1. Recombination Mapping SNP mapping
Trait Mapping
Recombination Mapping
SNP mapping
BIO520 Bioinformatics Jim Lund

2. Why do we care about variations?
underlie phenotypic differences
cause inherited diseases
allow tracking human history (ancient and modern)

3. Traits Mendelian Quantitative single locus, few alleles
high penetrance, high expressivity
eg color, enzyme, molecular, genetic diseases (CF, hemophilia…)
Quantitative
multiple allele, multilocus
variable penetrance, expressivity
epistasis, environmental effects
eg. blood pressure, weight, IQ...

4. Traits How do we find their basis?
Association of variance in trait with variance in gene
Genetic linkage

5. Low LD -> Recombination
Basic Concepts
Parent 1
Parent 2
A B
a b
A B
a b X
A B
a b
A b
A B
A B
a b OR
a B
A B
A b
a b
A B
a B
A b
A B
a b
A B
etc…
High LD -> No Recombination
(r2 = 1) SNP1 “tags” SNP2
Low LD -> Recombination
Many possibilities

6. Mapping Issues Need many arbitrary, polymorphic markers for dense map
Molecular markers: RFLP, STS, SNP
Need many progeny
100 progeny for 1 cM map
1000/0.1 cM map, 100 kb in mouse
Map distance varies (the ratio of kb/cM not constant)
centromere suppression
inversion suppression

7. Genetic crosses Model organisms, e.g. Fungi, no problem Humans
rare woman who will bear >5, >10 children
controlled breeding problematic

8. Alternate Mapping Pedigree analyses Population-based mapping
likelihood estimation
The original method, now less common
Population-based mapping
association studies
linkage disequilibrium

9. Pedigree Analysis Likelihood Method (LOD scores)
LOD  3-4, 1/1000 – 1/10000 odds of linkage
genome-wide p-value of p < .05
Hard to extend to <1 cM
According to Lander and Kruglyak (1995), the genome-wide false-positive rate, αT* is related to the pointwise false positive rate, αT by the equation
alphaT* = [C pGT]
T is the threshold lod score; C = 23, the number of chromosomes, and G = 33, the total length of the genome in Morgans. The parameter ρ measures the crossover rate, and takes different values depending on the relationship being studied, so that the formula cannot be simply applied to complex pedigrees. For affected sib pairs the formula suggests genome-wide lod score thresholds of 3.6 for IBD testing and 4.0 for IBS testing. Note that the formula applies strictly only to large samples and to stringent thresholds.

10. Cloning Human Genes Quantitative Traits! Positional
Positional/Candidate
Candidate Only
Functional
Quantitative Traits!

11. Complex diseases Association mapping Disease gene: D, d Marker: M, m
M associated with D if
the probability of an individual having the disease given that they have allele M is much greater than the chance of having the disease if the individual has allele m. Written as: P(D|M) > P(D|m)
Linkage between the gene and marker increases the likelihood of association.
Association can be caused by
Causation
Population subdivision
Statistical artifact
Linkage disequilibrium
D M M M M4
M M6

12. Association Mapping Pedigree sampled Many Meiosis (>104)
Resolution: 10-5 Morgans (Kbases)
Limited by number of markers r M D
2N generations

13. Gene Mapping & the single mutation caseD M
At time t
Now

14. Complicating factors Major Disease Causing Mutation.
Minor Disease Causing Mutation
+ has the disease. + + + + + +
Oversampled
Non-genetic cause
Incomplete penetrance

15. Alzheimers & Apolipoproteins E

16. Definition of QTL?
A quantitative trait locus (QTL) is the location of individual or multiple loci that affects a trait that is measured on a quantitative (linear) scale. Examples of quantitative traits are blood pressure and grain yield (measured on a balance). These traits are typically affected by more than one gene, and also by the environment. Thus, mapping QTL is not as simple as mapping a single gene that affects a qualitative trait (such as an inborn error of metabolism).
QTL is the acronym fro Quantitative Trait Loci, genes which underlie quantitative traits (Gelderman, 1975).

17. QTLs-interesting traits
Heritability often ~0.5
Traits like:
Heart disease
Depression
Type II diabetes
High blood pressure
Arthritis
Most diseases!

18. QTLs-simple problems 30,000 markers 2 QTLS near one another
P-value=0.01
299 false hits, 1 real one
Correct for multiple testing
2 QTLS near one another
“ghost” QTL between them

19. Factors that lead to success in mapping QTLs
Simple, easily quantified trait
Genes of major effect
distinct chromosomal loci
Well-defined map
Large numbers of progeny
inbred
outbred

20. Significance Thresholds by Permutation Churchill and Doerge, 1994
Permute the data (create the null hypothesis)
H0: there is no QTL in the tested interval H1: there is QTL in the tested interval
Perform interval mapping
3. Repeat (1) and (2) many times
Choose Threshold
                                                                                                       
                                                                                            
                                                  

21. Human SNPs
About 10 million SNPs exist in human populations where the rarer SNP allele has a frequency of at least 1%.
A set of associated SNP alleles in a region of a chromosome is called a "haplotype".
SNPs are arranged in groups
SNPs within groups show little recombination
Nonrandom association of SNPs results in only a few common haplotypes
Patterns capture most of the variation in a region
The HapMap will describe the common patterns of genetic variation in humans.
The HapMap Project will identify the associations between SNPs and identify the SNPs that tag them (tagSNPs).

22. SNPs identification methods
Pairwise sequence comparison
Deep resequencing
High throughput mismatch detection methods
Denaturing high-performance liquid chromatography (DHPLC)
Single-strand Conformational Polymorphism (SSCP)

23. HapMap
Blocks of adjacent SNPs that show little recombination are called haplotype blocks.
Mean haplotype block length is tens of kb.
HapMap project started examining 270 individuals from 4 ethnic groups.
Now expanding to a more comprehensive sample.
Characterization of haplotype blocks means that fewer SNPs will need to be typed.
500,000 SNPs will identify 90% of haplotype blocks.

24. HapMap Glossary
LD (linkage disequilibrium): For a pair of SNP alleles, it’s a measure of deviation from random association (i.e., a measure of lack of recombination). Measured by D’, r2, LOD
Phased haplotypes: Estimated distribution of SNP alleles. Alleles transmitted from Mom are in same chromosome haplotype, while Dad’s form the paternal haplotype.
Tag SNPs: Minimum SNP set to identify a haplotype. r2= 1 indicates two SNPs are redundant, so each one perfectly “tags” the other.

25. HapMap International Consortium
HapMap Project
Phase 1
Phase 2
Phase 3
Samples & POP panels
269 samples
(4 panels)
270 samples
1,115 samples
(11 panels)
Genotyping centers
HapMap International Consortium
Perlegen
Broad & Sanger
Unique QC+ SNPs
1.1 M
3.8 M
(phase I+II)
1.6 M (Affy 6.0 & Illumina 1M)
Reference
Nature (2005) 437:p1299
Nature (2007) 449:p851
Draft Rel. 1
(May 2008)

26. Phase 3 Samples
* Population is made of family trios

27. SNP databases dbSNP (NCBI) SNP frequency information
12 million human SNPs
5 million validated SNPs
SNP frequency information
Mapped to the current genome build
HapMap (haplotypes)

28. How to use markers to find disease?
genome-wide, dense SNP marker map
question: how to select from all available markers a subset that captures most mapping information (marker selection, marker prioritization)
problem: genotyping cost precludes using millions of markers simultaneously for an association study
depends on the patterns of allelic association (haplotypes) in the human genome

29. The promise for medical genetics
within blocks a small number of SNPs are sufficient to distinguish the few common haplotypes  significant marker reduction is possible
CACTACCGA
CACGACTAT
TTGGCGTAT
chromosome
if the block structure is a general feature of human variation structure, whole-genome association studies will be possible at a reduced genotyping cost
blocks
this motivated the HapMap project
Gibbs et al.
Nature 2003

30. The promise for medical genetics
Discover genes contributing to complex diseases
Use these markers to test for inherited disease risk
Find SNPs associated with drug side effects
Make drugs safer.
Rescue drugs abandoned due to significant side effects.

31. Pathway of Drug Development
Lead or Target (Clinical Candidate)
Animal Model Testing
Toxicity, Efficacy
Phase I Pre-Clinical (toxicity)
Phase II (efficacy)
Phase III (efficacy)
NDA (new drug application)
$100M 2000
$0.5M 100
$0.5M 20
$5M 3
$50M 2 1

32. Why pharmacogenomics? Where do you find the next profitable drug?
The 19/20 drugs that failed AFTER phase 1, but are still efficacious!
How do you decrease the cost of clinical trials?
Don’t enroll people of the “wrong” genotype!
Only give drugs to patients likely to benefit and at a low genetic risk of side effects!