application of marker-assisted selection...
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APPLICATION OF MARKER-ASSISTED SELECTION TO
BREEDING OF COMMON BEAN (PHASEOLUS VULGARIS L.)
A Thesis
Presented to
The Faculty of Graduate Studies
of
The University of Guelph
by
AARON D. BEATTIE
In partial fulfilrnent of requirements
for the degree of
Master of Science
May, 1998
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APPLICATION OF MARKER-ASSISTED SELECTION TO BREEDING OF COMMON BEAN (PHASEOLUS WLGARIS L.)
Aaron D. Beattie University of Guelph, 1998
Advisors: Dr. T.E. Michaels and Dr. K.P. Pauls
A rapid screening procedure was developed for Common Bacterial BLight (CBB) in the
first study. Two RAPD markers linked to CBB resistance were cloned, sequenced and
converted to SCAR markers. Ethidium bromide added directly to PCR reactions allowed
discrimination between the presence or absence of the marker when viewed under UV
light.
In the second study, QTLs were mapped for nine agronornic and architectural traits. A
RAPD linkage map consisting of 107 markers on 12 linkage groups and covenng 5 10 CM
was developed. A totai of 21 QTLs were identified with at least one QTL located for each
trait.
The fmal study examined the relationship between genetic distance and trait data within
an elite population. The population was clustered using UPGMA cluster analysis. Trait data
of individuals within groups was combined and correlated to genetic distance. Correlation
values were large and significant for ail traits except height and yield.
1 would iike to express my thanks to the many peopie who have assisted me throughout
this thesis. 1 have benefitted from the different insights and guidance provided by my two
advisors, Dr. Tom Michaels and Dr. Peter Pauls. Peter's attention to detail and d d y
involvement ensured a complete and thorough thesis, while my conversations with Tom were
a source of new ideas and kept the application of the work to breeding at the forefront. 1
would also like to th& Dr. Duane Falk whose knowledge of breeding was a valuable
iesource during the classes he taught me and the conversations we had outside the classroom.
The completion of this thesis would not have k e n possible without the work of Tom
Smith, Judy Krusky, and Lori Herteis. Tom was always a source of energetic enthusiasm,
humour and practical knowledge about the bean program. His expenence and observations
in the field were a welcorne part of my education. A special thanks also to the people who
ran the winter nursery in New Zealand, Mark Johnson and Jeremy Drrvison and his family.
Their gracious and fiiendly hospitality made my work in New Zealand an enjoyable and
unforgettable experience.
1 am grateful for the support and necessary distractions provided by my family and feliow
graduate students. To Cecilia 1 would like to express my appreciation for her support,
patience and love during this period.
Fiially. I am indebted for the financial support received h m NSERC and the University
of Guelph through the Soden Memonal Scholarship, Ta@ Davison Memonal Travel Grant
and the University Graduate Scholars hips.
TABLE OF CONTENTS
. . . . . . . . . . . . . . . . ACKNOWIEDGEMENTS i . .
TABLE OF CONTENTS . . . . . . . . . . . . . . . ii . . . . . . . . . . . . . . . . . . LIST OF TABLES iv . . . . . . . . . . . . . . . . . . LEST OF F!iGURES v
. . . . . . . . . . . . . . . . . . . 1.0 Introduction 1
. . . . . . . . . . . . . . . . . 2.0 Literature Review 4 2.1 Markers . . . . . . . . . . . . . . . . . . 4
2.1.1 Isozymes . . . . . . . . . . . . . . . . . 5 2.1.2 Restriction Fragment Length Polymorphisms (RFLPs) . 6 2.1.3MicrosateliitesorSirnpIeSequenceRepeats(SSRs). . 7 2.1.4 Random Arnpiified Polymorphic DNA (RAPDs) . 8 2.1.5 Amplified Fragment Length Polyrnorphisrns (AFLPs) . 9 2.1.6 Sequence Characterized Amplified Regions (SCARs) . 10
2.2 Linkage Mapping . . . . . . . . . . . . . . . . 11 2.2.1 Mapping in Common Bean . . . . . . . . . . . . 14
2.3 Quantitative Trait Loci (QTLs) . . . . . . . . . . . . 14 2.3.1 Estimating Gene Number . . . . . . . . . . . . 15 2.3.2 Size of Gene Effect . . . . . . . . . . . . 16 2.3.3GeneAction . . . . . . . . . . . . . . . . 17 2.3.4 Environmental Interaction . . . . . . . . . . . . . 18 2.3.5 S trategies for Mapping QTLs . . . . . . . . . . . . 19
2.4 Ideotype Breeding . . . . . . . . . . . . . . . . 20 2.5 Genetic Distance Measures and Cluster Analysis . . 23
2.5.1 Methods to Calculate Genetic SimilaritylDistance Coefficients . 24 2.5.2 Cluster Analysis . . . . . . . . . . 27 2.5.3 Effect of Marker System . . . . . . . . . . . . 27 2.5.4 Applications . . . . . . . . . . . . . . . . 28
3.0 Disease Screening Using PCR-Based Markers: Application to Comrnon Bacterial Blight Resistance in Phaseolus vulgaris L . . . . . . . . . . . . 30 3.1 Introduction . . . . . . . . . . . . . . . . . . 30 3.2 Materials and Methods . . . . . . . . . . . . 32
3.2.1 Plant Material . . . . . . . . . . . 32 3.2.2DNAExtraction . . . . . . . . . . . . . . 32 3.2.3 Cloning CBB RAPD Markers . . . . . . . . . . . 32 3.2.4 Sequencing of Clones . . . . . . . . . . 33 3.2.5 PCR Reactions with SCAR Primers . . . . . . . . . 33 3.2.6 Direct Staining of PCR Reactions with Ethidium Bromide . 34
3.3 Results . . . . . . . . . . . . . . . . . . 34 3.3.1 Cloning of R73 13 and R4865 RAPD Markers . 34
. . . . . . . . . . . . 3.3.2PCRUsingSCARMarken 35 3.3.3 Direct Staining of PCR Reactions . . . . . 37
. . . . . . . . . . . . . . . . . . 3.4Discussion 38
. . . . 4.0 Mapping QTLs for Yield and Yield-Related Traits in Common Bean 41 . . . . . . . . . . . . . . . . . . 4.1 Introduction 41
. . . . . . . . . . . . . . . 4.2 Materials and Methods 42 . . . . . . . . . . . . . . . . 4.2.1 Plant Material 42
4.2.2 Field Design and Data Collection . . . . . . . 43 4.2.3 M D Analysis . . . . . . . . 44 4.2.4 Linkage Analysis . . . . . . . . . . . . 44 4.2.5 Trait Analysis . . . . . . . . . . . . . . . . 45 4.2.6 QTL Mapping . . . . . . . . . . . . 45
. . . . . . . . . . . . . . . . . . . 4.3 Results 46 4.3.1 Marker Segregation Analysis and Map Construction . 46 4.3.2 Trait Analysis . . . . . . . . . . . 46 4.3.3 QTL Analysis . . . . . . . . . . 48
. . . . . . . . . . . . . . . . . . 4.4Discussion 51
5.0 Using Genetic Distance to Estimate Progeny Performance . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . 5.2 Materids and Methods . . . . . . . . . . . . - .
5.2.1 Plant Material . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Field Design and Data Collection
5.2.3 DNA Extraction and RAPD Analysis . . . . . . . . 5.2.4 Data Analysis . . . . . . . . . . . . . . .
5.3 Results . . . . . . . . . . . . . . . . . . 5.3.1 Trait Analysis . . . . . . . . . . . . . . . 5.3.2 Correlation Between Jaccard Distance Coefficients and Trait Values 5.3.3 Cluster Analysis and Correlation with Trait Values . . . . . 5.3.4 Phenotypic Ratings . . . . . . . . . . . . .
5.4 Discussion . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . REFERENCES 71
Appendix 1: Sequences of the R73 13 and R4865 Comrnon Bacteriai Blight resistance markers . . . . . . . . . . . . . . . . 84
Appendix II: Photographie index of RAPD markers selected for linkage mapping and QTL identification . . . . . . . . disk file (back cover)
Appendix III: Raw data used for linkage mapping in MAPMAKER and QTL . . . . identification in MAPMAKEWQTL disk fde (back cover)
LIST OF TABLES
Table 1. Quantitative traits measured on the RILs fiom the W0339 1 X OAC Speedvale cross . . . . . . . . . . . . . .
Table 2. Quantitative trait data for the RILs and parents averaged across repiications . . . . . . . . . . . . . . .
Table 3. Pearson phenotypic correlation coefficients for nine agronomic traits using means of the RILs from the W0339 1 X OAC Speedvaie cross . 49
Table 4. Location and description of QTLs identified for nine agronomic traits . . 50
Table 5. Pearson correlation coefficients between nine agromonic traits and genetic distances calculated by the Jaccard method or by the UPGMA cluster anaiysis . . . . . . . . . . . . . 64
Table 6. Genetic distances and trait rneans (standard deviations) for dusters generated by UPGMA cluster analysis . . . . . . . . . . . 66
Table 7. Phenotypic rating of individuals within clusters and the distribution of top yielding individuals in the population . . . . . . . . . 67
LIST OF FIGURES
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
A scale of genotypic values describing gene action . . . . . . . 18
Screening of parents and 20 BLT lines with RAPD primers UBC 73 (A) and UBC486(B) . . . . . . . . . . . . . . . 35
Clone inserts produced for RAPD markers R73 13 and R4865 . . . . 35
Sequences of cloned RAPD markers R73 13 (A) and R4865 (B) . . . . 36
Screening of parents and BLT lines with SCAR primers designed for markers R7313 (A) andR4865 (B) . . . . . . . . . . . . . . 37
Direct staining of PCR reactions with ethidium bromide . . . . . - 38
A RAPD linkage map of common bean and QTL likebhood plots for nine agronomic traits . . . . . . . . . . . . . . . . 47
Phenotypic variation within the population denved from the cross W0339 1 X OAC Speedvale . . . . . . . . . . . . . . . . 56
Histogram showing the distribution of GD coefficients calculated for the 1 10 F, individuais derived from the cross W03391 X OAC Speedvale . . . 63
Fig. 10. Plots of height (A), poddplant (B) and pod distribution (C) with genetic distances calculated using the Jaccard method . . . . . . . . 64
Fig. 1 1. Dendrogram produced by UPGMA cluster analysis of 1 10 F, individuals and parents from the cross W0339 1 X OAC Speedvale . . . . . . . 65
1.0 Introduction
Archeological records indicate that the common bean, Phaseolus vulgaris L., was
domesticated 7-8.000 years ago in both the Peruvian region of South America and the
Mexican-Guaternalan region of Centrd Amerka (Kaplan, 198 1). m e production of this
crop was initiaüy centreci in South and Central Amerka. it is now grown in North America,
West and south-east Europe, eastem Afnca, and eastem Asia (Smartt, 1990). World wide
production is estimated at 18.6 million tonnes per year (FAO, 1997).
Dry beans have been grown in Ontario since the mid 1850s (Heard et al., l988a). White
beans (navy beans) account for the largest acreage in the province but, coloured bans are
also grown. Kidney and cranberry beans make up 95% of the coloured beans grown with
blacks, pintos, browns, great northems, and pinks ail king grown occasionaily (Heard et al..
1988b).
White bean production was originaiiy based in Essex and Kent counties, but at present,
is based primarily in the counties of Huron, Middlesex, and Perth. Recent trends show white
bean acreage expanding into areas north of these counties and east into the Ottawa Valley
region. The production area has grown siowly over the past 50 years, increasing h m 29,500
hectares ( 1945-5 1 average) to 42,500 hectares ( 1990- 1995 average) (OMAFRA, 1952 and
1996). Dry edible beans are now a $50 million industry in Ontario involving 3500 growers
(Ontario hilse Cornmittee, 1997). Over 85% of the dry beam produced are exported, mainly
to the United Kingdom, for use as canned beans. White beans have a higher relative value
when compared to the commonly grown cash crops in the province, namely, corn, soybeans,
and winter wheat. White beans have been valued at an average price of $480 per tonne in
the past five years compared to $273 per tome for soybeaas (Glycine m u L.), $137 per
tonne for winter wheat (Tnticm aestivum L.), and $1 13 per tonne for grain corn (Zen mays
L.) (OMAFRA, 1996)
However, white bems are considered a high-risk crop by growers. Their susceptibiiity
to a range of diseases, sensitivity to poor weather (especially during emergence) and the
fluctuations in price have contributed to the limited acreage devoted to them. An additional
concem, particularly to breeders, is the slow rate of p ropss ihat has been made in improving
yield in white bans (Adams, 1973). The regular release of varieties which show improved
performance in trials has not translated into sigdîcant improvements in grower yields.
White beans have shown an annual yield increase of 0.18% over the past 35 years, as
opposed to 0.5% in soybeans (Voldeng, personal communication in, Kannenberg and Falk,
1995), 0.68% for barley (Hordeum vulgure L.) (Bulman et al., L993), and 1.89% in corn
(Russell, 199 1).
Current efforts by breeders to improve white bean production involve incorporating
resistance to diseases, increasing yield, decreasing days to maturity, and modifjkqg plant
architecture to a more upright form for use in narrow row planting and direct combining.
This thesis wiil examine the use of molecular marken for improving the selection
process in the white bean breeding program at the University of Guelph. Molecular markers
represent a potentiaily valuable tool which can be utilized to develop superior cultivars more
efficiendy. We propose that rnolecular markers c m decrease the time and labour required
for disease screening, a component of the breedhg prograrn at Guelph. Markea can also aid
in the selection of improved varieties by i d e n t m g genetic loci responsible for a number of
agronomic traits such as yield, maturity, and architecture. Such marken may act as a
preliminary screen of breeding material so that later selections are made fiom a superior
subset of genotypes. This would reduce the number of inferior genotypes carried to later
generations. FmaiIy, a method of selection is explored based on genetic similarity to a target
genotype. This method does not emphasize selection based on specific loci but rather, on a
complete "package."
2.0 Literature Review
2.1 Mkrkers
Marken are easily selectable, highly heritable genetic "tags" that are linked to traits
which are more difficult to select directly. Difficulty in selection may arise from the trait
having a low heritability, like yield, or because it is difficult to create the correct conditions
for selection, as with disease resistance. The concept of using markers is not new. Sax
(1923) reported that seed coat colour in beans was correlated with seed size and could,
therefore, be used as a selectable marker. Using markers to map and characterize genes
controlhng traits of interest was later suggested by Thoday (196 1). However, these ideas
could not be widely applied because the only markes available at that time were
morphological. Such markers are often the result of macromutations and are relatively rare
in nature because they are usually associated with deleterious effects causing death or
decreased viability of the individual carrying the mutation. Many of the rnorphological
markers that were available were associated with undesirable phenotypic effects like
dwarfism and aibinism.
In the past twenty-five years, the development of molecular markers (isozymes and DNA-
based markers) has created a potentiaily endless number of markers for analysing genomes.
There are many advantages of molecular markers over morphological markers. Marker
genotypes can now be determined at any stage of plant development as opposed to waiting
until the expression of the morphological marker at a certain point in the plant life cycle.
There are rnany more polyrnorphisms detectable with rnolecular markers. Molecular marken
are phenotypicaily neutral and usually two or more allelic markers can be distinguished per
locus whereas, morp hological markers are limited b y dominant-recessive interactions.
Finaliy, epistatic interactions do not occur between molecular markers. There are presently
a variety of marker systems available to the breeder, each with it's own strengths and
weaknesses.
2.1.1 Isozymes
Isozymes, or isoenzymes, are any two distinguishable proteins that catalyse the same
biochemicd reaction (Weeden, 1989). Isozymes arise in a variety of ways and have been
classified into seven categories: conformational, polymeric, chemicaily modified,
partially degraded, microheterogeneous, allelic, and genic (Markert and Whitt, 1968).
Hunter and Markert (1957) were the first to successfully separate isozymes by starch gel
electrophoresis and detect them using enzyme activity stains. This 1ed to more
widespread use of isozyme analysis and represented the first alternative to morphological
markers.
Although starch gels are still most cornmonly used for isozyme analysis, new methods
have extended the ability to resolve isozyme patterns. Polyacrylamide gel
electrophoresis (PAGE) provides better resolution of isozyme bands than starch gels,
especidly when gradients are employed. This procedure has gained only limited
acceptance because of the toxicity of acrylamide used to prepare the gels. IsoeIectric
focussing provides even better separation, however the cost of equipment and materiais
is higher than other methods.
Isozymes are CO-dominant markers and they provide many of the advantages
mentioned previously for molecular markers. However, despite being more abundant
than rnorphological markers, the number of isozyrne markers is still too lirniting for
extensive genome coverage (Tanksley et al., 1989; Dudley, 1993). Different
environmental conditions can also result in aitered isozyme expression (Weeden, 1989).
2.1.2 Restriction Fregment Length Polymorphisms ( R n P s )
Restriction Fragment Length Polymorphisms were the fmt DNA-based markers
described. Botstein et al. (1980) developed the technique and demonstrated how these
markers couid be used to create saturated linkage maps. RFLPs are generated by digesting
genornic DNA with restriction endonucleases into a large number of DNA fragments of
varying sizes. After electrophoresing the digestion products through an agarose gel, the
DNA is transferred and bound to a charged membrane (eg., nylon or nitrocellulose) by the
Southem transfer technique (Southem, 1975). Individual fragments are visualized by
hybridizing radioactively-labeiied probes, which are smail cloned pieces of DNA, to the
membrane. Polymorphisms between individuals are seen when the probe binds to
differently-sized DNA fragments. Polymorphisms rnay arise from point mutations which
create or destroy restriction sites but, more often arise by insertion or deletion of DNA
between restriction sites (McCouch et al., 1988). Probes are chosen from cDNA or genornic
libraries and normally are screened so that they only hybridize to one or two loci in the
genome. Landry et al. (1987) found that probes used from cDNA libraries yielded more
polymorphisms than probes h m genornic libraries. It has also been reported that the use of
certain restriction enzymes like EcoRI, Hindm and XbaI, often reveal the most
polymorphisms (Miller and Tanksley, 1990).
The use of RFLPs offer several advantages. There are virtuaily an unlimited number of
these markers available, making it possible to construct saturated genetic maps. Secondly,
RFLPs are codominantly inherited which rnakes it possible to detect heterozygotes.
Codominance also makes it easier to map QTLs than with dominant markers. FinaIly,
m a g e maps constructed with RFLP markers can be used with populations other than the
original mapping population.
This marker system is not without it's disadvantages. RFLP analysis is more labour
intensive and costly than other marker systems. Large amounts of purified DNA are
required, a library of cloned fragments fiom the organism k i n g studied must be available.
radioactivity must be used and the required technical expertise is higher. Additionally, RFLP
analysis does not generate as many polymorphisms as some of the other marker systems
(Vogel et al., 1994).
2.13 Microsatellites or Simple Sequence Repeats (SSRs)
Simple Sequence Repeats represent a variety of simple di-, tri-, tetra- and penta-
nucleotide repeats located throughout the genome which display length polymorphism in a
codominant manner. They are thought to be created and expanded by slippage of DNA
polymerase during replication (Queller et al., 1993). The role of SSRs in the genome is
unclear but, their ability to form the Z-DNA structure has led some to beheve they play a role
in genetic recombination, gene regulation, or chromosome condensing (Weber and May,
1989). By designing primers targeted to consewed regions which Bank the repeat region,
polymorphisms between individuals can be shown using the PCR reaction (Weber and May,
1989).
Microsatellites are very abundant in aii organisms. Approximately one SSR occurs every
30 kb in plants but, they are more common in animals. Lagercrantz et al. (1993) found there
were five times more SSRs in ariimals than in plants. The frequency of various repeat units
also differs between plants and mammals. The most frequent mammalian unit is (CA),
(34%) while in plants the (AT), repeat is most common (43%) (Lagercrantz et al., 1993).
It is known thaî microsateliites are distributed evenly throughout the mammalian genome but
few studies have demonstrateci their distribution in plants. Work camied out by Schmidt and
Heslop-Harrison (1996) and Bell and Ecker (1994) did reveai even coverage of the genome
with SSRs, although particular repeat units were distributed to particular locations.
Compared to other marker systems, microsatellites show very high levels of polymorphism.
In one study, 28 and 37 alleles were seen at two loci (Saghai Maroof et al., 1994).
Despite these advantages, there are several limitations associated with using
microsateiiites. Firstly, the identification of polymorphic SSRs in a species is costly and time
consumùig. Secondly, there is some difficulty in scoring bands which are very close in size,
particularly when they are generated from dinucleotide repeat regions. Finally, very high
mutation rates in the most variable rnicrosateiiites can give misleading information about
patemity in genetic relationship studies (Weissenbach et al., 1992).
2.1.4 Random Amplified Polymorphic DNA (RAPDs)
Random Ampiified Polymorphic DNA markers were developed by Williams et al. ( 1990)
using arbitrary prirners in a PCR reaction. The pnmers are typically 10 bp long, have a GC
content of at least 50% and are used individuaily at low annealing temperatures. This differs
h m the typical PCR reaction in which two different primes. each 17-25 bp in length, are
used at a stringent annealing temperature. Primers may bind to any part of the genome and
fragments are created when two primer binding sites are in correct orientation on opposite
DNA strands and are located close enough to each other to allow Taq polymerase to travel
between them during the elongation step of the PCR cycle. One PCR reaction can produce
up to twenty Eragments which are usuaiiy between 500 bp and 3000 bp in length. What is
amplified and where these fragments reside in the genome is not important. Again, this
contrasts with most PCR reactions in which a single, specific, known DNA fragment is
amplified. Visualization of these products is accomplished by electrophoresis through an
agarose gel and staining with ethidium bromide.
Polymorphisms arise if a primer binding site is destroyed or created by mutation or. if
primer sites are moved closer or fbrther apart by deletion or insertion of DNA between the
primer sites. RAPD markers are scored as present/absent, as opposed to dielic, which means
that the rnarkers are inherited in a dominant manner.
RAPD markers offer several advantages over RFLPs and SSRs. They are technically
simpler and less expensive to produce and they do not require such a large initial investment
of time as SSRs. Less genomic DNA of lower quality is sufficient for RAPD analysis
compared to RFLP analysis. RAPD analyses produce more polyrnorphisms per reaction than
RFLP (Vogel et al., 1994) and reveal polyrnorphisms in species that other methods c a ~ o t
(Paran et al., 1991).
However, some authors have noted that RAPD markers do not segregate in a Mendelian
fashion as consistently as other rnarkers. Only 57% of 392 polyrnorphic RAPD markers
segregated in the expected ratio in an Arabidopsis population (Reiter et al., 1992).
Homology between bands of sirnilar size is not always certain because primers ampli@ from
different areas of the genome (Whitkus et al., 1994). Reproducibility and scoring errors are
also a concern. Weeden et al. (1992) found a 5- 10% error rate when scoring RAPD data
while S b h and Nienhuis (1995) scored onIy 76% of polyrnorphic bands similady between
replicates.
2.1.5 Amplifed Fragment Length Polymorphisms (AFLPs)
Amplified Fragment Length Polymorphisms were f m t described by Vos et al. (1995).
To generate these markers, genomic DNA is digested with two restriction enzymes foilowed
by Ligation of oligonucleotide adapters to the restricted ends of the DNA. A PCR reaction
is then carried out to ampli@ specific restriction fragments. Specificity is obtained in the
PCR by using primers that are compiimentary to the adaptedrestriction site sequences and
a few nucleotides of plant DNA. By altering the combination of restriction enzymes that are
used and the end sequences of the PCR primers, the nurnber and variety of markers produced
can be controlled. Fifty to one hundred hgments are normaiiy produced and can be
visualized on a denaturing polyacrylamide gel.
The number of bands generated per reaction by the AFLP method, often called the
effective multiplex ratio, is higher than any other marker system. Powell et al. (1996)
developed the marker index to describe the utility of a marker system. It is defined as the
product of the multiplex ratio by the expected heterozygosity (the ability to distinguish
between alleles) and was highest for AFLPs (6.14). as compared to SSRs (0.56), RAPDs
(0.48) and RFLPs (0.10). Lin et al. (1996) found that this method produced more
polymorphic bands than the other marker system and the markers were highly reproducible.
The limitations of this method are the greater expense, time, and level of technical expertise
required compared to al1 other methods.
2.1.6 Sequence C haracterized Amplified Regions (SCARs)
Sequence Characterized Amplified Regions were fmt described by Paran and
Michelmore (1993) as genomic DNA fragments located at single genetic loci which are
identified by PCR using specific primers. These markers were developed to address two
problems associated with RAPD markers from which they originated. Firstly, they are less
sensitive to PCR conditions than RAPD marken so there are fewer problems with
reproducibility. Secondly, they c m be used for physical mapping. RAPD markers often
contain repetitive DNA sequences, and thus can not be used as hybridization probes for
physical mapping. The uniqueness of SCARs is defined by the PCR reaction rather than by
hybridization.
SCARs are developed by cloning RAPD markers, sequencing hem, and designing long
primes (18-24 bp) specific to the marker. Subsequently. when a PCR reaction is carried out
using these primes, only a single band corresponding to the cloned marker is produced. as
opposed to the ladder of bands normally seen with RAPD reactions.
SCAR markers cm give nse to either, a presencelabsence banding pattern where a single
band is associated with one "dele" and no band is associated with the alternate "allele", or
two bands of different size where one band is linked to one "allele" and the other band with
the second "ailele." In the latter case, such CO-dominant SCARs are usefùl in genetic studies
as they provide more information about genotypes, much like RFLP rnarkers. However, the
presencehbsence pattern does lend itself to the development of screening assays where the
presence of a marker is visualized with a simple fluorescent or colorimetric indicator added
to the PCR reaction.
There has k e n an increasing number of p a p a reporting the conversion of RAPD
rnarkers to SCARs. Garcia et ai. (1996) developed a SCAR linked to a nematode resistance
gene in peanut (Arachis hypogaea L-). and Naqvi and Chatt00 (1996) designed a SCAR for
a blast resistance gene in rice (Oryza sativu L.).
2.2 Linkage Mapping
The creation of well-saturated genetic linkage maps was not possible for most organisms
untii recently. Only in organisms like yeast (Succharomyces spp.)or fmit flies (Drosophilia
rnelanog~er) were there enough visible morphological mutations present to develop
extensive maps (Lander et al., 1987). Even then, it required decades to create these maps.
As isozymes were developed, mapping becarne possible in crops like corn (Goodman and
Stuber, 1983), tomato (Lycopersicon spp.) (Tanksley and Rick, 1980) and pea ( P i s m
sativm L.) (Weeden and Marx, 1987). Today, DNA-based markers have allowed detailed
mapping to be carried out and completed within a few years in a wide variety of species
(Jiang et al., 1997; Keim et al., 1997; Kurata et ai., 1994).
Most genetic maps are constructed with populations (usually greater than LOO
individuals) generated by controlled matings. The choice of parents used to create the
population can affect the map created. Wide crosses may have decreased levels of
recombination due to chromosome pairing problems and, therefore, produce maps with
reduced linkage distances. However, wide crosses which do not have such problems can
result in more polyrnorphic marker patterns than narrow crosses, thus giving rise to more
saturated maps (Staub et al., 1996).
Maps are cornmonly generated fiom BC, (Causse et al., 1994), F, (Concibido et al.,
1997) and F, populations (Serquen et al., 1997) or, fiom recombinant inbred lines ( R b )
(Paran et al., 1997) and doubled haploids (Tinker et ai., 1996) in species which are normally
self-pollinated or can tolerate inbreeding. The most genetic information can be derived from
an F, population using codominant markers. Ras are advantageous because they represent
a permanent mapping population that can be tested over many locations and years. With
such a population, dominant and codominant markers provide equal information. A BC,
population, in which the recurrent parent is homozygous, provides less information than a
F, population because recombination is k i n g evaiuated fiom only one parent.
The traditional method of consûucting linkage maps was to use two or three point analysis.
The fact that only a few informative meioses were foiiowed at a time resulted in only
approximaîe genetic distances. If loci were unidormative in a large number of the meioses,
larger numbers of markers needed to be analysed simultaneously to ensure that informative
markers were present in every meiosis. This is known as multiple linkage analysis but, it
becomes very difficult if five or more markers are followed.
The ideal method of mapping wouid be to analyse ali markers at once, consider a l i possible
orders of these markers and from this, select the best possible order. This method, known as
maximum likelihood analysis, was k t proposed by Mather ( 1963). It attempts to fit a
recombination value for a set of genetic loci which wiil maxirnize the likelihood of the
observations. The significance of the recombination value is then evaluated by testing the
probability that the loci are Linked compared to the chance that they are not iinked. Haldane
and Smith (!947) proposed using the LOD, or log of the odds ratio, which is defmed as:
LOD=logL(r)/L(O.S)]
where L is the Wcelihood of association of linkage between loci, and r is the recombination,
or linkage estimate. A LOD score of 3.0, or 1000 times more likely, is often used to state
that there is linkage. Such computations with large populations and large numben of
markers are oniy possible with the aid of computers. Lander et al. (1987) developed the first
practical computer package, caiied MAPMAKER, which incorporates maximum likelihood
analysis to constmct primary genetic linkage maps. This program is able to analyse data
generated from the above mentioned crosses which have been screened with dominant,
codorninant, or recessive markers. MAPMAKER is now cornmoniy used to create linkage
maps (Concibido et al., 1996; Jourdren et ai., 1996).
The fmt iinicage map of common bean was published nearly forty years ago (Lamprecht,
1961) and consisted of 26 markers on eight linkage groups. The markers were ail
morphological characteristics controlling traits for pigmentation of fiowers, pods and seeds,
plant and seed shape, and pod fibre content. This M a g e map was last updated in 199 1 and
contained 13 linkage groups with 46 markers (Bassen, 1991). Most of the marken were
morphological but a few isozyme and protein marken were included.
Several linkage maps have been constructed since that thne and are based predominantly
on DNA markers. Vallejos et ai. (1992) created the fmt and most comprehensive linkage
map using a BC, population of 68 individuals. The rnap contained 145 RFLP and
morphological loci on 1 1 linkage groups which spanned 960 CM of the estimated 1 2 0 CM
bean genome. From a flow cytometric anaiysis Arurnuganathan and Earle ( 199 1 ) calculated
the size of the bean genome to be 637 Mbp or 0.66 pg/lC. Using the Vallejos map, this
would yield an average 530 kb/cM. Several other less complete maps have been constructed
that are based on RFLP, RAPD and morphological marken (Nodari et al.. 1993: Adam-
Blondon et al., 1994; Bai et al., 1997).
2.3 Quantitative Trait Loci (QTLs)
Mendel's (Whitehouse, 1969) work was based on sirnply inhented qualitative traits. that
is, traits controiled by alleles with major phenotypic effects. While such traits were essential
in developing the concepts of segregation and independent assortment, they were by no
means common. Johannson's (Whitehouse, 1969) experiments with dwarf b a n s
demonstrated that seed size was not sirnply inherited, but rather, showed continuous variation
in size. This led him to suggest that phenotype is influenced by both genotype and
environment. Sooa after. Emerson and East ( 19 13) found evidence to support the "multiple
factor" hypothesis for continuously vaiying traits.
ideas such as continuous variation, environmental iduence and multiple factors are key
to the dennition of quantitative traits (Fakoner. 1989). The phenotype of a quantitative trait
shows a continuous (often normal) distribution, is affected to a greater degree by the
environment and is controlled by many loci (called polygenes), each having a small effect
on the trait (Falconer, 1989). These characteristics have precluded the use of Mendelian
methods to study quantitative traits.
Much work by Fischer (Whitehouse, 1969). among others, helped to develop the field
of quantitative genetics that cm be used to analyse the problerns that are particular to
quantitative traits. Typically, quantitative genetics can be used to estirnate the approximate
number of loci aKecting a trait in question, the type of gene action (averaged across the
genome), and the magnitude of the environmental influence on the expression of the trait.
However, it is impossible with these methods to determine the exact number of genes, the
type of action, the magnitude or the strength of the environmental effects on specific loci.
This basic inability to study the trait at the gene Ievel has limited Our understanding of what
a quantitative trait is and how it is expressed in the plant.
2.3.1 Estimahg Gene Number
One of the initial advantages realized using molecular markers to detect QTLs is the
ability to discover the number of polygenes affecthg a trait. However, these estimates tend
to underestimate the number of genes for several reasons. Only those loci with large enough
effects will be detected, which means there are loci with smaller effects which are not
identifieci. The number of loci detected is dependant on the size of the population studied.
As the size increases, more QTLs with smaller effects cm be discovered. A second reason
for underestimation stems from the problem that independent QTLs which are less than 20
CM apart are not distinguishable with population sizes under 500 individuals (Tanksley.
1993). The choice of parents h m which a mapping population is developed can also
influence the nurnber of QTLs found. QTLs may be rnissed because parents have identical
alleles in the region of a QTL which may have k e n detected if different parents had been
selected,
23.2 Size of Gene Effect
A review of some of the QTLs located for specific traits shows there is variability in
magnitude of effects among the loci controlling a particular trait. Most loci contribute very
little to the overail phenotypic variation, c 5%, with a few loci explaining greater proportions
of the variation, upwards of 40%. In a study by deVicente and Tanksley (1993), using an
interspecific tomato cross, 45 loci each accounted for less than 5% of the phenotypic
variation, 20 loci each explained 545% of the variation, while only three loci individualîy
accounted for more than 15% (the largest king 34%). However, there are many cases where
relatively few loci account for a large proportion of the variation. Veldboom and Lee (1994)
found that two loci controlled 40% of the phenotypic-variation in the number of ears per
plant in corn and Veldboom et al. (1994) found that five loci controlled 67.1% of the
variation in corn height.
Totalling the phenotypic variation explained by the QTLs detected provides an estimate
of how thoroughly one has identifieci the QTLs controiiing a trait. The range of explained
variance c m be quite large. Koester et al. (1993) explained 23% of the variation for height
in a corn population and Paran et al. ( 1997) discovered QT[s explaining 2 1 % of fresh weight
variability in tomato. At the other extreme, Kjaer et ai. (1995) found QTLs accounting for
75.2% and 79.9% of the variation for heading date and ear length, respectively, in a barley
population. It appears that, in most cases, 30-408 of the phenotypic variation can be
explained.
The ability to identiS a greater or lesser amount of variation depends on the
completeness of the linkage map developed for the population king used, the size of the
population, and the heritability of the trait. As marken cover a greater arnount of the
genome, the distance a QTL is from a marker decreases, which then permits detection of
QTLs with smailer effects (Lander and Botstein, 1989). A larger population size increases
the probability of detecting QTLs. Edwards et al. (1987) used a population of -1800
individuais and detected QTLç which explained as Little as 0.3% of the phenotypic variation
of certain traits in corn whüe, deVicente and Tanksley ( 1993) used a population of 142 plants
and located QTLs explaining no less than 3.0% of the variation in tomato. Finally, the
heritability of a trait will affect how much variation is accounted for since environmental
variation can decrease the likelihood of detecting QTLs. Traits which are more simply
inhented, iike plant height, tend to be more completely explained, for example. 67.1 % of the
variance for this trait was detected in a study of corn by Veldboom et al. ( 1994). Traits such
as yield are more cornplexly inhented and often less completely explained, as in the study
of Edwards et al. (1987), which found QTLs only accounted for 14% of corn grain weight.
2.3.3 Gene Action
Determinhg gene action at individual QTLs has not previously k e n possible. Pnor to
molecular marker studies of QTLs, gene action estimates were based on a whole genome
basis. The mode1 commonly used in quantitative genetics to describe quantitative traits can
4 values, d represents the degree
-44 44 44 [
' beseen in Fig. 1. The -aand+a
spresent homozygous genotype
homozygote value.
1 a=--=(+ 4- A+JQ The degree
of dominance and O is the mid-
1 da=c&edcbmnoe of dominance, dla, describes Fi. 1. A scale of genotypic values describing gene action. The degree of dominance (cüa) is the most cornmon means of expressing gene action (Falconer, 1989). gene action. If there is no
dominance, d=O, which is known as additive gene action. If A, is dominant to A,, d is
positive, but d is negative if A, is dominant to A,. Complete dominance occurs when d
equals +a or -a, and overdominance is expressed when d is > +a or c -a. QTL mapping
studies have shown that most gene action values at individual loci fdl between +1 and are
distributed approximately normally about a value of O with the extremes extending past +l
(Edwards et al., 1987; devincente and Tanksley, 1993).
2.3.4 Environmental Interaction
Since lohannson's (Whitehouse, 1969) suggestion that genotype was also influenced by
environment, this interaction has not k e n understood at the level of specific QTLs until
recently. Severai studies which mapped QTLs with the same population grown in multiple
environments have dernonstrateci that some QTLs (predominantiy ones explaining > 10% of
the phenotypic variation for a trait) rernain constant across environments while other QTLs
are only detected in certain environrnents. In one QTL s t ~ d y canied out at two locations, 33
QTLs were detected for nine traits (Serquen et al., 1997). Of the eight largest QTLs detected
in this study, six were detected in both locations. A study of 1 I corn traits detected 70 QTLs;
2 1.4% of these were important in aU three locations, 34.3% at two of the locations and 44.3%
at a single location (Ragot et al., 1995).
2.35 Strategies for Mapping QTLs
Single point analysis is the uaditional approach for detecting QTLs (Soller and Brody,
1976). Individual markers are analysed one at a time and the phenotypic means of the
progeny soxted according to marker classes (eg., AA vs. aa) and compared. The effect of an
allele (A vs. a) can be estimated by examinhg the Merence between the phenotypic means
of the marker classes. The initial investigations into QTL mapping in corn (Edwards et al .,
1987) and tomato (Tanksley et al.. 1982) used this approach. This method has severai
drawbacks including: the more distant a QTL is from a marker the more unlikely it will be
detected since crossover events occurring between the marker and QTL will cause
misclassification; the magnitude of the QTL is generally underestimated because of
recombination between the marker and the QTL; and large numbers of progeny are required
for this method (Lander and Botstein, 1989).
The most commonly used method for QTL mapping is interval mapping (Lander and
Botstein. 1989). Evidence for a QTL is demonstrated by the maximum likelihood estimate,
or LOD score. This indicates the likelihood that the data could have been arrived at by
assuming the presence of a QTL rather than assuming it's absence. The location of the QTL
does not necessarily have to coincide with the location of a marker but, can be placed
between rnarkers by analysing small intervals in the region between markers for the most
probable location. Such a method of mapping is superior if marken are 4 0 CM apart
(Tanksley, 1993) because it compensates for recombination between markers and QTLs.
Paterson et al. (1988) were the first to use the maximum likelihood method to map QTLs
affecthg nuit mas, soluble solids concentration and pH in tomato. Since then, many QTLs
have been mapped in a variety of crops using interval analysis, often using the program
MAPMAKER-QTL (Lee et al., 1996; Nandi et al., 1997; Veldboom and Lee, 1996).
Screening hundreds of individuals can be very labour intensive. To increase the
efficiency of screening for QTLs, Lander and Botstein (1989) suggested only using the
individuals that fdl in the upper and lower 5% of the distribution for the trait king mapped.
This is known as selective genotyping. It has k e n shown that this method yields similar
results to screening individuais from the entire phenotypic distribution (Ragot et al., 1995)
and has k e n used in a nurnber of studies (Mansur et al., 1993; Nandi et al., 1997).
Michelmore et al. (1991) took this a step further by developing bulked segregant analysis
(BSA). This involves combining the DNA from the individuals representing each
phenotypic extreme into separate bulks and then screening the two buiks with markers. BSA
avoids the tirne consuming construction of near isogenic lines which previously served the
same role. This has proven to be a popular method for detecting QTLs when used alone
(Thomas et al., 1995) or in combination (Miklas et al., 1996) with selective genotyping.
2.4 Ideotype Breedfng
lncreasing yield is the main goal of plant breeding prograrns. This is typically done by
selecting for higher yield per se, or for traits that are believed to positively affect yield. The
idea of breeding a mode1 plant, or ideotype, was proposed by Donald ( 1968). This concept
sets predefmed goals for plant characteristics which would constitute the ideal plant. Such
a plant would be specificdy suited to certain growing regions and/or specific cropping
systems (ie., planting density, herbicide use, and fertilizer application).
There are several examples of how selection for yield-related traits has improved yields.
The development of semi-dwarf wheat (Reitz and Salmon, 1968) led to yields in excess of
100 bu/acre in some areas. Also, selection of short rice plants with erect leaves (Jennings,
1964) initially increased yields by 18% over the common varieties of the day. Common bean
has also benefited from ideotype breeding. Adams (1982) proposed an ideotype based on an
erect plant profüe that would be suitable for machine harvesting. This work later led to the
release of the registered navy bean variety 'C-20' (Kelly et al., 1984).
The model plant proposed by Adams (1982) was defined prirnarily on the basis of
architectural traits. These included a thick hypocotyl, namow profüe, 3-5 upnght basai
branches. tail stature (50-55 cm) with 12- 15 main stem nodes and a Type II growth habit.
It has aiso been proposed that plants with such traits would be superior with respect to
lodging and disease incidence (Coyne, 1980). A study by Acquaah et al. (199 1) rated
architecture in progeny from 124 crosses to determine important indicators of plant
architecture. They identified hypocotyl diameter, plant height, branch angle, and pod
distribution as important characters for distinguishing between plants with good or poor
arc hi tecture.
Other ideotypes have been suggested for commoa bean based on physiological critena.
Wallace and Masaya (1988) suggested using biomass, days to flower, days to pod fill, days
to manirity, harvest index, seed growth rate, biomass growth rate, and economic growth rate
as selection criteria. This model has not proven to be popular for several reasons. The
number of measwements is excessively time consuming to be practical and, more
importantly, there are negative correlations between a number of these traits. For example,
Davis and Evans (1977) found a negative correlation between yield and days to matunty.
Scully et al. (1991) reported positive correlations between yield and the traits suggested by
Wallace and Masaya (1988) but, found negative correlations between harvest index and
biomass, seed growth rate and days to pod fdT and &ys to flower and harvest index, to name
a few. They also concluded that the relative selection efficiencies with these parameten were
t w low ( 4 .O) to warrant their use in indirect selection.
Rasmusson (1987) identified three steps in ideotype breeding. The breeder must fmt
identify the traits to focus on and defme the desired goal for each. This requires
understanding the relationships between the traits and yield, as weli as, arnong the traits. The
ease of scoring the trait must also be considered. Secondly, there must be sufficient genetic
diversity to justiQ using a trait. Without diversity no progress can be made. Finally, traits
are placed into one or more genetic backgrounds and evaluated. These were essentially the
steps taken by Jennings (1964) and Donald (1968) to develop shoa rice plants and semi-
dwarf wheat. respectively, except that they included reassessments of the ideotypes to
detennine if there were better ones.
Ideotype breeding aiso has it's criticisms. Many breeders feel that there is, at present,
insufficient knowledge about the morphological, physiological, and genetic components of
yield to make an informed decision about what feanires the ideal plant should have (Blixt and
Vose, 1984). An example of how selecting for an ideotype may work against the breeder is
the phenornenon of compensation. Total yield in a bean plant can be thought as being the
sum of poddplant, seeddpod and seed weight. Attempting to increase al1 of these traits
would be a logical rneans of increasing yield. At fmt these traits would seem to be ideal for
selection because they have high correlations to yield (0.77 for pods/m2, 0.57 for seeddpod
and 0.47 for seed weight) (Scully et ai., 199 1; Nienhuis and Singh, 1988) and have relatively
large narrow sense heritabilities (0.20, 0.57 and 0.74 respectively) (Scully et al., 199 1;
Nienhuis and Sin& 1988). However, Adams (1967) described negative correlations in navy
bean among pods/plant, seeds/pod and seed size. Nienhuis and Singh (1986) also reported
negative correlations between pods/m2 aod seed weight and, seeddpod and seed weight.
These studies emphasize the importance of examining the relationships among the selected
traits, and not just their individual relationships to yield.
Pleiotropy is another problem facing the breeder. For example, barley plants possessing
the e gene have multiple awns, so it was thought that e gene plants would have larger kernel
sizes than plants lacking the e gene because of enhanced photosynthetic capability.
However, plants with the e gene had fewer and smaller kemels than normal awned plants
(Rasmusson and Crookston, 1977). Researchers reasoned that the e gene influence on
growth was not restricted to the head, but occurred throughout the plant, as the multipie-
awned plants were also shorter and had smaller spikes.
The bean breeding program at the University of Guelph has adopted the t d , upnght plant
profile as a goal to work towards. This ideotype is of interest because it can exploit the yield
increases expected with denser (ie., narrow row) planting and cm facilitate harvest through
direct cornbining. Trials at five Ontario locations over a seven year period with cultivars
which had not yet incorporated the narrow profile ideotype showed an average 12% increase
in yield under such conditions (Heard et al., 1990). These values may be somewhat
misleadhg because the plots were hand harvested. However, the results indicate increased
yield potential for new varieties, such as OAC Laser, which are taller, narrower and have
pods that are held higher in the plant.
2.5 Genetic Distance Measures and Cluster Analysis
Categonzing individuals based on their genetic relatedness to one another has been
an important part of plant breeding. Plant breeders' rights have k e n established based
on the ability to distinguish varieties from one another. Accessions in breeding programs
or germplasm banks are characterized to evaluate the variability that is available for use
by plant breeders. In crops which utilize heterosis as a means of improving yield, such
as corn, assigning inbreds to heterotic groups is an important part of the breeding
program.
A common method to determine relationships between individuals in pedigreed
populations is to calculate a coefficient of kinship (f). However, it is not always possible to
calculate a f value because pedigree data are not always complete (Hahn et al., 1995). A
variety of other approaches have k e n used to assess relatedness of individuals including
comparisons of morphology, anatomy, cytology, and secondary compounds. Relating these
indicators to the extent of genetic relatedness is often difficult because the genetic basis of
the traits are often unknown and, they are present in limited number so that they do not
refiect the level of genetic diversity that is present (Whitkus et ai.. 1994). Molecular marken
are now cornmoniy used to analyse genetic relationships because of the advantages they
offer, as outlined in Section 2.1.
25.1 Methoch to Calculate Genetic Similarity/Distance Coefficients
The fmt step in establishing relationships among individuals is to calculate a similarity
coefficient. According to Sneath and Sokal (1973), there are four types of similarity
measures: distance coefficients, association coefficients, correlation coefficients, and
probabilistic similarity coefficients. The similarity rneasures associated with rnolecular
markers fall into the association coefficient and distance coefficient categories. There are
many measures available, only some of the most common will be covered. The following
data matrix wiil help illustrate each type of measure:
where n,, is the nurnber of bands shared by individuais x and y, n,, is the number of bands
present in individual y but not in individual x, q,, is the number of bands present in
individual x but not in individual y and, n, are the number of bands not present in either
genotype but seen in other individuds in the population.
In the category of association coefficients, the different methods can be divided into two
groups, those which ignore negative matches (ie., b) and those that do not. Methods which
ignore such matches are generaüy favoured because identifjing negative matches between
individuais depends on the individuals comprishg the population (ie., a negative match is
only known if the band is seen in another individual) and, absence of a band may be the
result of methodology (Vierling and Nguyen, 1992). Popular measures which ignore
negative matches include:
Nei and Li (1979):
NZy=2n,J (2n ,1 +rb, +n,o)
Jaccard ( 1908):
J, = n1J (n11 + %, + n10)
Link et al. (1995) suggested using the Nei and Li measure with RFLP data and the Jaccard
measure for RAPD data. They reasoned that RFLPs produce fwo bands of different size in
cultivars that differ at a locus but, o d y one band is produced for the comparison when the
cultivars match at a locus. Thus, matches should receive double weight, as with the Nei and
Li measure. RAPDs on the other hand tend to produce only a presence/absence pattern so
no such weighting is necessary. There have been many reports using the Nei and Li measure
(Poweii et al., 1996; Lanham et ai., 1995) and the Jaccard measure (Ford et al., 1997). The
most popular measure which does not ignore negative matches is the simple matching
coefficient:
Sokal and Michener (1958):
SMX, = (n,, + %) / (n,, + rb, + n,, +
Diers et al. (1996) used this methoci to calculate genetic distances in a population of canoia
(Brassica napus L.) cultivars.
There are three comrnod y used similarity measures in the dis tance coe ficien t category:
Euclidean distance (Sokai, 196 1):
Exy = (CI (xi - y,)')%
Rogers' distance (Rogers, 1972):
RD,, = (OSXI (xi - yi)2)H
Nei's standard distance (Nei, 1987):
4, = J., 1 (JXJyr where xi and yi are the frequencies of bandhllele 1 at a locus in the genotype x and y,
respectively. j,, = zxiyi, j, = EX:, j = z y / and J * J ,and J ,,,are the averages of j , j +ind
j, over al1 loci. Novy et al. (1994) used the Euclidean distance measure to assess
cranberry diversity, Becerra Velasquez and Gepts (1994) classified bean germplasm into
two distinct germplasm pools using Nei's standard distance and, both Smith and Smith
(1992) and Dudley et al. (1991) used Rogers' distance to measure diversity in corn
populations.
2.52 Cluster Analysis
After calcuiation of ail painvise similarities between individuals, the relationship among
them is expressed by perfonning a cluster analysis. Cluster analyses are ofien graphically
represented as a dendrogram. There are two prïnciple types of dendrograrns, those which
describe the evolutionary relationship among organisms (cladograms) and, those that
describe the relationship among organisms existing today (phenograms).
The phenetic clustericg rnethods are widely used for agricultural applications. There are
many clustering methods available but, most share the foliowing four properties: application
of a repetitive sequence of steps to produce sepuation of individuals; successive grouping
of individuals into a fmal, large set containing a i l individuals; hierarchal ordering of the
individuals; and placement of individuals into a single cluster.
The Unweighted Pair-Group Method using Anthmetic averages (UPGMA) is a very
popularclustering method (Ford et al., 1997; Mackill, 1995; Powell et al., 1996;). It initially
clusters the individuals which are most sirnilar and then recalculates the distance between
this cluster and aii other individuais. The next individuai added to the cluster will have the
smallest average distance to each of the individuals already in the cluster. Other popular
clustering methods. U e the nearest neighbour (Yang et ai.. 1996) and furthest neighbour
methods, follow the sarne procedure except individuais are added to the cluster based on
having the smallest or largest, respectively, distance to any one individual in the cluster.
2.5.3 Eff't of Marker System
Just as the choice of a similarity measure and clustering method can affect the grouping
of individuals, so to can the choice of a marker system. A comprehensive study by Powell
et al. ( 1996) compared RFLPs, RAPDs, AFLPs, and SSRs for analyshg soybean germplasm.
They found that each marker system produced weli correlated sirnilarity estimates when
individuals representing two species were anaiysed, but the correlation was rnuch lower
when only cultivated soybean accessions were used. They felt that the various sequences
sampled by each marker class (eg., low copy vs. high copy regions) and the particular
efficieocy of each class to detect polymorphisrns may become important in establishing
congruent genetic similarity estimates when germplasm is more closely related. Also,
RAPDs were less able to distinguish betwen the two species. This has also been reported
by Thormann et al. ( 1994) who suggested that RAPDs are better suited for discriminating at
the intraspecies or sub-species ievel and not at the interspecies level.
Other reports comparing rnarker systerns have found better correlations at the intraspecies
level. Prabhu et al. (1997) found good correlation between RFLP and DNA Amplification
Fingerprinting (DAF) similarity data using a set of ten elite soybean varieties and, Yang et
al. (1996) calculated comparable similarity estimates in a population of 39 sorghum
(Sorghum bicolor L.) lines using RFLP and RAPD marken.
2.5.4 Applications
There have been a number of studies which have used rnolecular markers to fîngerprint
cultivars in crops such as cranbeny [Vaminium macrocarpon (Ait.) Pursh] (Novy et al.,
1994), blackcurrent (Ribes nignun L.) (Lanharn et al., 1993, rice (Cao and Oard, 1997),
soybeans (Prabhu et al., 1997) and corn (Hahn et al., 1995). As well, genetic diversity of
germplasm has been assessed in beans (Becerra Velasquez and Gepts , 1994). Ientil (Lens
culinaris Med.) (Ford et ai., 1997). rice (Macküi, 1995), and sorghum (Yang et al., 1996).
Many reports have noted good agreement between existing pedigree data and the molecular
marker information (Cao and Oard, 1997; Prabhu et al., 1997; Dudley et al., 199 1).
However, in aU cases the genetic distances calculated with molecular marken were
approximately half the values obtained with pedigree data. As pointed out by Cao and Oard
(1997), markers are able to estimate relatedness not possible with pedigree data because
markers do not assume that unrelated cultivars have no genes in common, as pedigree
analysis does. Likewise, Prabhu et al. (1997) note that unknown relationships between
parents of two individuais artificially inflates genetic distance estimates. Markers are able
to directly sample the DNA composition of individuals which provides more accurate
estimates of genetic distance.
Using molecular markers to sort individuals into heterotic groups and predict hybrid
performance, without having to test hundreds of crosses, has k e n the focus of much study
in crops like corn, wheat and canola. Frei et al. (1986) reported significant correlation
between isozyme dissirnilarity and hybrid yield when hybrids originated from closely related
parents in corn. As parents became more unrelated, prediction of hybrid performance
became unreliable. This fuiding has not changed, even with the use of molecular marken
capable of producing a much greater number of polyrnorphic markers on which to base
genetic distance estimates. Burstin et al. (1995) reported the same findings in corn using
RFLP-based estimates of genetic diversity. Melchinger et al. ( 1990) reported that marker-
based measures of genetic distance were not useful for predicting heterotic performance of
crosses made between unrelated lines in corn. Studies in wheat (Martin et al., 1995) and
canola (Diers et ai., 1996) found similarly poor predictive ability of hybrid performance
using marker-based genetic distance estimates.
3.0 Disease Screening Using PCR-Based Markers: Application to
Common Bacterial Büght Resistance in Phasedus vulgarZr L.
3.1 Introduction
Incorporating disease resistance into commercial crop varieties is an important part of
ail breeding programs. Annual production losses throughout the world caused by diseases
amount to approximately 12% (Cramer, 1967). However, evaluating plants for disease
resistance is a timeconsuming, labour-intensive task which does not produce consistegt
results. The inability to conml factors which affect the uniformity of disease establishment,
penistence, and scoring can cause imprecise results (S tuber, 1992).
The development of simpler, quicker, and more accurate screening methods to identiQ
resistant plant breeding materid is a desirable goal. To this end, molecular markes Linked
to disease resistance pnes could significantly improve the ability to select disease resistant
individuals. While there are several marker systems available to tag resistance genes,
RAPDs are preferred for several rasons. RAPD analyses require less labour than other
rnethods and are technically simpler to produce. Additionally, they do not utilize
radioactivity, require smaller amounts of DNA, and are often able to reveal polymorphisrns
in species that show few polymorphisms with other marker systems (Whitkus et al., 1994).
RAPD markers have k e n linked to resistance genes controlling diseases in many crops such
as wheat (Tdbert et al., 1996), barley (Kutcher et al., 1996). rice (Zhang et al., 1 W6), and
common bean (Johnson et al., 1995; Young and Keily, 1997).
Although RAPD markers linked to disease resistance genes are useful, their utilization
by breeding programs is still low because of several limitations. For example,
reproducibility and scoring errors associated with RAPD data are concerns. Weeden et al.
(1992) found a 5-10% error rate when scoring W D data, and Skroch and Nienhuis (1995)
obsenred that only 76% of the bands scored in one replicate were sirnilarly scored in a second
replicate. This lack of consistency may be caused by the sensitivity of the PCR reaction to
subtle variations in the reaction environment. Also, Whitkus et al. (1994) point out that
bands of the same molecdar weight can result h m amplifications h m different regions of
the genome so band homology is uncertain. To overcome such problerns, longer primers can
be designed for the end-sequences of the RAPD fragment after it is cloned and sequenced.
Such primers, refemd to as sequenceîharacterized-amplified-regions (SCARs) (Paran and
Michelmore, 1993), can be used in PCR reactions at stringent annealing temperatures (55-
65°C) which produce more reliable results.
Another liability associated with RAPD markers is the method of detection. When
scoring markers, samples are electrophoresed through an agarose gel and subsequentiy
stained with ethidium bromide. This procedure is labounous and time consuming if there
are many samples to screen. The development of SCAR markers from RAPD markers
reduces the complexity of the products of a PCR reaction from a mixture of fragments to a
single product This allows for the development of a simple screening technique which can
differentiate between the presence or absence of DNA directiy in the reaction tube. There
are several compounds which can accompiish this (bisBenUrnide, 'Stains AU', DAPI) but,
ethidium bromide is most commonly used in rnolecular labs. This would ailow for direct
staining of PCR reactions and eliminate the electrophoresis step.
It is with these ideas in mind that we developed a rapid, reliable method to screen for
cornmon bacterial blight (CBB) resistance in common bean (Phaseolus vulgaris L.). Recent
work done by Bai et aL(1997) has linked two RAPD markers with CBB resistance genes.
CBB is caused by the pathogenic bacteria Xmthomonas campestris pv. phosedi (Xcp) and
is considered a major problem in most bean producing countries of the world where losses
can reach 2046 in severely affécted crops (Sherf and MacNab, 1986). However, the technique
is not ihited to this specific application but is applicable to any screenïng situation for
w hich molecular markers are available.
3.2 Materiais and Methods
3.2.1 Plant Materid
Twenty recombinant inbred lines ( R U ) , denoted BLT lines, derived from the
interspecific cross 'ICA Pijao' / PI 440795 (Phaseolus acutifolius A. Gray) // 'ExRico 23'
were utilized in this study (Parker, 1985). 'ICA Pijao' and 'ExRico 23' are both CBB
susceptible while PI 440795 is CBB resistant (Parker, 1985). Disease rating of the 20 RILs
was carried out by Scott and MichaeIs (1992).
3.2.2 DNA Extraction
The method of Edwards et al. (199 1) was used, with minor modifications, to extract
DNA from unifoliate leaves of 5-day-old bean plantlets. Specifically, two leaf punches were
homogenized in 400 pl of extraction buffer using a motorized pestle (Catnffamo, Ltd.)
rotating at 1500 rpm for 30 S. A centrifugation at 10,000 g for 1 min was included to remove
debris from the DNA solution.
3.2.3 Cloning CBB RAPD Markers
Two RAPD markers linked to CBB resistance, designated R7313 and R4865, were
identified by Bai et al. ( 1997). R73 13 is a 700 bp band generated by University of British
Columbia (UBC) primer 73 (5'-GGGCACGCGA-3') and is located 9.7 CM from the major
resistance gene. R4865, generated by the UBC primer 486 (5'-CCAGCATCAG-3'), is a 950
bp band which is linked to a minor resistance gene. Multiple PCR reactions were camied out
with each RAPD primer and electrophoresed at 60 V through a 1.4% agarose gel for 5 hrs.
Marker bands generated from each primer were excised kom the gel with a sterile razor
blade, pooled together and the DNA was extracted fiom the agarose using Geneclean (Bio
10 1, Inc.) according to the manufacturer's instmctions. The bands were cloned using the TA
Cloning Kit (Invitrogen) following the manufacturer's guidelines. Correctly-sized inserts
were identified by EcoRI (Gibco BRL) digestion of recombinant clones.
3.2.4 Sequencing of CIones
For each marker, three comctly-sized inserts were sequenced using the T7 and M 13
Reverse primers (Invitrogen). Sequencing was done on the AB1 Mode1377 DNA Sequencer
(Applied Biosystems. Inc.) using the P R I S W Ready Reaction DyeDeoxyTM Terminator
Cycle Sequencing Kit (Applied Biosystems, ïnc.). Plasrnids containing cloned insert DNA
were isolated using a modified mini alkaline-IysisIPEG precipitation procedure suggested
by ABL Reaction mixtures consisted of 9.5 pl of tenninator premix, 450 ng of plasmid DNA
(100 @pl) and 2 pmol of primer (2 pmoUp1) in a 20 pl volume. Cycling reactions were
carried out in the GeneAmp PCR System 9600 (Perkin Elrner) with the following cycle
parameters: block preheated to 96°C' 96°C for 10 s, 50°C for 5 s, 60°C for 4 min for 25
cycles. An ethanol precipitation of the spin column eluent was included to remove excess
terminators fiom the completed sequencing reaction before the final drying step in the
vacuum centrifuge.
3.2.5 PCR Reactions with SCAR Primers
SCAR primers specific to each RAPD marker fragment were designed from the DNA
sequences with the aid of the compter software program Gene Rumer (Hastings Software,
Inc.). These were used in a PCR reaction mixture consisting of 2.5 pl of 10X reaction
buffer (200 mM Tris-HC1 (pH 8.4), 500 mM KCI) (Gibco BRL), 0.1 mM of each dNTP
(Promega), 1.5 rn
(Gibco BRL) and 25 ng of template DNA in a 25 pl reaction. PCR reactions were carried
out in the GeneAmp PCR System 2400 (Perkin Elmer) with the following cycle
parameters: an initial hold at 94°C for 2 min, then 35 cycles of 94°C for 45 S. 60°C for 1
min, 72°C for 90 s, and a fmal hold at 72°C for 5 min. The SC.*, primers were used with
DNA from individuals whose RAPD genotype was known to c o n f m the accuracy of the
primers.
3.2.6 Direct Staining of PCR Reactions with Ethidium Bromide
PCR reactions were directly stained with 2 pl of 60 pg/ml ethidium bromide and then
visualized under UV light. The reactions were electrophoresed through agarose gels to
determine if the fluorescence results were correlated with the presence or absence of a SCAR
band.
3.3 Results
3.3.1 Cloning of R7313 and Mû65 RAPD Markers
The RAPD markers, R73 13 and R4865, are shown in Fig. 2 with the 3 parents and 20
BLT lines the RAPD pnmers were originaily tested against. R7313 was present in the
parental line PI 440795, the source of CBB resistance, and seven of the 20 BLT lines, while
R4865 was also present in PI 440795 but, only in four of the 20 BLT lines. Cloning these
markers from BLT #14 produced a range of insen sizes for both markers (Fig. 3). For each
marker, correctly shed inserts from three clones were sequenced from both ends. The
sequences of RAPD markers R73 13 and R4865 are shown in Fig. 4.
Fig. 2, Screening of parents and 20 BLT lines witb RAPD primers UBC 73 (A) and UBC 486 (B). Marker bands R7313 (A) and R4865 (B) are indicated.
33.2 PCR Using SCAR Primers
Several sets of SCAR primers were synthesized for each RAPD marker (Appendix I).
Each set was evaluated at several annealing temperatures between 58°C and 65°C for the
ability to produce a single, strongly-fluorescent, polyrnorphic PCR product. Some
combinations of R73 13 SCAR primers produced a single band of correct size in genotypes
known to contain the marker but not in genotypes known to lack the marker. Other sets did
not have this specificity. Above 64OC the band disappeared fiom genotypes containîng the
marker and below 59°C an identicai band
was dso seen in genotypes known to lack
the marker (results not shown). Similar
results were seen with the R4865 SCAR
prirners except the unique band disappeared
at annealing tempe ratures above 62°C
(results not shown). The SCAR primer sets Fig. 3. Clone iuserts produceci for RAPD markers R7313 and R4û65. Markers were cloned from BLT
used for each marker are shown in Fig. 4 . h e #14. Correct inscrt si= are indicated.
Fig. 4. Sequences of cioned RAPD markers R7313 (A) and R4865 (B). Letters in italics denote Eco RI sites, bold text indicates RAPD primer binding sites, underüned text denotes SCAR primer bin- ding sites. Only sequence data from the TI primer is given.
These primers were selected because they produced the most intensely fluorescent
bands and reliably distinguished between RILs segregating for the two RAPD
markers linked to CBB resistance. As shown in Fig. 5, iines that had the R73 13 or
R4865 marker in the RAPD assays with primers #73 and #486, respectively (Fig. 2),
also had the SCAR markers when primer pairs 73.1173.2 and 486.31486.4 were used,
respec tively.
Fig. 5. Screening of parents and BLT lines with SCAR primes designeci for markers R7313 (A) and R4865 (B). The SCAR marker bands are indicated, Note the presence/absence pattern is the same as in Fi. 2.
3.33 Mreft Staining of PCR Reactions
The PCR reactions with the SCAR primes were initialiy carried out in a PTC-100
Thermal Cycler (MJ Research, Inc.), which required minerai oil to be placed on reaction
mixtures. However, the minera1 oil interfered with the visualization of fluorescence when
PCR reactions were stained with ethidium bromide. It was possible to avoid using mineral
oil in the amplification reaction by performing it with the GeneAmp PCR System 2400
(Perkin Elmer) because this mode1 utilizes a heated sample cover to reduce sample
evaporation. The amount of ethidium bromide used to stain the DNA in the PCR reactions
(2p1 of 60nglpl solution) was optimal to aUow alternate marker genotype States (positive or
negative) to be distinguished. Higher or lower concentrations of ethidium bromide made the
difference less obvious. Fig. 6 shows the accuracy with which this technique can distinguish
the presence or absence of the R7313 and R4865 markers. For each well in Fig. 6, which
shows an increased level of fluorescence compared to the control reaction, there is a
correspondhg SCAR marker band in Fig. 5 and a RAPD marker band in Fig. 2.
- - - - - - -
Fig. 6. Dlreet sginhig of PCR maetions witb bromide, (A) PCR reactious using SCAR primers designed for marker R7313, (B) PCR reactions ushg SCGR primers designed for marker R4865- DNA c o n t d is a PCR reacîion containiag no template DNA and Taq control is a PCR reaction containing no Taq polymerase,
3.4 Discussion
The nonelectophoretic method that was developed in the present study for detecting
markers associated with bacteriai blight resistance in cornmon bean reduces the time and
labour of screening for this trait. Although the SCAR primers that were identified are only
applicable to screening for bacterial blight resistance, the general approach could be used for
any trait for which rnolecular markers are available.
SCAR markers do not have the same reproducibility problems as RAPD markers because
they are generated fkom prirners that are more than twice as long as the random primers. The
longer primers ailow more sûingent annealing temperatures to be used in the PCR reactions
so when a band is not seen during screening, it can more contidently be attributed to lack of
the marker in a sample rather than to problems with primer binding (as with RAPDs).
Severai primer sets and annealing temperatures had to be investigated before a set of
primers and conditions were identified that produced diagnostic SCARs. Some primer sets
could not be used to distinguish between the resistant and susceptible lines. The annealing
temperature also affected the ability of primer sets to distinguish resistant lines from
susceptible lines. In particular, an annealùig temperature below S8OC was sufficient to allow
mispriming between one or both primers with template from the susceptible lines which
caused a PCR product to be synthesized. This suggests that the template sequences of the
two genotypes are very homologous in the primer binding regions and that there may only
be a smail number of nonhomologous nucleotides which give rise to the polyrnorphisms.
The SCAR markea for bacterial blight resistance developed in the present study had a
presencdabsence pattern. Paran and Michelmore (1993) showed that SCAR primers can also
produce bands of different size from different aileles. Such codomuiant SCARs cm be
useful in genetic snidies but are not suited for rapid screening assays. The advantage of a
presence/absence SCAR pattern is that a simple staining procedure with ethidium bromide
of the PCR reactions, which can be performed directly in the reaction tubes. c m be used to
detect amplification h m the primers. This procedure elirninates the need to electrophorese
samples through an agarose gel which saves several hours. However, it was found that it was
necessary to use a thermal cycler which did not require mineral oil because the oil interfered
with visualization of fluorescence. In the future, the method rnight be hirther irnproved by
using fluorescent dyes coupled to the PCR primers and detection systems (eg., TaqManm,
Perkin-Elmer) that allow up to three different markers to be detected at one time.
The method of screening for common bacterial blight resistance developed in the present
study has many desirable features compared to conventional screening. Disease screening
usually occurs in the greenhouse and is a lengthy, labour intensive task which requires 45
days to complete. Additionally, results are sometimes ambiguous because of poor
inoculation of the Xanthomonas campesiris or variable growth conditions for both plants and
bacteria. Mohan et al. (1997) also point out that screening with different pathogens,
simultaneously or consecutively, is nearly impossible with conventional screening. In
addition. pathogens must be continuaily cultured and there is always concern about
maintaining vinilence. Molecular screening can be completed in a few days, it cm be
accomplished without subjecting the plant to the pathogen and provides clear results.
A marker-based screening system wiU be valuable to any breeding program. The
technique is simple to leam and the equipment that is required is ninimal, the largest running
cost is the price of Taq polymerase.
4.0 Mapping QTLs for Yield and Yield-Related Traits in Common Bean
4.1 Intmduction
Common bean (Phuseolus vulgaris L) has been the focus of several molecuiar mapping
studies. To date, the on1
as anthracnose (Collet~t~cItum LùIdemurhiancmr) (Adam-Blondon et al., 1994; Young and Kelly,
1997), mst (Uromyces appendculam) (Haiey et al., 1993; Johnson et al., 1995), cornmon
bacterial blight (Bai et ai., 1997) bean common mosaic Wus (Haley et al.. 1994), bean golden
mosaic virus (Miklas et al., 1996) and, one report mapping loci afEecting nodule number (Nodari
et al., 1993). While incorporating disease resistance into a breeding program is essential, there
are also many agronomie haits like yield, mahuity, and plant architecture which are important
to plant breeders.
Breeding to inarase yield is the main objective of plant breeders. This is typicaily done by
selecting for yield per se, or for traits which positively affect yield. Ideotype breeding,
introduced by Donaid (1968), is based on selecting a model plant which incorporates ali the
desirable traits that enhance yield. Adams ( 1 9 82) adopted this idea for comrnon bean breeding
and proposai a model plant defined prharily b
hypocotyl, narrow plant profile, upright basal branches and a tall, Type II growth habit. These
traits were successfully incorporated into a navy bean background h m which a registered
variety, C-20, was released (Keiiy et al., 1984).
Aquaah et ai. (199 1) determined which of the architectural traits defineci by Adams ( l982),
plus severai others, were effective indicators of good architecture. They found that hypocotyl
diameter, plant height, branch angle, and pod distribution to the upper tw~thircis of the plant
were the most important traits for which to select.
While Donald's (1968) onginal concept of ideotype included only morphological markers,
Rasmusson (1987) suggested widening this definition to include physio
phenological traits. At this point in time, one might also include rnolecular markers in the
definition. Using markers linked to QTLs wouid be advantageous because the breeder would
be able to evaluate traits free h m environmental interaction, and could evaluate the potential
of individuals at earlier generations.
QTL mapphg is king conducted for many traits in various crop species, such as corn
(S tuber, f 995a), tomato (deVicente and Tanksley, 1993), and barley (Tinker et al., 1996), and
is helping mearchers to better understand and manipulate these traits. Stuber et al. (1982)
provided convincing evidence thai selecting plants on the bais of markers can alter quantitative
traits when he reporteci improved grain yield h m selection at specific isozyme loci.
The goal of this research was to rnap loci associated with yield, as well as, the architectural
traits defined by Acquaah et al. (199 1) and other yield-related traits like rnaninty and lodging.
A better understanding of the genetic conü-ol of such traits will be usefid to plant breedes
wishing to alter them in a breeding program.
4.2 Materials and Methods
4.2.1 Plant Materid
The mapping population was developed from the cross W0339 1 X 'OAC Speedvale.' OAC
Speedvale is an Ontario registered varïety which has a determinate, Type 1 growth habit, early
maturity and good yield Typical of plants with Type 1 growth habits, OAC Speedvale is
relatively short and prostrate with pods held close to the ground. W0339 1 is a breeding line
h m the University of Guelph program with an indeterminate, Type II p w t h habit, late matttrity
and good yield. This Line showed promising architecturai traits such as a namw profile, tail
stature with pods held well off the ground. Single pod descent was used to advance the
population to the F,
the recombinant i n b d lines (RI&) for this study. Seed h m sel& plants was sent to the
winter nursery in New Zeaiand for increase and retumed to Canada as F,, lines.
4.2.2 Fied Design and Data Coiiecüon
In the surnrner of 1997. the 110 F,:, RILs, plus both parents, were grown in a randomized
complete block design with two replications at the Elora Research Station near Elora, Ontario.
The Elora site is situated at latitude 43" 39' N, longitude 80" 24' W on a London barn soil (silt
loam texture belonging to the Grey-Brown Podzolic great group). Plots were machine-planted
on June 3 as two, 6 m mws, with nfty plants per row and 60 cm spacing between rows.
Table 1 lists the traits measured on the population. AU trait data was taken at harvest
rnaturity (95% of the pods were brown). After reçording the maturity date, a 1 m sample
consisting of 8-10 plants was removed h m each plot to measure ail remaining traits except
yield. Yield was recordeci by harvesting the remainder of the plot and cornbining the seed with
that removed h m the 1 rn portion.
Table 1. Quantitative traits rneasured on the RILs from the W03391 X OAC Speedvale cross.
Trait Units Description
Angle de- Angle be~veen the outer bmches as rneasured through the centrai Ysis
Hiuvest lndex % Ratio of seed weight (cidjusted to 18%) to tolal above ground plmt biomass
Height cm Height o f plant from soi1 level to tip of c e n a l uis (exclriding the vine)
Hyp. Diameter mm Diamter of hypocotyl nnsured at soi1 level
% Severity of p h t lodging
Maturity &YS Nurnber of &ys h m p h t i n g to b e s t mturity (95% of pods are brown)
PodslPht count Number of pods per individuai plant
UPP P h % P d disaibuted to the uppcr two-thirds of the plant
Yield dm2 Weight of s e a d cidjusted to 18% moistwc
4.2.3 RAPD Anaiysis
RAPDs were used as genetic markers. The method of Edwards et al. (199 1) was used,
with minor modification. to extract DNA h m the unifoliate leaves of the 1 10 selected F,
plants. plus both parents. Specificaiiy. two leaf punches were homogenized at 1500 rpm for
30 s in 400 pl of extraction buffer using at motonzed pestle (Camfrarno, Ltd.). A final
centrifugation at 10,000 g for 1 min was included to remove debris fiom the samples after
dissolving the DNA in water.
PCR amplifications were canied out in 25 pl reaction volumes containing 2.5 pl of 10X
reaction buffer (200 rnM Tris-HCl pH 8.4, 500 rnM KCl; Gibco-BRL), 0.1 mM of each
dNTP (Promega). 3.0 mM MgCI, (Gibco-BRL), 0.3 pM 10-mer primer (Operon
Technologies; University of British Columbia), 2 U Tuq Polyrnerase (Gibco-BRL) and 25
ng of genornic DNA. PCR cycle parameters consisted of an initial hold at 94°C for 2 min
followed by 35 cycles of 94°C for 30 S. 36°C for 1 min, 72°C for 90 s, and then a final hold
at 94°C for 5 min. PCR products were loaded onto 1.4% agarose gels buffered in 1X TE3E
and electrophoresed for 5 hrs at 95 V in a BRL H4 gel apparatus. Gels were then stained for
10 min in 2 pg/ml ethidium bromide, destained for 20 min in distilled water and
photographed under W light.
Parental genotypes were screened with 160 RAPD pimers to identiQ polymorphic
bands. Twenty-three primers that resulted in a large number of high intensity polymorphic
bands were selected for the study. Together these primers produced 1 16 polyrnorphic bands.
The primers were then used to screen the 110 F, plants.
4-24 Linkage Analysis
A chi-square analysis was perforrned on each marker to test for deviations from the
expected 1 : 1 Mendelian segregation ratio using a 0.05 significance level.
MAPMAKER, version 3.0, (Lander et ai., 1987) was used to group and order the 1 16
RAPD markers. Initial linkage groups were defmed using the group command (LOD 3.0,
20 CM). Linkage groups which were small enough were ordered using the compare
command. Larger groups were ordered using three point analysis (LOD 3.0, 20 CM. 2
linkage group minimum) and tested with the ripple command. Linkages were expressed in
Kosambi (Kosambi, 1944) centimorgan units.
4-25 Trait Analysis
Analysis of variance was performed for dl traits to determine significant sources of
variance. Means, standard deviations, ranges, and correlations were determined using the
F,:, population data for aU traits listed in Table L with SAS (SAS Institute Inc., 1988). The
normality of the F,, distributions for each trait were tested using nomai probability plots.
4.2.6 QTL Mapping
QTL iikelihood maps were constructed for each trait with the method of interval mapping
using MAPMAKER/QTL, version 1.1 (Lander and Botstein, 1989). A LOD score of 3.0 was
used as the threshold to determine the presence of a QTL. This conservative level was
chosen, in part, because the mapping population consisted of RILs. in cornparison to
populations generated by backcrossing, the fiequency of crossovers is approximately doubled
which requires that the LOD score be increased by 0.4 (Lander. personai communication).
Each QTL detected was described by the value:
i ~ 2 = l-oL/~t
where d is the variance not explaineci by the QTL and, a: is the total phenotypic variance.
Additive gene action is also reported, dominance deviation is aot estimatable with RILs.
4 3 Results
43.1 Marker Segregation Analysis and Map Construcîion
A total of 116 polymorphic markea were identified in the mapping population. The
markers were generated from 23 RAPD prirners, giving an average of 5.0 marken per primer
(Appendix II). One hundred and seven of these rnarkers were mapped to 92 loci on 12
linkage groups (Fig. 7). Appendix III contains the marker data used in MAPMAKER to
construct the Linkage rnap. A chi square analysis showed that 86% of the rnarkers segregated
in the expected 1: 1 Mendelian ratio at a 0.05 level of signincance. The 15 markers showing
skewed segregation were confined to the central region of linkage group II.
The estimated length of the map was 5 10.1 clM (Kosambi, 1944) with an average distance
between markers of 6.4 CM. The largest interval between markers was 2 1.8 CM located on
linkage group II. There were two other large intervals, also occumng on linkage group II,
each 20.7 CM in length.
4.3.2 Trait Andysis
An analysis of variance based on RIL block means showed there were no significant
differences between blocks for ali traits tested. The assumption of additivity between blocks
and genotypes was also tested. However, there was only one replication per block so the iine
by block interaction could not be tested using the normal interaction term in an analysis of
variance. Instead, the method of Tukey (1949) was used to test for non-additivity by
partitioning one degree of freedom out of the error tem. There were no significant
interactions (p4.05) detected for any of the traits so data was combined across blocks. Al1
traits showed significant genotypic differences indicating genetic variation.
Means, standard deviations, and ranges were calculated for ai l traits (Table 2). Trait
distributions were approximately normai. AU showed transgressive segregation beyond the
upper and lower parental means with ranges of 4 to 6 standard deviations. The mid-parent
value was approximately equal to the RIL population mean for each trait except harvest index.
Table 2. Quantitative sait da& for the RILs and parents averaged across replications. Recombinant inhred Lines Partntal Meyrs
Trait (Units) M m Std. Dev. Minimum Maximum W0339 1 OAC Speedvde
A number of traits were significantly correlated (pc0.05) with yield (Table 3). For
example, branch angle. harvest index, height, lodging and podslplant were dl positively
correlated with yield. Poddplant was also positively correlated with branch angle. height,
hypocotyl diameter and lodging. The correlation between rnatunty and harvest index was
negative, but between rnaturity and height or hypocotyl diameter it was positive. Hypocotyl
diameter also showed a positive correlation with plant height and a negative correlation with
harvest index. Harvest index was negatively correlated to plant height. Lodging was
positively correlated with branch angle and plant height.
4.3.3 QTL Analysis
In total, 21 QTLs were detected for the nine traits analysed in this study (Table 4 and Fig.
7). Appendix ID contains the trait data used in MAPMAKEFUQTL to detect the QTLs. At least
Table 3. Pearson phenotypic correIation COepEiaents for nine agronomie traits using means of the RlLs from the W03391 X OAC Speedvale cross.
w c s t HYP- Yield Angle ladex Height Diameter LDdging M;inuity Pods/PLYit
Angle 0.36
Harvest index 0.27 n.s.
Height 0.46 n s -0.22
Hyp. Diameter ns. ns- 4.39 0.42
Lodging 0.44 0.48 n-S. 0.40 n.s.
Maturity n.s. DS. 4.54 0.35 0.68 n.s.
PodsiPht 032 0.25 n.s. 0.44 0.37 0.25 ns.
UPF POdS ns. 0.37 4.27 0.3 1 n.s. 0.38 0.36 n.s. n.s. = not signifimtly different h m O at P = 0.05.
one QTL was detected for each trait, with a maximum of four detected for both branch angle
and lodging. The range of phenotypic variation explained by individual QTLs was 12.9-
36.8%. QTLs were located on most M a g e groups except III. IV. V, and XI. Several
regions showed a clustering of QTLs controlling different traits. For example, linkage group
IV contained QTLs for lodging, yield and pods/plant, a portion of linkage group IX had
QTLs controlling height. yield and poddplant and, linkage group X held QTLs for harvest
index, hypocotyi diarneter and maturity.
Two QTLs were detected for yield. These mapped to linkage groups VI and IX and
together they accounted for 29.3% of the phenotypic variation for this trait. The aileles
contributed by OAC Speedvale at these loci were approximately equal in magnitude but,
opposite in effect. These same genornic regions were also shown to control podslplant and
again the alleles from OAC Speedvale were equal but opposite in effect. However, the
additive effects for this trait were not as strong as for yield. The QTLs on linkage group IX
had a negative influence on these traits wbile the QTLs on linkage group VI had a positive
influence. The QTLs for poddplant explained 25.0% of the phenotypic variation.
Table 4. Location and description of QTLs identiT~ed for nine agronomie traits. iïnkage Closest Distance LOD Additive Percent Total
Trait ~ U P Marker (cma Score variancec
Angle
Hmest hdex
Height
Hyp. Diameter
M @ n g
Mature
PodsiPlm t
Upper Pods
Yield
vm
IX
xn 1
VI11
X
n
x
X
VI
vn
vm
XII
ll
X
VI
IX
[X
VI
IX
UBC 460.4
UBC 388.1
UBC 189.3
UBC 174.5
OPE 04.6
UBC 174.3
UBC 485.4
UBC 51 1.3
OPE 043
UBC 485.4
UBC 146.4
OPE 04.4
UBC 484.1
UBC 174.5
UBC 51 1.3
UBC 485.4
UBC 146.4
UBC 376.1
UBC431.I
UBC 146.4
OPE 04.3
a Locririon of QTL LOD peak. Distance measured from closest m;ttjLer towrrrds the lower terminal end of the linkrige group. Vdue of the d e parent allele (OAC Speedvde). The ynount of phenotypic variation e x p b e d by each QTL The m u n t of phenotypic variation explained by dl Q T b found for a mit.
Only one QTL associated with pod distribution was detected and it controlled 17.2% of
the phenotypic variation and was located on linkage group K. The allele contributed by
OAC Speedvale had a negative effect on this trait. One QTL was identified on linkage group
X which affkcted hypocotyl diameter. The QTL explained 15.6% of the variation in this trait
and there was a slight negative effect associated with the QTL.
In the sarne region of linkage group X was a QTL affecting manirity. A second QTL
controlling this trait was located near the upper end of Linkage group IL Together they
accotmted for 36.8% of the phenotypic variation and both OAC Speedvale deles negatively
Muenced maturity.
Four genomic regions on Linkage groups II, Vm, IX, and XII contained QTLs affecting
branch angle. They explained 48.2% of the variability in this trait. Three of the loci
contributed by OAC Speedvale decreased branch angle while one locus increased branch
angle.
Two QTLs, located on W g e group IX and near the upper end of linkage group II, were
associated with plant height. Both alleles contributed by OAC Speedvale had a negative
effect on the trait and together they controlled 3 1.8% of the variation in height. Three QTLs,
located on linkage groups I, Vm, and X, were identified that controlled 37.0% of the
variation in harvest index. Forty-three percent of the variation in lodging was explained by
four QTLs located on linkage groups VI, VU, VIII, and XII. Al1 the QTLs had strong
additive effects, two positively affected and two negatively affected lodging.
4.4 Discussion
The bean ideotype proposed by Adams (1982) permits narrow row planting and direct
harvesthg. These production rnethods, not previously available to the grower, offer higher
yields, reduced weather-related difficulties at harvest and avoid the need for specialized
equiprnent (puiiers and windrowers). Early generation selection of upright varieties with
molecular marken would be a useful addition to traditional phenotypic selection.
The linkage map constnicted in this study is the sixth published for common ban .
RFLPs (Vallejos et al., 1992; Nodari et al., 1993), RAPDs (Miklas et al., 1996; Bai et al.,
1997) or a combination of both marker systems (Adam-Blondon et al., 1994) have b e n used
to construct these linkage maps. The current map contains 12 Iinkage groups and covers
5 10.1 CM, which is approximately 42.5% of the estimated 1200 CM genome (Vallejos et al.,
1992). Bean is a diploid and contains I l pairs of chrornosomes (2n=22). The presence of
an additional linkage gmup and the srnail size of the present map indicates a lack of genomic
information which could be addressed by mapping more markers in the population. This
would Iikely join some groups together and extend the size of others.
Fourteen percent of the markers used in the construction of the present map showed
segregation distortion. This level of skewed segregation is slightly higher than the 9% level
reported by Nodari et al. (1993) and Adam-Blondon et al. ( 1994) but. is similar to the 13%
reported by Kjaer et al. (1995) in barley, and better than the 33% level reported in rice (Nandi
et ai., 1997) and peanut (Halward et al., 1994). Skewness did not favour one parent or the
other and was not expected to affect QTL detection because the association between marker
class and phenotype rernains the same. Mmy other QTL studies have utilized maps
containing markers which showed skewed segregation (Koester et al., 1993; Veldboom et
al., 1994). Interestingly, aii of the markers showing segregation distortion were confined to
the central region of linkage group II.
Understanding correlations arnong traits is important when defining a plant ideotype.
The compensation phenornenon, or inverse relationship, arnong yield-related traits. as
described by Adams (1967), can hinder progress when attempting to increase yield. His
work and others (Nienhuis and Singh, 1988; Scully et al., 1991) have shown good
correlations between yield and podslm2, seeds/pod, and seed weight. However, negative
correlations between pods/m2 and seed weight and, seeds/pod and seed weight lirnit gains in
yield (Adam, 1967; Nienhuis and Singh, 1986).
The correlation values obtained in the present study are comparable to those reported in
other studies which examined some of the same traits. Scully et al. (1991) recorded a
positive correlation between yield and harvest index of 0.27. and a correlation of -0.66
between harvest index and maturity. Nienhuis and Singh (1986 and 1988) reported a
correlation of 0.67 between yield and height, and a value of 0.33 between yield and pods/rn2.
Tinker (1989) found a positive correlation between plant height and both stem diameter
(0.45) and lodging (0.29).
Genetic correlation between traits is, in part, the result of physically linked QTLs or
pleiotropy. There were several cases where traits shown to be correlated shared cornmon
locations for their respective QTLs. For exarnple, yield and pods per plant were positively
correlated and had QTLs at common locations on linkage groups VI and iX and had ailelic
effects that acted in the same direction. Maturity and height were correlated with one another
and had QTLs in the upper region of linkage group II with similar allelic effects. The other
QTL for maturity was located on Linkage group X dong with QTLs controlling hypocotyl
diameter and harvest index. These traits were correlated with one another and the allelic
contribution by OAC Speedvale was in the same direction for al l traits. Similar relationships
were present between height, yield and pods per plant at the lower end of linkage group K,
and between angle and lodging on linkage group XII.
Without more detailed mapping it is difficult to conclude if the QTLs in these regions are
single genetic elements that exhibit pleiotropic effects, or if they contain several tightly
linked QTLs. Nodari et al. (1993) also noticed loci in commoo bean controlling more than
one trait. Two host-bacteria interactions, Rhizobium noduiation and common bacteria blight
resistance, s h e d a common locus. Many other studies have noted QTLs controlling
53
different traits rnapped to the same chromosomal region (Kjaer et al., 1995; Paran et al.,
1997; Serquen et al.. 1997).
The total phenotypic variation explained for each trait raaged From 15.6% (hypocotyl
diameter) to 48.2% (branch angle). Thus, it is apparent that not ai l loci acting to control
these traits were identified. The missing variation might be attributed to the 50% of the
genome that was not mapped in the present study andor loci with small effects that could
not be detected because of the population size. Since a significant amount of variation can
be explained by a large number of loci, each with a smaii effect on the trait. Ragot et al.
(1995) found that 40% of the QTLs identified for eight traits each explained less than 10%
of the phenotypic variation, and in a study by deVicente and Tanksley ( 1993). approximately
85% of the QTLs found each accounted for 40% of the variation. The srnailest value
observed for explained variance by a single QTL was 12.9% (see harvest index in Table 4).
Several QTL studies using similas population sizes have reported comparable values for
minimum detectable QTL effects (Veldboom and Lee, 1996; Paran et al., 1997). In general,
as population size increases, smaller effects are detectable. In a study by Edwards et al.
(1987), a population of 1800 corn F2 families was used to detect QTLs explaining as Little
as 0.3% of the phenotypic variation.
This study used RILs to detect QTLs. With such a population, dominance gene action is not
estimatable because, in theory, al i loci are homozygous so that only additive gene action is
present For a bean breeder, knowledge about additive gene action is important because bans
are bred to homozygosity. It was intereshg to note the direction of the additive effect
contributed by the male parent (OAC Speedvaie) for each trait. For traits iike height, hypocotyl
diameter, matLUity and pod distribution, the effect was negative which is in agreement with the
parental value for these traits. However, OAC Speedvale contributeci alleles which positively
and negatively affected h t s such as yield, pods per plant, lodging and branch angle. M e r
studies have also reported the contribution of aileles with opposite effects to those expected,
based on the parental phenotype (Paterson et al., 1988; deVicente and Tanksley, 1993). These
results would explain the tmsgressive segregation seen for the traits in the progeny. Thaî is,
since each parent has a combination of positive and aegative aileles affecting a trait, when they
are crossed the progeny will receive a combination of positive and negaîive aiieles h m both
parents. This will result in some progeny with more positive deles than the superior parent and
some with more negative alleles than the iderior parent. The range of phenotypes produced by
such recombination of aileles was obvious in this study (Fig. 8).
This study has provided some initial insights into the genetic control of several
agronomically important traits in ban. It represents a first step towards utilizing rnarker-assisteci
selection for complex traits in bean breeding. To date, there have been only a few reports
comparing the efficiency of marker-assisted selection to conventional phenotypic selection with
respect to gain h m selection. However, results have been encouraging. Stuber et al. (1982)
showed that one cycle of selection based on seven isoenzyrnes previously identifid as king
associateci with higher yield could increase yield equivaiently to one-and-a-haif cycles of full-sib
selection. Frei et al. (1986) and Stuber and Edwards (1986) found that selection at isozyrne loci
produced equivalent results to phenotypic selection. In each of these studies, the selections took
place in populations h m which the favourable loci were identifieci.
As pointed out by Stuber (1 !XE), QTLç identifid for a trait should be assessed in severai
genetic backgrounds, in several envüonments, and be evduated for affects on other traits of
importance before utilizing ?hem in a breeding program. These questions are gradually k ing
addressed. Tanksley ( 1993) noted the^ is evidence accumulating that QTLs explaining
larger arnouots of phenotypic variation (>IO%) tend to remain important across
environments. Stuber ( 1995b) showed that marker facilitated backcrossing of important
QTLs into different corn iines can improve yield signifïcantly.
While more information must be obtained about the QTLs identified in this study, the
stringent LOD score used to detect hem (3.0) and the large amount of variation explained
by each make them promising candidates for use in marker-assisted selection.
5.0 Using Genetic Distance to Estimate Progeny Performance
5.1 Introduction
Genetic diversity estimates calculated from molecular marker data are used frequentiy
in plant breeding to categorize individuals, Lines and families. Diversity measurements have
been used to fingerprint cultivars (Novy et al., 1994; Cao and Oard. 1997; Prabhu et al.,
1997), assess germplasm divenity (Yang et al.. 1996; Ford et al.. L997), and predict hybrid
performance in crops like rice (Zhang et ai., 1994). canola (Diers et ai.. 1996), and corn
(Melchinger et ai., 1990; Dudley et ai.. 1991).
The relationship between genetic distance and heterosis has been studied most intensively
by corn breeders. The ability to predict heterosis without having to produce and assess
hundreds of single-cross hybrids would reduce the time and effort required to identib
promising combinations. Frei et al. (1986) fmt reported the limitations of this application.
They found that hybnd performance could be better predicted when the parents were closely
related. As the parents became more unrelated, predicting progeny performance became
unreiiable. While the Frei et ai. (1986) study was based on only a limited number of isozyme
markers, studies that have used better marker systerns iike RFLPs, RAPDs, and AF'LPs
which provide a greater number of polyrnorphic markers on which to estimate genetic
diversity, have corne to the same conclusion (Melchùiger et al.. 1990; Burstin et al.. 1995).
Bean breeding does not exploit heterosis as a means of yield improvement. ïnstead.
varieties are released as homozygous, homogeneous inbred lines. Progeny are often
advanced to the Fa-F, generation by single seed or single pod descent and then phenotypically
assessed on a single plant basis. Selection schernes based on single plants are advantageous
in ternis of the large number of individuals that can be carried in a breeding program, but
evaluating the tme potential of an individual is compromised by environmentai interactions.
These interactions reduce the ability to select superior genotypes. The avaiiability of diable
genetic selection criteria, to complement phenotypic selection, would increase the selection
efficiency whüe maintainhg the large base of individuals to select from.
Crosses that produce high performing progeny are often made with elite germplasm that
has been moulded to meet criteria that are defined for a particular growing region. The
current study was initiated to explore the relationship between genetic distance and
performance of progeny derived from an elite cross. This required selection of a "target"
individual to act as a reference point to which all individual plants could be compared.
Because there is less information about the individuals from a cross in a breeding program,
the target was limited to one of the parents, which would be better characterized. Yield is
often the best defined trait in a breeding prograrn, and ofien the most important objective,
so it was chosen as the defuiing criteria for selecting the target parent.
Several agronomie traits were examined, including those identified by Acquaah et al.
(1991) to be key indicators of good plant architecture. These traits were a thick hypocotyl
diameter, narrow plant profile, tall stature, and pod distribution to the upper canopy of the
plant. Architecturai traits were used by Adams (1982) to define a t d , upnght bean ideotype,
suitable for narrow row planting and machine harvesting, which has k e n incorporated into
some breeding programs. Other traits that were assessed included yield and yield-related
traits iike poddplant, maturit., and lodging resistance.
Elite germplasm has a narrow genetic base and the progeny from such crosses are
generaUy closely related. Based on the results from the corn studies. progeay from elite
crosses are appropriate materiais to use when investigating whether a correlation exists
between genetic distance and plant performance. The discovery of significant relationships
between traits and genetic distance could lead to the development of a selection scheme
based on a consideration of genetic distance and phenotypic observations.
5.2 Materials and Methods
5.2.1 Plant Material
The RIL population was developed from the cross W03391 X OAC Speedvale as
described in Section 4.2.1.
5.2.2 Field Design and Data Coiiection
As described Ui Section 4.2.2.
5.23 DNA Extraction and RAPD Anaiysis
RAPDs were used as genetic markers. The rnethod of Edwards et al. (1991) was used,
with minor modification, to extract DNA from the unifoliate leaves of the 110 selected F,
plants plus both parents. Specificaliy, two leaf punches were homogenized at 1500 rpm for
30 s in 400 pl of extraction buffer using at motorized pestle (Camframo, Ltd.). A final
centrifugation at 10,000 g for 1 min was included to remove debris from the samples after
dissolving the DNA in water.
PCR amplifications were carried out in 25 pl reaction volumes containing 2.5 pl of 10X
reaction buffer (200 mM Tris-HC1 pH 8.4, 500 mM KCI; Gibco-BRL), 0.1 rnM of each
dNTP (Promega), 3.0 m . MgCl, (Gibco-BRL), 0.3 pM IO-mer primer (Operon
Technologies; University of British Columbia), 2 U Taq Polymerase (Gibco-BRL) and 25
ng of genomic DNA. PCR cycle parameters consisted of an initial hold at 94°C for 2 min
followed by 35 cycles of 94°C for 30 s, 36°C for Z min, 72°C for 90 s and then a final hold
at 94°C for 5 min. PCR products were loaded ont0 1.4% agarose gels buffered in IX TBE
and electmphoresed for 5 hrs at 95 V in a BRL H4 gel apparatus. Gels were then stained for
10 min in 2 p g h l ethidium bromide, destained for 20 min in distiiled water and
photographed under W light.
Parental genotypes were screened with 160 RAPD primers to identify polymorphic
bands. Thirty p h e r s that resulted in a large number of high intensity polymorphic bands
were initially selected. Two additional, independent screenings of the parents were carried
out with these primea to ensure the consistency of the polymorphic bands. Twenty-thee
prime= which gave reproducible bands were selected for this study. Together these primers
produced 1 16 polyrnorphic bands. The primers were then used to screen the 1 10 F, plants.
52.4 Data Analysis
RAPD marken were scored as present ( 1 ) or absent (O). Only the 1 16 polymorphic
bands were scored, as opposed to a i l bands present on the gels, which meant that the parental
genotypes represented the extreme values of the similarity coefficients calculated. Similarity
coefficients among the 110 F, individuals and parents were caiculated with the Jaccard
(1908) method using SYSTAT, version 5 (SYSTAT Inc., 1992):
J, = n1,I (n, 1 + 40 + no,)
where n,, is the number of bands shared by two individuals, n,, is the number of bands
exclusive to one individual and, q,, is the number of bands exclusive to the second
individual. The similarity coefficients were subtracted from 1 to produce genetic distance
coefficients.
Analysis of variance was performed for ail traits to determine significant sources of
variation. Means, standard deviations, and ranges were detennined for al1 traits listed in
Table 1 using the F,, population data. Distance coefficients calculated between the F,:, lines
and the target parent were then correlated to the F,, line trait data using PROC CORR (SAS
hstitute Inc., 1988).
Distance coefficients calculated among dl F,:, lines and parents were used to cluster
individuais by the Unweighted Pair-Group Method using Arithmetic Averages (UPGMA)
(Sneath and Sokal, 1973). Using this information, individuais were grouped according to
their genetic distance from the target parent. Each successive branch point on the
dendrogram h m the target was used to partition the individuals. Trait data h m individuals
within clusters was combined to calculate means and standard deviations. These means were
correlated with the genetic distance using PROC CORR (SAS Institute Inc., 1988).
Finally, individuds were rated on the basis of their phenotypic characteristics. For yield,
pods/plant, hypocotyl diameter, and harvest index, an individual exceeding the best parent
value received a score of one for each trait, except for yield in which a score of two was
assigned. For traits like pod distribution, maturity, Iodging, height and branch angle, in
which the best parent was very close to the phenotypic extreme, the mid-parent value was
chosen as the Ievel to exceed in order to receive a score of one. This resdted in a scale fkom
O to 10, O k ing an agronomicaily poor individual and 10 k i n g an agronornically superior
individual.
5.3 Results
5.3.1 Trait Analysis
An analysis of variance based on RIL block rneans showed there were no significant
difierences between blocks for aU traits tested. The assumption of additivity between blocks
and genotypes was also tested. However, there was only one replication per block so the line
by block interaction could not be tested using the normal interaction term in an analysis of
variance. hstead the method of Tukey (1949) was used to test for non-additivity by
partitioning 1 de- of freedom out of the error term. There were no significant interactions
(p4.05) detected for any of the traits so data was combined across blocks. AU traits showed
significant genotypic differences, indicating genetic variation.
Means, standard deviations and ranges were cdculated for dl traits (Table 2). Trait
distributions were approxirnately normal. AU showed transgressive segregation beyond the
upper and lower parental means with ranges of 4 to 6 standard deviations. The mid-parent
value was approxùnately equal to the RIL population mean for each trait. OAC Speedvaie
was the better yielding parent, as was expected, so it was selected as the "target" on which
to base comparkons.
5.3.2 Correlation Between Jaccard Distance Coefficients and Trait Values
Fig. 9 shows the distribution 1
i of genetic distance (GD) 1 25 4
coefficients calculated between
the F,, lines and the target
parent. OAC Speedvaie. The
mean GD coefficient was 0.656
Fig. 9. Histograrn showhg the distribution of GD coeffiicients Correlations between calculated for the 110 F, individuais derived from the cross
W03391 X OAC Speedvale. GD coefficients are with respect to GD coefficients and branch the target parent OAC Speedvaïe.
1 [
l I
I I
l l
loIf 5
t 0-1 1 I 1 1 1 1
with a range from 0.382 to
angle, harvest index, hypocotyl diameter, lodging, maturity and yield were ail srnail and non-
O QI 02 Q3 a4 0.5 Q6 Q7 a8 a9 1
M c D i ç & n c e
significant (Table 5). Oniy height (r = 0.23), pods/plant (r = -0.30) and pod distribution (r
= 0.30) were significantly correlated to GD (Table 5 and Fig. 10).
Table 5. Pearson correlation coeffllcients between nine agromonic traÏts and genetic distances caicuiated by the Jaccard method or by the UPGMA cluster analysis.
20 -1 1 O OP 0.4 0.6 0.8 1
Genetic Distance
Genetic Distance
O OP 0.4 0.6 0.8 1
Genetic Distance
53.3 Cluster Anaiysis and
Comlation with Trait Values
Tbe dendrogram produced by the
TJPGMA cluster analysis separated aii
110 F,,, lines and parents fiom one
another (Fig. 11). From OAC
Speedvale, ten individual clusters were
identified with genetic distance (GD)
values ranging from 0.013 to 0.450.
Trait data from lines within each
cluster was combined (Table 6). With
the exception of cluster 9, which
contained only one line, the standard
deviations were relatively consistent
across clusters despite differences in
the number of iines contained within
each (ranging fkom two in clusters 1,8
Geneüc distance &fflaents were enlailateci &ing the Jaccard method. Comtations between GD vdues and
Fi. Il. See following page for caption.
4 Fig. 11. Dendrogram pduced by UPGMA cluster analysis of 110 Fs individuals and parents fmm the sas W03391 X OAC Speedvaie. Ciustermg was based on J a d distance M ~ a e n t s using 116 RAPD markers. Individuals are listed to the left of the deodrogram (indicated as numbers), Y#% indicate top yieiding liaes and brackets at the far left denote the 10 clusters identiried when OAC Speeàvale was used to initiate dustering. The distance scale is located a m the top with specific values listed at branching points for the 10 clusters.
Table 6. Cenetic distances and trait means (standard deviations) for clusters generated by UPGMA cluster analysis.
Ciuster Genetic Hyvest HYP- p w UPF No. ~ismœ' Angle index Height Diameter Mging M h t y Plant Pods Yield
(4.9) (5.3) (2.3) (0.063) (7.1) (0.0) (3.2) (2.0) (7.8)
mm the mget p n t OAC Speedvde
trait values were large and significant for ail traits except plant height and yield (Table 5).
The largest correlations with the cluster values were found for hypocotyl diameter (r = 0.90),
harvest index (r = -0.85), and upper pods (r = 0.84).
5.3.4 Phenotypic Ratings
Each parent raîed a 5 on the scale used in this study, with individual F, scores ranging fiom
O to 8. Ratings of individuals are presented in Table 7, summarized across clusters identified
ushg the UPGMA clustering method. The nIst four clusters containhg individuals most related
to OAC Speedvale had the highest ratings (except for cluster 9 which contained one individual)
and accounted for 13 (62%) of the 2 1 highest rated individuals. Seven of the top ten yielding
lines, including the top five, were also found in the fïrst four clusters (Table 7 and Fig. 1 1).
Tabk 7. Phenotypic rating of hdividuals within dusters and the distribution of top yielding: individnals in the population.
Cluster [ndividudd Avcnge k t No. (96) of fndividuals No. of Top 10 Yiclding No. Cluster Rating ht ing Exceediag a Score of 5 bdividuds
5.4 Discussion
The rate of yield improvements in bean has been slow in cornparison to other field crops
(Adams, 1973). One approach to this problem has been to define a bean ideotype, based on
architectural traits, which could produce higher yields (Adams, 1982). This approach has
resulted in the successful release of the variety C-20 which incorporated these traits (Kelly
et al., 1984).
Equaily important to progress is the ability to efficientiy select superior individuals From
a population. The evaluation of individuals based solely on their phenotypes can be
inefficient when traits are of low heritability. The use of molecular markers can potentially
improve the selection process in a breeding program. This study investigated the use of
molecularly defined genetic distance data as a basis of selection within a population derived
67
h m an elite cross.
There were poor and non-significant correlations between GDs from OAC Speedvale
coefficients and trait values for al1 traits except height, pods/plant, and pod distribution.
Even for the traits that were correlated to GD, the values were too s m d (r c 0.30) to be
useful for breeding purposes. Similar results have been reported in studies exarnining the
relationship be~veen GD values and heterosis within narrow crosses of corn (Melchinger et
al., 1990; Burstin et ai., 1995).
The difficulty with making such a simple cornparison is that the analysis includes random
differences between individuals as well as meaningful ones. This is because not al1 of the
marken included in this study represent loci affecthg the traits studied, so the unassociated
markers simply increase variability in a random way. This fmding has been noted by several
other authors (Bemardo, 1992; Martin et al., 1995). Also contributing to the poor correlation
is the large number of marker combinations that cm produce the same GD coefficient. This
results in trait variability king distributeci within specific values instead of dong a range of
GD coefficients, as demonstrated in Fig. 10.
A more rneaningful method of descnbing the reiationships among individuals is required
without increasing the work (ie., by taking more measurements in the population). Cluster
analysis is able to accomplish this by grouping similar individuals together and
distinguishing groups fiom each other. A hierarchal ordering is estabiished using the entire
population instead of focusing on one individual to order the population. The WGMA
cluster analysis produced ten distinct groups when OAC Speedvale was used as a seed to
initiate clustering. The GD values of the groups were very well correlated with seven of the
nine traits examineci in tiiis study. Yield was not one of the correiated traits but, a trait that
is often associated with yield, poddplant, was highly correlated. Such hi& correlation
values indicate that the clustering procedure placed the lkes into meaningful groups. Also
indicative of appropriate clustering was the fmding that the standard deviations across the
groups were simüar and were aimost always smaiier than the population standard deviation.
This information could be used by a breeder in the following way. After selecting a
target individual based on certain criteria, such as yield, the clustering around this individual
could be used to provide a "cut-df'' point. This point would help focus subsequent
phenotypic selections and could be used in earlier generations to reduce the number of
individuals carried in the program. Based on the idea that individuals clustered nearest to
the target are more similar, the cut-off point could be adjusted depending on how much one
was willing to deviate from the target.
Within this population, the fmt three clusters together contained 13 individuals (1 2% of
the population) and held four of the top ten yielding lines including the first and second
ranked. If the cut-off was extended to include the fourth cluster, a total of 39 individuals
would be encompassed (35% of the population) and seven of the top ten yielding individuals
would be captured including al1 of the top five. Additionally, when lines were ranked on a
scaie of 1 to 10 (based on their phenotypic data) 7 of the 2 1 lines having scores better than
5 (the parental scores) were within the first three clusters and 13 were within the fint four.
This indicates that there was incorporation of genetic material from the other parent into
individuah clustered around the target which improved their overall performance above that
of the parents.
This study has provideci some insights into the genetic composition of a breeding population
derived h m e h parents. There were stmng relationships between most of the traits studied and
GD values calcuiated h m a target individual using UPGMA cluster analysis. Additionally,
some of the most promising individuals were clustered mund the target, indicating that
beneficial attributes h m the second parent were incorporated into these individuais. These
findings suggest that GD values can be usehil for selecting a sub-sample of the population in
which a breeder wili have a better chance of selecting the superior individuals h m the
population.
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Sequences of the R73 1 3 and R4865 Cornmon Bacterial Blight resistance markers. Location of EcuFU restriction sites Located on the pCRII clonhg vector, RAPD primer sites and, SCAR primer sites are indicated on the sequence.
ATGACACCCTTCCCATATTATCCAATGAACATTTGCTTACAACCWTCAGATTTCTACAACTCA TTCTTGGCCATCATTAGGAAGTTCAAGTTCATGWAAGTAAAATTAAGTAAGGTTGATAGGAGAG AACAGCAT?Y;ATGAAGTTTAGACAG"SmWCW?Yj?V1VriIiTi'AGnGnWAGTATG TCAACAGAAAACGGCTTGCANAAAGCGGCCTCNTGTNANGGGACAGTTTGKCGAAAGTTGAATG GTGTTAATGTGGATCTTAATGGACAACCTACTTCCACTGATTATGATGAANAANA%ATGAANAA GAGGAAGACACCATGTTTCAAGAGATTCTCTCCTGANGATGCTTGAGATCTCAATGCACCT~ CCAWGGTGAA~ACCAATGAATTNTTTCTGCAGGAAATTAGCCTGTTGAGGACTTGATACTGT T C T T G A T C T n ; A T G G A A A A T T C A T C T A A C A C T A G T G G C A GGGTAAAGGATGCTGATCCAATTCCCCCTNTGCACCCTCCTCATCATTTGGTGGGGTCTCGCAAA TTTCACn"rCTCAAAZlAAAGAAAGATATATATCTTCCTGAAGGTGTACCn;CTGCAGGTCTTCCC CAAGTGGTNGAGTTGAAAGTATTACACTTTTGTTTAATtAAAACTTTTGGAACTATTATCTGAGA TTTACTGTCAAACAAATTTTACAACCAGCTCAAGCATATTCCTCTTCGTGATTATTACTATGTAC
lower-case text: EcoRI sites bold text: RAPD primer sites -: SCAR primers selected for screening assay italicized text: SCAR primers rejected for screening assay
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