The medical relevance of

genome variability

Gabor T. Marth, D.Sc.

Department of Biology, Boston Collegemarth@bc.edu

Lecture overview

1. Phenotypic effects caused by known genetic variants

2. Genetic mapping to find genetic variants that cause diseases – linkage analysis and association studies

3. Genome-wide association mapping resources – the HapMap

4. Structural and epigenetic variations in disease

1. Phenotypic effects caused by known genetic variants

Many SNPs do have phenotypic effects

Badano and Katsanis, NRG 2002

some notable genetic diseases:

cystic fibrosis cycle-cell anemia

Genetic variants in Pharmacogenetics

Evans and Rellig, Science 1999

Genetic variants in Pharmacogenetics

Evans and Rellig, Science 1999

Using genotype information in the drug development pipeline

Roses. NRG 2004

Are all genetic variants functional?

~ 10 million known SNPs

0.005.00

10.0015.00

20.0025.00

30.0035.00

40.00

4 kb4 kb

8 kb8kb

12 kb12 kb

16 kb16kb0

0.1

0.2

0.3

0.4

SNPs, on the scale of the genome, can be described well with the “neutral theory” of sequence variations the vast majority of SNPs likely to have no functional effects

How do we find the few functional variants in the background of millions of non-functional SNPs?

2. Genetic mapping to find genetic variants that cause diseases – linkage analysis and association studies

Genetic mapping

Allelic association (linkage)

• allelic association is the non-random assortment between alleles i.e. it measures how well knowledge of the allele state at one site permits prediction at another marker site functional site

• significant allelic association between a marker and a functional site permits localization (mapping) even without having the functional site in our collection

• allelic association, and the use of genetic markers is the basis for mapping functional alleles

Mendelian diseases have simple inheritance

genotype inheritance

genotype + phenotype inheritance

Linkage analysis compares the transmission of marker genotype and phenotype in families

Complex disease – complex inheritance

Badano and Katsanis, NRG 2002

Allele frequency and relative risk

Brinkman et al. Nature Reviews Genetics advance online publication;published online 14 March 2006 | doi:10.1038/nrg1828

Association study strategies

• region(s) interrogated: single gene, list of candidate genes (“candidate gene study”), or entire genome (“genome scan”)

• direct or indirect:

causative variant causative variantmarker that is co-inherited with causative variant

• single-SNP marker or multi-SNP haplotype marker

• single-stage or multi-stage

Association study strategies

2. LD-driven – based entirely on the reduction of redundancy presented by the linkage disequilibrium (LD) between SNPs; tags represent other SNPs they are correlated with

1. hypothesis driven (i.e. based on gene function)

causative variant

for economy, one cannot genotype every SNP in thousands of clinical samples: marker selection is the process where a subset of all available SNPs is chosen

Marker selection depends on genome LD

Daly et al. NG 2001

Case-control association testing

• searching for markers with “significant” marker allele frequency differences between cases and controls; these marker signify regions of possible causative alleles

AF(cases)

AF(

contr

ol

s)

clinical cases

clinical controls

• genotyping cases and controls at various polymorphisms

3. Genome-wide association mapping resources – the HapMap

The HapMap resource

• goal: to map out human allele and association structure of at the kilobase scale

• deliverables: a set of physical and informational reagents

LD structure in four human populations

International HapMap Consortium, Nature 2005

LD varies across samples

African reference (YRI)

there are large differences in LD between different human populations…

European reference (CEU)

… and even between samples from the same population.

Other European samples

Sample-to-sample LD differences make tagSNP selection problematic

groups of SNPs that are in LD in the HapMap reference samples may not be in a future set of clinical samples…

… and tags that were selected based on LD in the HapMap may no longer work (i.e. represent the SNPs they were supposed to) in the clinical samples…

… possibly resulting in missed disease associations.

Marker selection with additional samples

test if markers selected from the HapMap continue to “tag” other SNPs in their original LD group

Representative computational samples

Two methods of computational sample generation

“HapMap” “cases”

“controls”HapMap

Method 1. “Data-relevant Coalescent”. This algorithm uses a population genetic model to connect mutations in the HapMap reference to mutations in future clinical samples. Full model but computationally slow.

Method 2. The PAC method (product of approximate conditionals, Li & Stephens). This method constructs “new” samples as mosaics of existing haplotypes, mimicking the effects of recombination. An approximation but fast.

LD difference -- comparison to extra experimental genotypes

0.949 +/- 0.013

0.978 +/- 0.0100.963 +/- 0.014

• we have analyzed two extra genotype sets collected at the HapMap SNPs in three genome regions, from our clinical collaborators (Prof. Thomas Hudson, McGill; Prof. Stanley Nelson, UCLA)

Genome-wide scans for human diseases

Klein et al, Science 2005

SNPs in Complement Factor H (CFH) gene are associated with Age-related Macular Degeneration (AMD)

4. Somatic, structural and epigenetic variants in disease

Somatic mutations

© Brian Stavely, Memorial University of Newfoundland

the detection of somatic mutations, and their distinction from inherited polymorphism, is important to separate pre-disposing variants from mutations that occur during disease progression e.g. in cancer

1. detect the mutations

2. classify whether somatic or inherited

Detecting somatic mutations with comparative data

• based on comparison of cancer and normal tissue from the same individual

• often cancer tissue is highly heterogeneous and the somatic mutant allele may represent at low allele frequency

Detecting somatic mutations with subtraction

• if normal tissue samples are not available, we detect SNPs in cancer tissue against e.g. the human genome reference sequence

• subtract apparent mutations that are present in sequence variation databases

• search for evidence that these mutations are genetic

Detecting somatic mutations in murine mtDNA

• we have applied our methods for somatic mutation detection in murine mitochondrial sequences

heteroplasmy homoplasmy

• we will be applying our methods for human nuclear DNA from our collaborators

Structural variants in disease

Feuk et al. Nature Reviews Genetics 7, 85–97 (February 2006) | doi:10.1038/nrg1767

Structural variations and phenotype

Feuk et al. Nature Reviews Genetics 7, 85–97 (February 2006) | doi:10.1038/nrg1767

Epigenetics and cancer

Baylin at al. NRC 2006.

Informatics of detection / integration of varied genetic and epigenetic data

chromatin structure

gene expression profiles

copy number changes

methylation profiles

chromosome rearrangement

s

repeat expansions

somatic mutations