MICROSATELLITE GENETIC DIVERSITY BETWEEN AND WITHIN
FOUR HYBRIDIZING RED OAK SPECIES, AND THEIR
ECOLOGICAL IMPLICATIONS
by
FETUN DESTA
A THESIS
Submitted in partial fulfilment of the requirements
for the degree of Master of Science
in the Department of Biological and Environmental Sciences
in the School of Graduate Studies
Alabama Agricultural and Mechanical University
Normal, AL 35762
March 2014
Submitted by Fetun Desta in partial fulfilment of the requirements for the
degree of MASTER OF SCIENCE specializing in Molecular Biology in
BIOLOGICAL AND ENVIRONMENTAL SCIENCES
Accepted on behalf of the Faculty of the Graduate School by the Thesis
Committee:
Major Advisor
Dean of the Graduate School
Date
ii
Copyright by
FETUN DESTA
2014
iii
MICROSATELLITE GENETIC DIVERSITY BETWEEN AND WITHIN FOUR
HYBRIDIZING RED OAK SPECIES, AND THEIR ECOLOGICAL
IMPLICATIONS
Desta, Fetun, M.S., Alabama A&M University, 2014, 50pp.
Thesis Advisor: Dr. Khairy Soliman, Ph.D.
Genetic diversity between four red oak species, northern red oak (Quercus rubra L.),
southern oak (Quercus falcata), scarlet oak (Quercus coccinea) and black oak (Quercus
velutina), and diversity within these species were studied. Eight microsatellite loci were
used to detect genetic characteristics of red oak species in the Bankhead National Forest
of the Cumberland Plateau in Northwest Alabama. The highly polymorphic
microsatellite markers have been reported to be applicable in genetic diversity studies
of closely related species such as red oak species. Due to their extensive hybridization,
oak species have been known to defy the biological species concept. Beyond
morphology, genetic evidence of extensive hybridization between red oak species is
still scarce. Species differentiation was detected based on the total number of alleles
(AT) comparisons between species. An observed heterozygosity (Ho) comparison
indicated there was no significant difference in heterozygosity between the four red oak
species. The mean number of the observed heterozygosity was lower than the mean
number of the expected hetrozygosity in all species resulting in positive values of mean
inbreeding coefficients (Fis) in all species. Since significant levels of inbreeding are not
expected in outcrossing and wind pollinated oaks, the heterozygote deficiency detected
in this study seems to deviate from the expectation and may require further
examination.
iv
KEY WORDS: hybridization, introgressive hybridization, microsatellites
v
TABLE OF CONTENTS
CERTIFICATE OF APPROVAL .............................................................................................ii
ABSTRACT ............................................................................. Error! Bookmark not defined.
LIST OF TABLES .................................................................................................................viiii
LIST OF FIGURES ............................................................................................................ viiiiii
ACKNOWLEDGEMENTS .................................................................................................... ixx
CHAPTER 1
INTRODUCTION .......................................................................................... 1
CHAPTER 2
LITERATURE REVIEW ………………………………….…......................7
2.1
2.2
2.3
CHAPTER 3
The process of hybridization ........................................10
Hybridization between oak species .............................. 12
Molecular Marker Application .....................................14
MATERIALS AND METHODS .................................................................. 21
3.1
3.2
3.3
3.4
3.5
Sample Collection ........................................................ 21
DNA Extraction ............................................................ 22
DNA Quantification ..................................................... 26
Polymerase Chain Reaction.......................................... 26
Data Analysis ............................................................... 28
CHAPTER 4
RESULTS ..................................................................................................... 30
CHAPTER 5
DISCUSSION ............................................................................................... 35
REFERENCES ........................................................................................................................ 40
VITA
vi
LIST OF TABLES
Table 1.1.
Characteristics and adaptations of the four red oak species……….4
Table 3.1.
Amount of samples per species collected from eight sites ………21
Table 3.2.
Analyzed DNA samples and corresponding sites………………...23
Table 3.3.
PCR Protocol......................................................................................... 27
Table 3.4.
Primer names, 5’3’ sequences and annealing temperatures .................. 28
Table 4.1.
Microsatellite loci and their allele size ranges. ..................................... 30
Table 4.2.
Microsatellite allelic genetic diversity between the four red oak
species ................................................................................................... 31
Table 4.3
Distribution of alleles differentiation between sites……………...32
Table 4.4.
Microsatellite genetic diversity between the four red oak species
per locus, and within species ................................................................. 33
Table 4.5.
Anova results for number of alleles comparison between species ........ 34
Table 4.6.
Anova results for observed heterozygosity comparison between
species ................................................................................................... 34
vii
LIST OF FIGURES
Figure 3.1.
Bordered region in map is location of Bankhead National Forest in
Alabama. Letters A-H are clusters where samples were collected
from. ...................................................................................................... 25
viii
ACKNOWLEDGEMENTS
I would like to thank my advisor, Dr. Khairy Soliman, for giving me the
opportunity to do this research. I would also like to thank Dr. Soliman for the guidance
and support he has given me throughout this research. I would like to thank my advisory
committee, Dr. Yong Wang, Dr. Andrew Scott, and Dr. Luben Dimov for the
constructive feedback they have offered me during my proposal defense and through
the times when I needed their advice. I would like to thank our lab manager, Timley
Watkins, for being there to share her expertise in the many lab procedures and
troubleshooting problems with equipment. I would also like to thank the Bankhead
National Forest service staff for guiding me through different sites during the sample
collection process. Finally, I would like to thank the NSF for funding CREST projects
which allowed this research to go on.
ix
CHAPTER 1
INTRODUCTION
Ability of a population to respond to environmental changes can be severely
impacted by reduced genetic diversity (Moran, 2010). Continued adaptability and
evolution of forest tree populations facing natural or human-induced population
changes depend on maintenance of genetic diversity (Buchert et al., 1997). Therefore,
effective management of forest populations for sustainable forestry require assessment
of distribution of genetic variation at compositional, functional, and structural levels
(Grant, 2010). Genetic composition relates to the number and type of alleles and
genotypes present in a population (Grant, 2010). While genetic structure implies
heterozygosity and allelic distribution within and among populations, genetic function
refers to fitness (Geber and Griffin, 2003; Grant, 2010). Heterozygosity measures
genetic variation of a locus in a population and refers to the frequency of individuals
that have different alleles in the two chromosome sets for the particular locus (Griffiths,
2005). Studies have suggested a direct correlation between heterozygosity levels and
fitness of populations, which indicates that a loss in heterozgosity is linked to reduced
population fitness through inbreeding depression (Reed and Frankham, 2003). Chances
of a population maintaining viability, and more individuals surviving environmental
changes are enhanced by genetic diversity and the presence of more alleles in the
population which increases the likelihood of some of those alleles being associated with
1
beneficial phenotypes (Grant, 2010; Hanski, 1999). Ecological Consequences of
genetic diversity affect ecosystems significantly in relation to processes such as
disturbance recovery, species competition, population structures, and fluxes of
resources (Hughes et al., 2008). For extensively hybridizing species such as oaks,
natural hybridization plays a huge role in maintaining genetic variation within species
(Curtu et al., 2007). It is believed that hybridization can increase not only genetic
diversity but also, as a result enhance adaptive potential in oaks due to beneficial
introgression such as high heat and drought tolerance allow adaptive responses to
environmental and climate changes (Moran, 2010).
The utilization of molecular markers in genetic diversity studies has advanced
through the years, but it seems that, despite these advancements, much remains to be
explored due to the fact that inferring accurate phylogenies remain challenging (Sang,
2002). Few universal molecular markers such as RFLPs, RAPDs, AFLPs, and SSRs
have been relied upon for genetic diversity studies. Each marker has its own advantages
and drawbacks. Identifying an appropriate strategy and employing an appropriate
molecular marker depend on the nature and extent of the study and the availability of
resources as some markers are much more demanding and costly than others. For
example, although non-PCR based RFLPS are more demanding in terms of costs and
labor, PCR based SSRs require prior knowledge of DNA sequences which can be a
drawback in their application. PCR technology as given rise to a widespread utilization
of microsatellite markers to study conservation and evolutionary biology, and to
analyse genetic composition of populations (Balloux and Lugon-Moulin, 2002). The
extent of allele sharing between related populations can also be analysed using the
highly polymorphic microsatellite markers (Muir and Schloetterer, 2005.),
2
Microsatellite markers have been utilized for estimation of genetic differentiation, and
effect of hybridization on allelic frequencies among closely related species (Roy et al.,
1994). It has been noted that microsatellite markers are appropriate for detecting genetic
variations in oak because of their high levels of polymorphism (Aldrich et al., 2003;
Kampfer et al., 1998; Steinkellner et al., 1997).
Oak (Quercus) is a genus that is part of the Fagacea family (Aldrich et al.,
2003a). Red oak (Quercus section Lobatae) is one of the oak subgenus that is native to
the range that extends from North America to Central America and the northern areas
of South America, constituting approximately 200 species (Kashani and Dodd, 2002;
Jensen, 1997). Along with other oak subgenus native to North America, it is one of the
most economically important hardwoods of the region (Romeo-Severson et al., 2003).
Due to its adaptive tendencies (Table 1.1) to exist in a wide range of soil and climatic
conditions, oak is considered a good model for genomic exploration of adaptive
significance (Jones, 1986; Rampant et al., 2011, USDA and NRCS, 2006). The success
of the genus Quercus has been attributed to its high levels of phenotypic plasticity
(Jensen et al., 1984; Jensen et al., 1988) and genetic variation (Dodd and Kashani, 2003;
Guttman and weigt, 1988; Kremer and Petit, 1993). These same attributes are also
responsible for posing difficulties in estimating genetic differentiation between species,
genetic architecture of populations, and defining definite taxonomic relationship among
species (Kashani and Dodd, 2002). Recent reports also suggest a decline in oak
recruitment (Aldrich et al., 2005) which has been linked to competition from other
species due to decreased fire frequencies and other factors such as increased deer
herbivory (McDonald et al., 2002; Moran, 2010; Spitech, 2004).
3
Table 1-1. Characteristics and adaptations of the four red oak species.
Northern Red
Sothern Red
Scarlet
Scientific
name
Quercus rubra L.
Flowering
monoecious
monoecious
monoecious
monoecious
Fruit
acorn
acorn
acorn
acorn
Leaves
Quercus falcata Quercus coccinea
Black
Quercus
velutina
dark green leaves, dark green to pale deeply cut, bristle lathery, shiny,
toothed and
green leaves with tipped leaves
dark green
pointed tipped
pointed bristle
with pointed leaves, up to 10”
lobes, 7-11” long tipped lobes and lobes, 3-6” long
long
rounded bases, 49” long
Growth (ft)
65-98
60-80
60-80
60-80
Bloom Time
May
April-May
April-May
April-May
Distribution
south eastern
Canada, extends
from eastern
United States
southward and
westward up to
Minnesota,
Nebraska,
Arkansas,
Oklahoma, North
Carolina, Georgia,
Alabama
extends from New extends from
extends from
York southward
main southSouthern
up to northern westward to New Ontario southFlorida, westward
York, Ohio,
ward to
across gulf states,
Michigan,
Minnesota,
Texas, Illinois, Indiana, Illinois, Iowa, Nebraska,
Ohio, Arkansas
Missouri,
Kansas,
Mississippi,
Oklahoma,
Georgia,
Texas, Georgia,
Alabama
Alabama,
Florida
humid and
humid regions,
moderate
Adaptation mesic slopes, well
drained uplands,
temperate
well drained, dry climates, upland
cool moist soils in
uplands, dry,
sandy soils
hills, dry to
north, and warm, sandy, clay soils
medium
moist soils
moisture, moist,
elsewhere
well drained
soils
The significance of genetic diversity studies on populations such as red oak
species relates to the identifications of genetic properties of adaptive significance and
4
quality enhancement of forest management to improve health and productivity by
conserving genetic variation (Rampant et al., 2011). Inferences from such studies can
also be made on determinants of gene flow and genetic drift based on the knowledge of
population genetic structure (Epperson, 1990; Loveless and Hamrick, 1984; Montalvo
et al., 1997; Slatkin and Barton, 1989). Beyond morphological identification,
understanding the impact of gene flow in oak species can play a critical role from
management stand point beacause cryptic introgression may influence the efforts of
forest managers interested in promoting certain traits (Moran et al., 2012). Oak species,
known for their natural inclination to hybridize, often defy the concept of biological
species based on genetic isolation (Burger, 1975; Curtu et al., 2007). The ecological
species concept based on adaptive zone or niches where conspecific organisms interact
has been proposed to be applicable to oaks (Valen, 1977). This concept is believed to
compensate for the discrepancies of reproductive isolation criteria that the biological
species concept presents (De Queiroz, 2007; Valen, 1977). According to population
studies, patterns of hybridization follow paths of either hybrid zone formation or
occurrence of gene flow from species to species (introgression) (Anderson, 1953;
Rushton, 1993). Either way, morphological intermediacy becomes the manifestation of
hybridization where either intermediate characters or combinations of suspected
parental characters are expressed by the presumed hybrids (Phipps, 1984; Rushton,
1993). Cryptic introgression might also occur without any bearing on morphology
(Dodd & Afzal‐Rafii, 2004; Moran, 2010). Though oak hybridization is a welldocumented phenomenon, extensive studies and genetic analysis on oak hybrids have
not been conducted (Rushton, 1993). Also, despite evidence of ecological divergence
5
driven by selection, extensive studies in reproductive barriers and their influence in the
rate of gene flow in oak species are lacking (Abadie et al., 2012).
The primary objective proposed at the beginning of this study was to detect the
genetic diversity between and within four red oak species based on microsatellite
markers. Phylogenetic tree reconstruction based on nuclear and chloroplast
microsatellites was the secondary objective proposed. The primary objective has been
met using fragment analysis technology performed by an ABI 3100 Genetic Analyzer
(Life Technologies, Carlsbad, CA). All four red oak species were studied and analyzed.
Due to time and resource constraints, chloroplast DNA analysis was not included in
this study. The hypotheses tested are:
1
Genetic diversity based on microsatellite markers differs between the four red
oak species.
2
There is significant genetic variation within each species between individuals
based on microsatellite markers.
6
CHAPTER 2
LITERATURE REVIEW
Genetic variation within populations has been reported to be much higher than
between populations in oak species (Dodd et al., 2002, Hamrick and Godt, 1989).
Interspecific hybridization is believed to be an important source for genetic variation
for the outcrossing, long lived and wind pollinated oaks (Dodd et al., 2002), suggesting
gene flow to be accountable for the low interspecific differentiation (Craft et al., 2002;
Moran, 2010). The significance of gene flow in population dynamics lies upon the
maintenance of genetic diversity, which prevents the inability of populations to respond
to environmental changes due to depleted diversity and strong selection (Moran, 2010).
On the other hand, fitness at local environment in wind pollinated and dispersed forest
species such as oaks may be attributable to local adaptation of functional loci in the
face of extensive gene flow at neutral loci, causing heterozygotes for the particular
functional loci to be more disadvantaged at parental environment than the homozygotes
(Mimura and Aitken, 2010; Moran, 2010). Despite the well-known biological species
concept controversy oaks present due to the widespread interspecific hybridization,
their maintenance of species morphological and ecological distinctness is suggestive of
some reproductive barriers (Coyne and Orr, 2004; Moran, 2010).
Hybridization isolates the novel taxon from the parental type due to the
formation of new genetic combinations of semi-compatible genes (Tovar-Sanchez and
Oyama, 2004). The concept of biological species explained through reproductive
isolation is challenged by hybridization (Mallet, 2005). It can be difficult to
morphologically identify plant hybrids due to the fact that ancestral genetic
7
polymorphisms and mutations can lead to the appearance of hybrids (Mallet, 2005).
Sympatric populations of closely related species exhibit unfixed genetic differences,
therefore requiring statistical techniques to detect hybridization, and genetic markers
can provide evidence for both hybridization and introgression (Mallet, 2005). The role
of hybridization as a widespread evolutionary phenomenon ranges from speciation to
the introgression of essential phenotypic traits between species (Martinsen et al., 2001).
In plants, up to 25% of species are known to hybridize with at least one other species
(Mallet, 2005), and up to 50 – 75 % of angiosperms are estimated to have arisen from
hybridization (Martinsen et al., 2001). While it has been realized that introgression is a
powerful evolutionary force (Anderson, 1949; Stebbins, 1959), its impact has been
classified in a number of different ways. One defined extreme consequence is increased
reproductive isolation through selection for conspecific mating (Howard, 1986). The
other extreme implies genetic disintegration (O’Brien and Mayr, 1991) through the
merging of hybridizing species (Martinsen et al., 2001). The role of introgression in
adaptive evolution has also been explained as hybrids acting as evolutionary filters
(Harrison, 1986) through which beneficial genes pass and negative genes do not
(Martinsen et al., 2001). Molecular studies have suggested the asymmetrical patterns of
introgression (Barton and Hewitt, 1985; Orive and Barton, 2002) seem to follow the
rout of adapted alleles spreading from species to species (Barton and Hewitt, 1985;
Whitney et al., 2006) through fit hybrids (Broyles, 2002; Borge et al., 2005).
Explaining the concept of plant speciation has been a major challenge in
botanical studies (Rieseberg and Willis, 2007) due to the high rate of interspecific gene
flow (Morjan and Rieseberg, 2004). Defining species has been a point of arguments
and taxonomic conflicts where more than twenty concepts on species exist based on
8
empirical and operational definitions (Agapow et al., 2004; Mayden, 1997). Despite
their differences, primarily due to secondary defining properties of a lineage, there
exists a primary defining common property in all species concepts which treats species
simply as lineages that evolve separately (De Queiroz, 1999). Processes that lead to
speciation such as mutation, migration, genetic drift, and natural selection have
genotypic, phenotypic, and behavioural effects that give rise to diverging lineages with
distinguishable and fixed characters and mate recognition systems that make them
unrecognizable to each other (De Queiroz, 1999). Since all of these changes do not take
place at the same time or in a particular order, and since the different concepts of species
subscribe to different aspects of these changes to define species category, problems of
conflicting views arise (De Queiroz, 1999). Unlike other clads within which speciation
is divergent, hybridization is believed to account for a large portion of plant speciation
events (Soltis & Soltis, 2009). Morphologic and biologic based concepts on species
have faced serious challenges in plant systematics (Soltis and Soltis, 2009). Although
widely used in plant taxonomy, the morphological based application, which assembles
morphologically similar individuals that differ from other assemblages, is considered
to be subjective (Rieseberg and Willis, 2007), and also to be overlooking autopolyploid
speciation (Soltis and Soltis, 2009). The application of biological concept (Mayr, 1942),
which defines species as reproductively isolated, and interbreeding populations, has
almost been abandoned in plants systematics studies due to high levels of interspecific
hybridization (Soltis and Soltis, 2009). For example, entities joined by hybrid zones
with semipermeable reproductive barriers cannot be distinguished as conspecific or as
different species, prompting the idea of a gene by gene application to define species
(Barton and Hewitt, 1985; Harrison, 1993). Other applications such as the evolutionary
9
concept (Wiley, 1978), and variety of phylogenetic approaches (Judd et al., 1999) have
also been proposed, but they are not straight forward, and do not seem to fully account
for hybrid-derived lineages (Soltis and Soltis, 2009). The ecological concept on species
(Valen, 1997) has made headway in explaining the extensively hybridizing oak species
which are known to defy biological species concept based on reproductive isolation
(Burger, 1975; Curtu et al., 2007). Under the ecological concept, different species are
constituted in differences in ecology, and hybridizing species and species involved in
gene exchanges while maintaining ecological distinctions are tolerated (Mayden,
1997).
2.1
The process of hybridization
The definitions of hybridization and hybrids have varied, many contrastingly,
since systematic studies began centuries ago (Harrison, 1993). For example, the
prevailing perspective of Nineteenth Century biologists, who established distinction
between “varieties” and “species,” was that hybrids were products of crosses between
“species” and not “varieties,” and hybrids represented sterility (Harrison, 1993). This
Nineteenth Century view was challenged when Darwin, in 1872, pointed out that the
distinction between “varieties” and “species” may be subjective based on external
appearances (Darwin, 1859; Harrison, 1993). Contrastingly, other literature on plant
and animal breading defined hybrid simply as offspring of genetically distinct parents
where the parents exhibited differences in one or more traits. By this definition, every
individual of the next generation would be considered a hybrid since every individual
in sexually reproducing species is a unique genotype (Harrison, 1993). A more
objective approach defines hybridization as interbreeding of individuals from different
10
populations or groups with distinguishable heritable characters where the populations
are distinct for the character. This approach excludes applications of subjective
judgments on species concepts, taxonomic categories, and relative fitness of hybrids
(Cracraft, 1983; Harrison, 1990; Harrison, 1993; Nixon and Wheeler, 1990; Woodruff,
1973).
In order to further scrutinize the variety of definitions of hybridization and
understand the process, it is beneficial to also look into the occurrence and historical
explanations of hybrid zones. While hybrid zones have roughly been explained as
narrow regions where hybrids are produced through the meeting and mating of
genetically distinct populations, at the genetic level, most literature defines them as
clines or a set of clines between hybridizing parapatric taxa for morphology or gene
frequency at one or more loci (Barton and Hewitt, 1985; Hewitt, 1988). The primary
and secondary zones, in principle, have been used to explain the ways that hybrid zones
originate (Hewitt, 1988). Primary and secondary zones mainly differ in the evolution
of the differences between the hybridizing taxa where primary refers to continuous
distribution and secondary refers to differences while geographically isolated (Hewitt,
1988). Although clines have been restricted by many to secondary zones, a difficulty in
distinguishing between a primary and secondary contact has been reported (Woodruff,
1973) since differences may have originated continuously even if the clines may have
occurred at secondary contact (Barton and Hewitt, 1985). Stable clines at loci
responsible for barriers to neutral gene flow and reproductive isolation become the
result of hybrid zones which are formed when equilibrium between the effects of
dispersal and selection (Harrison, 1993) occurs (Barton and Bengtsson, 1986; Bierne et
al., 2003; Hewitt, 1988). It is apparent that the nuisance nature of hybrid zones poses
11
difficulties in defining species because of the ambiguity it creates about the status of
the diagnosable distinct parental populations accommodated by the variable species
concepts (Harrison, 1993; Nixon and Wheeler, 1990). In spite of the differences of
opinions in which species concept to apply when it comes to hybridization and hybrid
zones, it is clear that hybrid zones provide information on the degrees of divergence
between populations (Harrison, 1993), the level of gene flow between populations
(Hewitt, 1985), and the range of genotypes present, allowing analysis of genetic
variation between the hybridizing taxa (Barton and Hewitt, 1985).
2.2
Hybridization between oak species
Although the phenomenon of oak hybridization has been known, extensive
studies in the patterns and frequencies of interspecific genetic exchanges between many
oak species are scarce (Gonza´lez-Rodrı´guez et al., 2004). Distinguishing oak taxa at
species level has primarily been relied upon leaf morphology in the past (Rushton,
1993). However, environmental modifications may prevent morphological application
from being an absolute discriminator (Rushton, 1993). Consequently, detecting putative
hybrids with intermediate features becomes challenging due to ambiguity and wide
variability in parental morphologies (Curtu et al., 2007; Rushton 1993). However,
despite extensive introgressive hybridization within the oak taxonomic group,
individual species seem to maintain their identities at morphological, genetic and
ecological levels (Williams et al., 2001). Introgressive hybridization explains the
phenomenon of repeated backcrossing of hybrids to parental species causing the
infiltration of germ plasm from species to species (Martinsen et al., 2001). Correlation
between hybrids and intermediate morphology seems to be weak, which suggests
12
selection favors individuals with parental phenotypes (Moran, 2010; ValbuenaCarabana et al., 2007). Some literatures have presented contrasting views on the
mechanisms responsible for differentiations of hybridizing oaks species. On one hand,
ancestral polymorphism instead of gene flow (Muir and Schlotterer, 2005) has been
credited for oak species differentiation. However, the ancestral polymorphism theory
has been seriously rebutted by a recurrent gene flow explanation, which points out the
unevenness of interspecific differentiation across the genome, and the impact of
selection leading to increased differentiation at limited loci (Lexer et al., 2006). Due to
their complexity exhibited through distinct species with weak reproductive barriers
(Curtu et al., 2007; Rushton, 1993; Williams 33), oaks have become model taxon
(Valen, 1977; Williams et al., 2001) to address the issues of species concepts (Petit et
al., 2003).
Mechanisms such as prezygotic barriers, reduced fertility rates of hybrids, and
differences in flowering times have been suggested as at least partial reproductive
barriers (Moran, 2010; Rushton, 1993). Despite evidence of ecological divergence
driven by selection, extensive studies in reproductive barriers and their influence in rate
of gene flow in oak species are lacking (Abadie et al., 2012). The literatures in
reproductive barrier have classified the mechanisms mainly into pre-and postzygotic
with pre-and postmating sub categories (Naisbit et al., 2001; Nosil, 2004; Rundle and
Whitlock 2001). While prezygotic barriers refer to temporal, habitat and ecological
isolations as well as mechanical and gametic incompatibilities, poszygotic barriers
implicate zygotic mortality, habrid inviability and/or sterility and eventual breakdown
of f2 generations (Nosil, 2004). When it comes to interspecific reproductive barriers,
the mechanisms control the level of gene exchanges between populations that may or
13
may not lead to species isolation (Boavida et al., 2001). Reported reduced fertility in
oak hybrids may suggest a postzygotic barrier even though under the right conditions
hybrids often seem to develop healthy and produce viable seeds (Moran, 2010;
Rushton, 1993; Salvini et al., 2009). Instances of species differentiation in flowering
time (Rushton, 1993) may also indicate temporal prezygotic isolation mechanism
(Nosil, 2004) allowing species distinctness in oaks (Moran, 2010).
2.3
Molecular Marker Application
Varieties of molecular marker systems have been utilized in genetic diversity
studies. These systems generate data that allow scoring of total number of alleles across
all population at a given locus and per population, calculations of observed and
expected heterozygosities, calculations of deviations from Hardy-Weinberg
expectations, and Wright’s F-statistics (Grant, 2010; Weir and Cockerham, 1984). The
benefit of molecular markers lies on not only their ability to highlight polymorphisms
within a nucleic sequence between different individuals, but also being applicable to
any part of the genome (coding, noncoding) that do not necessarily produce phenotypic
variation (Grant, 2010). Although molecular markers should not be considered as
normal genes, but rather as constant landmarks in the genome (Semagn et al, 2006), the
inheritance of these markers is easily monitored since they segregate as single genes
and are not affected by the environment (Kumar et al., 2009). Although it is impossible
to identify a molecular marker that employs all attributes of a perfect marker, the ideal
marker should exhibit traits such as polymorphic nature, co-dominant inheritance,
frequent occurrence in genome, selective neutral behaviors, high reproducibility, easy
14
access, easy and fast assay, and easy exchange of data between laboratories (Kumar et
al., 2009).
Each of molecular marker techniques that are widely used for genetic diversity
studies come with its own advantages and drawbacks. Identifying a strategy and
selecting a marker depend on the type and extent of the study, as well as availability of
resources. Molecular markers vary from each other with respect to their
polymorphisms, abundances, locus specificity, technical requirements and cost
(Agarwal et al, 2008). Marker selection for plant genetic diversity studies has evolved
from primarily the traditional chloroplast genome markers to nuclear and eventually to
parallel utilization of both. Novel approaches have also recently emerged that seem to
have greater potentials that can provide high resolution in defining phylogeny at low
taxonomic levels. Appropriate marker selection for a given situation should address
issues such as amount of polymorphism requirement, availability of analytical and
statistical approaches, and cost and time demand (Grant, 2010; Parker et al., 1998).
Molecular marker techniques are typically classified into two major groups: non PCR
based ad PCR based (Agarwal et al, 2008). RFLPs are the major non-PCR based
molecular markers utilized, while RAPD, AFLP, and Microsatellites (SSRs) are the
most widely used PCR-based markers in genetic diversity studies. The methodology of
PCR-based markers is based on the use of PCR technology for in vitro amplification of
DNA sequence or loci using arbitrarily chosen primers (Kumar et al., 2009).
RFLP markers are the most widely used non-PCR based markers. The
application of RFLP is based on the special class enzyme, restriction endonucleases,
which digests Southern blot DNA for hybridization to a chemically labeled probe in
order to detect polymorphisms (Agarwal et al., 2008). The RFLP technique reveals
15
differences in nucleotide sequences between individuals by detecting variations in
restricting fragment lengths (Kumar et al., 2009). Due to their abundance in the genome
and their co-dominant nature, RFLPs can be advantageous in discriminating
homozygous and heterozygous individuals for a trait (Kumar et al., 2009). However,
limitations caused by the requirement of large amounts of DNA, time consuming RFLP
assays, and expensive radioactive labels that are also hazardous, led to the development
of less technically complex PCR-based methods (Agarwal et al., 2008; Kumar et al.,
2009).
RAPDs are applied in such a way that randomly amplified DNA segments
indicate polymorphisms due to alterations of primer binding site as a result of base
changes, or insertions or deletions (indels) within amplified regions (Parks et al., 1991;
Tingey and Tufo 1993; Williams et al., 1990). While RAPDs are advantageous for their
abundance, and requirement of no prior knowledge of the studied genome, their low
reproducibility, and their sensitivity to reaction conditions are major drawbacks (Kumar
et al., 2009).
AFLPs are considered to be intermediate markers between RFLPs and PCR
markers, since they couple the power of RFLP with the flexibility of PCR-based
technology (Agarwal et al., 2008). Selectively amplified DNA fragment subsets from a
mixture of DNA fragments obtained from restriction endonuclease treatment are
screened for polymorphisms based on differences in length. Although AFLPs are
designed to overcome the low reproducibility of RAPD, and fill the application gap
between RFLP and PCR-based techniques, their dominant nature limits their
application (Kumar et al., 2009).
16
Microsatellites (simple sequence repeats) are molecular markers that occur in
all eukaryotic genome as short tandem repeat motifs. Length variations of these tandem
repeats are caused by strand slippage during replication, and account for their
polymorphisms, which can be detected by PCR and southern hybridization (Kumar et
al., 2009). SSRs are highly advantageous due to their co-dominant nature, high
reproducibility, and high variability, which make them highly suitable for
distinguishing closely related genotypes (Kumar et al., 2009). Besides cost and
requirement of prior knowledge of DNA sequences, major drawbacks of SSRs lie on
mutations at primer annealing regions that may cause null alleles, which may also lead
to underestimation of heterozygosity (Kumar et al., 2009). Concerns such as high
mutation rates, allele size range (Goldstein and Pollock, 1997), and size homoplasy
(Bruford and Wayne, 1993) have been raised as constraints that may limit SSRs in
inferring phylogenies especially among divergent taxa (Ochieng et al., 2007). Bias
caused by the high levels of polymorphism due to high rates of mutation at
microsatellite loci in relation with stepwise changes in allele size (Hardy et al., 2003),
and homoplasy are to be taken into account in genetic diversity studies. Size homoplasy
in microsatellites refers to DNA fragments of identical size but not identical in descent
due to convergent mutations that connect the gene copies. Since their genetic
divergence is due to random drift, homoplasy may not constraint phylogenetic
reconstruction among closely related populations, (Estoup et al., 2002). Genetic
diversity studies may also address size homoplasy in microsatellites by accounting for
the large amount of variability of SSR loci that can compensate for homoplasious
evolution (Estoup et al, 2002). Allele size constraint is also a major factor that plays a
critical role in the complex microsatellite mutational mechanism. Studies have
17
suggested that mutational variations seem to be bounded by allele sizes (Bowcock et
al., 1994; Feldman et al., 1997; Goldstein et al., 1995; Nauta and Weissing, 1996) that
follow a pattern of large size alleles mutating to smaller size and vice versa
(Zhivotovsky et al., 1997). Two major models - infinite allele mutation model and
stepwise mutation model - have been proposed to address the mutation process of
microsatellites. While infinite allele model assumes the birth of unprecedented infinite
allelic states from a unique allele produced by each mutation (Anmarkrud et al., 2008;
Estoup et al, 2002; Kimura and Crow, 1964), stepwise mutation infers an equal
probability of gain or loss of one or small number of repeat motif that will produce
distinguishable alleles (Barkley et al., 2009). However, direct mutational mechanism
studies on yeast (Henderson and Petes, 1992), and human pedigree (Weber and Wong,
1993) seem to suggest that stepwise mutational model may be the main mutational
mechanism of microsatellites (Feldman et al., 1997). Studies have also shown that when
microsatellites mutate in stepwise manner, the difficulty in distinguishing effects of
mutation from that of migration can be overcome, and evolutionary distances between
populations can be inferred since size difference is correlated with genetic distance
(Balloux and Goudet, 2002; Goldstein et al., 1995; Michalakis and Excoffier, 1996;
Rousset, 1996; Slatkin, 1995). Based on this information, a method of measuring
genetic distance in linear relation with time has been demonstrated by taking the
squared differences between the means of the two populations, which has the ability to
estimate separation time in unbiased manner (Goldstein et al., 1995; Slatkin, 1995;
Feldman et al., 1996). There have been notable cases such as one involving eucalyptus
microsatellites (Shepherd et al., 2006), with limitations addressed, cross-genera SSRs
showed potential in phylogenetic studies such that high transferability has been
18
reported (Ochieng et al., 2007). Their multi genome representation and faster evolution
may lead to more informative characters suggesting that SSRs may have more
advantages over DNA sequencing in phylogenetic studies (Ochieng et al., 2007). While
allozymes, RFLPs, RAPDs, and AFLPs can still be deployed as different approaches
for different dimensions of population genetic studies, due to their high variability and
rapid mutation rate, microsatellite have proven to be ideal for genetic diversity studies
for their ability to allow closely related individuals and populations be distinguished
from each other (Grant, 2010).
While chloroplast DNA (cpDNA) served as a resource in plant genetic diversity
studies in the past, understandings of nuclear DNA capabilities to compensate for the
limitations of cpDNA has resulted in the adoption of nrDNA to be as commonly used
as cpDNA in plant systematics (Small et al., 2004). Polymorphisms of chloroplast
microsatellites provide complementary information to that of nuclear, and form the
bases for genetic variation analysis in plant phylogenetic studies (Powell et al., 1995).
The value of organelle genomes lies on their simple, uniparental modes of inheritance
(Bryan et al., 1999). Recognition of the mutational mechanisms that are responsible for
molecular evolution in noncoding sequences can be vital in accuracy of character
homology assessment (Kelchner, 2000). The recognition of these multi-dimensional
mutational mechanisms as generators of specific mutations can lend itself to the concept
of a more conserved and structured chloroplast DNA evolution (Kelchner, 2000). While
the conserved and slower evolving cpDNA have been used at lower taxonomic level,
even the noncoding regions do not provide complete information at that level, and that
is where nuclear DNA (nrDNA) may be able to fill in the information gap (Small et al.,
1998; Small et al., 2004). It has been noticed that, although cpDNA can provide easily
19
amplifiable sequences, and orthologous genes that are not complicated by gene family
duplication, their limited variability and their tightly linked markers on single nonrecombining molecule that reflect only maternal linkage, restrict their utility at low
taxonomic level (Duarte et al., 2010; Naumann et al., 2011; Small et al., 1998). On the
other hand, despite their variability, numerous copies of nrDNA gene regions in the
nucleus which evolve by tandem duplication events may complicate phylogeny
relationships due to their paralogous nature, making it almost impossible to determine
orthology from such large copy number (Naumann et al., 2011). However, the primary
advantage of nuclear genes over chloroplast DNA for diversity analysis is their elevated
rate of sequence evolution, allowing more variation to be detected per unit of sequence
yielding greater efficiency in sequencing, making their purpose more acute at lower
taxonomic level (Small et al., 2004).
20
CHAPTER 3
MATERIALS AND METHODS
3.1
Sample Collection
The study was focused on four red oak species from the Bankhead National
Forest of the Cumberland Plateau. The four red oak species that were collected are
northern red oak, southern red oak, scarlet oak and black oak. Eight collection sites
were picked with the help of field guides as diversified representations of the Bankhead
National Forest (Figure 1). A total of 217 samples were collected and extraction
performed on (Table 3-1). Northern red oak sample were collected from four sites while
southern red and black oak samples were collected from six sites, and all scarlet oak
samples were collected from one site. Although southern red and black oak samples
were collected from six sites, most of the samples for both species came from two sites
(Table 3-1).
Table 3-1. Amount of samples per species collected from eight sights.
Site
# of samples
Northern
# of Samples
Southern
# of Samples
Scarlet
# of Samples
Black
A
29
2
0
5
B
16
9
0
0
C
0
0
46
0
D
0
0
0
17
E
8
19
0
7
F
11
3
0
4
G
0
12
0
4
H
0
3
0
22
Total
64
48
46
59
21
Samples were collected at first contact with juvenile leaves. Selection of
samples was based on leaf morphologies. Most juvenile leaves were collected from
newly sprouting trees, and trees with branches that were reachable by hand. Trees were
separated from each other by few feet to several yards. After collection, samples were
immediately placed in a cooler packed with dry ice, until their transfer to -80°C freezer
at the laboratory. Out of the 217 samples that extractions were performed on, 128 DNA
samples were collected. The rest of the samples did not yield any or sufficient DNA,
and did not amplify by PCR reactions. As a result, 32 DNA samples from each of the
four species were extracted and analysed (Table 3-2).
3.2
DNA Extraction
Dneasy plant mini-kit and Dneasy plant maxi-kit (Qiagen, Valencia CA) were
used to perform DNA extractions, although the majority of the samples were extracted
using the min-kit. Dneasy plant extraction kits are optimized for leaf tissues which
makes them appropriate for DNA extraction of red oak leaves. Protocol for the Dneasy
plant Mini-Kit was slightly modified by increasing the amounts of buffer AP1 and AP2
to 600 μl and 195 μl respectively, as suggested by the troubleshooting guide for
insufficient yield. Newer version of DNeasy plant mini-kit was also used for a portion
of the samples. The newer version is similar in procedure and reaction with the older
version. The difference in the two versions is mostly in labeling where buffer AP3/E
was relabeled as buffer P3 and buffer AW was partitioned as AW1 and AW2 for the
two washing steps. Buffer P3 of the new version is not supplied as concentrate so no
dilution with ethanol is necessary (Dneasy Plant Handbook, 2012). Before extraction
was performed, a water bath was heated to 65°C. Also prior to extraction, Buffers AP1
22
Table 3-2. Analyzed DNA samples and corresponding sites.
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Site H
1-N
8-N
86-SC
118-B
39-S
24-N
57-S
184-B
2-N
9-N
87-SC
119-B
41-S
25-N
58-S
185-B
3-N
11-N
88-SC
121-B
42-S
56-S
61-S
187-B
4-N
23-N
89-SC
164-B
44-S
63-S
62-S
188-B
5-N
35-S
90-SC
165-B
47-S
94-N
64-S
197-S
6-N
53-S
91-SC
166-B
48-S
95-N
176-B
198-S
7-N
54-S
100-SC
167-B
51-S
81-B
196-S
199-S
66-N
55-S
101-SC
168-B
72-N
160-N
213-S
200-B
67-N
69-N
102-SC
179-B
75-B
214-S
201-B
68-N
70-N
103-SC
181-B
82-B
215-S
202-B
92-N
71-N
104-SC
183-B
84-B
216-S
203-B
93-N
96-N
105-SC
85-B
217-S
204-B
160-S
97-N
106-SC
98-N
205-B
171-B
161-N
107-SC
117-B
206-B
173-B
193-N
108-SC
158-N
207-B
208-S
109-SC
192-N
209-S
110-SC
210-S
111-SC
211-S
112-SC
212-S
113-SC
114-SC
134-SC
135-SC
136-SC
137-SC
149-SC
150-SC
151-SC
152-SC
153-SC
154-SC
155-SC
23
and AP3/E that may have formed precipitates upon storage were warmed to 65°C to
redissolve, and buffers AW and AP3/E were diluted with instructed amount of ethanol
to make working solutions (Dneasy Plant Handbook, 2006).
Extraction with Dneasy Plant Mini-Kit
Leaf tissue was ground to a fine powder using mortar and pestle in a liquid
nitrogen bath. Ground tissue (< 100mg) was transferred to a 2ml centrifuge tubes using
a metal spatula. (Dneasy Plant Handbook, 2006). Immediately upon transfer, 600μl
AP1 and 4 μl RNase were added to each tube containing the ground tissue sample.
Then, the tubes were vigorously vortexed until no tissue clumps were visible. The
mixtures were then incubated in the prepared 65°C water bath for 10 minutes to allow
cell lyses, while mixing by inverting the tube a few times. 195 μl of buffer AP2 was
then added to the lysate. Each lysate was then mixed and incubated in ice for 5 minutes,
allowing the precipitation of detergent, proteins and polysaccharides. Next, samples
were centrifuged at 14,000rpm for five minutes, followed by the transfer of the
supernatant from each tube to QIAshredder mini spin column (lilac) placed in 2 ml
collection tube. The columns were centrifuged at 14,000rpm for 2 minutes allowing the
removal of precipitates and cell debris, although a small amount may pass through and
form a pellet in the bottom of the collection tube. Without disturbing the pellet at the
bottom of collection tubes, the flow through was transferred to new 2 ml tubes. 1.5
volume of buffer AP3/E was then added to each tube containing the cleared lysate
followed by pipetting to mix.
24
Figure 3.1. Bordered region in map highlights the location of the Bankhead National
Forest in Alabama. Letters A-H are clusters where samples were collected
from.
650 μl of the mixture from each tube was transferred to DNeasy mini spin
columns placed in 2 ml collection tubes and centrifuged for one minute at 8,000 rpm.
The step was repeated for the remaining samples of the mixture from each tube. Two
rounds washing steps were then performed by placing the spin columns in 2 ml
collection tubes then adding 500 μl AW buffer, and centrifuging for one minute at 8,000
rpm for the first round and two minutes at 14,000 rpm for the second round. If the
previous washing steps had not resulted in dry membranes, then additional step of just
centrifugation was performed. The spin columns were then transferred to 2 ml
microcentrifuge tubes and 100 μl buffer AE was added to each Dneasy spin column
25
membrane followed by incubation at room temperature for five minutes. Elution step
was then performed by centrifuging the tubes for one minute. Elution step was repeated
in the same microcentrifuge tube (Dneasy Plant Handbook, 2006). Eluted samples were
then stored at -20°C until ready to use.
Extraction with Dneasy plant Maxi-Kit
Extraction protocol of the Dneasy Plant Maxi-Kit (Qiagen, Valencia CA) is
similar with the mini-kit in regards to the flow of the procedures except the maxi-kit
was designed to accommodate larger quantities of starting material as the QIAshredder
Maxi spin columns and the Dneasy Maxi spin columns come in 50 ml collection tubes.
Extraction with the Dneasy plant Maxi-Kit was performed as instructed without
modifications (Dneasy Plant Handbook, 2006)
3.3
DNA Quantification
Nanodrop spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA)
and nano-drop software (Thermo Fisher Scientific Inc., Waltham, MA) were used to
perform quantification of extracted DNA. Measurement of Extracted DNA quantity for
the samples was used to determine volume of sample solution to make working solution
based on C1V1 = C2V2 formula (Grant, 2010).
3.4
Polymerase Chain Reaction
Custom labeled fluorescent oligo primer pairs (Table 3-4) were made by
Erophins MWG Operon (Huntsville, AL) with each forward primer labeled with (6FAM) fluorescent dye for visualization. Same PCR protocol (Table 3-3) was used for
26
all primers. Master Mix from Promega (Promega, Madison, WI) was used for PCR
reactions. Components of the Master Mix for a reaction include, PCR Master Mix 2X,
upstream primer, downstream primer, DNA template and nuclease free water
(Promega, Madison, WI). PCR was performed in a 25 μl or 12.5 μl reaction volume.
Table 3-3. PCR Protocol.
Step
Phase
Temperature (°C)
Time (min)
1
Denaturing
95°C
2:00
2
Denaturing
94°C
0:45
3
Annealing
Annealing Temp
0:45
4
Extension
72°C
1:00
5
30 cycles
Denaturing, Annealing and
extension
0:45
6
Extension
72°C
1:00
7
Pause
4°C
For ever
8
End
27
Table 3.4. Primer names, 5’3’ sequences, and annealing temperatures.
Primer Name
3.5
Primer
Annealing
Temperature (Ta)
quru-GA-1i06
*CAAGCTCCACTGAGTCGTCGGT
CTCTCGCTTTGATTTCACTCCA
58ºC
quru-GA-1L05
*AAGATGCATGGTATTGTAGCAGG
GCTTGTTGCGGTAGGTTA
50ºC
quru-GA-2H14
*ATTCGCGAGCGTGCAGT
GTGCTCCACGAATGCTCTAGCCA
58ºC
quru-GA-1F02
*CCAATCCACCCTTCCAAGTTCC
TGGTTGTTTTGCTTTATTCAGCC
56ºC
quru-GA-0M05
*CTACAAGTTACATGCCCAATCA
CTTTGCGCAGGTCCATTAC
53ºC
quru-GA-0C11
*ATACCCAGCTCCCATGACCA
TCCCCAAATTCAGGTAGTGT
53ºC
quru-GA-0i01
*GGGCTATCAAGTAAGTGCTTAAC
ACGCCATCCCTATAACACA
56ºC
quru-GA-1G13
*AAAACTCACACAGCCGATTACTA
GATTCCATTGTCAACTGCGAAGA
50ºC
Data Analysis
Fragment analysis was performed using an ABI 3100 genetic analyzer (Life
Technologies, Carlsbad, CA). The ABI genetic analayzer for microsatellite markers
analyzes fluorescently labelled PCR products by capillary electrophoresis which
separates and sizes alleles by comparison to size standards (Life Technologies,
Carlsbad, CA). PCR products were mixed with Hi-Di Formamide-ROX 500 size
standard or Hi-Di Formamide-600 LIZ size standard solutions (Life Technologies,
Carlsbad, CA) prior to visualization. The GeneMapper software was used for
visualization of microsatellite peaks, scoring, and distinguishing of alleles based on
fragment sizes (Grant, 2010). The GeneMapper software calls quality alleles, and
28
provides automated analysis of genotyped samples (Life Technologies, Carlsbad, CA).
Genepop version 4.2 (Raymond and Rousset, 1995a; Rousset, 2008) was used to
compare populations for genetic diversity between the four red oak species. Input files
were created from data gathered by the GeneMapper software for data analysis by
Genepop. Genepop is a population genetics software that is widely used for data
analysis in genetic diversity studies. Genepop computes unbiased estimates for haploid
or diploid data based on Hardy-Weinberg equilibrium. Genepop performs exact tests
for group differentiation and outputs allelic compositions in each group per locus.
Allele size ranges, total number of alleles (AT), and mean number of alleles (A) were
scored using Genepop analysis. Option 1 of Genepop on the web was used to perform
Hardy-Weinberg exact tests. Option 3 and option 5 of Genepop on the web were used
to calculate population differentiation and allele frequencies respectively, allowing
calculations of observed heterozygosity (Ho) and expected heterozygosity (He).
(Raymond and Rousset, 1995a; Rousset, 2008). ANOVAs were used for statistical
analysis to compare between and within species diversity.
29
CHAPTER 4
RESULTS
A total of 146 alleles were observed across the eight loci in northern red oak,
142 in southern oak, 136 in scarlet oak and 174 in black oak (Table 4-2). These number
of alleles are reflective of the different length in base pair of the particular microsatellite
markers exhibited in each species. All loci were also polymorphic with the total number
of alleles per locus ranging from 27 to 45 (Table 4-4). This means, 27 is the lowest
number of different forms in length exhibited by one microsatellite, and 45 is the
highest. The smallest allele size detected was 154 base pairs in locus 1F02, and the
largest was 288 base pairs in locus 1i06 (Table 4-1).
Table 4-1. Microsatellite loci and their allele size ranges.
Locus
Repeat Unit
Allele Size Range (bp)
0C11
(GA)15
195-224
1F02
(GA)15
154-182
1i06
(GA)23
191-288
1L05
(GA)23
240-274
2H14
(GA)18
207-270
0M05
(GA)20
158-234
0i01
(GA)16
190-220
1G13
(GA)14
165-194
Total number of alleles per species ranged from 136 to 174 (Table 4-2). Mean
total number of alleles of the four species was 149.5 (Table 4-2). The mean number of
alleles of per species averaged across the eight loci was 18.69 (Table 4-2). Total number
of private alleles per species ranged from 14 to 27, with the mean number of private
30
alleles being 18.5 (Table 4-2). Private alleles refer to alleles that were only found in a
single species. Effective number of alleles per locus ranged from 11.71 to 15.37 for the
four species. Effective number of alleles is a measure of equally frequent alleles it
would take to create the same hererozygosity as observed in the population. The highest
mean number of allele across the eight loci was 21.75, which was exhibited by black
oak. The lowest mean number of alleles of the four red oak species was 17 by scarlet
oak. Northern red oak and southern oak had 18.25 and 17.75 mean number of alleles
respectively (Table 4-2).
Table 4-2. Microsatellite allelic genetic diversity between the four red oak species.
Species
AT
A
Ae
Private
Ho
He
Fis
Northern Red
Oak
146
18.25
12.06
16
0.5077
0.9171
0.4459
Southern Red
Oak
142
17.75
15.37
17
0.6102
0.9349
0.3228
Scarlet Oak
136
17
11.71
14
0.4505
0.9146
0.5044
Black Oak
174
21.75
12.71
27
0.6308
0.9398
0.3378
18.5
0.5498
0.9266
0.4027
Mean
149.5 18.6875 12.9625
AT = Total number of alleles, A = Mean number of alleles per locus, Ae =
Effective number of alleles per locus, Private = private number of alleles, Ho = Observed
heterozygosity, He = Expeted heterozygosity. Fis = inbreeding coefficient
Mean observed heterozygosity (H0) per species ranged from 0.4505 to 0.6308
with the lowest value coming from scarlet oak and highest value from black oak (Table
4-4). Mean H0 for northern red oak and southern oak were 0.5077 and 0.6102
respectively (Table 4-4). Ho was lower than expected heterozygosity (He) within all the
four red oak species indicating heterozygote deficiency. While observed heterozygosity
31
(Ho) refers to the frequency of heterozygote individuals per locus, expected
heterozygosity (He) refers to gene diversity which measures genetic variation based on
allele frequencies. Inbreeding coefficient values (Fis) of the four species averaged over
the eight loci ranged from 0.3228 to 0.5044. (Table 4-2). Inbreeding coefficient (Fis) is
a measure of the probability of individuals having identical alleles at a particular locus.
High Fis values indicate higher number of homozygotes per locus in the population due
to inbreeding.
Between sites comparison were also performed for the three species that were
collected from different sites. Since all scarlet oak samples were collected from one
site, it was not possible to perform site comparison. To avoid sample size bias in site
comparison, two sites per species were considered because most DNA samples
collected in each of the came from two sites (Table 3-2). P-values were computed based
on distribution of alleles between sites per locus. Only one locus was significantly
different between sites in northern re oak. Three loci in southern red, four loci in black
oaks were significantly different between sites (Table 4-3)
Table 4-3. Distribution of alleles differentiation between sites.
Locus
Northern
Southern
Black
OC11
0.08043
0.8455
0.9954
1F02
0.28121
0.87733
0.00392
1i06
0.13283
0.01985
0.00395
1L05
0.5976
0.21226
0.03061
2H14
0.12261
0.00512
0.53858
0M05
0.14739
0.11839
0.1652
0i01
0.3376
0.13696
0.57355
1G13
0.04588
0.0282
3.00E-05
32
33
12
17
17
20
20
17
20
1F02
1i06
1L05
2H14
0M05
0i01
1G13
Fis
0.9643 0.2222
He
0.931
0.356
0.9434 0.417
0.2258 0.9522 0.7628
0.6
0.55
0.5667 0.9328 0.3925
0.5417 0.9004 0.3984
0.3793 0.9323 0.5931
0.4483 0.7802 0.4254
0.75
Ho
17
17
20
19
16
25
12
16
# of
Alleles
He
Fis
0.9649 0.4818
0.6552 0.9273 0.2935
0.6897 0.9323 0.2602
0.5714 0.9557 0.4021
0.4286 0.957 0.5522
0.6296 0.9188 0.3147
0.5
0.9231 0.9054 -0.195
0.4839 0.9177 0.4728
Ho
Southern Red Oak
17
16
21
17
16
16
15
11
24
# of
Alleles
Fis
0.9506 0.211
He
22
21
24
18
20
27
20
22
# of
Alleles
He
Fis
0.54
0.6786 0.959 0.2934
0.7407 0.936 0.2091
0.5185 0.9601 0.4599
0.5455 0.9394 0.4194
0.4194 0.9403
0.5938 0.9022 0.4279
0.7419 0.9258 0.1986
0.8077 0.9554 0.1546
Ho
Black Oak
0.4505 0.9146 0.5044 21.75 0.6308 0.9398 0.3379
0.1154 0.9508 0.8786
0.4194 0.957 0.5618
0.1724 0.8836 0.8049
0.6538 0.9223 0.2911
0.3214 0.923 0.6519
0.5161 0.9022 0.4279
0.6552 0.827 0.2077
0.75
Ho
Scarlet Oak
Ho = observed heterozygosity, He = expected heterozygosity, Fis = inbreeding coefficient
18.25 0.5077 0.9171 0.4459 17.75 0.6102 0.9349 0.3228
23
0C11
Mean
# of
Alleles
Locus
Northern Red Oak
Table 4-4. Microsatellite genetic diversity between the four red oak species per locus, and within species.
Table 4-5. Anova results for number of alleles comparison between species.
Source of Variation
Between Groups
SS
df
MS
F
106.375
3
35.4583
2.95047
336.5
28
12.0179
442.875
31
Within Groups
Total
P-value
F crit
0.049803
2.94669
8
The difference between Ho and He was the greatest within scarlet oak (table).
Black oak exhibited the smallest difference between Ho and He (table). Total number of
alleles between species significantly differed (p=0.0498) (Table 4-5). Comparison
between species showed that Ho did not significantly differ between species
(P=0.14980) (Table 4-6).
Table 4-6. Anova results for observed heterozygosity comparison between species.
Source of Variation
SS
df
MS
Between Groups
0.17472
3
0.05824 1.916689 0.149787 2.946685
Within Groups
0.85079
28
0.030385
Total
1.02551
31
34
F
P-value
F crit
CHAPTER 5
DISCUSSION
Allele size ranges (Table 4-1) of each of the eight loci conforms to other studies
that utilized the same microsatellite primers (Aldrich et al., 2002; Grant, 2010). The
eight microsatellite markers, which were developed from northern red oak species
proved to be transferrable by amplifying not only in other red oak species considered
in this study, but also amplifying in European white oak and chinese chestnut species
that were analysed in a different study (Aldrich et al., 2003a). Studies have shown the
ability of microsatellite markers for cross species amplification (Soto et al., 2003).
Transferability of microsatellites, especially within the same genus or even to some
extent across genera within the same family, is one of the attributes that make them
attractive in genetic diversity studies by facilitating their use and reducing cost,
particularly when working with taxa with low SSR frequency (Oliveira et al., 2006).
Although differentiated in regards to total number of alleles per species per locus (p =
0.0498) (Table 4-4), the mean number of alleles per species for the eight loci were
similar (northern red = 18.69, southern = 17.75, scarlet = 17, black = 21.75) (Table 43), but black oak seemed to have deviated from the other three species, and appeared to
have more genetic diversity than the other three species. Similarity in mean number of
alleles between species was also exhibited in a study that amplified white oaks and
Chinese chestnut from northern red oak microsatellites (Aldrich et al., 2003). Allelic
richness represented as the number of alleles at a locus is a basic measure of genetic
diversity in a population (Kalinowski, 2004). Allelic richness is affected by samples
size as large samples yield more alleles (Kalinowski, 2004). Equal number of samples
35
per species were considered in this study, so comparison of allelic richness between
species is not expected to be biased by sample size. In the same token, private alleles
measure genetic distinctiveness, and are also affected by sample size (Szpiech et al.,
2008). The extent of allele sharing between the four species was significant. However,
northern red, southern red, and scarlet oak species exhibited approximately ten percent
private alleles, and black oak exhibited about fifteen percent private alleles (Table 42). Two theories that have been proposed to explain low genetic differentiations in oak
species are the ancestral allele polymorphism, and the recurrent gene flow theories
(Lexer et al., 2006; Muir and Schlotterer, 2005). Since portions of samples for northern
red oak, southern red oak, and black oak were collected from sympatric populations,
contribution of gene flow and cryptic introgression due to hybridization in the low
differentiation is plausible. Allele sharing due to shared ancestral variation is known to
be extensive in closely related species, and has been shown to be evident in oak species
(Lexer et al., 2006). Sympatric species were distinguishable during sample collection,
which reiterates the maintenance of morphological and ecological integrity of red oak
species even though they are known to readily and easily hybridize. Genic
differentiation per species per locus between sites, which relates to the distribution of
alleles in samples (Raymond and Rousset, 1995a; Rousset, 2008), was low particularly
in northern red and southern red oaks, and black oak exhibited significant difference
between sites in half of the loci (Table 4-3). Reduced population differentiation
between sites of the same species are often explained by the outcrossing, and wind
pollinated nature of oaks (Kremer and Petit, 1993).
Observed heterozygosity appeared to be lower than expected across all loci that
created a mean positive inbreeding coefficient (Fis) values, indicating heterozygote
36
deficiency (Table 4-4). Other studies have also shown relatively higher Fis values than
expected in oaks species within which high level of gene flow is common (Grant, 2010;
Moran, 2010). Causes that create heterozygote deficiency include inbreeding and
population structure (Rousset and Raymond, 1995). Heterozygote deficiencies should
be taken with caution when relying on Fis estimates due to the variability of Fis values
at different loci (David et al., 2007). As a result, there have been reports of unreasonably
large heterozygote deficiency for species that do not exhibit that level of inbreeding
(David et al., 2007; Zouros and Foltz, 1984). The outbreeding wind pollinated oak
species (Dodd et al., 2002) are not expected to show high level of inbreeding, although
population structure may have played a role in causing the deficiency seen in this study.
Selection at specific loci that favores homozygotes rover heretozygotes has also been
pointed out in other oak species studies in relation to local adaptation of species (Lexer
et al., 2006). Although it is difficult to study the extent of local adaptation of specific
loci from just neutral microsatellite markers, population structure may have played a
role in low levels of heterozyosity since significant portions of species samples were
not collected from sympatric populations (Table 3-1). When working with
microsatellites, the occurrence of null alleles has also been known to contribute to
heterozygote deficiency, resulting in overestimation of homozygotes (Kumar et al.,
2009; Moran et al., 2012). Microsatellite null alleles refer to unamplified alleles by PCR
at a microsatellite locus due to mutations at primer annealing sites (Dakin and Avise,
2004; Kumar et al., 2009). Therefore, it may be reasonable to take into account the
possibility of microsatellite null alleles causing less than expected heterozygosity. In
closely related inter-fertile species such as oaks, hybridization may be the mechanism
that the problem of heterozygote deficiency can be overcome leading to the introduction
37
of advantageous alleles into the gene pool (Chybicki et al., 2012; Soltis and Soltis,
2009).
Intraspecific diversity appeared to be higher than interspecific diversity. Alleles
were highly variable between individuals per locus per species. Out of the eight loci
considered in this study, 146 alleles were observed in northern red oak, 142 in southern
red oak, 136 in scarlet oak, and 174 in black oak (Table 4-2). The total number of alleles
observed in each species are reflective of the variability that was observed between
individuals per locus per species (Table 4-4), which correlates with high intraspecific
variability of microsatellite loci in each of the species. Out of the total number of alleles
observed in each species, approximately 90% of alleles for northern red, southern red,
and scarlet oaks were shared, and black oaks exhibited 85% sharing of alleles (Table 42) which indicates low interspecific differentiation. Three out of the four species
expressed over fifty percent heterozygosity, and the remaining one species expressed
just below 50% (Table 4-2). On the other hand, comparison between species indicated
that, while the number of alleles differed, difference in heterozygosity was insignificant
between the four red oak species. This correlates with several studies that have revealed
weak gene pool differentiation between oak species (Aldrich et al., 2003b). Low
variability of molecular markers, ancestral polymorphisms, and introgressive
hybridization have been reported to contribute to poor resolution of species (Aldrich et
al., 2003b). Microsatellites, due to their highly polymorphic nature, prove to be
powerful markers in this regard and in this study with each locus exhibiting high
variability. Oak genetic diversity studies have reported low level interspecies
differentiation between oak species (Bodénès et al., 1997; Craft and Ashley 2006),
particularly red oak species (Aldrich et al., 2003b). Overall black oaks expressed more
38
diversity than the other three species, and seem to have deviated in similarity from the
other species in terms of having more alleles per locus, more private alleles, and
exhibiting more allelic differentiation between sites per locus.
39
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VITA
Fetun Desta, the son of Desta Anore and Almaz Beyene, was born on
February 28, 1978 in-Showa, Ethiopia. In 2007, he received the Bachelor of Science
degree in biology from -Temple University located in Philadelphia, Pennsylvania. He
joined the graduate school program at Alabama A&M University in 2011 to work
towards obtaining a Master of Science degree in biology. Fetun Desta graduated in
May 2014 with a degree in biology and a specialization in molecular biology.