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Admixture Mapping
Qunyuan Zhang
Division of Statistical Genomics
GEMS Course M21-621
Computational Statistical Genetics
March 24, 2010
dsgweb.wustl.edu/qunyuan/presentations/AdmixtureMapping2011.pptx
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Three Mapping Strategies
Linkage Analysis (linkage): genotype &
phenotype data from family (or families)
Association Scan (LD): genotype & phenotype
data from population(s) or families
Admixture Mapping (LD): genotype data from
admixed and ancestral populations, phenotype
data from admixed populations
(1) Ancestry-phenotype association mapping
(2) Ancestry info for population structure
control
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Genetic Admixture
Ancestral Population 1
Ancestral Population 2
Africans
Caucasians
Admixture Information
(Ancestry Analysis)
Admixed Population
African Americans
Admixture Mapping
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Rationale of Admixture Mapping
If a disease has some genetic factors, and the disease gene
frequency in pop 2 is higher than in pop 1. After the admixture of
pop 1 and 2, the diseased individuals in admixed generations will
carry disease genes/alleles that have more ancestry from pop 2 than
from pop 1.
If a marker is linked with disease genes, because of linkage
disequilibrium, the diseased individuals will also carry the marker
copies that have more ancestry from pop 2 than from pop 1.
Inversely, if we find a marker/locus whose ancestry from pop 2 in
diseased group is significantly different from that in non-diseased
group, we consider this marker/locus to be linked with (or a part of )
disease gene.
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Advantages of Admixture Mapping
Admixed population has more genetic variation and
polymorphism than relatively pure ancestral populations.
Admixture produces new LD in admixed population. Compared
with ancestral populations, shorter genetic history of admixture
population keeps more LD (long genetic history will destroy LD),
In admixed population, LD could be detected for relatively loose
linkage.
Ancestry information can be used to control population
stratification caused by genetic admixture.
According to simulation, admixture mapping demonstrates
higher power than regular methods, needs less sample size.
Flexible design: case-control or case-only, qualitative or
quantitative traits, no need of pedigree information
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Ancestry
Proportion of genetic materials descending from each
founding population
Population level : population admixture proportion
Individual level: individual admixture proportion
Individual-locus level: locus-specific ancestry
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Two Ways of Using Ancestral Info.
Individual Ancestry (IA) can be used as a genetic
background covariate for population structure control
Phenotype= a + b * Genotype + c * IA + Error
Locus-specific Ancestry (LSA) can be directly used to
detect association (admixture mapping)
Phenotype=a + b * LSA
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Individual Ancestry (IA) Estimation
using MLE
G: Observed genotypes of admixed and ancestral populations
Q: Allelic frequencies in ancestral populations
P : Individual Ancestry to be estimated
Goal: obtain P that maximizes Pr(G|P,Q)
1. Assign prior values for Q (randomly or estimated from ancestral population
genotype data) & P (randomly)
2. Compute P(i) by solving
3.
Compute Q(i) by solving
(G | Q, P)
0
( P)
(G | Q, P)
0
 (Q)
4. Iterate Steps 1 and 2 until convergence.
Tang et al. Genetic Epidemiology, 2005(28): 289–301
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Locus-specific Ancestry Estimation
using MCMC
Observed G : genotypes of admixed and ancestral populations
Unknown Z : admixed individuals’ locus specific ancestries from ancestral populations
Problem: How to estimate Z
?
Maximum Likelihood Estimate(MLE):
How to obtain a
Z that maximizes
Pr(G|Z) ?
Z is a huge space of parameters, in which search is difficult for likelihood method.
Bayesian and Markov Chain Monte Carlo (MCMC) methods
1. Assume ancestral population number
K
2. Define prior distribution Pr(Z) under
K
3. Use MCMC to sample from posterior distribution Pr(Z|G) = Pr(Z)∙ Pr(G|Z)
4. Average over large number of MCMC samples to obtain estimate of
Z
Falush et al. Genetics, 2003(164):1567–1587
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Distribution of Locus-specific Ancestries from Africans
Ancestry from Africans
(An Example of African American)
Maker Position
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Software
STRUCTURE
Falush D, Stephens M, Pritchard JK (2003)
Inference of population structure using multilocus genotype data: linked
loci and correlated allele frequencies. Genetics 164:1567–1587.
ADMIXMAP
Hoggart CJ, Parra EJ, Shriver MD, Bonilla C, Kittles
RA, Clayton DG, McKeigue PM (2003) Control of confounding of genetic
associations in stratified populations. Am J Hum Genet 72:1492–1504.
ANCESTRYMAP
Patterson N, Hattangadi N, Lane B, Lohmueller
KE, Hafler DA, Oksenberg JR, Hauser SL, Smith MW, O’Brien SJ, Altshuler
D, Daly MJ, Reich D (2004) Methods for high-density admixture mapping of
disease genes. Am J Hum Genet 74:979–1000
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References
D.C.Rife. Populations of hybrid origin as source material for the detection of
linkage. Am.J.Hum.Genet. 1954, (6):26-33
R.Chakraborty et al. Adimixture as a tool for finding linked genes and detecting
that difference from allelic association between loci. Proc.Natl.Acad.Sci.
1988,Vol.85:9119-9123
N. Risch. Mapping genes for complex disease using association studies with
recently admixed populations. Am.J.Hum.Genet.Suppl. 1992, 51:13
…
P.M.McKeigue. Prospects for admixture mapping of complex traits.
Am.J.Hum.Genet. 2005, Vol.76:1-7
X.Zhu et al. Admixture mapping for hypertention loci with genome-scan markers.
Nature Genetics. 2005,Vol.37(2): 177-181
Q Zhang et al. Genome-wide admixture mapping for coronary artery calcification in
African Americans: the NHLBI Family Heart Study. Genet Epidemiol. 2008
Apr;32(3):264-72.
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