Molecular & Genetic Epi 217 Association Studies: Indirect John Witte

Molecular & Genetic Epi 217

Association Studies: Indirect

John Witte

Homework, Question 4: Haplotypes

ID MTHFR_C677T MTHFR_A1298CHaplotypes?

959 CC AA C-A / C-A

1044 CC AC C-A / C-C

147 CT AA C-A / T-A

123 CT AC C-A / T-C or

C-C / T-A

• Genotypes 677TT and 1298CC never observed together: Suggests most

Probable haplotype, and potential selection or chance.

• Rare variants: not necessarily lethal, especially those that are associated

with late onset diseases.

3 SNPs in the TAS2R38 Gene

P A V

A V I

P A I

A A V

P V I

P V V

A A I A V V

TASR: 3 SNPs form Haplotypes

P A V

A V I

Taster

Non-taster

TAS2R38 Haplotype Function

0

0.2

0.4

0.6

0.8

1

1.2

0.1 1 10 100 1000

PTC concentration (M)

Rat

io P

TC

/ S

ST

PAV

PAI

PVV

PVI

AAV

AAI

AVV

AVI

ID Taster rs10246939 rs1726866 rs713598 Haplotypes Amino Acid

10 0 CT AG CG CGG*/TAC PAV/AVI


14 1 . . . . .

17 0 CC GG GG CGG/CGG PAV/PAV



22 . TT AA CC TAC/TAC AVI/AVI


26 . CT AG CG CGG*/TAC PAV/AVI


29 1 CC GG CG CGG/CGC PAV/PAI

30 0 TT AA CC TAC/TAC AVI/AVI


TASR Genotyping Results

Too many MTHFR SNPsSolution: Tag SNP Selection

SNPs are correlated (aka Linkage Disequilibrium)

Carlson et al. (2004) AJHG 74:106

high r2 high r2 high r2

AATT

GC

CG

ACCC

GC

CG

TCCC

GGAA

A/T1

G/A2

G/C3

T/C4

G/C5

A/C6

Pairwise Tagging:

SNP 1SNP 3SNP 6

3 tags in total

Test for association:

SNP 1SNP 3SNP 6

Coverage: Measurement Error in TagSNPs

Common Measures of Coverage

• Threshold Measures– e.g., 73% of SNPs in the complete set are in LD with

at least one SNP in the genotyping set at r2 > 0.8

• Average Measures– e.g., Average maximum r2 = 0.84

Coverage and Sample Size

• Sample size required for Direct Association, n• Sample size for Indirect Association

n* = n/ r2

• For r2 = 0.8, increase is 25%• For r2 = 0.5, increase is 100%

Tag SNPs Database Resources

http://www.hapmap.org

http://gvs.gs.washington.edu/GVS/index.jsp

HapMap

• Re-sequencing to discover millions of additional SNPs; deposited to dbSNP.

• SNPs from dbSNP were genotyped• Looked for 1 SNP every 5kb• SNP Validation

– Polymorphic– Frequency

• Haplotype and Linkage Disequilibrium Estimation– LD tagging SNPs

HapMap Phase III Populations

• ASW African ancestry in Southwest USA • CEU Utah residents with Northern and Western

European ancestry from the CEPH collection • CHB Han Chinese in Beijing, China • CHD Chinese in Metropolitan Denver, Colorado • GIH Gujarati Indians in Houston, Texas • JPT Japanese in Tokyo, Japan • LWK Luhya in Webuye, Kenya • MEX Mexican ancestry in Los Angeles, California • MKK Maasai in Kinyawa, Kenya • TSI Toscani in Italia • YRI Yoruba in Ibadan, Nigeria

Tag SNPs: HapMap

Tag SNPs: HapMap

Tag SNPs: HapMap & Haploview

http://www.broad.mit.edu/mpg/haploview/





Identified 33 common MTHR SNPs (MAF > 5%) among Caucasians

Forced in 3 potentially functional/previously associated SNPs

Identified tag based on pairwise tagging

15 tags SNPs could capture all 33 MTHR SNPs (mean r2 = 97%)

Note: number of SNPs required varies from gene to gene and from population to population

Tag SNPs: HapMap Summary

1K Genomes Project

Genome-wide Assocation Studies (GWAS)

1,2,

3,…

……

……

……

……

,N

1,2,3,……………………………,M

SNPs

Sam

ples

One-Stage DesignOne-Stage Design

Stage 1

Sta

ge 2

samples

markers

Two-Stage DesignTwo-Stage Design

1,2,3,……………………………,M

SNPs

Sam

ples

1,2,

3,…

……

……

……

……

,N

One- and Two-Stage GWA DesignsOne- and Two-Stage GWA Designs

SNPs

Sam

ples

Replication-based analysisSNPs

Sam

ples

Stage 1

Stag

e 2

One-Stage DesignOne-Stage Design

Joint analysisSNPs

Sam

ples

Stage 1

Stag

e 2

Two-Stage DesignTwo-Stage Design

Multistage Designs

• Joint analysis has more power than replication

• p-value in Stage 1 must be liberal

• Lower cost—do not gain power

• http://www.sph.umich.edu/csg/abecasis/CaTS/index.html

Complex diseases

Diabetes

Obesity

Diet

Physical activity

Hypertension

Hyperlipidemia

Vulnerable plaques

Atherosclerosis MI

Genetic susceptibility

Complex diseases: Many causes = many causal pathways!

Pathways

• Many websites / companies provide ‘dynamic’ graphic models of molecular and biochemical pathways.

• Example: BioCarta: http://www.biocarta.com/

• May be interested in potential joint and/or interaction effects of multiple genes in one pathway.

http://www.biocarta.com/

http://www.biocarta.com/

Interactions

• “The interdependent operation of two or more causes to produce or prevent an effect”

• “Differences in the effects of one or more factors according to the level of the remaining factor(s)”

• Last, 2001

AA Aa aa

BB At risk At risk No risk

Bb At risk At risk No risk

bb No risk No risk No risk

Why look for interactions?

• Improve detection of genetic (& environmental) risks.• Understand etiology/biology• New hypotheses?• Diagnostics• Prevention and interventions

Dilution of effects

OR=1.5

5.2

2.1

0.1

2.8

Dri

nke

r?M

icro

nutr

ient

X

2.7

0.6Envir

onm

enta

l exposu

re Y

Gene A

19

0.1

25

21

0.2

0.1

16

Oth

er

gene Z

Within particular subgroups, effect of genemay be quite high or low

Statistical vs. Biological Interactions• Not identical. • One hypothesizes biological interaction

• But ‘tests’ for statistical interaction

• Does statistical evidence support our biological hypothesis?

Multiplicative vs. Additive Interactions

g G

e 1.0 1.4

E 2.0 2.4

g G

e 1.0 1.4

E 2.0 2.8

g G

e 1.0 1.4

E 2.0 7.8

Multiplicative “effect”(ORs, RRs)

Multiplicative interaction(ORs, RRs)

2.8/2.01.4/1.0

= = 1.0

7.8/2.01.4/1.0

= = 2.8

Departure from =1 is a multiplicative interaction

Additive “effect”

RER = (OR(E,G)-1)/((OR(E,g)-1)+(OR(e,G)-1))

= (2.4-1)/((2.0-1)+(1.4-1)) = 1.0RER = relative excess risk

Brennan, P. Carcinogenesis 2002 23:381-387

Two possible causal pathways: additive and multiplicative interaction for colorectal cancer

Additive interaction: G1 and E5: independent risk factors

Multiplicative interaction: G2 and E2: work through same pathway

If factors are not known to act independently, use multiplicative.

Analysis of Multiple Genes

• Joint / Additive

• Multiplicative

• Increasing complexity

321)(1

)(ln 321 GGG

DP

DP

3*13*22*1

321)(1

)(ln

321

321

GGGGGG

GGGDP

DP

More Complex Modeling

• Multifactor-dimensionality reduction– (Moore & Williams, Ann Med 2002)

• Logic regression– (Kooperberg & Ruczinski, Genetic Epi 2005)

• Multi-loci analysis– (Marchini, Donnelly, Cardon, Nat Genet 2005)

• Bayesian epistasis association mapping – (Zhang & Liu, Nat Genet 2007)

Documents

Molecular & Genetic Epi 217 Association Studies: Indirect John Witte