20
Modeling the unobservable developmental stability using a Bayesian latent variable model Stefan van Dongen Dept. Biology, University of Antwerp

Modeling the unobservable developmental stability using a Bayesian latent variable model

  • Upload
    wilma

  • View
    33

  • Download
    0

Embed Size (px)

DESCRIPTION

Modeling the unobservable developmental stability using a Bayesian latent variable model. Stefan van Dongen Dept. Biology, University of Antwerp. OUTLINE. Introduce biological problem developmental stability Develop statistical model Show some simulation results - PowerPoint PPT Presentation

Citation preview

Page 1: Modeling the unobservable developmental stability using a Bayesian latent variable model

Modeling the unobservable developmental stability using a Bayesian latent variable model

Stefan van Dongen

Dept. Biology, University of Antwerp

Page 2: Modeling the unobservable developmental stability using a Bayesian latent variable model

OUTLINE

• Introduce biological problem– developmental stability

• Develop statistical model

• Show some simulation results

• Interesting results in humans– keep paying attention this is important

Page 3: Modeling the unobservable developmental stability using a Bayesian latent variable model

• We develop from 1 cell to what we are now

• During development mistakes occur due to random noise (DN)– Fluctuations in concentrations– Somatic mutations– Death of cells

• There are mechanisms that correct for these mistakes: DEVELOPMENTAL STABILITY

What is developmental stability?

Page 4: Modeling the unobservable developmental stability using a Bayesian latent variable model

What is developmental stability?

• Our genotype and environment in which we live determine our size and shape– unknown in most cases

• BUT there is some degree of stochasticity• PROBLEM: how to estimate this stochastic contribution

which has two components (noise and stability)

• SOLUTION: Look at symmetric traits

Page 5: Modeling the unobservable developmental stability using a Bayesian latent variable model

How to estimate developmental stability?

• Left and right side develop often under exactly the same environmental conditions (but e.g. handedness) and obviously share the same genotype

• In the absence of noise they should develop to the same size or shape

• Noise will cause asymmetry

• Stability will counteract this effect

Page 6: Modeling the unobservable developmental stability using a Bayesian latent variable model

How to estimate developmental stability? Measure asymmetry

• Asymmetry estimates the joint action of noise and stability

Page 7: Modeling the unobservable developmental stability using a Bayesian latent variable model

The picture is not so clearDirectional asymmetry or antisymmetry: adaptive and genetically determined

GynandromorphsNO STRESS EFFECT

Page 8: Modeling the unobservable developmental stability using a Bayesian latent variable model

The picture is not so clear

Subtle forms of asymmetry

Early action of stressContinuous scale

Phenodeviants & deformations

Under severe stress onlyBinary

Page 9: Modeling the unobservable developmental stability using a Bayesian latent variable model

Why study developmental stability?

Charles Darwin

EVOLUTION CONSERVATION BIOLOGY

FITNESS (reproductive success, survival, ….)

Difficult to measure in field

May be related to stabilityHigh stability => sufficient energy to achieve high fitness

Page 10: Modeling the unobservable developmental stability using a Bayesian latent variable model

Why study developmental stability?

Charles Darwin

EVOLUTION CONSERVATION BIOLOGY

FITNESS (reproductive success, survival, ….)

Difficult to measure in field

May be related to stabilityHigh stability => sufficient energy to achieve high fitness

INDIVIDUALLEVEL

Page 11: Modeling the unobservable developmental stability using a Bayesian latent variable model

1

2

3

Page 12: Modeling the unobservable developmental stability using a Bayesian latent variable model

Asymmetries are usually small:take bias due to measurement error into account

measure[i,j]~N(0,2ME)

DIRECTIONAL ASYMMETRY

FLUCTUATING ASYMMETRY

muind[i,j]=interc[i]+side[i,j] unb_FA[i]

where: interc[i]~N(0,2)and: unb_FA[i]~N(0,2

FA[i])

expected[i,j]=intercept+slopeside[i,j]

ERROR

Page 13: Modeling the unobservable developmental stability using a Bayesian latent variable model
Page 14: Modeling the unobservable developmental stability using a Bayesian latent variable model

unb_FA[i]~N(0,DI[i])

Assume DN to be constant

DS[i]~beta(1,2)

DI[i]=DN (1-DS[i])

The association between asymmetry and stability

Page 15: Modeling the unobservable developmental stability using a Bayesian latent variable model

N=500, 2500 and 5000DN constant, DS beta-distr.

‘good’ estimate of DSoverestimate of DN

‘worse’ estimate of DS‘good’ estimate of DN

‘failure’ to estimate bimodal pattern of DS

The association between asymmetry and stability

Page 16: Modeling the unobservable developmental stability using a Bayesian latent variable model

The association between asymmetry and stability

Page 17: Modeling the unobservable developmental stability using a Bayesian latent variable model

fitness[i]=interfit+slopefitDS[i]

The association between asymmetry and fitness

Page 18: Modeling the unobservable developmental stability using a Bayesian latent variable model

Further developments

• Multiple-trait analyses

• robustness against deviations of model assumptions (normality vs. log-normality)

• Include variation in DN (stochastic or constant)

Page 19: Modeling the unobservable developmental stability using a Bayesian latent variable model

Interesting results in humans

• Symmetric persons have higher IQ• Criminals have higher asymmetry• Symmetric males and females are more attractive• Symmetric males have more sexual partners in their life• Symmetric males are ‘better lovers’• Female breast asymmetry decreases around period of ovulation• Females are more selective for symmetric males around this

ovulation period• …..

Page 20: Modeling the unobservable developmental stability using a Bayesian latent variable model

One case study