Lecture 5

Question 1

translate hypotheses about r to make them testable in cholesky

1) rA = 1
2) rA = 0

Answer 1

1) a22 = 0

>>> only one set of genes is responsible for variation in both phenotypes

1) a21 = 0

>>> for each phenotype there is an distinct set of genes that explains variation

Question 2

bivariate saturated model

> how many parameters?

Answer 2

22

> 10 cov MZ

> 10 cov DZ

> 2 means (2 pheno)

Question 3

bivariate ACE model

> how many parameters)

Answer 3

bivariate ACE model 11 parameters

> 3 for A

> 3 for C

> 3 for E

> 2 means

Question 4

interpretation of bivariate models (pheno’s AD and WB)

> cov(AD1, WB1)

> cov(WB1, AD2)

> cov(AD1,AD2)

Answer 4

1. cov(AD1,WB1) - within individual, cross trait covariation

> common genetic influences for both traits?

2. cov(WB1, AD2) - cross twin, cross trait covariation

> are common genetic influences familial?

3. cov(AD1,AD2) - cross individual, within trait covariation

> informs about familiar influences on phenotype

Question 5

how to interpret a correlation rA = 1 between two phenotypes when the confidence interval is [0;1]

when does this happen?

Answer 5

interpret with care, not exact estimate can be made

> this happens when one of the variance parameters is small

Question 6

what are possible applications of multivariate analysis?

Answer 6

1. study comorbidity (2 or more phenotypes)
2. study development (multiple measurements)
3. study gene -“ environment” interactions

> e.g. do negative life events correlate with depression