1. Medical genomics BI420 Department of Biology, Boston College
1. Phenotypic effects caused by known genetic variants
- Diseases of Mendelian inheritance: sickle cell, cystic fibrosis
- known relationships between drug metabolic phenotypes and drug metabolizing enzyme polymorphisms
2. Genetic mapping to find genetic variants that cause diseases – linkage analysis and association studies
- the principle of genetic mapping, genetic markers
- monogenic diseases
- family based linkage analysis
- complex diseases
- case-control association studies: strategies, marker selection and association testing
3. Genome-wide association mapping resources: the HapMap
- the reason for the HapMap
- HapMap physical and informational resources
- the association structure of the human genome
4. Structural and epigenetic variations in disease
BI420
Department of Biology, Boston College

2. 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. Somatic variations in disease

3. 1. Phenotypic effects caused by known genetic variants

4. Many SNPs have phenotypic effects
Some notable genetic diseases:
cystic fibrosis
(Mendelian recessive)
sickle-cell anemia
Badano and Katsanis, NRG 2002

5. Genetic variants may affect drug metabolism: Pharmacogenetics
Evans and Relling, Science 1999

6. Are all genetic variants functional?
~ 40 million known SNPs
SNPs, on the scale of the genome, can be described well with the “neutral theory” of sequence variations, i.e. the vast majority of SNPs are likely to have no functional effects
How do we find the few functional variants in the background of millions of non-functional ones?

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

8. Recall from population genetics…
sequence variations are the result of mutation events
TAAAAAT
TAACAAT
TAAAAAT
TAACAAT
MRCA
mutations are propagated down through generations
and determine present-day variation patterns

9. Mendelian diseases have simple inheritance
genotype inheritance
Mendelian diseases have simple relationship between
genotype + phenotype inheritance

10. Modulators: Recombination
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because of recombination, DNA sequences may not have a unique common ancestor, hence phylogenetic analysis may not apply

11. Genetic mapping

12. Linkage analysis compares the transmission of marker genotype and phenotype in families
Sequence regions of the genome to determine which loci are linked with the trait.
Works well for Mendelian diseases

13. However, some diseases have complex inheritance
Multiple genes may influence the trait.
E.g. retinitis pigmentosa requires heterozygosity for two genes.
Badano and Katsanis, NRG 2002

14. General problem: Allele frequency and relative risk
Brinkman et al. Nature Reviews Genetics 14 March 2006
For complex diseases, it is difficult to find the causal loci, since each may contribute a only a little to the disease.
To simplify this problem, the “common disease-common variant” hypothesis is often invoked. This is the hypothesis that common genetic diseases are caused by adding up the effects of several loci, each with a common bad variant. However, the relative risk associated with the bad variant at any individual locus is thought to be low.
If this is true, to understand complex disease, you only need to sequence sites with common variants.

15. Allelic association (linkage disequilibrium, LD)
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

16. Case-control association testing
clinical cases
clinical controls
genotyping cases and controls at various polymorphisms
searching for markers with “significant” marker allele frequency differences between cases and controls; these marker signify regions of possible causative alleles
AF(cases)
AF(controls)

17. 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
marker that is co-inherited with causative variant
single-SNP marker or multi-SNP haplotype marker
single-stage or multi-stage

18. Association study strategies
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
1. hypothesis driven (i.e. based on gene function)
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
causative variant

19. Marker selection depends on genome LD
Daly et al. NG 2001

20. 3. Genome-wide association mapping resources – the HapMap

21. The HapMap resource
goal: to map out human allele and association structure of at the kilobase scale
We wish to determine which SNPs provide the most information about human genetic diversity

22. Linkage Disequilibrium (LD) structure in four human populations
International HapMap Consortium, Nature 2005

23. Genome-wide scans for human diseases
SNPs in Complement Factor H (CFH) gene are associated with Age-related Macular Degeneration (AMD)
Klein et al, Science 2005

24. How successful have association studies been?
201 SNPs associated with height could explain about 16% of genetic variance, 142 SNPs associated with Crohn's disease could explain about 20%, and 67 SNPs could explain about 17% of genetic variance in each of three common cancers.
Baker Nature.
Much information about inheritance is still unaccounted for!

25. Where is the “missing heritability”
An alternative to the “common disease-common variant” idea is that common diseases are actually caused by variants that are rare, but that there is more than one way to cause the disease.
If this is true, it is important to sequence every base in the genome to understand complex disease.

26. Most newly discovered SNPs are rare
Ryan Poplin
12M
10M 8M 4M 2M 6M
number of sites
frequency of alternate allele
0.001
0.01
0.1
1.0

27. Functional prediction vs. allele frequency
Jin Yu, Fuli Yu, Baylor College of Medicine
Marth et al. Genome Biology 2011

28. Disease causing alleles vs. allele frequency

29. Assessing the impact of rare variants
Manolio et al. Nature 2009
Traditional GWAS unlikely to work for rare variants
New methods are under development

30. VAAST
Instead of individual variants, use a larger unit for comparison e.g. a gene
Weight predicted impact of variant (e.g. non-synonymous change, large allele frequency difference etc.)

31. 4. Somatic variants in disease

32. 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

33. 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 be present at low allele frequency