1. SNP Applications
statwww.epfl.ch/davison/teaching/Microarrays/snp.ppt

2. Human Genome and SNPs
Now that the human genome is (mostly) sequenced, attention turning to the evaluation of variation
Alterations in DNA involving a single base pair are called single nucleotide polymorphisms, or SNPs
Map of ~1.4 million SNPs (Feb 2001)
It is estimated that ~60,000 SNPs occur within exons; 85% of exons within 5 kb of nearest SNP

3. SNP Initiatives Industrial Academic – Industry Consortium Governmental
Genset
Incyte
Celera
CuraGen
Academic – Industry Consortium
Governmental US
Japan
Non-industrial scale academic programs

4. Goals of SNP Initiatives
Immediate goals:
Detection/identification of …
The hundreds of thousands of SNPs estimated to be present in the human genome
Interest also in other organisms, e.g. potatoes(!)
Establishment of SNP Database(s)

5. Longer term goals: Areas of SNP Application
Gene discovery and mapping
Association-based candidate polymorphism testing
Diagnostics/risk profiling
Response prediction
Homogeneity testing/study design
Gene function identification
…etc.
See Schork, Fallin, Lanchbury 2000

6. Polymorphism
Technical definition: most common variant (allele) occurs with less than 99% frequency in the population
Also used as a general term for variation
Many types of DNA polymorphisms, including RFLPs, VNTRs, microsatellites
‘Highly polymorphic’ = many variants

7. Use of Polymorphism in Gene Mapping
1980s – RFLP marker maps
1990s – microsatellite marker maps

8. SNPs in Genetic Analysis
Abundance – lots
Position – throughout genome
Haplotype patterns – groups of SNPs may provide exploitable diversity
Rapid and efficient to genotype
Increased stability over other types of mutation
Recombination patterns – e.g. ‘hot spots’

9. Gene Discovery and Mapping
Linkage Analysis
Within-family associations between marker and putative trait loci
Linkage Disequilibrium (LD)
Across-family associations

10. One locus: Founder genotype probabilities
Founder: individual whose parents are not in the pedigree
Usually obtain genotype probs. assuming Hardy-Weinberg Equilibrium (HWE):
Say P(D) = p, P(d) = 1-p;
Then P(DD) = p2, P(Dd) = 2p(1-p), P(dd) = (1-p)2
Genotypes of founder couples treated as independent:
P(Father Dd and Mother DD) = 2p(1-p)3

11. One locus: Transmission probabilities (I)
Offspring get their genes according to Mendel’s rules…
Independently for different offspring
P(3 dd | 1 Dd & 2 Dd) = ½ x ½ Dd Dd 1 2 dd 3

12. One locus: Transmission probabilities (II)Dd Dd 1 2 3 4 5 dd Dd DD
P(3 dd & 4 Dd & 5 DD| 1 Dd & 2 Dd)
= (½ x ½) x (2 x ½ x ½) x (½ x ½)

13. P(affected|DD) = p (<1)
One locus: Penetrance
Usual to assume that the chance of having a particular phenotype (being affected with a disease, say) depends only on the genotype at one locus
Complete penetrance:
P(affected|DD) = 1
Incomplete penetrance:
P(affected|DD) = p (<1)

14. One locus: putting it all together1 2
Assume:
P(Aff|dd) = .1
P(Aff|Dd) = .3
P(Aff|DD) = .8
P(D) = .01 Dd Dd 3 4 5 dd Dd DD
P(pedigree) = (2 x .01 x .99 x .7) x (2 x .01 x .99 x .3) x (½ x ½ x .9) x (2 x ½ x ½ x .7) x (½ x ½ x .8)

15. Crossing over and Recombination

16. Two loci: Linkage and RecombinationDd TT Dd tt 1 2 Dd Tt 3 T D
(1-)/2
/2 d
3 produces gametes in proportions:

17. Recombination Fraction
 = ½ : independent assortment (Mendel)
 < ½ : linked loci
 = 0 : tightly linked loci (no recombination)
In 3, if the loci are linked then D-T and d-t are parental haplotypes, D-t and d-T are recombinant haplotypes

18. LOD-score Linkage Analysis
LOD(*) = log10 of the odds ratio L:
L = P(data|*)/P(data|½)
LOD(*) measures the relative strength of the data for  = * rather than  = ½
Can compute LOD() at several values
Can find the value  maximizing the LOD

19. IBD Allele Sharing

20. Allele-sharing Methods
Based on number (or proportion) of alleles shared identical by descent (IBD) of related individuals
Can be done either assuming (likelihood-based) or not assuming (nonparametric) a genetic mode of inheritance for a trait

21. Errors
Genotyping errors can result in false positive or false negative findings
Data checking/cleaning necessary (although there are approaches which model error)
Must be especially careful with SNP genotypes, because errors often pass simple Mendelian checks

22. Disease-Marker Association
A marker locus is associated with a disease if the distribution of genotypes at the marker locus in disease-affected individuals differs from the distribution in the general population
A specific allele may be positively associated (over-represented in affecteds) or negatively associated (under-represented)

23. Examples: Alzheimer’s
Alzheimer’s disease and ApoE
E4 present
E4 absent
Patients 58 33
Controls 16 55
The E4 allele appears to be positively associated with Alzheimer’s disease:
Odds Ratio = (58/16)/(33/55) = 6

24. Examples: HLA Disease Allele RR Ankylosing spondylitis B27 87
Myasthenia gravis B8
4.1
Systemic lupus erythematosus
2.1
Hemachromotosis A3
8.2
(and many more…)

25. Linkage Disequilibrium
Disease locus
Alleles D, d LD
penetrance
Marker locus
Alleles M, m
Disease

26. Linkage Disequilibrium
Concept of the ‘historical recombinant’
Explanations for observed association between marker and disease:
Marker locus may be a disease susceptibility locus
Marker locus may be linked to disease susceptibility locus
Spurious result due, e.g. to admixture, population stratification, heterogeneity

27. Linkage and LD Mutation occurs Allele D is created Nearby marker
Allele M was nearby
D and M subsequently transmitted together

28. Candidate Polymorphism Testing
Linkage and LD assume markers have indirect association with the trait
Large SNP collections may allow testing for direct, physiologically relevant associations with trait

29. Diagnostics/Risk Profiling
Identified SNP associations can potentially be used to develop diagnostic tools
Applicability will require large-scale studies, since most diseases of interest now are influenced by many genetic and nongenetic factors

30. Response Prediction Related to diagnosis/risk assessment
Strategy: stratify populations to improve effectiveness of interventions
Pharmaceutical companies especially interested in this:
Aim to identify those likely to respond
Predict toxicity reactions in susceptible individuals
Response to any kind of substance; creation of ‘functional foods’

31. Homogeneity Testing
Test to protect against false inferences about the relationship between endpoints (e.g. disease) and risk factors
Assess generalizability of results
Can assess the homogeneity of the genetic background of study participants using a panel of randomly distributed SNPs

32. Gene Function Identification
Alternative to other experimental procedures (e.g. knock-outs, which cannot be used in humans)
Studies to compare individuals with and without naturally occurring disease predisposing genetic profiles

33. Haplotype Variation
The large databases already available (and increasing in size) should allow characterization of haplotype variation across the genome in different populations
Can help population geneticists trace evolution and reveal connections between populations/ethnic groups