Chapter 24 Flashcards by Rylee Cohoon

Phenotypes fall on a range without clear division: quantitative, continuous. Why are they called continuous?

exhibit a spectrum of possible values
trait can vary smoothly across a continuum than falling into distinct categories

How well did you know this?

Not at all

Perfectly

Why is the relationship of phenotype to genotype complex?

What type of inheritance does this contribute to?

due to multiple genes/loci
polygenic inheritance

How well did you know this?

Not at all

Perfectly

What is the unabbreviated version of QTL?

quantitative trait loci

How well did you know this?

Not at all

Perfectly

What strongly influences multifactorial characteristics?

loci
environment

How well did you know this?

Not at all

Perfectly

What does meristic mean?

whole numbers
traits that are countable and can’t be continuous

How well did you know this?

Not at all

Perfectly

Define threshold characteristics?

two phenotypes
underlying quantitative inheritance (disease susceptibility)

How well did you know this?

Not at all

Perfectly

What does it mean when alleles are additive at all loci?

for every gene involved in a trait, each allele contributes a predictable amount to the overall phenotype
no interaction between alleles at different loci

How well did you know this?

Not at all

Perfectly

Give an example of “additive, but not equal”

A+A+B-B- = A-A-B+B+
A+A+ does not = B+B+

How well did you know this?

Not at all

Perfectly

Can environmental influences make characteristics more continuous?

yes

How well did you know this?

Not at all

Perfectly

What does a normal distribution curve look like?

a simple hill
“bell-shaped”

How well did you know this?

Not at all

Perfectly

What does a skewed distribution curve look like?

large hill, then decrease in hill height

How well did you know this?

Not at all

Perfectly

What does a bimodal distribution curve look like?

two hills
one large, one medium

How well did you know this?

Not at all

Perfectly

For normal distribution, what is the “mean”?
What’s its unit?

average
(line over) X

How well did you know this?

Not at all

Perfectly

What is standard deviation (s)?

calculated from the variance
measuremnet of how far the typical individual is from the mean

How well did you know this?

Not at all

Perfectly

What does it mean when the value are high for standard deviation?
Low?

high:
- wide curve

low:
- narrow curve

How well did you know this?

Not at all

Perfectly

How do you calculate variance form standard deviation (s)?

variance = (standard deviation)^2
= s^2

What does correlation measure?

how strongly the values of two different quantitative characteristics relate to each other

What does it mean when the correlation value is 1?
0?
-1?

1:
- perfect correspondence (one is perfectly proportional to the other)

0:
- no correspondence (no relation between the two characteristics)

-1:
- perfect correspondence (inversely proportional)

What does it mean when the correlation between parental values and offspring is high?

higher genetic contribution to the characteristics
lower environmental contribution

What does regression measure?
What is it used for?

how two characteristics relate to each other
predict the actual value of one characteristic, given the value of the other

What is regression coefficient?

the slope of the regression line

What is the formula that corresponds to population variance?

Vp = Vg + Ve + Vge

Within the population phenotypic variance function Vp = Vg + Ve + Vge, what is Vg?
Ve?
Vge?

Vg:
- genetic variance
- all different contributions to phenotype

Ve:
- environmental variance
- contribution of environmental factors to phenotypic variance

Vge:
- genetic-environmental interaction variance
- contributes to phenotypic variance
- environment factors may have different effects on genotypes

What are the components of genetic variance (Vg)?

Vg = Va + Vd + Vi

Withing the genetic variance formula (Vg = Va + Vd + Vi), what is Va? Vd? Vi?

Va: - additive variance - genetic component of phenotypic variability at a loci - phenotype of Aa may be the average of AA and aa Vd: - dominance genetic variance - Aa may not be the average of AA and aa Vi: - genic interaction variance - alleles at different loci may interact and not be additive

What does the phenotypic variance formula look like when Vg is substituted with its components?

Vp = (Va + Vd + Vi) + Ve + Vge

Whar are the two types of heritability?

- broad-sense heritability - narrow-sense heritability

What does it mean when broad-sense heritability is equal to zero? 1?

0: - no genetic component - all phenotypic variation is environmental - offspring would resemble their parent similar as a stranger would 1: - genetic component completely accounts for all phenotypic variance - offspring will resemble parents exactly

What is commonly the value of broad-sense heritability? What does this mean?

0.5 - genetic and environmental components of variance are similar magnitudes

What is the formula for broad-sense heritability? narrow-sense heritability?

broad: H^2 = Vg/Vp narrow: h^2 = Va/Vp

What is the difference between broad-sense heritability and narrow-sense heritablility?

broad: - includes all genetic components narrow: - includes just additive and phenotypic variance

What is narrow-sense heritability used for?

predicting the inheritance of quantitative traits

Why can't you eliminate environmental variance by growing/raising individuals in identical environments?

it is impossible for two environments to be exactly the same

What is one way to eliminate genetic variance (Vg)? What would be the new phenotic variance formula?

- inbreeding or cloning individuals Vp = Ve

What is another formula for broad-sense heritability?

H^2 (varying population) = (Vp (varing population) - Vp (identical population)) / Vp (varying population)

What is artificial selection applied to?

members of a parental population

What is the formula for narrow-sense heritability (h^2)?

h^2 = R/S

Within the formula for narrow-sense heritability (h^2) (h^2 = R/S), what is R? S?

R = response to selection = x (original) - x (offspring) S = selection differential = x (original) - x (selected)