BSc Course: "Experimental design“ Genome-wide Association Studies Sven Bergmann Department of...

BSc Course:

"Experimental design“

Genome-wide Association Studies

Sven BergmannDepartment of Medical Genetics

University of LausanneRue de Bugnon 27 - DGM 328

CH-1005 Lausanne Switzerland

work: ++41-21-692-5452cell: ++41-78-663-4980

http://serverdgm.unil.ch/bergmann

Overview• Population stratification

• Associations: Basics

• Whole genome associations

• Genotype imputation

• Uncertain genotypes

• New Methods

6’18

9 in

divi

dual

s

Phenotypes

159 measurement

144 questions

Genotypes

500.000 SNPs

CoLaus = Cohort Lausanne

Collaboration with:Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)

ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG…

ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG…


ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG…


Genetic variation in SNPs (Single Nucleotide Polymorphisms)

Analysis of Genotypes only

Principle Component Analysis reveals SNP-vectors explaining largest variation in the data

Example: 2PCs for 3d-data

http://ordination.okstate.edu/PCA.htm

Raw data points: {a, …, z}



Normalized data points: zero mean (& unit std)!



Identification of axes with the most variance

Most variance is along PCA1

The direction of most variance

perpendicular to PCA1 defines

PCA2

Ethnic groups cluster according to geographic distances

PC1 PC1

PC

2P

C2

PCA of POPRES cohort






• New Methods

Phenotypic variation:

0

0.2

0.4

0.6

0.8

1

1.2

-6 -4 -2 0 2 4 6

What is association?chromosomeSNPs trait variant

Genetic variation yields phenotypic variation

Population with ‘ ’ allele Population with ‘ ’ allele

Distributions of “trait”

Quantifying Significance

T-test

t-value (significance) can be translated into p-value (probability)

Association using regression

genotype Coded genotype

phen

otyp

e

Regression analysis

X

Y

“response”

“feature(s)”

“intercept”

“coefficients”

“residuals”

Regression formalism

(monotonic)transformation

phenotype(response variable)of individual i

effect size(regression coefficient)

coded genotype(feature) of individual i

p(β=0)error(residual)

Goal: Find effect size that explains best all (potentially transformed) phenotypes as a linear function of the genotypes and estimate the probability (p-value) for the data being consistent with the null hypothesis (i.e. no effect)






• New Methods

Whole Genome Association

http://www.biotech.umb.edu/Affymetrix.htm

Whole Genome AssociationCurrent microarrays probe ~1M SNPs!

Standard approach: Evaluate significance for association of each SNP independently:

sig

nif

ican

ce

Whole Genome Associationsi

gn

ific

ance

Manhattan plot

ob

serv

edsi

gn

ific

ance

Expected significance

Quantile-quantile plot

Chromosome & position

GWA screens include large number of statistical tests!• Huge burden of correcting for multiple testing!• Can detect only highly significant associations (p < α / #(tests) ~ 10-7)

GWAS: >20 publications in 2006/2007

Massive!

Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the

calcium-sensing receptor (CASR) gene

Karen Kapur, Toby Johnson, Noam D. Beckmann, Joban Sehmi, Toshiko Tanaka, Zoltán Kutalik, Unnur Styrkarsdottir, Weihua Zhang, Diana Marek, Daniel F. Gudbjartsson, Yuri Milaneschi, Hilma Holm, Angelo DiIorio, Dawn Waterworth, Andrew Singleton, Unnur Steina Bjornsdottir, Gunnar Sigurdsson, Dena Hernandez, Ranil DeSilva, Paul Elliott, Gudmundur Eyjolfsson, Jack M Guralnik, James Scott, Unnur Thorsteinsdotti, Stefania Bandinelli, John Chambers, Kari Stefansson, Gérard Waeber, Luigi Ferrucci, Jaspal S Kooner, Vincent Mooser, Peter Vollenweider, Jacques S. Beckmann, Murielle Bochud, Sven Bergmann

Current insights from GWAS:

• Well-powered (meta-)studies with (ten-)thousands of samples have identified a few (dozen) candidate loci with highly significant associations

• Many of these associations have been replicated in independent studies

Current insights from GWAS:

• Each locus explains but a tiny (<1%) fraction of the phenotypic variance

• All significant loci together explain only a small (<10%) of the variance

David Goldstein:

“~93,000 SNPs would be required to explain 80% of the population variation in height.”

Common Genetic Variation and Human Traits, NEJM 360;17

The “Missing variance” (Non-)Problem

Why should a simplistic (additive) model using incomplete or approximate features possibly explain anything close to the genetic variance of a complex trait?

… and it doesn’t have to as long as Genome-wide Association Studies are meant to as an undirected approach to elucidate new candidate loci that impact the trait!

1. Other variants like Copy Number Variations or epigenetics may play an important role

2. Interactions between genetic variants (GxG) or with the environment (GxE)

3. Many causal variants may be rare and/or poorly tagged by the measured SNPs

4. Many causal variants may have very small effect sizes

5. Overestimation of heritabilities from twin-studies?

So what do we miss?






• New Methods

Intensity of Allele G

Inte

nsi

ty o

f A

llele

A

Genotypes are called with varying uncertainty

Some Genotypes are missing at all …

… but are imputed with different uncertainties

… using Linkage Disequilibrium!

Markers close together on chromosomes are often transmitted together, yielding a non-zero correlation between the alleles.

Marker 1 2 3 n

LD

D

Conclusion

• Genotypic markers are always measured or inferred with some degree of uncertainty

• Association methods should take into account this uncertainty

Two easy ways dealing with uncertain genotypes

1. Genotype Calling: Choose the most likely genotype and continue as if it is true(p11=10%, p12=20% p22=70% => G=2)

2. Mean genotype: Use the weighted average genotype(p11=10%, p12=20% p22=70% => G=1.6)

Overview• Associations: Basics


• Population stratification



• New Methods

1. Improve measurements:- measure more variants (e.g. by UHS)- measure other variants (e.g. CNVs)- measure “molecular phenotypes”

2. Improve models:- proper integration of uncertainties- include interactions- multi-layer models

How could our models become more predictive?

Towards a layered Systems Model

We need intermediate (molecular) phenotypes to better understand organismal phenotypes

Organisms

Data types

Conditions

Developmental

Physiological

Environmental

Experimental

Clinical

– Genotypic data– Gene expression data– Metabolomics data– Interaction data– Pathways

The challenge of many datasets: How to integrate all the information?

Network Approaches for Integrative Association Analysis

Using knowledge on physical gene-interactions or pathways to prioritize the search for functional interactions

Using expression datafrom Hapmap panel we computed all 1012 pairwiseinteractions for several expression values:

The strongest interactions do not occur for SNPs with high (marginal) significance!

Bioinformatics Advance Access published online on April 7, 2010 Bioinformatics, doi:10.1093/bioinformatics/btq147

Transcription Modules reduce Complexity

SB, J Ihmels & N Barkai Physical Review E (2003)

http://maya.unil.ch:7575/ExpressionView

http://maya.unil.ch/

Association of (average) module expression is often stronger than for any

of its constituent genes

Overview• Associations: Basics


• Population stratification



• New Methods

• Analysis of genome-wide SNP data reveals

that population structure mirrors geography

• Genome-wide association studies elucidate

candidate loci for a multitude of traits, but

have little predictive power so far

• Future improvement will require– better genotyping (CGH, UHS, …) – New analysis approaches (interactions,

networks, data integration)

Take-home Messages:

BSc Course: "Experimental design“ Genome-wide Association Studies Sven Bergmann Department of...

Documents

Transcript of BSc Course: "Experimental design“ Genome-wide Association Studies Sven Bergmann Department of...