Genetic epidemiology of complex traits: issues and methods

M.W.Zuurman, Werkbespreking Medische Biologie28 november 2005

Breedtestrategie

The presentation

1.Background

2.Issues

3.Methods

We want to cure disease!

What do we want anyway?

We want to explain disease!

We want to counteract disease!

Let’s explain disease

Breedtestrategie: Let’s explain Cardiovascular and Renal disease

What is disease?

What is disease?

Disease is a condition in the organism that impairs normal function of the organism

Disease is a condition in the organism that impairs normal function of the organism

Disease:

•Conforming with or constituting a norm or standard or level or type or social norm•In accordance with scientific laws •Being approximately average or within certain limits •Convention: something regarded as a normative example•A statistical measure of usually observed structures, typical, or representative type

Normal:

Subjectivity of ‘normal’ vs ‘diseased’:

A disease is any abnormal condition of the body or mind that causes discomfort, dysfunction, or distress to the person affected or those in contact with the person.(Wikipedia)

Disease (Platonic)

Symptoms (Phenotypes!) Causes:Nurture/Nature

Intervention

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++

+-

Medical/Research practice:

+

Nature versus Nurture

Genetic versus environmental influence

In reality, you can’t have one without the other:

Mulcaster (1581): “that treasure bestowed on them by nature, to be bettered in them by nurture”

Organisms are born with a set of genes in a certain environment

Disease

Genotype Environment

When seeking to explain disease:

Summary (1)

-Define disease clearly

-What is normal and why? It will determine the extend of ‘abnormal’

-Define phenotypes clearly Make them quantifiable with sufficient

Specificity :the probability to detect a negative result (e.g. ‘healthy’ or ‘control’)

and

Sensitivity the probability to detect a positive result(e.g. ‘diseased’ or ‘case’)

Genetic epidemiology of complex traits

What is the genetic basis of complex traits (=disease/disease phenotypes)?

Main issue

Research Question:

Genetic variation

Single Nucleotide Polymorphisms (SNP) (genotyping):

~AATGCCGA~ ~AATACCGA~~TTACGGCT~ ~TTATGGCT~

Divided in wild type and mutated allelesHas the genotype form of AA AB BBCan be functionalCan be a neighbor of a functional variation (haplotype)Can be none of those

Locus (e.g. QTL)

Gene (e.g. expression arrays)

Complex Traits

Mendelian traits: a single gene phenotype

- e.g. eye colour, curly hair etc. - also called dichotomous traits

- irrespective of environment in most cases

Continuously variable trait: polygenic and/or pleiotropic

polygenic : multiple genes affect a single traitpleiotropic : one gene affects multiple traits

Note: pure polygenic/pleiotropic (without environmentalinfluences) hardly exist

Complex Trait: polygenic- and pleiotropic gene-environment interaction

Examples: stature, atherosclerosis, blood pressure regulation, and many many more.

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A B C

D E F

G H I

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HDL

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HDL1,00 2,00 3,00

HDL

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HDL

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Context effect of genetic variance in complex traits

1,24 1,30 1,34

1,32 1,33 1,39

1,41 1,39 1,40

1,27

1,33

1,40

2 SNPs (4 alleles, 9 possible combinations)

2236 393

3126 587

1214 246

AA

AB

BB

SNP1N

No

N

Yes

Obesity

Power drainage

One SNP

1326 219

1340 259

407 87

785 150

1473 263

588 112

119 23

307 64

216 46

AA

AB

BB

SNP1CC

AA

AB

BB

CD

AA

AB

BB

DD

SNP2N

No

N

Yes

Obesity

Two SNPs

Three SNPsSNP2 CC CD DDSNP1 AA AB BB AA AB BB AA AB BBObesity N N N N N N N N N

SNP3 EE No 115 126 32 74 131 50 4 32 14Yes 19 20 13 9 28 6 1 4 4

SNP3 EF No 531 554 166 327 604 250 45 127 86Yes 77 116 36 63 105 51 7 25 20

SNP3 FF No 673 658 204 377 734 284 69 146 115Yes 120 123 38 77 127 55 15 35 22

Methods (1)

We need methods to:

-Preserve power-Reduce noise-Lift shadows of stronger determinants

FGClustor

Hypothesis driven Exploration via FGClustor

Conceptual thinking:

Given any outcome parameter measured in a population one is able to detect differences in frequency of a combination of geno- or phenotypes along the range of the parameter when compared to the prevalence of that combination in the whole population.

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f1c f2c f3c f4c

f1d f2d f3d f4d

f1e f2e f3e f4e

f1f f2f f3f f4f

f1g f2g f3g f4g

f1h f2h f3h f4h

FGClustor principle

FGClustor

FGClustor

FGClustor

0% 20% 40% 60% 80% 100%

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96,5

108

119,5

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165,5

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188,5

SB

P r

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_01

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FGClustor and strong confoundersPhenotype: Systolic blood pressureComplex Trait: Quartiles Cholesterol + Gender Chi-square Test

F2 F1

M1 M2

F3 F4

M3 M4

F2 0,000217

M1 0,000703

M2 0,000606

F3 0,019796

F4 0,001564

M3 0,000205

M4 0,000192

F1 6,67E-05

FGClustor

0% 20% 40% 60% 80% 100%

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Phenotype : HDL-cholesterolComplex Trait: SNP1 + SNP2

AACC

ABDD

ABCC

Chi-square Test

cc p

AACC 0.0008

ABDD 0.0073

ABCC 0.0069

FGClustor and SNPsFGClustor

FGClustor

Summary

Pro:

FGClustor used in hypothesis driven approach can shed light on relationships of covariates of interest

FGClustor can visualize context-based main effects of parameters of interest

“Standard” statistical methods are needed in conjunction with FGClustor output to confirm context-based main effects

Con:

FGClustor is not statistically powerful

MDR

Multifactor Dimensionality Reduction

What is MDR?

-Nonparametric and genetic model-free -Alternative to logistic regression -Detecting nonlinear interactions among discrete genetic and environmental attributes.

The MDR method combines

attribute selection, attribute construction, classification, cross-validation and visualization

http://www.epistasis.org/mdr.html

Moore (Expert Review of Molecular Diagnostics, 4:795-803, 2004)

MDRWorked example: SBP (dichotomous by median)Covariates: Sex and Quartiles of Total cholesterol

Combination Class 1 Class 0 Ratio New Class

0,1 479 480 0,9979 0

0,2 662 462 1,4329 1

0,3 769 341 2,2551 1

0,4 752 313 2,4026 1

1,1 258 913 0,2826 0

1,2 319 686 0,465 0

1,3 476 546 0,8718 0

1,4 572 491 1,165 1

MDRMDRWorked example: SBP (dichotomous by median)Covariates: Sex and Quartiles of Total cholesterol

Best Model output:

Combination Class 1 Class 0 Ratio

0 408 1131 0,3607

0,1 221 713 0,31

0,2 26 114 0,2281

1 341 1255 0,2717

1,1 357 1372 0,2602

1,2 59 306 0,1928

2 77 413 0,1864

2,1 105 593 0,1771

2,2 44 217 0,2028

Best Model output:

MDRMDRWorked example: HDL-c (dichotomous by <= 1 mmol/L)Covariates: SNP1 SNP2

AACC

ABDD

ABCC

MDR

Summary

Pro:

Includes cross-validation in the same population

Can be used as dataminer, not necessarily hypothesis driven

Statistically powerful to uncover also weak (genotype) effects

Con:

Can be used as dataminer, not necessarily hypothesis driven

Limited by categorical data only

Discussion

-Standard methods in genetic epidemiology only show very strong association in case of direct or extremely close relationship between gene and outcome parameter of interest.

-Complex traits are build of individual contributors (genetic variants, environmental parameters) that each in itself have a weak main effect on the trait.

-Noise and strong confounders limit detection of the weaker contributors in complex traits by standard statistics

-Main effects of the individual contributors can be visualized using novel tools (e.g. FGClustor, MDR) in a context dependent approach at the background of solid hypothesis