Lecture 6

Question 1

what genetic variations in linear regression are affected by the dominance deviation parameter d

Answer 1

not only Aa, but also AA and aa

>>> both get lower compared to when d = m

Question 2

what are the 5 assumptions of the classical twin design

Answer 2

1. equal environments assumption
2. no GE correlation (passive, actieve or evocative)
3. no GxE interaction
4. no assortative mating - random mating is assumed
5. no gene-gene interaction

> non additive epistatic effects

Question 3

what happens when the following assumptions are violated

1. C = 0
2. D = 0
3. equal environment
4. random mating
5. Cov (AC) = 0
6. AxE = 0
7. AxC = 0

Answer 3

violation

1. C = 0 > A overestimated, C underestimated
2. D = 0 > A overestimated, D underestimated
3. equal environment

> C mimc A >> A overestimated, C underestimated

4. random mating

> can result in r(A,A)DZ > 0.5 >> C overestimated, A/D underestimated

5. Cov(AC) = 0

> mimic C, C overestimated, A underestimated

5. AxE = 0

> mimic E, E overestimated, A underestimated

6. AxC = 0

> mimic C, C overestimated, A underestimated

Question 4

how to choose starting model

Answer 4

rDZ < 0.5 rMZ >>> ADE

rDZ = 0.5 rMZ >>> AE

rDZ > 0.5 rMZ >>> ACE

>>> but you never know

Question 5

explain

AxE/AxC

interaction

Answer 5

1.AxE: effects of E depends on level of A

> genetic control of environmental effects

2.AxE: effects of A depends on level of E

> environmental control of genetic effects

Question 6

how to test for GxE interaction?

Answer 6

test for heteroskedasticity

> regress phenotype on A/E

> is the variance of the residuals equal over all levels of A/E?

> if so, then homoskedasticity, no interaction

> if not, then the model is heteroskedastic, GxE interaction

Question 7

what are 2 binary examples of moderating effects

Answer 7

1. unmarried women show greater levels of genetic influence on depression
2. religious upbringing diminishes A effects on the personality trait of disinhibition

Question 8

GxE moderation: what to do when M seems to be a measured variable, but really is influenced by genotype?

Answer 8

treat M like a phenotype and include it in your model

>> go full cholesky

Question 9

what model can be used to analyse moderation in bivariate modeling?

what are the advantages/disadvantages?

Answer 9

Purcell’s model (especially when moderator is continuous)

> path loadings are allowed to vary as function of moderator

> moderator features twice: as phenotype and as moderator

advantage: covariance between M and T can also fluctuate as a function of the moderator itself

disadvanteages:

1. lots of parameters need to be estimated
2. moderator and trait variables need to have the same measurement level