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## PowerPoint Slideshow about ' Introducing Bayesian Approaches to Twin Data Analysis' - quincy-boyd

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### Introducing Bayesian Approaches to Twin Data Analysis

### Some references

### The Traditional Approach:Via Likelihood

### Typically

### BUT….

### Bayesian approach

### P(q)

### How do we get integral?

### “Markov Chain” Monte Carlo…

### If we succeed…

### One algorithm that can generate chains in large number of cases…

### Application to Twin Data

Lindon Eaves,

VIPBG, Richmond.

Boulder, March 2001

Outline

- Why use a Bayesian approach?
- Basic concepts
- “BUGS”
- Live Demo of simple application
- Applications to twin data

Why Use Bayesian Approach?

- Intellectually satisfying
- Get more information out of existing problems (distributions of model parameters, individual“genetic” scores)
- Tackle problems other methods find difficult (non-linear mixed models – growth curves; GxE interaction)

Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (1996) Markov Chain Monte Carlo in Practice. Chapman & Hall, London.

Spiegelhalter, D., Thomas, A., Best, N. (2000) WinBUGS Version 1.3, User Manual, MRC BUGS Project: Cambridge.

Eaves, L.J., Erkanli, A. (In preparation) Markov Chain Monte Carlo Approaches to Analysis of Genetic and Environmental Components of Human Developmental Change and GxE Interaction. (For Behavior Genetics).

Given Data D and parametersq:

The likelihood function, l, is

l=P(D|q).

We find qthat maximizes l.

Maximize likelihood numerically

Fairly easy for linear models and normal variables (“LISREL”)

Mx works well (best!)

Some things don’t work so well

For example:

- Getting confidence intervals etc.
- Non-linear models (require integration over latent variables – hard for large # of parameters)
- Estimating large numbers of latent variables (e.g. “genetic factor scores”)

Markov Chain Monte Carlo Methods:(MCMC)

- Allow more general models
- Obtain confidence intervals and other summary statistics
- Estimates missing values
- Estimates latent trait values

All as part of the model-fitting process

ML works with

l=P(D|q).

Bayesian approach seeks distribution of parameters given data:

B=P(q|D).

A couple of problems

- We don’t know P(q)
- What is P(D)?

“Prior” distribution not known but

may know (guess?) its form, e.g.,

Means may be normal

Variances may be gamma

If we know P(q) we could sample q many times and evaluate function. Integral is approximated to desired accuracy by mean of k (=large) samples

(“Monte Carlo” integration)

Simulate a sequence of samples of q that ultimately converge to (non-independent) samples from the desired distribution, P(q).

When the sequence has converged (“stationary distribution”, after “burn in” from trial q) we may construct P(q) from sequence of samples.

…The “Gibbs Sampler”, hence:

“Bayesian Inference Using Gibbs Sampling”

“BUGS” for short

Spiegelhalter, D., Thomas, A., Best, N. (2000) WinBUGS Version 1.3, User Manual, MRC BUGS Project: Cambridge.

Obtaining WinBUGS

- Find MRC BUGS project on www (search on WinBUGS)
- Download educational version (free)
- Register by email (at site)
- Install educational version (Instructions at site)
- Follow instructions in reply email to convert to production version (free)

list(n=50)y[] 14.1110 9.5125 13.2752 10.5952 8.6699... 10.0673 10.7618 8.2337 9.2170 7.3803 8.9194 4.9589list(mu=10,tau=0.2)

Data and Initial Values for Mean-Variance Problem

Values of Mean (mu):

First 200 iterations of MCMC Algorithm

Values of Variance (Sigma2):

First 200 iterations of MCMC Algorithm

MCMC Estimates of Mean and Variance:

5000 iterations after 1000 iteration “burn in”.

Fitting the AE model to bivariate twin data

Parameter

ML estimate

Population value

mu[1]

9.993

10.0

mu[2]

10.047

10.0

sigma2.g[1,1]

0.704

0.8

sigma2.g[1,2]

0.371

0.4

sigma2.g[2,2]

0.741

0.8

sigma2.e[1,1]

0.194

0.2

sigma2.e[1,2]

0.098

0.1

sigma2.e[2,2]

0.254

0.2

Table 1: Population parameter values used in simulation of bivariate

twin data and values realized using Mx for ML estimation

(N=100 MZ and 100 DZ pairs).

list(N=2,nmz=100,ndz=100,mean=c(0,0),precis =structure(.Data=c(0.0001,0, 0, 0.0001),.Dim=c(2,2)),omega.g=structure(.Data=c(0.0001,0,0,0.0001),.Dim=c(2,2)),omega.e=structure(.Data=c(0.0001,0,0,0.0001),.Dim=c(2,2)))ymz[,1,1] ymz[,1,2] ymz[,2,1] ymz[,2,2] 9.9648 9.4397 10.1008 9.6549 8.9251 10.5722 9.5299 10.5583 10.7032 9.9130 11.1373 10.2855 10.8231 11.5187 11.0396 10.7342 11.3261 12.4088 11.4504 11.9600 9.4009 10.7828 9.5653 11.8201

Start of Data for Bivariate Twin Example

Summary statistics for 5000 MCMC iterations of bivariate AE model after 2000 iteration "burn in"

node mean sd MC error 2.5% median 97.5%

deviance 985.7 67.7 2.91 856.7 985.4 1118.0

mu[1] 9.991 0.05906 0.001452 9.877 9.992 10.11

mu[2] 10.05 0.06184 0.001871 9.924 10.05 10.17 g[1,1] 0.705 0.08122 0.003302 0.5567 0.7018 0.8731 g[1,2] 0.3719 0.06418 0.0024 0.252 0.3711 0.5065 g[2,2] 0.7394 0.08337 0.002786 0.5885 0.7367 0.9113

e[1,1] 0.197 0.02872 0.001223 0.1476 0.1943 0.2606 e[1,2] 0.09904 0.02398 9.144E-4 0.05537 0.09788 0.1486 e[2,2] 0.2581 0.03528 0.00123 0.1977 0.2555 0.3337

Illustrative MCMC estimates of genetic effects:

first two DZ twin pairs on two variables

Observation Est S.e. MC error 2.5% Median 97.5%

g1dz[1,1,1] 11.53 0.3756 0.007243 10.77 11.53 12.27

g1dz[1,1,2] 10.94 0.4263 0.007021 10.1 10.94 11.78

g1dz[1,2,1] 11.44 0.3763 0.006501 10.7 11.43 12.18 g1dz[1,2,2] 10.93 0.4286 0.007602 10.08 10.95 11.76 g1dz[2,1,1] 9.231 0.3776 0.007866 8.499 9.228 9.992 g1dz[2,1,2] 10.25 0.4248 0.007331 9.409 10.25 11.09 g1dz[2,2,1] 9.331 0.373 0.005969 8.606 9.329 10.07

g1dz[2,2,2] 9.505 0.4188 0.007975 8.694 9.5 10.31

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