to flowering, flowering

length, pod length, No. seeds and ovules

per pod

Cruz-

Izquierdo et

al. (2012)

Quality traits

Low vicine-convicine vc unknown RAPD markers linked to low vicine-

convicine content and their conversion to

SCARs (F2)

Gutierrez et

al. (2006)

Zero tannin zt1 unknown RAPD markers linked to zt1 and their

conversion to SCARs (F2)

Gutierrez et

al. (2007)

zt2 unknown RAPD markers linked to zt2 and their

conversion to SCARs (F2)

Gutierrez et

al. (2008)

24

2 AIMS OF THIS STUDY

The main aim of this study was to investigate the genetic potential for drought adaptation in

faba bean by developing tools for germplasm utilization and marker assistant selection

(MAS) that can be used in pre-breeding. To reach this end, two collections of faba bean,

each of 201 accessions, originating from the wet and dry regions of the world and chosen

using a novel approach (FIGS), were screened for leaf traits related to drought adaptation

(stomatal morphology and function, and leaf water relations). Advanced biparental

populations were developed to detect QTLs governing these traits, and synteny with M.

truncatula as a model legume species was employed to identify candidate genes. During

population development, the genetic control of some Mendelian characters (e.g., stipule spot

pigmentation, SSP) was also considered.

The specific aims of this study were:

1) to compare the drought adaption-related traits of two faba bean sets originating from

environments with contrasting seasonal moisture availabilities, selected using machine-

learning algorithms. The primary hypothesis was that ecotypic differences occurred so traits

associated with plant moisture regulation and lifecycle would differ between the two sets

(Publication I).

2) to test whether faba bean germplasm from drought-prone and non-drought-prone

environments differed in drought adaption related traits under both well watered and water

deficit conditions. In addition, inter-relationships between studied traits were investigated

(Publication II).

3) a, to develop a gene-based linkage map using SNPs; b, to identify QTLs controlling

drought adaption-related traits; and c, to exploit synteny between faba bean and M.

truncatula to examine the putative gene content of the detected QTLs for candidate genes

(Publication III).

4) to investigate the genetic control of SSP and to exploit synteny between faba bean and

M. truncatula to detect putative candidate genes (Publication IV).

25

3 MATERIALS AND METHODS

The experimental part of the work is described here as a general outline.

Table 3 Summary of methods used in this work are listed below. More details are described

in the original publications (I − IV) and following text

Method Publication

Focused Identification of Germplasm Strategy (FIGS) I, II

Machine-learning algorithms* I

Screening drought adaptation-related phenotypes I, II, III

Evaluation of water deficit response II, III

Multivariate statistics II

SNP genotyping III, IV

DNA extraction* III, IV

QTL analysis III

Synteny analyses with M. truncatula III, IV

Developing a genetic linkage map III

Crossing and population development III, IV

KASPar assay* III, IV

Backcrossing IV

Microsatellite loci screening (confirmation of hybrids) IV

Methods marked with asterisk were not carried out by HK

26

3.1 Germplasm survey

3.1.1 Construction of the FIGS sets

The “dry” and “wet” sets each comprised 200 accessions (listed in supplementary Table 1

publications I and II), plus one of known response from earlier investigations (ILB 938/2 and

Aurora/2, respectively) (Khan et al. 2007; Khazaei et al. 2010). Collection sites were

identified where the long-term average annual rainfall was between 300 and 550 mm/year

(dry set) and over 800 mm / year (wet set), respectively. After the sites were identified, one

accession per site was chosen at random. A hierarchical cluster analysis was performed using

the collection site agro-climatic parameters (long-term yearly precipitation, long-term yearly

aridity index, long-term yearly minimum temperature, long-term yearly maximum

temperature, temperature seasonality, precipitation seasonality, precipitation of wettest

quarter and precipitation of coldest quarter; http://www.worldclim.org/bioclim) in order to

achieve a balanced representation of sites (more details can be found in publication I and II).

3.1.2 Germplasm screening

The two sets were evaluated using randomized complete block design with 4 replicates for

some morphological, physiological and phenological traits during 2010−2011 under well

watered conditions (measurements listed in Table 4). In the next step, on the basis of

principal component analysis (PCA) on studied traits, ten representative accessions from

each set (wet and dry, called “subsets”) were selected for exposure to transient moisture

stress following Khan et al. (2007) (publication II).

3.2 Molecular breeding

3.2.1 Mapping population development and selection

Four parental lines [Aurora/2 (drought-sensitive), Mélodie/2 (High EUW), Disco/2 (zt2) and

ILB 938/2 (high WUE)] differing in water-deficit response and other valuable traits were

chosen at the start of the project for crossing and development of RIL populations. Crosses

Mélodie/2 × ILB 938/2, Mélodie/2 × Disco/2, ILB 938/2 × Aurora/2 and ILB 938/2 ×

Disco/2 were prepared by hand, along with their reciprocals, in the insect-proof greenhouse.

The standard sets of quantitative-genetics generations (F1, F´1, F2, and BC1F1) were prepared.

The F2 seeds were advanced to the next generation (F3–F4) by single seed descent, each

comprising ~ 350 individuals. The parental lines were in a collection of 35 accessions used

in the development of 756 SNPs that have been validated in an international panel (Cottage

et al. 2012b; Webb et al. in preparation). Mélodie/2 and ILB 938/2 showed the highest

genetic distance from each other, with 222 polymorphic SNPs, so this cross was advanced to

F5 and chosen for deeper investigation (publication III and IV). Mélodie/2 was selected from

a French low vicine-convicine cultivar with high EUW, while ILB 938/2 was a selection

from an Ecuadorian landrace with relatively high WUE.

3.2.2 Genotyping

High throughput genomic DNA was extracted by LGC Genomics laboratory (LGC

Genomics, Hoddesdon, UK) according to the manufacturer’s instructions

27

(http://www.lgcgenomics.com/nucleic-acid-extraction/services/dna_extraction_services/).

SNP genotyping was accomplished using the KASPar™ (Kompetitive Allele Specific PCR)

assay (KBioscience, UK) platform, a single-plex SNP genotyping methodology using allele-

specific amplification followed by fluorescence detection for genotyping

(http://www.kbioscience.co.uk/reagents/KASP_manual.pdf). The SNP sequences and

specific alleles for parental lines used in this study a listed in Supplementary Table 2,

publication III.

3.2.3 Phenotyping

The F5 population (Mélodie/2 × ILB 938/2) and its with parental lines were phenotyped in a

climate-controlled glasshouse during 2013−2014 for some morphological and physiological

traits (see Table 4) using a completely randomized design with 3 replicates under well

watered and water deficit conditions (publication III). Furthermore, the population was

phenotyped for vicine and convicine content (data not shown in this thesis).

3.2.4 QTL mapping, synteny and candidate genes

Details of genetic linkage map construction and QTL analysis are presented in Table 5. SNP

segregation was subjected to the chi square goodness-of-fit test to assess deviations from the

expected Mendelian segregation ratio (1:1). SNPs showing normal diploid segregation (P ≥

0.05) were chosen for the linkage map construction. The Kosambi function was used to

calculate the map distance in centiMorgans (cM) (Kosambi 1943). Composite interval

mapping (CIM) was used to detect the relationship between each linkage group and putative

QTL locations. Significant QTLs were analysed with CIM. Cofactors were determined using

the forward and backward method in the standard CIM model with the probability in and out

of 0.1. The 95% significance threshold for QTL detection was determined by the phenotype

permutation using 1,000 permutations at an experiment-wise P < 0.05.

The overall level of collinearity between the detected putative QTLs (using sequences

flanking SNPs) and M. truncatula was examined. Reciprocal best BLAST hit E-values were

employed to determine the strength of the orthologous relationship. Gene family,

phylogenetic context, and tissue-specific gene expression profiles of the orthologous genes

from model plant species were examined using the LegumeIP database

(http://plantgrn.noble.org/LegumeIP/) (M. truncatula, Gene Model, Mt3.5v3).

3.3 Measurements

Details of measurements are listed in Table 4. For the leaf morphology (stomatal

morphology and leaflet area), gas exchange traits and leaf temperatures the youngest, fully

expanded leaflets of 8-week-old plants were used in all experiment (publications I, II and

III).

3.3.1 Induction of water deficit (Publications II and III)

For publication II, transient drought response (subsets in 2011), 2 L plastic pots were filled

with 1.42 kg of the mixture (sand / peat, 3:1 v/v) that had a water holding capacity (WHC) of

20% (w/w), using a completely randomized factorial design with 4 replicates. Each pot was

brought to WHC by adding 285 ml of water. Pots were weighed every 2 days and amounts of

water equal to the loss in weight were added. Ten days after sowing, 60 g of perlite was

28

added to the top of each pot (to reduce soil evaporation). In the well watered treatment, the

pots were irrigated as described above until harvesting at 13 weeks (when the main tiller of

the plants turned yellow). Half of the plants were exposed to a gradual and uniform water

stress starting at 5 weeks by reducing water by 2% (w/w) of available water per every two

days to bring the moisture level down from the field capacity (20% w/w) to moisture stress

(2–4% w/w). Pots were weighed and where water use exceeded 2%, irrigation was applied.

Measurements were done 2 weeks after the gradual drying was started.

For publication III in 2013, 4 L pots were filled with 2.48 kg of the mixture (sand / peat,

2:1 v/v) that had a WHC of 24% (w/w). Each pot was brought to WHC by adding 595 ml of

water. Pots were weighed three times per week and amounts of water equal to the loss in

weight were added to maintain soil moisture level at field capacity. Immediately after 8-

week-old plants were phenotyped under well watered conditions, a uniform water stress was

started by reducing relative available water by 2% (w/w) every day to bring the moisture

level down from the field capacity (24% w/w). Pots were weighed every day and where

water use exceeded 2%, irrigation was applied (Khan et al. 2007). When the soil moisture

content had reached 2−4%, 10 days after the induction of water deficit was started, canopy

temperature was measured. Thereafter normal irrigation was continued until harvest.

3.4 Growth conditions and statistical analysis

All experiments (germplasm survey and mapping population phenotyping) were conducted

in the climate-controlled glasshouse of the Department of Agricultural Sciences, University

of Helsinki, Finland. Soil moisture level was maintained at field capacity with automatic

irrigation for all plants in the well watered experiments. Seeds of all accessions were

inoculated with Rhizobium leguminosarum biovar. viciae (faba bean strain, Elomestari Oy,

Tornio, Finland) before sowing. A mixture of sand (size: 0.5–1.2 mm, Saint-Gobain Weber

Oy Ab, Helsinki, Finland) and peat (White 420 W, Kekkilä Oy, Vantaa, Finland) containing

all essential nutrients was used as the potting medium. For all experiments, at three and five

weeks after sowing, 70 ml of nitrogen-free fertilizer (equivalent to 20 kg of P and 24 kg of K

per ha) was added to each pot. Photoperiod was adjusted to 14 h light and 10 h dark, and the

temperature was maintained at ~ 21°C day/15°C night ±2°C. Photosynthetic photon flux

density (PPFD) was approximately 300 µmol m-2

s-1

at the canopy level. Relative humidity

was maintained at 60%. The temperature, humidity and light conditions were automatically

recorded throughout the experiments. For the germplasm survey experiments, 2 L pots used

with mixture of sand and peat (3:1 v/v) was used. In the population phenotyping experiment,

4 L plastic pots filled by sand and peat (2:1 v/v) used. Statistical analysis and software used

to analyse data in this thesis are presented in Table 5.

29

Table 4 Measurements and methods used in the experiments

SD stomatal density, SL stomatal length, SW stomatal width, SA stomatal area, SAAL stomatal area per

unit area of leaflet, Anet photosynthetic rate, gs stomatal conductance, E transpiration rate, WUEi

Intrinsic water use efficiency (Anet/gs), PPFD photosynthesis photon flux density

a In publications I and II, the canopy temperature differences from air temperature and leaflet

temperature differences from air temperature were calculated

Measurements Germplasm survey Mapping

population

Instrument / Method / Reference Publication

Stomatal

morphology

Yes (SD, SL, SW, SA,

SAAL)

Yes (SD, SL) Impression method (Wang and Clarke

1993), using abaxial surface of leaflet

I, II, III

Leaflet area Yes No LI-6200 (LI-COR Inc., Lincoln, NE,

USA)

I, II

Number of tillers Yes No Counting I

Gas exchange traits Yes (Anet, gs, E, WUEi) Yes (gs) LI-6400 (LI-COR Inc.), equipped

with a 2×3 cm leaf chamber with a

LED light source (6400-02B, 90% red

and 10% blue), reference CO2

concentration = 400 µmol mol-1

, flow

rate = 400 µmol s-1

, PPFD = 1,000

μmol m-2

s-1

I, II, III

Leaf temperaturea Yes No LI-6400 (LI-COR Inc.) I, II

Canopy temperaturea Yes Yes Infrared thermometer (IRT, FLUKE®

thermometer gun 574, Everett, WA,

USA)

http://www.plantstress.com/methods/I

RT_protocol.htm

I, II, III

Water use subsets No Weighing pots (according to Khan et

al. 2007)

II

Biomass WUE subsets No Total biomass / water used II

Relative water

content

Yes No According to Barrs and Weatherley

(1962)

I, II

Days to flowering Yes No Number of days to the onset of

flowering

II

Seed weight No Yes Weighing 10 mature dried seeds per

plant

III

Stipule spot

pigmentation

No Yes Scoring the absence / presence of

pigmentation

IV

30

Table 5 Statistical analysis and software used in the experiments

Statistics / Function Software Publication

One way ANOVAa R

b II

Two way ANOVA R and SPSS v. 21c II, III

Machine-learning algorithm R [caret, randomForest, svm (e1071) and ksvm

(kernalab) libraries]d

I

Correlation and Regression R II, III

T-test R I

Contrast analysis SPSS v. 21 II

PCA R II

Skewness, kurtosis and Shapiro-Wilk test R III

Chi-square R III, IV

Linkage map construction MapDisto v. 1.7.7.0. (LOD = 3, recombination fraction

= 0.3) (Lorieux 2012)

III, IV

Composite interval mapping Windows QTL Cartographer v 2.5_011 (control

marker = 5, window size = 10, walk speed = 1) (Wang

et al. 2012)

III, IV

Linkage groups and QTL drawing MapChart v. 2.2 (Voorrips 2002) III, IV

a Analysis of variance

b R Development Core Team (2012)

c IBM

® SPSS

® Statistics for Windows, v. 21, Armonk, NY, USA

d Models used in this study: Classification and regression training, random forest and support vector

machines (publication I)

31

4 RESULTS AND DISCUSSION

4.1 Germplasm screening

The morpho-physiological and phenological measurements of the dry and wet FIGS sets

differed significantly under well watered conditions. The dry set had significantly larger

stomata, greater stomatal area per unit area of leaflet, higher relative water content (RWC),

leaflet area and transpiration rate, and in contrast lower leaflet and canopy temperature,

fewer fertile tillers and fewer days to flowering than the wet set. Greater variation for most

traits was observed within the wet set. Stomatal density and stomatal area per unit area of

leaflet were negatively correlated with gas exchange parameters and positively correlated

with intrinsic water use efficiency, while stomatal size showed the opposite trends

(publications I and II).

Three machine-learning algorithms (classification and regression training, random forest

and support vector machines) were employed to examine difference between the two FIGS

sets (publication I). The outcome revealed that sets were classified into two groups based on

the phenotypic evaluation with little or no overlap between sets, showing a high degree of

discrimination between sets. Among the traits, leaflet and canopy temperature and RWC

contributed most to the discrimination between sets (Table 5 in publication I). Machine-

learning algorithms have been little used in this context, so their results were confirmed by

using a well known multivariate statistical methods, principal component analysis, that

further assessed the pattern of variation in the two sets and confirmed the results of the

machine-learning algorithms results. The first four principal components (PC1-4) explained

81.3% of the total variation, of which the first component (PC1) accounted for around half.

Gas exchange traits (photosynthetic rate, gs, intrinsic WUE and transpiration rate) made the

highest contribution to PC1. The second PC was highly dependent on leaflet and canopy

temperature along with RWC. These traits contributed more than other measurements to

discriminate the sets from each other (Figure 5), and also made the greatest contribution to

separating the two sets according to machine-learning algorithms (see publication I). The

next components were mostly based on stomatal morphology. Distribution of the two FIGS

sets shows how FIGS successfully demonstrates that ecotypic differentiation driven by

moisture availability occurred in faba bean germplasm (Figure 5). Similarly, several salt-

tolerant accessions in wild Phaseolus species were identified from arid, coastal, or saline

areas close to their centre of diversity in Central and South America (Bayuelo-Jiménez et al.

2002). When applied to Lathyrus species, FIGS also allowed selection of manageable

subsets with a high probability of finding accessions adapted to drought and heat (Shehadeh

2011). Furthermore, FIGS has been used to detect numerous sources of resistance to biotic

stresses in wheat (Triticum ssp.) and barley (Hordeum vulgare L.) (e.g., Kaur et al. 2008;

Bhullar et al. 2009; Endresen et al. 2011; Bari et al. 2012, 2014b).

32

Figure 5 Principal component analysis scatter diagram based on morpho-physiological traits

in the wet set (black circles) and dry set (grey circles) of faba bean germplasm

Faba bean has been grown for hundreds or thousands of years in a wide range of

environments, including those subjected to often severe drought constraints (Duc et al.

2010), so we expected notable variation between sets from contrasting seasonal moisture

availabilities. Ecosystem disturbance has been shown to add further selection pressure in

annual plant evolution (Peleg et al. 2005). Other methods demonstrated the existence of

ecological adaptation in several plant species (De Micco and Aronne 2012; Sarris and

Koutsias 2014). All of these results support the hypothesis that accessions with desirable

adaptive traits can be identified through the process of predicting the environmental profiles

that select for these traits, and the question was whether FIGS would adequately predict

these profiles, and would do so better than earlier methods. Analysis of natural genetic

variation leads to the discovery of novel genes and alleles, especially in the field of plant

adaptation to the environment (Shindo et al. 2007). Crop wild relatives are the most

promising sources of novel alleles to improve drought adaptation in crops (Nevo et al. 2002),

but in the case of faba bean, there is no wild relative available, so novel methods for utilizing

PGR such as FIGS play a key task in this important aspect. In contrast, the use of core

collections often misses adaptive and rare alleles (Gepts 2006; Pessoa-Filho et al. 2010).

Evaluation of drought adaptation requires phenotyping by exposing plants to contrasting

water regimes. So, the next step sought natural variation within and between the wet and dry

sets for response to water deficit using manageable subsets, 10 accessions representing the

average of each set based on PCA. A moderate, transient, mid-season water deficit was used,

as this is typical of what a crop may experience in the field almost anywhere in the world

(Araus et al. 2002). Water deficit affected most traits and a significant set × water stress

interaction was observed (publication II). Most traits, including canopy temperature

depression (Figure 6), clearly separated two sets under well watered conditions, and the

-6 -4 -2 0 2 4 6-4

-2

0

2

4

PC

2 (

16

%)

PC 1 (46.2%)

33

response of most traits to water deficit also clearly differed between the two sets. The most

outstanding drought adaptation capacity was found in accessions from the dry set, directing

future exploration to these ecosystems. Genetic variation has been shown in leaf temperature

RWC, Δ13

C and gs of a small set of drought-adapted and drought-sensitive lines of faba bean

(Khan et al. 2007). Our result showed that water stress caused stomatal densities to increase

significantly in the dry set while stomatal size decreased in both sets. Stomatal area and

density were negatively correlated to each other in all experiments, so the stomatal area per

unit of leaflet remained fairly constant in the two sets. Stomata do not move. The results

relates partly to reduced leaf size in both sets during the drought interval, due to reduced cell

elongation from low turgour. Differences in stomatal density between the sets suggests that

something else occurred, either more differentiation of guard cells in the dry set, or greater

noise in one set that obscured the differences. These findings were in agreement with those

on several other species (Franks and Farquhar 2007).

Under water deficit conditions, gas exchange fell strongly. The clearest differences

between sets were in leaf temperatures and RWC. The dry set accessions had cooler

canopies under well watered conditions and their leaf temperatures increased more under

water deficit than those of the wet set (Figure 6). This demonstrates that accessions from dry

regions have more effective stomatal control than those from wet regions in both wet and dry

conditions. Overall, these results confirm the key role of stomatal function in water relations

in this species. In the field, lower canopy temperature under drought stress conditions

indicates a better capacity to take up water from deep soil and hence better water status

(Blum 2011a). Fast and accurate phenotyping criteria are currently seen as a major issue for

improvement of drought adaptation in crops (Richards et al. 2010). Canopy temperature can

be measured by an infrared thermometer (IRT) as a rapid and cost-efficient alternative for

preliminary screening for drought adaption under controlled and uniform conditions in faba

bean (Khan et al. 2007, 2010). Canopy temperature has also been shown as an efficient field

phenotyping tool for drought adaptation in other legume species (e.g., soya bean, McKinney

et al. 1998; chickpea, Kashiwagi et al. 2008b; cowpea, Hall 2012). Canopy temperature

measured by IRT as a screening technique for dehydration avoidance was first reported by

Blum et al. (1982). Under drought conditions, this is the most drought adaptive trait

associated with yield performance and has high heritability (Olivares-Villegas et al. 2007).

Good practice in using the IRT, such as those protocol at

http://www.plantstress.com/methods/IRT_protocol.htm, should be followed carefully to

avoid large error variance and non-repeatable results. Water stress caused a reduction in

RWC in both sets, but it was greater reduction in the wet set (27%) than in the dry set (19%).

The ability of genotypes to maintain RWC when moisture is limiting is a good indicator of

drought adaptation (Barrs and Weatherley 1962; Anyia and Herzog 2004; Blum 2011a).

In the current study we screened a large number of faba bean accessions (402), which

showed a wider range of variation for morpho-physiological measurements than previous

reports, suggesting a high potential for breeding for these traits. For example, in our

germplasm gs ranged from ~ 0.040 to 0.620 mol m-2

s-1

, in contrast to the range of 0.125 to

0.176 mol m-2

s-1

in six accessions known to differ for drought response (Khan et al. 2007).

Similarly, ten faba bean accessions (Tanzarella et al. 1984) showed a much narrower range

(77% less) for stomatal density than our work. These comparisons clearly show the enhanced

identification of genotypic variation that results from the large germplasm set analysed in

this work, and from using FIGS to seek extremes in a trait.

34

Figure 6 Effects of water treatments on the canopy temperature depression in dry and wet

set: well watered (white column); water deficit (gray column). Error bars show mean ± 1

SEM

4.2 Molecular breeding

4.2.1 Response of parental lines to water deficit

The parents used for map construction showed different responses to water deficit.

Mélodie/2 had a cooler canopy under well watered conditions and a sizeably higher increase

in canopy temperature under water deficit conditions than ILB 938/2. The gs showed the

reverse trend. Water deficit had 3-fold more effect on biomass production in Mélodie/2 than

in ILB 938/2 but biomass in Mélodie/2 under water deficit conditions was the same as ILB

938/2 under well watered conditions (publication III). These results show the ability of ILB

938/2 to maintain higher water status under water deficit conditions, as it was selected for

high WUE with relatively low yield. In contrast Mélodie/2 with higher EUW showed better

productivity under drought conditions than ILB 938/2, with better water uptake ability

through a well developed root system (Khazaei and Stoddard unpublished data). These two

parental lines nicely explain the concept of WUE and EUW in sense of Blum (2009).

Selection of parental lines, which are genetically and geographically far enough from each

other, is the key point to build a promising RIL populations for successful genetic mapping

and QTL detection (Würschum 2012).

4.2.2 Linkage map and chromosome assignment

Two hundred and eleven individuals were randomly chosen (of 350) from the population

Mélodie/2 × ILB 938/2 for phenotyping and genotyping (publication III). After considering

Wet set

Dry set

-2.0 -1.5 -1.0 -0.5 0.0 0.5

***

Canopy temperature depression (C)

***

35

rejected markers based on the grounds of excess missing data (> 5% missing data per

marker) or excessive expected chi-squared goodness of fit ratio, 188 SNPs, as along with

seed coat colour (publication III), hilum colour and SSP (publication IV) were successfully

mapped in nine linkage groups that covered ~ 928 cM of faba bean genome. The linkage

groups were composed of 2–43 loci and their length ranged from 3.07 to 223.02 cM, with an

average inter-marker density of 5.8 cM. The map showed a high degree of synteny with the

genome of M. truncatula (publication III). High-throughput SNPs have greatly accelerated

the development of high density linkage maps, with 2.3 cM average marker density having

been achieved in faba bean recently (Kaur et al. 2014b; more information in Table 1).

The number of genetic linkage groups should be the same as chromosome number in the

species, so six LGs is expected in faba bean. Nevertheless, the number of LGs generally

exceeds the chromosome number, ranging from 7 to 48 in faba bean, and map length varied

from 231.4 to 4,613 cM (Table 1). Our genetic map had seven main linkage groups and two

fragments which is slightly more than the appropriate number, and the map was slightly

smaller than some others (Table 1). These differences may be related to factors such as the

large size of the genome, the number and types of markers, the software analysis used for

construction of the genetic map (e.g., recombinant fraction and LOD value), the number and

generation of progeny, and especially the genetic background and genetic distance of the

parents (Young 2000; Broman 2006; Semagn et al. 2006; Cheema and Dicks 2009).

The correspondence of linkage groups to their respective chromosomes was performed

using markers with identified chromosome location or linkage according to Cruz-Izquierdo

et al. (2012) and the current consensus genetic map published by Satovic et al. (2013). The

assignments made here are consistent with those possible using the higher syntenic

resolution possible using a 768-locus consensus genetic map (Webb et al. in preparation)

(Figure 7). Seven of the nine linkage groups were assigned to the six faba bean

chromosomes (I–VI) (publication III).

36

Figure 7 Synteny between M. truncatula (Mt) and faba bean (Vf) chromosomes. This figure

was extracted from Webb et al. (in preparation)

4.2.3 Putative QTLs

There is an urgent need to identify chromosome regions associated with the climate change-

related traits in crops in order to assure food security in the near future. Progress in MAS for

a biotic stresses such as drought in faba bean is relatively modest compared to that on biotic

stresses, although some progress have been made for yield and its components (Table 2 and

Torres et al. 2010). There is no corresponding previous report on morpho-physiological traits

related to drought adaptation such as stomatal morphology and function, so the QTLs

detected in this study would be a good starting point for future studies. QTLs were identified

on two of the six faba bean chromosomes, II and IV. Fourteen QTLs were detected from five

studied traits (stomatal density and length, gs, canopy temperature and seed weight) under

well watered conditions over two years (2013 and 2014) and one QTL for canopy

temperature under drought conditions in 2013. The LOD threshold at P = 0.05 varied from

2.8 to 3.0. The proportion of phenotypic variation explained by each individual QTL ranged

from 5.7% to 9.3%. Thirteen QTLs were located on chromosome II, with eight of them

clustered in the same genomic region (qSD-2013-1, qSD-2014, qSL-2013, qSL-2014, qgs-

2013, qCT-2012, qCT-2013 and qCT-2014), in the marker interval, Vf_Mt4g014430_001.

These traits also showed strong phenotypic correlations.

All traits were phenotyped in two years under well watered conditions, and the detected

QTLs remained stable between years except that for gs. Canopy temperature was also

37

phenotyped under water deficit conditions in 2013 and its QTL (qCTd-2013) was mapped in

the same chromosome, chromosome II, ~ 125 cM from those found under well watered

conditions (publication III). The direction of additive effect illustrates that alleles driving

warmer leaves under well watered conditions probably came from Mélodie/2 and those

conferring cooler leaves under stress conditions were from ILB938/2, which is in agreement

with the concept of WUE and EUW (Blum 2009).

A QTL was detected in chromosome II for seed weight, which remained stable during

the two years of phenotyping (qSW-2013-1 and qSW-2014-1). The second stable QTL

governing seed weight was located on chromosome IV (qSW-2014-1 and qSW-2014-2).

QTLs for this trait have been previously reported in chromosome VI (Vaz Patto et al. 1999).

This difference is at least partially attributable to the different parents used, with the study by

Vaz Patto et al. (1999) using paucijuga and major lines whereas the present study used

equina (ILB 938/2) and minor (Mélodie/2) lines, and partly to the different distribution of

markers in the two studies.

Many QTLs governing flowering time and reproductive traits associated with drought

avoidance in faba bean were already mapped on chromosomes I, IV, V and VI (Cruz-

Izquierdo et al. 2012; Satovic et al. 2013; Table 1). In this work, however, transient rather

than terminal drought was the objective, and it was a glasshouse-based experiments,

assessment of grain yield was not considered appropriate. Yield is related to high gs,

photosynthetic rate and cooler canopies in several crops (Fischer et al. 1998; Khan et al.

2010), so the region in chromosome II that made a significant contribution to stomatal

function warrants further investigations.

A short-term mapping population (e.g., F2 and BCs) developed through the BSA

approach is considered a good starting point for molecular mapping, while a long-term

mapping population such as one comprised advanced RIL is suitable for characterization of

traits of importance. Several QTLs in this species were detected at the F2 generation,

especially before 2008, and some of them were later validated at advanced generations, e.g.

ascochyta blight resistance (Díaz-Ruiz et al. 2009a) (see Tables 1 and 2). In more recent

studies, mapping populations are generally advanced to the F6 or F7 generation (Table 1 and

2). In this study, we used an F5 RIL population and Takuno et al. (2012) pointed out that

QTL mapping does not necessarily require RILs at F6 or higher generations, and that F4 (or

even F3) populations would be almost as useful as highly advanced populations.

Two QTLs governing stomatal density and gs (qSD-2013-2 and qgs-2014) were mapped

in regions of distorted SNPs (publication III). The precision of the linkage map (map

distances and marker ordering) may be biased in regions of many distorted markers (Lorieux

et al. 1995a, b). Additionally, QTL regions often overlap with regions undergoing

segregation distortion, because favourable QTL alleles are in excess frequencies in those

regions (Boopathi 2013). Therefore, QTLs found in these regions are more uncertain than

those located elsewhere.

4.2.4 Synteny

Despite the large difference between the genome size of faba bean and M. truncatula, a tight

relationship was found between the two genomes (Figure 7), with strong evidence for

extensive collinearily between linkage groups as it suggested by Rispail et al. (2010). Based

on annotated genic SNPs derived from M. truncatula, candidate genes were found within

38

detected QTL intervals. Candidate genes for M. truncatula and Arabidopsis were almost

identical, and were mostly related to growth and development, stress and transport

(publication III). A segment of M. truncatula chromosome IV that harbours receptor-like

protein kinase (RLKs) was consistently linked with most of the detected QTLs, including

those for stomatal density and length, gs and canopy temperature under well watered

conditions. The candidate gene for canopy temperature under water deficit was ribose-

phosphate pyrophosphokinase 4 in M. truncatula chromosome VI. RLKs are the best known

stress-induced genes and control a wide range of physiological responses such as growth

regulation and specific adaptations in several plant species (e.g., Casson and Gray 2008;

Marshall et al. 2012; Osakabe et al. 2013). Gene ontology for both intervals with detected

QTLs for seed weight were related to regulation of seed germination (Arginyl-tRNA-protein

transferase 1) (Manahan and App 1973; Holman et al. 2009) and embryonic development

ending in seed dormancy (Splicing factor 3B subunit 4) (Raab and Hoth 2007; Aghamirzaie

et al. 2013; http://www.medicagohapmap.org/?genome).

There is another success story for candidate gene discovery in faba bean. An ortholog of

CEN/TFL1-like genes was recognized to be responsible for determinate growth habit in faba

bean, and this was confirmed in several plant species and later used to develop a CAPs

marker for use in MAS (Avila et al. 2007).

4.3 Mendelian traits

During population development, we noticed that parental lines used for population

development also differed in several Mendelian traits, such as seed coat, hilum and flower

colour, and stipule spot pigmentation (SSP). These characters were recorded during

population development (F1 to F5) as well as in backcrosses, their heritability was

investigated and they were mapped along with SNPs. The most detailed findings were on

genetic control of SSP by using four segregating populations in the F1 to F5 generations

(Mélodie/2 × ILB 938/2, Mélodie/2 × Disco/2, ILB 938/2 × Aurora/2 and ILB 938/2 ×

Disco/2) as well as reciprocal crosses. The results revealed that two unlinked genes, ssp1 and

ssp2, determined this trait. A novel locus, ssp1, decoupling pigmentation in the flower from

that in the extra-floral nectarines in ILB 938/2 was detected and mapped onto chromosome I

along with hilum colour using the Mélodie/2 × ILB 938/2 population (publication IV).

Another gene, ssp2 (zt2) in Disco/2 was already mapped in this species (Gutierrez et al.

2008). In addition, the SSP as an early-expressed morphological marker confirmed hybridity

in the crosses, so this trait can be recommended for use as a simple marker in programs

focused on low tannin or vicine-convicine along with white flower and colourless hilum.

Since ssp1 is recessive, but rare (it was present in 6% of the 402 faba bean accessions), it

could be used in the long run if a sufficient number of parents carry it. Another practical

application might be to use the ssp1 gene as a co-marker for visual evidence of outcrossing

during pedigreed seed production if a particular cultivar carries ssp1 as a homozygote. If the

same cultivar were homozygous recessive for a trait that was much more difficult to track,

for example, low vicine-convicine, the appearance of nectary spots would be a simple co-

marker in many situations. If this were desirable, a closely linked molecular marker could be

used to design crosses appropriately for pyramiding because heterozygotes and homozygotes

would be distinguishable. The ssp1 locus was tightly linked into a well conserved region of

M. truncatula chromosome VI with some candidate Myb and bHLH (basic helix-loop-helix)

transcription factor families that have been shown to regulate the anthocyanin pigmentation

39

pathway in many species (e.g., Quattrocchio et al. 1998; Tai et al. 2013). Green seed coat

colour was mapped on faba bean chromosome IV (publication III), while black seed coat

colour was shown to be on faba bean chromosome II (Sjödin 1971; Díaz-Ruiz et al. 2010;

Cruz-Izquierdo et al. 2012).

40

5 CONCLUSION AND FUTURE PROSPECTS

This study has combined the novel use of FIGS for identifying sources of abiotic stress

resources and synteny with M. truncatula for gene mapping and discovery in faba bean. An

appropriate phenotyping method, mapping population and suitable marker system are the

key requirement for a successful molecular breeding program. The differences between the

two FIGS sets (drought-adapted and non-drought-adapted) allowed demonstration of a

reliable and cost-efficient phenotyping tool for screening traits related to drought adaptation

(i.e. canopy temperatures). In the next step, using a mapping population with wide variation

at both DNA sequence and phenotypic level and using M. truncatula-derived SNPs, all

brought success in detecting the genetic regions and even candidate genes (e.g., RLKs) for

studied traits with potential for further functional genomics analysis. RLKs have already

been widely shown to be related to abiotic stress responses in model plants as well as in

several crop species. These results confirmed that genetic information from model species

can be easily translated to faba bean as a “genome orphan”.

When traits other than yield associated with drought adaptation are considered for

mapping, poor phenotyping can often cause failures (Varshney et al. 2011) and hence

generate confusing results. A fast and reliable phenotyping method is needed for the

particular crop in the specific drought profile. Significant correlations allow utilizing simpler

traits as indirect selection criteria for yield and drought adaptation. In this species, leaf

temperatures seems a reliable phenotyping method but it is highly recommended that a

subset of samples be subjected to a more accurate screening method such as Δ13

C. The most

severe droughts mostly occur in poor countries with little access to expensive equipment

such as sensor technologies or even Δ13

C, so economical tools like IRT are exceptionally

important.

Our findings support the assertion that FIGS can be an effective tool in pre-breeding

programmes for traits related to drought adaption in faba bean, and this may be extruded to

other traits related to abiotic stresses and adaptation to climate change. The core-collection

approach asks "what is the range of variation in the species" while the FIGS approach asks

"where can we find the extremes of variation in this trait in the species". Thus FIGS is

complementary to core collections. The major demand for germplasm collections from

agricultural genebanks is mostly for adaptive traits (Kenneth Street personal

communication), so in contrast to core collections, FIGS represents a dynamic, direct and

practical approach that focuses on the sought-after trait rather than on generalized measures

of genetic diversity. Overall, CGIAR suggests the FIGS approach, alongside current core

collection activities, should be part of breeding strategies. Several FIGS sets developed by

ICARDA are available to speed up research on climate change adaptive traits

(http://www.icarda.org/figs-%E2%80%93-new-approach-mining-agricultural-genebanks-

speed-pace-innovation-and-food-security).

This work was carried out under climate-controlled greenhouse conditions and these

findings should be confirmed under field conditions. When preparing to introduce detected

putative QTLs as MAS tools, they should be validated in different environments. The

identification of candidate genes takes away some of the apparent complexity of drought

adaptation in faba bean. Co-localisation of these genes with known QTLs involved in

drought adaptation may help select regulatory genes involved in this respect and to define a

rational approach to developing drought-adapted cultivars. Candidate genes also can be

41

transferred to elite lines of faba bean by MAS or genetic transformation. Limited progress

has been shown on gene transfer or pyramiding using MAS in legumes, but some progress

has been made in soya bean, pea and common bean (Dwivedi et al. 2007).

Research on the root, the hidden part of plant, lags behind that on shoots. Physiologists

and breeders are becoming positioned to breed crops with better rooting systems that

improve productivity under water-limited conditions. Preliminary results on root

phenotyping showed that drought-adapted accessions had relatively better rooting systems

(branching, density and architect) than the wet adapted set, but root length did not differ.

More research needs to be conducted on root morphology and function and its relation to

shoot characteristics in this species, since drought adaptation in rice, for example, was

associated with a more extensive root system as well as reduced stomatal density and

improved WUE (Yu et al. 2013).

42

ACKNOWLEDGMENTS

I am very lucky to have Dr. Frederick L. Stoddard as my main supervisor. His profound

knowledge in crop science, breeding, genetics and statistics along with his kind support,

guidance and encouragements culminated this work. I am very grateful to my co-supervisor

Professor Mikko J. Sillanpää for his advices and supports during molecular biology work.

Many thanks to Professor Pirjo Mäkelä leader of crop science group, for her support for my

PhD course management, thesis review and pre-reviewer suggestions and also Dr. Arja

Santanen for her advice and practical help during lab work. My PhD monitoring team,

Professor Teemu Teeri and Dr. Mervi Seppänen are acknowledged for their valuable

comments and suggestions. I would like to thank Dr. Petr Smýkal and Dr. Simo Hovinen,

pre-reviewers of the thesis for their constructive comments.

Many interesting ideas to develop the current work came from our international

collaboration for developing the FIGS sets by Dr. Kenneth Street, Dr. Abdallah Bari

(ICARDA) and Dr. Michael Mackay (The University of Queensland, Australia), and for

developing the SNPs platform by Professor Donal M. O’Sullivan (University of Reading,

UK). I wish to thank Professor Wolfgang Link (Georg-August-University, Germany) for

providing the seeds of the parental lines for population development.

I owe great gratitude to Markku Tykkyläinen, Sanna M. Peltola, Sini Lindström and

Daniel Richterich, technical staff of the glasshouses of the University of Helsinki, for their

kind assistance during the experiments.

I would like also to acknowledge Damian Wach, Guillermo Mínguez Vélaz, Sandro

Maria Cavaleri, and Stefano Zanotto, visiting students, and Yao Lu, MSc student of this

university, for their for their kind assistance at different stages of this project. Many thanks

to all friends and colleagues at the crop science group, HY.

This work was financially supported by CIMO (the Centre for International Mobility),

the Emil Aaltonen Foundation (Emil Aaltosen Säätiö), Niemi Säätiö, Department of

Agricultural Sciences, University of Helsinki and the Doctoral Program in Plant Science.

Part of the research reported here was conducted in conjunction with "Legume Futures

(Legume-supported cropping systems for Europe)", a collaborative research project funding

from the European Union's Seventh Programme for research, technological development and

demonstration under grant agreement No. 245216.

I give special thanks to my wife, Aida for being patient and kind during the work and

thesis writing, and to my parents for their encouragements during my studies which led me

to the doctoral degree.

Helsinki, September 2014

Hamid Khazaei

43

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