Chapter 7 Quantitative Genetics Read Chapter 7 sections 7.1 and 7.2. [You should read 7.3 and 7.4 to deepen your understanding of the topic, but I will not cover these topics in lecture].
Transcript
Slide 1
Chapter 7 Quantitative Genetics Read Chapter 7 sections 7.1 and
7.2. [You should read 7.3 and 7.4 to deepen your understanding of
the topic, but I will not cover these topics in lecture].
Slide 2
Quantitative Genetics Traits such as flower color in peas
produce distinct phenotypes. Such discrete traits, which are
determined by a single gene, are relatively rare. Most traits are
determined by the effects of multiple genes (polygenic traits) and
these show continuous variation in trait values.
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Complex traits vary continuously
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Continuous variation For example, grain color in winter wheat
is determined by three genes at three loci each with two
alleles.
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Additive effects of genes Genes affecting color of winter wheat
interact in a straightforward way. They have additive genetic
effects. Thus the phenotype is obtained by summing the effects of
individual alleles. The more alleles for being dark (prev. slide)
or large (next slide) an individual has, the darker/taller it will
be. A continuous distribution results.
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Continuous variation Examples of humans traits that show
continuous variation: height, intelligence, athletic ability, skin
color.
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Quantitative traits For continuous traits we cannot assign
individuals to discrete categories (e.g. tall or short). Instead we
must measure them. Therefore, characters with continuously
distributed phenotypes are called quantitative traits. Quantitative
genetics is the study of the genetic basis of quantitative
traits.
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Value of quantitative traits Quantitative traits determined by
influence of (1) genes and (2) environment. The value of a
quantitative trait such as height is determined by the organisms
genes operating within their environment. Both the genes it
inherited from its parents and the conditions under which it grows
up affect an individuals height.
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Value of quantitative traits For a given individual the value
of its phenotype (P) (e.g., a persons height in cm) can be
considered to consist of two parts -- the part due to genotype (G)
and the part due to environment (E) P = G + E. G is the expected
value of P for individuals with that genotype. Any difference
between P and G is attributed to environmental effects.
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Genetic and environmental influences create continuous
distribution
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Measuring Heritable Variation Quantitative genetics takes a
population view and tracks variation in phenotype and whether this
variation has a genetic basis. Variation in a sample is measured
using a statistic called the variance.
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Measuring Heritable Variation We want to distinguish between
heritable and nonheritable factors affecting the variation in
phenotype. It turns out that the variance of a sum of independent
variables is equal to the sum of their individual variances.
Because P = G +E Then Vp = Vg + Ve where Vp is phenotypic variance,
Vg is variance due to genotypic effects and Ve is variance due to
environmental effects.
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Measuring Heritable Variation Heritability is defined as the
fraction of total phenotypic variation that is due to variation in
genes
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Measuring Heritable Variation Heritability = Vg/Vp Heritability
= Vg/Vg+Ve This is broad-sense heritability (H 2 ). It defines the
fraction of the total variance that is due to genetic causes.
(Heritability is always a value between 0 and 1.)
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Measuring Heritable Variation The genetic component of
inheritance (Vg) includes the effect of all genes in the genotype.
If all gene effects combined additively an individuals genotypic
value G could be represented as the sum of individual gene
effects.
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Measuring Heritable Variation However, interactions between
alleles (dominance effects) and interactions between different
genes (epistatic effects) can affect the phenotype and these
effects are non-additive.
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Measuring Heritable Variation To account for dominance and
epistasis we break down the equation for P (value of the phenotype)
P = G +E Comonent G (genetic effects) is the sum of three
subcomponents A [additive component], D [dominance component] and I
[epistatic or interaction component]. G = A + D + I So therefore P
= A + D + I + E
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Measuring Heritable Variation Similarly, assuming all
components of the equation P = A + D + I + E are independent of
each other then the variance of this sum is equal to sum of the
individual variances. Vp = Va + Vd + Vi + Ve
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Measuring Heritable Variation Only Va is directly operated on
by natural selection. The effects of Vd and Vi are strongly context
dependent i.e., their effects depend on what other alleles and
genes are present (the genetic background).
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Measuring Heritable Variation Va, however exerts the same
effect regardless of the genetic background. Therefore, its effects
are always visible to selection.
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Measuring Heritable Variation We defined broad sense
heritability (H 2 ) as the proportion of total variance due to any
form of genetic variation H 2 = Vg/Vg+Ve We similarly define narrow
sense heritability h 2 as the proportion of variance due to
additive genetic variance h 2 = Va/(Va + Vd + Vi + Ve)
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Measuring Heritable Variation Because narrow sense heritability
is a measure of what fraction of the variation is visible to
selection, it plays an important role in predicting how phenotypes
will change over time as a result of natural selection. Narrow
sense heritability reflects the degree to which offspring resemble
their parents in a population.
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Estimating heritability from parents and offspring Narrow sense
heritability is the slope of a linear regression between the
average phenotype of the two parents and the phenotype of the
offspring. Can assess the relationship using scatterplots.
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Plot midparent value (average of the two parents) against
offspring value. If offspring dont resemble parents then best fit
line has a slope of approximately zero. Slope of zero indicates
most variation in individuals due to variation in
environments.
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If offspring strongly resemble parents then slope of best fit
line will be close to 1.
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Most traits in most populations fall somewhere in the middle
with offspring showing moderate resemblance to parents.
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When estimating heritability its important to remember parents
and offspring share an environment. We need to make sure there is
no correlation between the environments experienced by parents and
their offspring. This requires cross-fostering experiments to
randomize environmental effects.
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Smith and Dhondt (1980) Smith and Dhondt (1980) studied
heritability of beak size in Song Sparrows. Moved eggs and young to
nests of foster parents. Compared chicks beak dimensions to parents
and foster parents.
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Smith and Dhondt estimated heritability of bill depth as about
0.98.
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Evolutionary response to selection Once we know the sources of
variation in a quantitative trait we can study how it evolves. If
selection favors certain values of a trait then we expect the
population to evolve in response. The effect on the distribution of
the trait will depend on which phenotypes are being favored (see
next slide).
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Directional selection for oil content in corn
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Disruptive selection for bristle number in Drosophila
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Evolutionary response to selection To quantify the amount and
direction of change in a trait value from one generation to the
next (i.e., how a trait evolves) we need to quantify heritability
and the effect of selection. To assess the effect of selection we
have to measure differences in survival and reproductive success
among individuals.
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Measuring differences in survival and reproduction Need to be
able to quantify difference between winners and losers in whatever
trait we are interested in. This is strength of selection.
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Measuring differences in survival and reproduction If some
members of a population breed and others dont and you compare the
mean values of some trait (say mass) for the breeders and the whole
population, the difference between them (and one measure of the
strength of selection) is the selection differential (S). This term
is derived from selective breeding trials.
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Selection Differential
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Response to Selection We want to be able to measure the effect
of selection on a population. This is called the Response to
Selection and is defined as the difference between the mean trait
value for the offspring generation and the mean trait value for the
parental generation i.e. the change in trait value from one
generation to the next.
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Evolutionary response to selection Knowing heritability and
selection differential we can predict evolutionary response to
selection (R). Given by the simple formula: R=h 2 S R is predicted
response to selection, h 2 is heritability, S is selection
differential. R is the proportional (or if you multiply by 100, the
percentage) change in a trait value.
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Effect of difference in heritability (h 2 ) on a populations
response to selection (R) with same selection differential (S).
Plots of parent offspring regressions for two populations.
Intersection of axes is midpoint of parental (x-axis) and offspring
(y-axis) trait values.
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Alpine skypilots and bumble bees Alpine skypilot perennial
wildflower found in the Rocky Mountains. Populations at timberline
and tundra differed in size. Tundra flowers about 12% larger in
diameter. Timberline flowers pollinated by many insects, but tundra
only by bees. Bees known to be more attracted to larger
flowers.
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Alpine skypilots and bumble bees Candace Galen (1996) wanted to
know if selection by bumblebees was responsible for larger size
flowers in tundra and, if so, how long it would take flowers to
increase in size by 12%.
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Alpine skypilots and bumble bees First, Galen estimated
heritability of flower size. Measured plants flowers, planted their
seeds and (seven years later!) measured flowers of offspring.
Concluded 20-100% of variation in flower size was heritable (h 2
).
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Alpine skypilots and bumble bees Next, she estimated strength
of selection by bumblebees by allowing bumblebees to pollinate a
caged population of plants, collected seeds and grew plants from
seed. Correlated number of surviving young with flower size of
parent. Estimated the selection differential (S) to be 5%
(successfully pollinated plants 5% larger than population
average).
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Alpine skypilots and bumble bees Using her data Galen predicted
response to selection R. R=h 2 S R=0.2*0.05 = 0.01 (low end
estimate) R=1.0*0.05 = 0.05 (high end estimate)
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Alpine skypilots and bumble bees Thus, expect 1-5% increase in
flower size per generation. Difference between populations in
flower size plausibly due to bumblebee selection pressure.