MEDG 505Pharmacogenomics

March 18, 2004

A. Brooks-Wilson

Reminder: What is Genomics?

According to http://genomics.ucdavis.edu/what.html:

“Genomics is operationally defined as investigations into the structure and function of very large numbers of genesundertaken in a simultaneous fashion”

Pharmacogenetics

• “The study of how genes affect people’s response to medicines” (NIH) • A subset of complex genetics where the traits relate to drugs• First observed in 1957• Part of “personalized medicine”• 20-95% of variability in drug disposition and effects is thought to be genetic• Non-genetic factors: age, interacting medications, organ function• Drug metabolism: >30 families of genes

Pharmacogenetics: Examples

• Drug metabolism genes• NAT2, isoniazid anti-tuberculosis drug hepatotoxicity• CYP3A5, many drugs• Thiopurine S-methyltransferase (TPMT), 6-thioguanine• Drug targets (receptors)• B2 Adrenergic Receptor, inhaled B agonists for asthma• Drug transporters• P-glycoprotein (ABCB1, MDR1), resistance to anti-epileptic drugs• The examples known today are those that come closest to simple genetic traits

Potential Consequences

• Extended pharmacological effect• Adverse drug reactions• Lack of pro-drug activation• Increased effective dose• Metabolism by alternative, deleterious pathways• Exacerbated drug-drug interactions

General Population

Increased Surveillance:

Proteomics: New Tumour MarkersFrequent screening: Blood-based tests Mammography Colonoscopy Cervical screening

Gene expression profilingSomatic mutation detectionPharmacogenomics

Customized Treatment+

Clinical Care

Healthy individuals each provide a blood sample for testing at

a clinic

A cancer patient visits a BC Cancer

Agency clinic

A

B

No appreciable cancer susceptibility

Susceptible to developing a cancer

Diagnosed with cancer

LegendSusceptibility Testing: Genotyping Genetic analysis

Early detection andCharacterization of lesion

Cancer Patients

Gene expression profilingSomatic mutation detectionPharmacogenomics

Genomics and the Future of Cancer Care: Where does PGX fit?

Concepts

• Family studies vs. population studies

• Penetrance

• Genetic heterogeneity

• Linkage vs. association

• Haplotypes in family and association studies

• Genetic variation, SNPs

• Genotyping

Types of Genetic Studies

• Family studies– multi-generation families

• Association studies– Case / control (easiest to collect)

Penetrance

• Penetrance = the proportion of carriers who show the phenotype

• Expressivity = severity of the phenotype

Genetic Heterogeneity

• Locus heterogeneity (what we usually refer to when we talk about genetic heterogeneity)

• Allelic heterogeneity

• Examples:– in a family study, effect– in an association study, effect

Family Studies Identify Highly Penetrant Mutations

High penetrance disease allele(s)

Availability of suitable families is the limiting factor

Family studies are effective for only a minority of conditions

THE IMPORTANCE OFGENETIC BACKGROUND

**

* = mutation carrier

Few tumors Many tumors

HETEROGENEOUSGENETIC BACKGROUNDS

* ** ***

Few tumors

Many tumorsTumor type A only

Tumor type B only

Few A or B tumors

Many A and B Tumors

GENETICS WITH ENVIRONMENT

***

* **

** *

Fireman

Accountant

Farmer

Dyes hair

Eats too much

No exercise

Smokes

Smokes

Smokes

Smokes

Smokes

Eats too much

No exercise No exercise

Chronic stress Hates broccoli

Chronic stress

Chemist

RANDOM MATING

***

* **

* *

Fireman

Accountant

Farmer

Dyes hair

Eats too much

No exercise

Smokes

Smokes

Smokes

Smokes

Smokes

Eats too much

No exercise No exercise

Chronic stress Hates broccoli

Chronic stress

Chemist

* *

**

*

*

* *

*

So how do we map complex traits in the human gene pool?

Association Studies Can Identify Variants with High or Low Penetrance

• Case / control groups

• Not limited to high penetrance alleles

• Amenable to the study of gene-environment interactions

• Preferred approach for the majority of complex genetic disorders

Complex Diseases

• Multigenic (genetic heterogeneity)

• Environmental effects (multiple)

• Gene-gene interactions

• Gene-environment interactions

• Association studies will hold up under these complications but family studies will not!

Linkage vs. Association

• Linkage is to a locus– different families can be linked to the same

locus but have different disease alleles– how to take advantage of this in proving a gene

is responsible for a disease

• Association is with an allele– done in groups or populations– the allele arose and was propagated in the

population; the haplotype was degraded by recombination

Case / Control Studies

1. Collect blood samples from patients and controls, with consent

2. Establish database of clinical and epidemiological data3. Select ‘candidate’ genes of interest for each trait4. Sequence the candidate genes in a small group of

patients5. Genotype selected variants in case / control groups6. Analyze for association with a phenotype7. Analyze for gene-gene and gene-environment

interactions

Genetic, Ethical, Legal and Social (GELS) issues investigations

LD and Association

• Direct association– asks about the effect of a variant– if negative, the gene may still be involved!

• Indirect association– uses LD– can be conclusively negative if all haplotypes

are assessed

Human haplotype blocks . . .

Ancestral chromosomes

Observed pattern of historical recombination in common haplotypes

Rather than

50 kb

. . . Simplify association studies

Gene

Ancestralchromosomes

C

T

A

G

SNP1 SNP2

A disease-causingmutation arises

C

T

A

G

A

G

C

T

A

G

A

GAssociation withnearby SNPs

*

*

Location of mutation

A

G

A

G

A

G

C

T

C

T

C

T

Haplotype Blocks

• Became clear in October 2001• 87% of the genome is in blocks ~> 30 kb• Average block 20 kb (Gabriel et al, 2002)• A few common haplotypes at a given locus in a

given population• African populations generally have the greatest

number of haplotypes and the shortest haplotype blocks

• Strength of LD and size of blocks varies greatly between regions

• ‘Haplotype tagging’

How to Generate Haplotypes

• Haplotyping in families

• Impute haplotype frequencies– Expectation Maximization algorithm– generate frequencies for case group, control

group separately

• Physical determination– long-range PCR, separation of molecules– cloning of single molecules– very labor intensive

Haplotypes and Association Tests

A negative result of a single SNP association test rules out the SNP, not the gene or the region !

Uses of Haplotypes

• In the context of family studies

• In the context of association studies

• In which context will the haplotypes extend further physically?

Strategies

• Candidate gene based studies– hypothesis-driven– must guess the right gene!!– Current state of the art

• Genome scans– “hypothesis-free”– true genome scans not currently done– scans of ~ 50,000 markers possible

Genetic MarkersSNPs: Substitutions, for example, C / T Most common type of genetic variation 1 SNP every ~ 300 base pairs The SNP Consortium db contains 1.4 M mapped SNPs Ideal for association mapping over short distances

Microsatellites: (CA)n or other short repeats More polymorphic than SNPs Less common than SNPs 1 polymorphic microsatellite per ~ 100,000 base pairs Best for linkage mapping over long distances, in families

Minisatellites: Also known as VNTRs (variable number of tandem repeats) Highly polymorphic Forensic applications and paternity testing

SNPs

• Single Nucleotide Polymorphisms

• Can also use “Indels”, though some investigators throw them away!

• Synonymous, non-synonymous SNPs

• Mutation vs. polymorphism vs. variant

• The 1% definition (and why I don’t like it)

Genetic Markers• Linkage is seen over large distances

– think about why!– Microsatellites, repeats of 2, 3 or 4 bp units– 400 markers for a “10 cM” genome scan

• Association (LD) is seen over short distances– Think about why!– SNPs– Could need ~500,000 markers for a true

genome scan

SNP Databases

• dbSNP

• Human Genome Variation Database

• At least 11 others!

• ~ 50,000 non-synonymous SNPs in the human genome

• ~ 10 million SNPs with minor allele >1%

• ~ 7 million SNPs with minor allele >5%

Using Human Genome Information

SNP Discovery is Still Necessary

• Most have been found by multi-read sequence mining

• Directed SNP discovery in certain genes but not most

• Individuals used were “unaffected”• John Todd’s group: the current databases of

human variation are inadequate to specify all the common haplotypes in most gene regions

SNP Discovery

All exons and regulatory regions of each gene

Identify regulatory regions by comparative genomics

Bi-directional sequencing

Denaturing High Performance Liquid Chromatography (DHPLC)

Other methods

SNP Discovery: PolyPhred and Consed

Sample Output

GG

GA

AA

Does a variant have a functional effect?

• Worthy of excitement:

– Nonsense mutations (create a stop codon)

– Splice site mutations

– Coding region deletions

• To assay further:

– Missense mutations (a.a. substitutions)

– Promoter variants

• Probably not causal:

– Synonymous variation

• Using LD to infer function:

– is it the only likely variant in the block?

Genotyping

• Determining the allele(s) present in a particular sample at a particular (SNP) marker

• Many methods

TaqMan (Applied Biosystems)

TaqMan

TaqMan Output

Homozygous 1,1Heterozygous

Homozygous 2,2

Extended Primer (26-mer)

A C T

Extended Primer (24-mer)

T C T

ACT

Allele 2Same Primer (23-mer)

+Enzyme+ddATP

+dCTP/dGTP/dTTP

A T G A

Allele 1

TCT

Unlabeled Primer (23-mer)

EX

TE

ND

Pri

mer

Alle

le 1

EX

TE

ND

Pri

mer

Alle

le 1

Alle

le 2

EX

TE

ND

Pri

mer

Alle

le 2

MassEXTEND REACTION

Diagram courtesy of Sequenom

* A G

* A G

* T C

* A G

* C T

Multiplex Genotyping

Diagram courtesy of Sequenom

Backups

Linkage Disequilibrium

• The difference between the observed frequency of a haplotype and its expected frequency if all alleles were segregating randomly

• For adjacent loci: A,a B,b• D = PAB - PA x PB

• D is dependent on allele frequencies

• 2 most commonly used measures now:

– D’ = absolute value of D/Dmax

– D’ = 1 if complete LD, but inflated in small samples

– r2 = D2 / product of the 4 allele frequencies

– r2 = 1 only if the markers have not recombined

– r2 is emerging as the preferred measure of LD

The Common Disease / Common Variant Hypothesis

• Vs. the Common Disease / multiple rare variant hypothesis

• Combinations

PCR products

Template aliquotting:Robbins Hydra

1

PCR Set-up:Packard Multiprobe II liquid handler

2

PCR and cycle sequencing: MJ Tetrads

3

Purification of PCR Products: Agencourt

4

5

Sequencing: ABI 3700s

6

Cycle Sequencing

Determining Allele Frequencies

• Reduce the number of genotypes and genotyping cost, particularly for whole genome scans

• Pool of case DNAs vs. pool of control DNAs• DNAs must be mixed in precisely equimolar

proportions in the pools!• Requires a quantitative genotyping technique• E.g. 40% in cases vs. 20% in controls• Verify positives by genotyping individual samples

Daly et al., 2001

Tag SNPs

Chromosome copy 1

Chromosome copy 2

Chromosome copy 3

Chromosome copy 4

The HapMap

• 100,000 - 200,000 markers

• 3 years

• Reference map for association studies

• Will reduce the number of markers required to conduct effective genome scans for association

• Functional pharmacogenomics• Investigates a response to a drug that is relevant to a disease (asthma)• Functional assessment of haplotypes, not just individual variants• The haplotypes are more important (predictive) than single variants (SNPs)• This group (Genaissance with Liggett) seem to dominate the genetic / functional analysis of this gene

Functional Assessment of Haplotypes

ADRB2 Haplotypes

13 SNPs found in 12 haplotypes out of 8,192 possible haplotypesDivergence between ethnic groups

ADRB2 Haplotype Pairs in Asthmatics

Functional Analysis of ADRB2 Haplotype Pairs

• Mean responses by haplotype pair varied by >2-fold• Response significantly related to haplotype pair• Response not related to individual SNPs• Note that the 2/4 haplotype pair has a B2AR agonist response halfway between that of the 2/2 pair and the 4/4 pair

Conclusions

• Haplotypes are more predictive of bronchodilator response than individual SNPs

• Unique interactions of multiple SNPs within a haplotype can affect biologic and therapeutic phenotype

Illumina BeadArray System

• 1152-fold multiplexing

• 0.26 ng of genomic DNA per genotype

• $ 0.05 USD per genotype

Excitation Beam FluorescenceEmission

Photons(out)Fiber Cladding

Photons(in)

Fiber Core

Total Internal Reflection

cladding

Excitation Beam FluorescenceEmission

Photons(out)Fiber Cladding

Photons(in)

Fiber Core

Total Internal Reflection

claddingcladding

Illumina BeadArray SystemA B

D e c o d e r O l i g o 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6D e c o d e h y b 1D e c o d e h y b 2

D e c o d e h y b . 1 D e c o d e h y b . 2

D e c o d e r O l i g o 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6D e c o d e h y b 1D e c o d e h y b 2

D e c o d e h y b . 1 D e c o d e h y b . 2

AG

Address’ Allele Specific Extension

PCR with common primers

P1’P2’

P1P2

Product captureby hybridizationto array

P3’

P3

/\/\/\/

Address

T/C

AG

Address’ Allele Specific Extension

PCR with common primers

P1’P2’

P1P2

Product captureby hybridizationto array

P3’

P3

/\/\/\//\/\/\/

Address

T/C