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Molecular & Genetic Epi 217 Association Studies: Indirect. John Witte. Homework, Question 4: Haplotypes. ID MTHFR_C677T MTHFR_A1298C Haplotypes? 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.

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homework question 4 haplotypes
Homework, Question 4: Haplotypes

ID MTHFR_C677T MTHFR_A1298C Haplotypes?

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
3 SNPs in the TAS2R38 Gene

P A V

P A I

P V V

P V I

A A V

A A I

A V V

A V I

tasr 3 snps form haplotypes
TASR: 3 SNPs form Haplotypes

P A V

Taster

Non-taster

A V I

too many mthfr snps solution tag snp selection

G/C

3

G/A

2

T/C

4

G/C

5

A/T

1

A/C

6

G

G

A

A

G

T

G

A

C

C

C

C

C

C

C

C

T

T

A

A

G

G

C

C

high r2

high r2

high r2

  • SNPs are correlated (aka Linkage Disequilibrium)

Too many MTHFR SNPsSolution: Tag SNP Selection

Pairwise Tagging:

SNP 1

SNP 3

SNP 6

3 tags in total

Test for association:

SNP 1

SNP 3

SNP 6

Carlson et al. (2004) AJHG 74:106

common measures of coverage
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
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

Tag SNPs Database Resources

http://www.hapmap.org

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

hapmap
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
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 haploview

Tag SNPs: HapMap & Haploview

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

slide21

Tag SNPs: HapMap Summary

  • 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
slide24

One- and Two-Stage GWA Designs

Two-Stage Design

One-Stage Design

SNPs

SNPs

1,2,3,……………………………,M

1,2,3,……………………………,M

1,2,3,………………………,N

1,2,3,………………………,N

samples

Stage 1

Samples

Samples

Stage 2

markers

slide25

One-Stage Design

SNPs

Samples

Two-Stage Design

Joint analysis

Replication-based analysis

SNPs

SNPs

Samples

Stage 1

Stage 1

Samples

Stage 2

Stage 2

multistage designs
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
Complex diseases

Physical activity

Genetic susceptibility

Obesity

Hyperlipidemia

Diet

Diabetes

Complex diseases: Many causes = many causal pathways!

Vulnerable plaques

Hypertension

MI

Atherosclerosis

slide28

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.
interactions
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
why look for interactions
Why look for interactions?
  • Improve detection of genetic (& environmental) risks.
  • Understand etiology/biology
  • New hypotheses?
  • Diagnostics
  • Prevention and interventions
dilution of effects

19

2.8

Micronutrient X

0.6

0.2

0.1

Environmental exposure Y

25

2.7

5.2

Other gene Z

Drinker?

16

2.1

0.1

0.1

Within particular subgroups, effect of gene

may be quite high or low

21

Dilution of effects

Gene A

OR=1.5

statistical vs biological interactions
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

Additive “effect”

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

= (2.4-1)/((2.0-1)+(1.4-1)) = 1.0

2.8/2.0

7.8/2.0

= 1.0

= 2.8

 =

 =

1.4/1.0

1.4/1.0

Multiplicative “effect”

(ORs, RRs)

Multiplicative interaction

(ORs, RRs)

Departure from =1 is a multiplicative interaction

Multiplicative vs. Additive Interactions

RER = relative excess risk

slide34

Additive interaction: G1 and E5: independent risk factors

Multiplicative interaction: G2 and E2: work through same pathway

Two possible causal pathways:

additive and multiplicative interaction for colorectal cancer

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

Brennan, P. Carcinogenesis 2002 23:381-387

analysis of multiple genes
Analysis of Multiple Genes
  • Joint / Additive
  • Multiplicative
  • Increasing complexity
more complex modeling
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)