trait mapping
DESCRIPTION
Trait Mapping. Recombination Mapping SNP mapping. BIO520 BioinformaticsJim Lund. cause inherited diseases. Why do we care about variations?. underlie phenotypic differences. allow tracking human history (ancient and modern). Traits. Mendelian single locus, few alleles - PowerPoint PPT PresentationTRANSCRIPT
Trait Mapping
•Recombination Mapping•SNP mapping
BIO520 Bioinformatics Jim Lund
Why do we care about variations?
underlie phenotypic differences
cause inherited diseases
allow tracking human history (ancient and
modern)
Traits
• Mendelian– 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...
TraitsHow do we find their basis?
• Association of variance in trait with variance in gene
• Genetic linkage
Basic Concepts
A B
a b
A Ba b
High LD -> No Recombination(r2 = 1) SNP1 “tags” SNP2
A B
A B
A B
a b
a b
a b
Low LD -> RecombinationMany possibilities
A b
A ba Ba b
A BA B
a B
A b
etc…
A B
A B
X
OR
Parent 1 Parent 2
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
Genetic crosses
• Model organisms, e.g. Fungi, no problem
• Humans– rare woman who will bear >5, >10 children
– controlled breeding problematic
Alternate Mapping
• Pedigree analyses– likelihood estimation
– The original method, now less common
• Population-based mapping– association studies
– linkage disequilibrium
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
Cloning Human Genes
• Positional• Positional/Candidate• Candidate Only• Functional
Complex diseasesAssociation mapping• Disease gene: D, d• Marker: M, m
M associated with D ifthe 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 M1 M2 M3 M4 M5 M6
•Pedigree sampled
•Many Meiosis (>104)
•Resolution: 10-5 Morgans (Kbases)
•Limited by number of markers
2N gen
erations
rM
D
Association Mapping
At time t
Now
D M
D M
Gene Mapping & the single mutation case
+ + ++ ++
Major Disease Causing Mutation.
+ has the disease.
Incomplete penetrance
Minor Disease Causing Mutation
Non-genetic cause Oversampled
Complicating factors
Alzheimers & Apolipoproteins E
Definition of QTL?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).
http://gnome.agrenv.mcgill.ca/tinker/pgiv/whatis.htm
QTLs-interesting traits
• Heritability often ~0.5• Traits like:
– Heart disease– Depression– Type II diabetes– High blood pressure– Arthritis – Most diseases!
QTLs-simple problems
• 30,000 markers– P-value=0.01
– 299 false hits, 1 real one
– Correct for multiple testing
• 2 QTLS near one another– “ghost” QTL between them
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
Significance Thresholds by PermutationChurchill and Doerge, 1994
1.Permute the data (create the null hypothesis)
H0: there is no QTL in the tested intervalH1: there is QTL in the tested interval
2.Perform interval mapping
3. Repeat (1) and (2) many times 4.Choose Threshold
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).
SNPs identification methods
• Pairwise sequence comparison• Deep resequencing• High throughput mismatch detection
methods– Denaturing high-performance liquid
chromatography (DHPLC)– Single-strand Conformational
Polymorphism (SSCP)
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.
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.
HapMap Project
Phase 1
Phase 2
Phase 3
Samples & POP panels
269 sample
s(4
panels)
270 sample
s(4
panels)
1,115 sample
s (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:p1
299
Nature (2007) 449:p8
51
Draft Rel. 1 (May 2008)
Phase 3 Samples
label population sample # samples QC+ Draft 1ASW* African ancestry in Southwest USA 90 71
CEU*Utah residents with Northern and Western
European ancestry from the CEPH collection180 162
CHB Han Chinese in Beijing, China 90 82CHD Chinese in Metropolitan Denver, Colorado 100 70GIH Gujarati Indians in Houston, Texas 100 83JPT Japanese in Tokyo, Japan 91 82LWK Luhya in Webuye, Kenya 100 83MEX* Mexican ancestry in Los Angeles, California 90 71MKK* Maasai in Kinyawa, Kenya 180 171TSI Toscans in Italy 100 77
YRI* Yoruba in Ibadan, Nigeria 180 1631,301 1,115
* Population is made of family trios
SNP databases
• dbSNP (NCBI)– 12 million human SNPs– 5 million validated SNPs– http://www.ncbi.nlm.nih.gov/SNP/get_html.cgi?whichHtml=overview
• SNP frequency information• Mapped to the current genome build• HapMap (haplotypes)
How to use markers to find disease?
• problem: genotyping cost precludes using millions of markers simultaneously for an association study
genome-wide, dense SNP marker map
• depends on the patterns of allelic association (haplotypes) in the human genome
• question: how to select from all available markers a subset that captures most mapping information (marker selection, marker prioritization)
The promise for medical genetics
CACTACCGACACGACTATTTGGCGTAT
• within blocks a small number of SNPs are sufficient to distinguish the few common haplotypes significant marker reduction is possible
• if the block structure is a general feature of human variation structure, whole-genome association studies will be possible at a reduced genotyping cost
• this motivated the HapMap project
Gibbs et al. Nature 2003
blocks
chromosome
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.
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
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!