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Dating the Origin of the CCR5-Δ32 AIDS-Resistance Allele by the Coalescence of Haplotypes. J. Claiborne Stephens, David E. Reich, David B. Goldstein, Hyoung Doo Shin, Michael W. Smith, Mary Carrington, Cheryl Winkler, Gavin A. Huttley, Rando Allikmets,

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dating the origin of the ccr5 32 aids resistance allele by the coalescence of haplotypes

Dating the Origin of the CCR5-Δ32 AIDS-Resistance Allele by the Coalescence of Haplotypes

J. Claiborne Stephens, David E. Reich, David B. Goldstein, Hyoung Doo Shin,

Michael W. Smith, Mary Carrington, Cheryl Winkler, Gavin A. Huttley, Rando Allikmets,

Lynn Schriml, Bernard Gerrard, Michael Malasky, Maria D. Ramos, Susanne Morlot,

Maria Tzetis, Carole Oddoux, Francesco S. di Giovine, Georgios Nasioulas, David Chandler,

Michael Aseev, Matthew Hanson, Luba Kalaydjieva, Damjan Glavac, Paolo Gasparini,

E. Kanavakis, Mireille Claustres, Marios Kambouris, Harry Ostrer, Gordon Duff,

Vladislav Baranov, Hiljar Sibul, Andres Metspalu, David Goldman, Nick Martin,

David Duffy, Jorg Schmidtke, Xavier Estivill, Stephen J. O’Brien, and Michael Dean

American Journal of Human Genetics 62:1507–1515, 1998

Presented by: Chad Brock, Lisa Ellison, and Travis Hagey

cell communication
Cell Communication

One way cells communicate is through receptors.

A chemokine receptor is a particular type of protein found in the cell membrane, used by the cell to send and receive chemical messages to/from other cells.

what hiv does
What HIV Does

The CCR5 gene produces the CCR5 chemokine receptor that, with CD4, serves as an entry port for HIV-1 strains that infect white blood cells.

HIV attaches to the CCR5 and CD4 proteins of the macrophage cell membrane, inserting it’s viral DNA into the cell.

ccr5 32

The CCR5-Δ32 mutation leads to truncation and loss of the receptor on lymphoid cells.

Homozygous individuals have nearly complete resistance to HIV-1 infection despite repeated exposure.

Heterozygous individuals have delayed onset of AIDS two to three years longer than do CCR5-+/+ individuals

frequency of the ccr5 32 allele in defined populations
38 ethnic populations including 4,166 individuals were tested for the CCR5-Δ32 allele (table 1). Frequency of the CCR5-Δ32 Allele in Defined Populations
  • CCR5-Δ32 deletion
  • High allele frequency among several Caucasian populations
  • Rarity or absence in non-Caucasian populations
  • Led to theory that mutation occurred only once in ancestry of Caucasians, after they migrated out of Africa
european distribution of the ccr5 32 variant
A north-to-south cline of allele frequency is affirmed as well as the absence of CCR5-Δ32 among East Asian, Middle Eastern, and American Indian populations.European Distribution of the CCR5-Δ32 Variant

ccr5 32 loci on chromosome 3
CCR5-Δ32 Loci on Chromosome 3
  • The time of origin of the CCR5-Δ32 mutation was estimated on the basis of the persistence of a common ancestral three-locus haplotype among modern CCR5-Δ32-bearing chromosomes.
  • This haplotype includes:
    • CCR5-Δ32 (gene of interest)
    • GAAT (microsatellite)
    • AFMB (microsatellite)
ccr5 haplotypes observed in modern caucasians
Of the people found to have the CCR5 mutation, 85% are of the haplotype Δ32-197-215. Thus, the authors suggest that this is the ancestral haplotype.

The authors suspect that this haplotype was elevated by natural selective pressures.

The estimated time to a common ancestor (time of origin of theΔ32 mutation) was estimated using coalescent methods based on the modern distribution of derivative Δ32 haplotypes.

CCR5 Haplotypes Observed in Modern Caucasians


Microsatellites are repeating sequences in the ‘junk’ DNA areas. Non-coding (don’t code for proteins) Not under selection High rate of mutation


The authors evaluated seven microsatellites on chromosome 3 for linkage with the CCR5-Δ32 allele.

GAAT and AFMB were found to show significant linkage the CCR5-Δ32 allele.


GAAT has 3 possible alleles: 197 base pairs 193 base pairs 191 base pairs

AMFB has 4 possible alleles:215 base pairs 217 base pairs 219 base pairs 213 base pairs

age of mutation under drift
Age of mutation under drift

Ne = 500025 years/generationp = .1 (current allele frequency)was either fixed (p=1) or very rare (p=0)

  • 4Ne [ p( ln p) + (1-p) ln (1-p)]
  • yields 6500 generations 162,500 years ago

Since CCR5-Δ32 isn’t present in non-caucasian populations, we can assume p was equal to 0, so we can use:

[ ]

p ( ln p) (p – 1)

  • 4Ne

yields 5100 generations 127,500 years ago


Age estimation based on haplotype variation

By looking at which alleles are most commonly found with CCR5-Δ32, the authors concluded that the ancestral haplotype for CCR5-Δ32 was GAAT-197 and AFMB-215 (85%)

In order to estimate the age of the CCR5-Δ32 mutation using the frequency of the ancestral haplotype, the authors first needed estimates for the rates of mutation and recombination.

age estimation based on haplotype variation estimation of r
Age estimation based on haplotype variationEstimation of r

r = total rate of change from ancestral haplotype = μ + c (μ = mutation rate) (c = recombination rate)

Used previous microsatellite mutation rate estimations from Weber and Wong (1993)

μ = .001 as an upper limit at GAAT and AFMB


Age estimation based on haplotype variationEstimation of c

Based on wild type haplotype frequencies we able to estimate 1 cM (cemtimorgan ‘recombination distance) is equal to 3.76 cR (centiray ‘physcial distance’)

Used radiation-hybrid analysis to estimate physical distances for CCR5, GAAT, and AFMB.


Age estimation based on haplotype variationEstimation of c

Used radiation-hybrid analysis to estimate physical distances for CCR5, GAAT, and AFMB.

CCR5 is .8 cR from GAAT (.21 cM)GAAT is 2.7 cR from AFMB (.72 cM)


This means the is a .21% recombination rate between CCR5 and GAAT and .72% recombination between GAAT and AFMB


Age estimation based on haplotype variationEstimation of c

Looking at CCR5-+ (wild type) populations, if recombinations were to occur, 36% of them would result in crossing over with the same haplotype, so 64% of recombinations that occurred inbetween CCR5 and GAAT would result in CCR5 moving next to a different microsatellites.

30.8+1.4+14.4+1.4 = 48

Also, 48% of the wild type haplotypes do not have the 215bp AFMB allele, so 48% of recombinations between GAAT and AFMB would result in a different AFMB allele switching chromosomes.


Age estimation based on haplotype variationEstimation of r

Combining these values:

c = .64 (.21%) + .48 (.72%) = .005

rate of recombination events which would lead the CCR5-Δ32-197-215 haplotype to transfer the CCR5-Δ32 gene to a different haplotype.

r = μ + c = .001 + .005 = .006

Ignoring mutations in which resulting in changing back to the original haplotype. (very rare)


Estimation of selection coefficient

To estimate the selection pressure to change CCR5-Δ32’s frequency from 0 to .1 in G generations:

p’ = p(pw11 + qw12) w

w11,w12 and w are dependent on if CCR5-Δ32 is dominant, codominant, or recessive.

If dominant, w11= w12 = 1w22 = 1 – sw = 1 – sq2

Trial values of s were used until p’ = 0.1 after G generations of selection.Initial p = .0005 and .0001 (1/2Ne if Ne = 1,000 and 5,000)

estimating the age of ccr 32
Estimating the age of CCR-32
  • Stephens et al. present an equation to calculate the age of the CCR-32 mutation based on it’s level of LD
  • Assuming the mutation was unique, at time zero it will be in complete LD with the alleles at the neighboring loci
  • With an estimate of the rate of recombination between the locus of interest and nearby loci, the age of the mutation may be gauged by the degree of decay of LD
  • In order to do this, the ancestral haplotype has to be identified
estimating the ancestral haplotype
Estimating the ancestral haplotype
  • As alluded to earlier, the authors use the relative frequencies of the different CCR-32-bearing haplotypes to estimate the ancestral haplotype
  • 32-197-215 is the most common CCR-32-bearing haplotype (84.8%) and is one mutational step away from the most common non-mutant haplotype, +-197-215
  • Thus, 32-197-215 was identified as the most likely ancestral haplotype for the CCR-32 mutation and all other CCR-32-bearing haplotypes were considered derived
calculating the number of generations since the mutation
Calculating the number of generations since the mutation
  • The probability that a given haplotype does not change from its ancestor G generations ago is the following:
    • P = (1-r)G ~ e-rG
  • Solving for G, we get the following:
    • G = -ln(P)/r
  • To estimate P, they use the proportion of observed haplotypes that are ancestral
  • Note: This estimate was originally derived for a dramatically expanding population but also holds for a constant-sized population in which many lineages are highly correlated (extensive periods of coancestry)
  • Variance in estimates of T, however, do depend on tree topology. Why?
calculating g and t
Calculating G and T
  • Substituting the present frequency of the ancestral haplotype (0.848) for P and the authors estimate of r (0.006) into Equation 2 gives a G = 27.5 generations
  • Assuming a 25-year human-generation time:
    • T = 25*27.5 = 688 Years
  • This calculation, however, assumes the present frequency and r are known without error
accounting for uncertainty in parameter estimates
Accounting for uncertainty in parameter estimates
  • The authors note two potential sources of error in their estimates of r and p
  • For r, the regression is consistent with the possibility of a 10%-20% reduction in recombination from their estimate of r in the region where the haplotype resides
  • When they considered lower values of r (i.e. 0.004 or 0.002) the G (and thus T) estimates were still well within the range of recorded human history (G = 41.3 and 82.5, and T = 1,032 and 2,064, respectively)
  • To check their estimates of p, ancestral haplotype frequencies were estimated from a larger sample of 1,400 chromosomes
  • Similar frequencies to those estimated from the smaller original sample were found and gave an estimated G = 16-31 and T = 402-766
coalescent simulations
Coalescent simulations
  • To further evaluate their estimate of G (= 28), the authors conducted coalescent simulations incorporating a complete Markov transition matrix (Reich and Goldstein, 1999)
  • This approach considers regeneration of the ancestral haplotype and incorporates a number of different population growth models (see Hudson, 1990)
  • From these simulations, 95% CI were derived
  • They performed 1,000 simulations for each combination of demographic parameters (population sizes ≤ 100,000, exp. growth rates from zero to rapid)
markov transition matrix
Markov transition matrix
  • The Markov transition matrix was calculated as follows:
    • K = cR + M + (1- c - )I


    • K = Markov transition matrix
    • R = Recombination matrix
    • M = Mutation matrix
    • I = No event occurring matrix (Identity Matrix)
    • c = recombination rate
    •  = mutation rate
mutation matrix
Mutation Matrix


b = proportion of alleles one mutation step away from AH

variance estimates for g and t
Variance estimates for G (and T)
  • The simulations considered each combination of demographic parameters separately
  • Of all the simulated genealogies, only those that resulted in the observed number of ancestral haplotypes were considered further
  • From this subset of trees, they produced a 95% confidence interval around T
  • Adding a further constraint on recent European population size (N > 5,000) a 95% CI for T was estimated to be 275-1,875 Years


  • The age of the CCR5- 32 mutation is estimated to be approx. 700 years via selective coalescent based estimates
  • Since the mutation probably occurred so recently and the fact the mutation gives strong resistance to AIDS,it is suspected that a strong selective pressure (possibly an ancient plague) was responsible for it’s high frequency in Eurasia.

Bubonic Plauge “Bacillus (yersinia pestis)”, Shigella, Salmonella, Mycobacterium, Tuberculosis are all candidate sources for the selective pressure since they all attack the immune system.

  • Additional possible diseases are Syphilis, Small Pox, and Influenza.

The Bubonic Plague which claimed the lives of 25%-33% of Europeans ~650 years ago is an obvious candidate.


Other deleterious mutations with positive side effects.

Sickle-cell anemia, thalassemia, duffy mutations all impart a resistance to malaria.

Similar hypotheses may also apply to Tay-Sachs disease and cystic fibrosis.

Red Blood Cell showing sickle shape due to sickle-cell anemia.


Key Words and Terms

  • Allele – A particular ‘flavor’ of gene. Ex. Gene-hair color. Allele-blond, or brown, or red…
  • Haplotype – A particular set of alleles. Ex. Blond hair, fair skin, and blue eyes is one haplotype. Brown hair, brown eyes, and olive complexion is another haplotype.
  • Genotype – All the alleles an organism possesses. Can be expressed or unexpressed.
  • Wild Type Haplotype – The ancestral, non-mutated haplotype. Signified by a + symbol. Ex. CCR5-+ (wild type) vs. CCR5-Δ32 (mutated type).
  • Microsatellite – A non-coding (isn’t used to make proteins) region of DNA which is inheritable, not under selection, and has a high rate of mutation. Because of these traits, they’re often used to establish relationships among individuals and/or populations. Different microsatellite alleles contain different numbers of base pairs. Ex. 215 base pairs vs. 217 base pairs.
  • Loci – A gene’s location on a chromosome.
  • Recombination – “crossing over” During meiosis (the formation of the gametes. ie. sperm & egg), the chromosomes from the mother and father cross over and exchange parts of the DNA strand, mixing up the alleles from each parent onto one chromosome, effecting gene linkage.
  • Gene Linkage – When genes are are close to one another on the chromosome, the alleles tend to be inherited together. Crossover events move alleles to different chromosomes so they are no longer inherited together.
  • Macrophages & Monocytes – Both are part of the non-specific immune system. Common name: White blood cells. Located in the blood and in tissues. When in the vicinity of a foreign object (invading virus or bacteria),they engulf and digest it.
  • Chemokine Receptor – A particular type of protein found in the cell membrane, used by the cell to send and receive chemical messages to/from other cells.
  • Explain what r, μ, and c represent in the context of this paper. r = μ + cr – The combined mutation and recombination rate. μ – The estimated microsatellite mutation rate. c – The estimated recombination rate.