Transcript Chapter 7 Quantitative Genetics

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].
Quantitative Genetics
 Traits such as cystic fibrosis or flower color in peas
produce distinct phenotypes that are readily
distinguished.
 Such discrete traits, which are determined by a single
gene, are the minority in nature.
 Most traits are determined by the effects of multiple
genes (such traits are called polygenic traits) and
these show continuous variation in trait values.
Complex traits vary continuously
Continuous variation
 For example, grain color in winter wheat is determined
by three genes at three loci each with two alleles.
Additive effects of genes
 The genes affecting color of winter wheat interact in a
particularly straightforward way.
 They have additive genetic effects.
 This means that the phenotype for an individual is
obtained just by summing the effects of individual alleles.
 For example, the more alleles for being dark (previous
slide) or large (next slide) an individual has the
darker/taller it will be and the continuous distribution
results.
Continuous variation
 Examples in humans of traits that show continuous
variation include height, intelligence, athletic ability,
and skin color.
Quantitative traits
 For continuous traits we cannot assign individuals to
discrete categories. Instead we must measure them.
 Therefore, characters with continuously distributed
phenotypes are called quantitative traits and the
study of the genetic basis of quantitative traits is
quantitative genetics.
Value of quantitative traits
 Quantitative traits determined by influence of (1)
genes and (2) environment.
 The value of a quantitative traits such as height or fruit
size or running speed is determined by the organism’s
genes operating within their environment.
 The size an organism grows is affected not only by the
genes it inherited from its parents, but the conditions
under which it grow up.
Value of quantitative traits
 For a given individual the value of its phenotype (P)
(e.g. the weight of a tomato in grams, a person’s 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.
Genetic and environmental influences
create continuous distribution
Measuring Heritable Variation
 The quantitative genetics approach 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. The variance measures how
different individuals are from the mean and estimates
the spread of the data.
 FYI: Variance is the average squared deviation from the
mean. Standard deviation is the square root of the
variance.
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.
Measuring Heritable Variation
 Heritability measures what fraction of variation is due
to variation in genes and what fraction is due to
variation in environment.
Measuring Heritable Variation
 Heritability = Vg/Vp
 Heritability = Vg/Vg+Ve
 This is broad-sense heritability (H2). It defines the
fraction of the total variance that is due to genetic
causes.
 (Heritability is always a value between 0 and 1.)
Measuring Heritable Variation
 The genetic component of inheritance (Vg) includes the
effect of all genes in the genotype.
 If all gene effects combined additively then an individual’s
genotypic value G could be represented as a simple sum of
individual gene effects.
 However, there are interactions among alleles (dominance
effects) and interactions among different genes (epistatic
effects) that can affect the phenotype and these effects are
non-additive.
Measuring Heritable Variation
 To account for dominance and epistasis we break down
the equation for P (value of the phenotype)
 P = G +E
 G (genetic effects) is the sum of three components – A
[additive component], D [dominance component] and
I [epistatic or interaction component].
G=A+D+I
 So therefore P = A + D + I + E
Measuring Heritable Variation
 Similarly, if we assume all the 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
Measuring Heritable Variation
 Breaking down the variances allows us to produce a simple
expression for how a phenotypic trait changes over time in
response to selection.
 Only one component Va is directly operated on by natural
selection.
 The reason for this is that 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). [E.g. a recessive allele only exerts its effect on
a phenotype when it is homozygous. If a dominant allele is
present the recessive allele is not expressed.]
Measuring Heritable Variation
 Va however exerts the same effect regardless of the
genetic background. Therefore, its effects are always
visible to selection.
Measuring Heritable Variation
 Remember we defined broad sense heritability (H2) as
the proportion of total variance due to any form of
genetic variation
 H2 = Vg/Vg+Ve
 We similarly define narrow sense heritability h2 as
the proportion of variance due to additive genetic
variance
 h2 = Va/(Va + Vd + Vi + Ve)
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.
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.
 Plot midparent value (average of the two parents)
against offspring value.
 If offspring don’t 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.
If offspring strongly
resemble
parents then best fit line
will be close to 1.
Most traits in most
populations fall
somewhere in the middle
with offspring showing
moderate resemblance to
parents.
 When estimating heritability it’s 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.
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.
Smith and Dhondt estimated heritability of bill depth as about 0.98.
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).
Directional selection for oil content in corn
Disruptive selection for
bristle number in Drosophila
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 [as we’ve
done] and the effect of selection.
 We need to be able to measure differences in
survival and reproductive success among
individuals to assess the effect of selection.
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.
Measuring differences in
survival and reproduction
 If some members of a population breed and others
don’t 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.
Selection Differential
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.
Evolutionary response to selection
 Knowing heritability and selection differential we
can predict evolutionary response to selection (R).
 Given by the simple formula: R=h2S
 R is predicted response to selection, h2 is
heritability, S is selection differential.
Effect of difference in heritability (h2) on a population’s response to
selection (R) with same selection differential (S).
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.
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%.
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 (h2).
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).
Alpine skypilots and bumble bees
 Using her data Galen predicted response to selection
R.
 R=h2S
 R=0.2*0.05 = 0.01 (low end estimate)
 R=1.0*0.05 = 0.05 (high end estimate)
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.