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Effetto “setaccio” in un gel uniforme
ELETTROFORESI
Migrazione di particelle cariche sotto l’azione di un campo elettrico.
Tecnica soprattutto ANALITICA ma anche PREPARATIVA.
E’ un mezzo di separazione molto potente, fra i piu’ usati in biochimica
Forza elettrica : Fel = q · E
Forza frizionale : Ffr = f ·v
(f = 6 r )
Quando le forze si bilanciano:
q ·E = f ·v

q
v= —·E
f
mobilita’ elettroforetica :
v
q
 = — = —
E
f
Formazione di un gel di poliacrilammide
NBT Nitro Blu di Tetrazolio
Coomassie Blu
Locus A
3 alleles
Locus B
a allele
Western blotting (blotting di proteine)
Solbrig and Simpson (1974)
• 284 T. officinale samples from a
500m×500m area in Ann Arbor, Michigan.
• Electrophoretic variations (allozymes) of
six enzymes were analyzed
Most Commonly Used Molecular
Markers in Plants
•
•
•
•
RFLP
RAPD
AFLP
SSR & STS
DNA extraction
• From any tissue but:
• - Young leaves still actively
dividing, so more DNA
available.
• - Older plant parts beginning
to senesce, and DNA is
breaking down so less useful.
• From any condition plant, but
green-house or growthchamber materials tend to be
cleaner (less fungus, dirt
carrying bacteria, insects, etc).
DNA extraction
• Extracted DNA checked
for quantity
(spectrophotometer or
quality gel)
• Extracted DNA checked
for quality (quality gel)
RFLP: Restriction Fragment
Length Polymorphism
• Plant genomic DNA digested with restriction
endonuclease
• DNA fragments separated via electrophoresis
and transferred to non-charged membranes
• Membranes exposed to probes labeled with
digoxigenin or P32 via southern hybridization
• Film exposed to the x-ray or light labeled
membranes
other methods of detecting DNA sequence variations
* RFLP: restriction fragment length polymorphism
restriction endonucleases: enzymes that cleave DNA at specific places
the produced DNA fragments are separated by electrophoresis
restriction sites are gained and lost through mutation
presence/absence of restriction fragments of a given length
are character states
different rates of evolution andor heredity can be analysed
RFLP: Continued
• Probes derived from cDNA or genomic DNA
(also digested with endonucleases)
• RFLPs have good repeatability
• Large quantities of DNA are needed and
procedure is difficult to automate
Polymorphism induced by size variation (number of copies)
VNTR: variable number of tandem repeats
relatively small tandemly repeated sequences: dispersed in the genome
e.g. minisatellites: repeat lengths of 10-100’s of base pairs
microsatellites: have repeated lengths of one or some base pairs
RAPD: random amplified polymorphic DNA
produces a large number of fragments, many species-specific
if inheritance verified: RAPD patterns can be used in population genetics
Genome Sizes
The genome of an organism is the complete set of genes specifying how its phenotype will develop
(under a certain set of environmental conditions). In this sense, then, diploid organisms (like
ourselves) contain two genomes, one inherited from our mother, the other from our father.
Table of Genome Sizes (haploid)
Base pairs
Phi-X 174
5,386
Genes
Notes
10
virus of E. coli
Pelagibacter ubique 1,308,759
1,354
smallest genome yet found in a free-living
organism (marine α-proteobacterium)
Agrobacterium
tumefaciens
4,674,062
5,419
Useful vector for making transgenic plants; shares
many genes with Sinorhizobium meliloti
Saccharomyces
cerevisiae
12,495,682 5,770
Budding yeast. A eukaryote.
Caenorhabditis
elegans
100,258,171 19,427
The first multicellular eukaryote to be sequenced.
Arabidopsis thaliana 115,409,949 ~28,000
a flowering plant (angiosperm) See note.
Drosophila
melanogaster
the "fruit fly"
122,653,977 13,379
Anopheles gambiae 278,244,063 13,683
Humans
3.3 x 109
20,000–
25,000
Rice
3.9 x 108
37,544
Amphibians
109 - 1011
?
Mosquito vector of malaria.
The human genome turns out to
have only about half or fewer
(30,000 to 40,000) genes than we
predicted (100,000). Why?
Drosophila – 13,000
Nematode – 19,000
• C value paradox: the amount of
DNA in the haploid cell of an
organism is not related to its
evolutionary complexity or
number of genes.
Human Genome Organization
HUMAN GENOME
Nuclear genome
3000 Mb
30-40000 genes?
~30%
Mitochondrial genome
16.6 kb
37 genes
~70%
Genes and generelated sequences
Extragenic
DNA
Unique or moderately repetitive
~10%
~90%
Coding
DNA
Pseudogenes
Noncoding
DNA
Gene
fragments
Introns,
untranslated
sequences, etc.
Two rRNA
genes
22 tRNA
genes
13 polypeptideencoding genes
80%
20%
Unique or
low copy
number
Moderate to
highly
repetitive
Tandemly
repeated
or clustered
repeats
Interspersed
repeats
Inventory of a eukaryotic genome
Moderately repetitive DNA

Functional




rRNA genes (250 cop ies)
tRNA genes (50 sites with 10– 100
cop ies each in hu man )
his tone genes in many species




Without known function










1–5 kb long
10–10 00 0 copies per genome
pseudogenes
composed of repeats of up to 13 bp
~100s of kb long
~ 106 copies/genome
most of the heterochromatin around
the centromere
telomeres

long interspersed elements
(LINEs)


Alu is an examp le (some function in
gene reg ulation)
200–30 0 bp long
100 000's of copies (300 000 Alu)
scat tered locati ons (not in tandem
repeats )
composed of repeats of 14–500 bp
segments
1–5 kb long
many different ones
scattered throughout the genome
microsatellites

short interspersed elements
(SINEs)

minisatellites
e.g. actin, glo bin
tandem gene family arrays



dispersed gene families


Highly repetitive DNA

contain a short repeat unit (typically
6 bp: TTAGGG in human genome,
TTGGGG in Paramecium, TAGGG in
trypanosomes, TTTAGGG in
Arabidopsis)
250–1 000 repeats at the ends of
each chromosome
RAPD: Random Amplified
Polymorphic DNA
• PCR-based marker with 10 – 12 bp primers
• Random amplification of several fragments
• Amplified fragments run in a agarose gel and
detected by ethidium bromide
• Unstable amplification leads to poor repeatability
PCR (Polymerase Chain Reaction)
The mixture after the PCR reaction is normally loaded on an agarose gel for electrophoresis to separate
amplification products according to their size. Staining of the gel with ethidium bromide, and subsequent
observation under ultraviolet light is the next step. Ethidium bromide will concentrate between the bases in
the DNA and give rise to yellow flourescent bands under the UV-light.
The present RAPD reactions show amplification of nine different chromosome segments in the agarose gel.
The B3 band is polymorphic with a longer amplification product from L1 than from L2. Because both
alleles can be seen in the F1 this marker is codominant. The B6 band is absent in the reaction from the line
L1, possibly because one of the primer sites has changed owing to a mutation. Dominant marker types are
the most frequent in RAPD analysis.
AFLP: Amplified Fragment Length
Polymorphism
• Restriction endonuclease digestion of DNA (EcoR
i/Mse I, EcoR i/Pst I)
• Ligation of adapters
• Amplification of the ligated fragments
• Separation of the amplified fragments via
electrophoresis and visualization
• AFLPS have stable amplification and good
repeatability
• AFLPs may not be mapped and are technically
difficult to perform
AFLP reactions can be analysed by separation according to fragment size on a polyacrylamide gel. Visualisation is often done by
autoradiography if one primer has P33labeling. The simplified autoradiograph from an AFLP (right) shows 27 different amplification
products.
The B9 is a rare codominant marker with the fragment from line L1 larger than the one form L2. Both alleles are visible in the hybrid.
B18 and B21 are of the much more frequent dominant types, where only one allele will amplify.
SSR: Simple Sequence Repeat or
Microsatellite
• PCR-based marker with 18-25 bp primers
• SSR polymorphisms are based on number of
repeat units, and are hypervariable (have many
alleles)
• Primers are mapped and reported in MaizeDB
(www.agron.Missouri.edu/probes.Html)
• SSRs have stable amplification and good
repeatability
• SSRs are easy to run and automate
STS: Sequence Tagged Sites
• PCR-based marker with 18 – 25 bp primers
• Derived from sequenced RFLP, RAPD or AFLP
fragments or known genes
• Stable amplification and good repeatability
• Generally mapped
• Easy to run and automate
• Not many polymorphic STSs currently available
Species
DNA Extraction
Sequencing and primer design
Clone
Genotype analysis of
fluorescent PCR
Analysis of
marker
polymorphism
atggctatcgtatcgattttgctatgc
gagcggttaaaatctatgttgggat
atggtatttgaatctcgtatcgctgta
tcgctgatgcgcgcgcgcgcgcg
cgcgcgcgcttatctgctatcgttsa
gctatcgttcgggttaggtttatatgt
agtctcgtcgctcatagttagctggt
tatcttatcgtctcgatct
Gel Electrophoresis
PCR
DNA sequencing:
* sequencing: nucleotide sequences of DNA or RNA:
greatest genotype resolution (looks at the nucleotide itself)
- direct sequencing of genomic DNA (PCR)
- or by cloning such regions and sequencing from the cloned fragment
* nuclear genes: single locus nuclear genes
detecting functional polymorphisms and population structure
* mitochondrial DNA: cytoplasmic mtDNA high number of copies,
inherited from female parent, gives insight into sex-biased population
structure, mtDNA highly variable, classically used in population biology
* transposons: polymorphism induced by insertion, horizontal gene
transfer (transfer of DNA segments between organisms), inter-organellar
gene transfer (between organelles within organisms)
Choice of molecular markers
to determine parentage depending on the level of evolutionary divergence
most molecular markers: variation in noncoding DNA regions
in general:
allozymes less variable than RAPD or RFLP markers
RAPD/RFLP markers less variable than micro/minisatellite fingerprints
Definitions
• Traits examined so far have resulted in
discontinuous phenotypic traits
– Tall or dwarf
– Round or wrinkled
– Red, pink or white
• Most of phenotypic traits vary on a continuous
basis:
– Height
– Weight
– Fitness
What is a Quantitative Trait?
A quantitative trait has numerical values that can be ordered
highest to lowest.
Describing Quantitative Traits:
The Mean
• Two statistics are commonly used to describe
variation of a quantitative trait in a population
1 The Mean - For a trait that forms a bell shaped
curve (normal distribution) when a frequency
diagram is plotted, the mean is the most common
size, shape, or whatever is being measured
SXi
X=
n Number of
individual
values
X
D Frequency
Sum of individual
values
D Trait
Describing Quantitative Traits:
Standard Deviation
2 Standard Deviation - Describes the amount of
variation from the mean in units of the trait
• Large SD indicates great variability
• 68 % of individuals exhibiting the trait will fall
within ±1 SD of the mean, 95.5 % ±2, 99.7 % ±3 SD
• 95 % fall within 1.96 SD
-1
+1
D Frequency
X
68.3%
D Trait
DEVIAZIONE
STANDARD
VARIANZA
COVARIANZA [COV X,Y]
COEFFICIENTE DI CORRELAZIONE=r=
ANALISI DELLA REGRESSIONE
Y=A + BX
B=COV [X,Y]
Varianza X
Separating Genetics from Environment
• Two experiments by Wilhelm Johannsen, from
Denmark, using the common bean (Phaseolus
vulgaris). Johannsen coined the words
“genotype” and “phenotype”.
• First Johannsen experiment: he weighed a
group of beans, then grew them up and
weighed their progeny (after selfing them).
In general, heavy parents gave heavy offspring and light parents gave
light offspring. That is, there is a significant correlation between parent
and offspring weights.
However, there is also a considerable variation among the offspring
weights.
Johannsen’s Second Experiment
• Johannsen then worked on separating environmental effects
from genetic effects. He did this by inbreeding the beans for
10 generations. .
• After 10 generations of selfing, the percentage of
heterozygotes is less than 1/1000 of the original level.
Results
• Johannsen created 19 inbred lines. The inbred lines
had some variation, but less than the original randombred population. The remaining variation was due to
environmental variations.
• The mean weight of each line was different, but it
was stable across generations. The reason is that the
lines are genetically different from each other, but
they are genetically (more or less) identical. The
variance was also stable between generations.
•
•
Start with a random-bred population.
Take the best ones to be parents of the
next generation. The next generation
has a mean that is shifted in the
desired direction .
This procedure doesn’t work for
inbred populations, because there is
no genetic variation to inherit. The
next generation’s mean is the same as
the previous generation’s mean
despite having selected the best
parents.
Mathematical Basis of Quantitative
Genetics
•
•
•
•
•
•
Recall the basic premise of quantitative genetics: phenotype = genetics plus
environment.
In fact we are looking at variation in the traits, which is measured by the width of
the Gaussian distribution curve. This width is the variance (or its square root, the
standard deviation).
Variance is a useful property, because variances from different sources can be added
together to get total variance.
However, the units of variance are the squares of the units used to measure the trait.
Thus, if length in centimeters was measured, the variances of the length are in cm2.
This is why standard deviation is usually reported: length ± s.d. --because standard
deviation is in the same units as the original measurement. Standard deviations
from different sources are not additive.
Quantitative traits can thus be expressed as:
VT = VG + VE
where VT = total variance, VG - variance due to genetics, and VE = variance due to
environmental (non-inherited) causes.
This equation is often written with an additional covariance term: the degree to
which genetic and environmental variance depend on each other. We are just going
to assume this term equals zero in our discussions.
Heritability
• One property of interest is “heritability”, the proportion of a
trait’s variation that is due to genetics (with the rest of it due to
“environmental” factors). This seems like a simple concept,
but it is loaded with problems.
• The broad-sense heritability, symbolized as H (sometimes H2
to indicate that the units of variance are squared). H is a
simple translation of the statement from above into
mathematics:
H = VG / VT
• This measure, the broad-sense heritability, is fairly easy to
measure, especially in human populations where identical
twins are available. However, different studies show wide
variations in H values for the same traits, and plant breeders
have found that it doesn’t accurately reflect the results of
selection experiments. Thus, H is generally only used in social
science work.
Additive vs. Dominance Genetic
Variance
•
•
•
•
•
The biggest problem with broad sense heritability comes from lumping all genetic
phenomena into a single Vg factor. Paradoxically, not all variation due to genetic
differences can be directly inherited by an offspring from the parents.
Genetic variance can be split into 2 main components, additive genetic variance
(VA) and dominance genetic variance (VD).
VG = VA + VD
Additive variance is the variance in a trait that is due to the effects of each
individual allele being added together, without any interactions with other alleles or
genes.
Dominance variance is the variance that is due to interactions between alleles:
synergy, effects due to two alleles interacting to make the trait greater (or lesser)
than the sum of the two alleles acting alone. We are using dominance variance to
include both interactions between alleles of the same gene and interactions between
difference genes, which is sometimes a separate component called epistasis
variance.
The important point: dominance variance is not directly inherited from parent to
offspring. It is due to the interaction of genes from both parents within the
individual, and of course only one allele is passed from each parent to the offspring.
Narrow Sense Heritability
• For a practical breeder,
dominance variance can’t be
predicted, and it doesn’t affect the
mean or variance of the offspring
of a selection cross in a systematic
fashion. Thus, only additive
genetic variance is useful.
Breeders and other scientists use
“narrow sense heritability”, h, as a
measure of heritability.
h = VA / VT
• Narrow sense heritability can also
be calculated directly from
breeding experiments. For this
reason it is also called “realized
heritability”.
Heritability in a Selection Experiment
• There are 3 easily measured parameters in a selection experiment: the mean
of the original random-bred population, the mean of the individuals
selected to be the parents, and the mean of the next generation. These
factors are related by the narrow sense heritability:
• The denominator is sometimes called the “selection differential”, the
difference between the total population and the individuals selected to be
parents of the next generation. The numerator is sometimes called the
“selection response”, the difference between the offspring and the original
population, the amount the population shifted due to the selection.
h
next _ generation_ meanoriginal_ mean
 parent _ meanoriginal_ mean
Example
• In Drosophila, the mean number of bristles on the
thorax (top surface only) is 6.4.
• From this population, a group was chosen which had
an average bristle number of 7.2.
• The offspring of the chosen group had an average of
6.6 bristles.
• h = (next gen - original) / (selected - original)
• h = (6.6 - 6.4) / (7.2 - 6.4)
• h = 0.2 / 0.8
• h = 0.25
Example Problem
• In a quest to make bigger frogs, scientists started with a
random bred population of frogs with an average weight of
500 g. They chose a group with average weight 600 g to be
the parents of the next generation. A few other facts: VE =
1340, VA = 870, VD = 410.
•
•
•
•
•
What is the genetic variance? VG = VA + VD = 1280
What is the total variance? VT = VG + VE = 2620
What is the broad sense heritability? H = VG / VT = 0.49
What is the narrow sense heritability? h = VA / VT = 0.33
What is the mean weight of the next generation?
h = (next gen - original) / (selected - original)
0.33 = (next_gen - 500) / (600 - 500) = 533 g
Additive Alleles
• If more than one gene with two alleles that
behave as incompletely dominant alleles are
involved, variability occurs over more of a
continuum
• If two genes with two alleles are involved, X
phenotypes can result
F2
1/4 AA
1/2 Aa
1/4 aa
Additive
alleles
1/4 BB -- 1/16 AABB
4
1/2 Bb -- 2/16 AABb
3
1/4 bb -- 1/16 AAbb
2
1/4 BB -- 2/16 AaBB
3
1/2 Bb -- 4/16 AaBb
2
1/4 bb -- 2/16 Aabb
1
1/4 BB -- 1/16 aaBB
2
1/2 Bb -- 2/16 aaBb
1
1/4 bb -- 1/16 aabb
0
1/16
4/16 = 1/4
6/16 = 3/8
4/16 = 1/4
1/16
Additive Alleles
• Additive alleles are alleles that change the
phenotype in an additive way
• Example - The more copies of tall alleles a person
has, the greater their potential for growing tall
• Additive alleles behave something like alleles
that result in incomplete dominance
• More CR alleles results in
F2 Generation
redder flowers CR
CW
CR CR CR CR CW
1:
R
R
2:
R
W
1
W W
CW CRCW CWCW
Estimating Gene Numbers
• The more genes involved in producing a trait, the
more gradations will be observed in that trait
• If two examples of extremes of variation for a trait
are crossed and the F2 progeny are examined, the
proportion exhibiting the extreme variations can be
used to calculate the number of genes involved:
1 = F extreme phenotypes in total offspring
2
4n
• If 1/64th of the offspring of an F2 cross of the kind
described above are the same as the parents, then
1 = 1
N = 3 so there are probably
64 43
about 3 genes involved