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Bayesian Estimators of Time to Most Recent Common Ancestry. Bruce Walsh. Ecology and Evolutionary Biology. Adjunct Appointments Molecular and Cellular Biology Plant Sciences Epidemiology & Biostatistics Animal Sciences. Definitions.

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bayesian estimators of time to most recent common ancestry

Bayesian Estimators ofTime to Most Recent Common Ancestry

Bruce Walsh

Ecology and Evolutionary Biology

Adjunct Appointments

Molecular and Cellular Biology

Plant Sciences

Epidemiology & Biostatistics

Animal Sciences


MRCA - Most Recent Common Ancestor

TMRCA - Time to Most Recent Common Ancestor

Question: Given molecular marker information from

a pair of individuals, what is the estimated time back

to their most recent common ancestor?

With even a small number of highly polymorphic

autosomal markers, trivial to assess zero (subject/

biological sample) and one (parent-offspring) MRCA

problems with autosomal markers
Problems with Autosomal Markers

Often we are very interested in MRCAs that are modest

(5-10 generations) or large (100’s to 10,000’s of


Unlinked autosomal markers simply don’t work over these

time scales.

Reason: IBD probabilities for individuals sharing a MRCA

5 or more generations ago are extremely small and hence

very hard to estimate (need VERY large number of


mrca i vs mrca g

We need to distinguish between the MRCA for a pair

of individuals (MRCA-I) and the MRCA for a particular

genetic marker G (MRCA-G).

MRCA-G varies between any two individuals over

recombination units.

For example, we could easily have for a pair of relatives

MRCA (mtDNA ) = 180 generations

MRCA (Y ) = 350 generations

MRCA (one a-globulin allele ) = 90 generations

MRCA (other a -globulin allele ) = 400 generations

mrca g mrca i





mtdna and y chromosomes
mtDNA and Y Chromosomes

So how can we accurately estimate TMRCA for modest

to large number of generations?

Answer: Use a set of completely linked markers

With autosomes, unlinked markers assort each generation

leaving only a small amount of IBD information on each

marker, which we must then multiply together. IBD

information decays on the order of 1/2 each generation.

With completely linked marker loci, information on IBD does not assort away via recombination. IBD information decay is on the order of the mutation rate.

y chromosome microsatellite mutation rates i
Y chromosome microsatellitemutation rates- I

Estimates of human mutation rate in microsatellites

are fairly consistent over both the Y and the autosomes


Basic Structure of the Problem

What is the probability that the two marker

alleles at a haploid locus from two related

individuals agree given that their MRCA was

t generation ago?

Phrased another way, what is their probability

of identity by state (IBS), given they are identical

by descent (IBD) when their TMRCA is t



Infinite Alleles Model

The first step in answering this question is to

assume a particular mutational model

Our (initial) assumption will be the infinite alleles

model (IAM)

The key assumption of this model (originally due

to Kimura and Crow, 1964) is that each new mutation

gives rise to a new allele.

The IAM was the first population-genetics model to

attempt to formally incorporate the structure of DNA

into a model





(1-u) t

Pr(No mutation from MRCA->A) = (1-u)t

Pr(No mutation from MRCA->B) = (1-u)t



Key: Under the infinite alleles, two alleles that are

identical in state that are also ibd have not

experienced any mutations since their MRCA.

Let q(t) = Probability two alleles with a MRCA

t generations ago are identical in state

If u = per generation mutation rate, then

q(t) = (1-u)2t


Building the Likelihood Function for n Loci











For any single marker locus, the probability of IBS

given a TMRCA of t generations is

q(t) = (1-u)2t ≈ e-2ut = e-t, t = 2ut

The probability that k of n marker loci are IBS is just

a Binomial distribution with success parameter q(t)

Likelihood function for t given k of n matches

ml analysis of tmrca















ML Analysis of TMRCA

It would seem that we now have all the pieces in

hand for a likelihood analysis of TMRCA given

the marker data (k of n matches)

Likelihood function (t = 2ut)

MLE for t is solution of ∂ L/∂t = 0

p = fraction of matches











Var( t ) =


In particular, the MLE for t becomes

Likewise, the precision of this estimator follows

for the (negative) inverse of the 2nd derivative

of the log-likelihood function evaluated at the



Finally, hypothesis testing, say Ho: MRCA = t0, is

easily accomplished by comparing -2* the natural

log of the ratio of the value of the likelihood function

at t = t0 over the value of the likelihood function at the

MLE t = t


Likewise, we can (numerically) easily find 1-LOD

support intervals for t and hence construct

approximate 95% confidence intervals to TMRCA

The resulting log likelihood ratio LR is (asymptotically)

distributed as a chi-square distribution with one degree of


trouble in paradise
Trouble in Paradise

The ML machinery has seem to have done its job,

giving us an estimate, its approximate sampling

error, approximate confidence intervals, and a scheme

for hypothesis testing.

Hence, all seems well.

Problem: Look at k=n (= complete match at all markers).

MLE (TMRCA) = 0 (independent of n)

Var(MLE) = 0 (ouch!)


What about one-LOD support intervals (95% CI) ?

With n=k, the value of the likelihood function is

L(t) = (1-u)2tn ≈ e-2tun

L has a maximum value of one under the MLE

Hence, any value of t that gives a likelihood value of

0.1 or larger is in the one-LOD support interval

Solving, the one-LOD support interval is from t=0 to

t = (1/2n) [ -Ln(10)/Ln(1-u) ] ≈(1/n) [ Ln(10)/(2u) ]

For u = 0.002, CI is (0, 575/n)


MLE(t) = 0 for all values on n

1 LOD ≈ t = 29

1 LOD ≈ t = 58

1 LOD ≈ t = 115

0.1 of max value (1) of

likelihood function

With n=k, likelihood function reduces to

L(t) = (1-u)2tn ≈ e-2tun






(Plots for u = 0.002)


What about Hypothesis testing?

Again recall that for k =n that the likelihood at t = t0 is

L(t0) ≈ Exp(-2t0un)

Hence, the LR test statistic for Ho: t = t0 is just

LR = -2 ln [ L(t0)/ L(0) ]

= -2 ln [ Exp(-2t0un) / 1 ]

= 4t0un

Thus the probability for the test

that TMRCA = t0 is just Pr( c12 > 4t0un)

the problem s with ml
The problem(s) with ML

The expressions developed for the sampling variance,

approximate confidence intervals, and hypothesis

testing are all large-sample approximations

Problem 1: Here our sample size is the number of

markers scored in the two individuals. Not likely to

be large.

Problem 2: These expressions are obtained by taking

appropriate limits of the likelihood function. If the

ML is exactly at the boundary of the admissible space

on the likelihood surface, this limit may not formally

exist, and hence the above approximations are incorrect.

the solution go bayesian

posterior distribution of

q given x

Likelihood function for q

Given the data x

priordistribution for q

The appropriate constant so that the posterior

integrates to one.

The Solution? Go Bayesian

An extension of likelihood is Bayesian statistics

Instead of simply estimating a point estimate (e.g., the

MLE), the goal is the estimate the entire distributionfor the unknown parameter q given the data x

p(q | x) = C * l(x | q) p(q)

Why Bayesian?

• Exact for any sample size

• Marginal posteriors

• Efficient use of any prior information

• MCMC (such as Gibbs sampling) methods

the prior on tmrca
The Prior on TMRCA

The first step in any Bayesian analysis is choice of

an appropriate prior distribution p(t) -- our thoughts on

the distribution of TMRCA in the absence of any of

the marker data

Standard approach: Use a flat or uninformative prior,

with p(t) = a constant over the admissible range of the

parameter. Can cause problems if the likelihood function

is unbounded (integrates to infinity)

In our case, population-genetic theory provides the

prior: under very general settings, the time to MRCA

for a pair of individuals follows a geometric distribution


Prior hyperparameter = 1/Ne





Previous likelihood function (ignoring constants

that cancel when we compute the normalizing factor



In particular, for a haploid gene, TMRCA follows

a geometric distribution with mean 1/Ne.

Hence, our prior is just

p(t) = l(1-l)t≈l e-lt, where l = 1/Ne

Hence, we can use an exponential prior with

hyperparameter (the parameter fully

characterizing the distribution) l = 1/Ne.

The posterior thus becomes

the normalizing constant









The Normalizing constant


I ensures that the posterior distribution integrates

to one, and hence is formally a probability distribution

what is the effect of the hyperparameter





What is the effect of the hyperparameter?

If 2uk >> l, then essentially no dependence on the

actual value of l chosen.

Hence, if 2Neuk >> 1, essentially no dependence on

(hyperparameter) assumptions of the prior.

For a typical microsatellite rate of u = 0.002, this is just

Nek >> 250, which is a very weak assumption. For example,

with k =10 matches, Ne >> 25. Even with only 1 match (k=1),

just require Ne >> 250.

closed form solutions for the posterior distribution




















Closed-form Solutions for the Posterior Distribution

Complete analytic solutions to the prior can be obtained

by using a series expansion (of the (1-ex)n term) to give

Each term is just a * ebt, which is easily integrated




















With the assumption of a flat prior, l = 0, this reduces to












Hence, the complete analytic solution of the posterior is

Suppose k = n (no mismatches)

In this case, the prior is simply an exponential

distribution with mean 2un + l,

analysis of n k case






Analysis of n = k case

Mean TMRCA and its variance:

Cumulative probability:

In particular, the time Ta satisfying P(t < Ta) = a is


For a flat prior (l = 0), the 95% (one-side) confidence

interval is thus given by -ln(0.5)/(2nu) ≈ 1.50/(nu)

Hence, under a Bayesian analysis for u = 0.02, the

95% upper confidence interval is given by ≈ 749/n

Recall that the one-LOD support interval (approximate

95% CI) under an ML analysis is ≈ 575/n

The ML solution’s asymptotic approximation significantly

underestimates the true interval relative to the

exact analysis under a full Bayesian model


n = 20, t0.95 = 38

n = 10, t0.95 = 75

Under a Bayesian analysis, we look

at the posterior probability

distribution (likelihood adjusted

to integrate to one) and find the

values that give an area of 0.95

Under ML, we plot the

likelihood function and look

for the 0.1 value

Pr(TMRCA < t)

n = 10, area to

left of t=75 = 0.95

n = 20, area to

left of t=38 = 0.95


Why the difference?


Posteriors for n = 10

Posteriors for n = 20

Posteriors for n = 50

n = 100


n = 100













p( t | k )

p( t | k )
















































Time t to MRCA

Time t to MRCA

Sample Posteriors for u = 0.002


Key points

• By using markers on a non recombining chromosomal

section, we can estimate TMRCA over much, much

longer time scales than with unlinked autosomal markers

• By using the appropriate number of markers we

can get accurate estimates for TMRCA for even

just a few generations. 20-50 markers will do.

• Hence, we have a fairly large window of resolution

for TMRCA when using a modest number of completely

linked markers.

extensions i different mutation rates








Extensions I: Different Mutation Rates

Let marker locus k have mutation rate uk.

Code the observations as xk = 1 if a match,

otherwise code xk = 0

The posterior becomes:

stepwise mutation model smm

Mutation 1

Mutation 2

Under IAM, score as a match, and hence no mutations

In reality, there are two mutations

Stepwise Mutation Model (SMM)

Microsatellite allelic variants are scored by their number

of repeat units. Hence, two “matching” alleles can actually

hide multiple mutations (and hence more time to the MRCA)

The Infinite alleles model (IAM) is not especially

realistic with microsatellite data, unless the fraction

of matches is very high.

y chromosome microsatellite mutation rates ii
Y chromosome microsatellitemutation rates- II

The SMM model is an attempt to correct for

multiple hits by fully accounting for the mutational


Good fit to array sizes in natural populations when

assuming the symmetric single-step model

• Equal probability of a one-step move up or down

In direct studies of (Y chromosome) microsatellites

35 of 37 dectected mutations in pedigrees were

single step, other 2 were two-step

smm0 model match no match under smm
SMM0 model -- match/no match under SMM

The simplest implementation of the SMM model is

to simply replace the match probabilities q(t) under

the IAM model with those for the SMM model.

This simply codes the marker loci as match / no match

We refer to this as the SMMO model








Formally, the SMM model assumes the following

transition probabilities

Note that two alleles can match only if they have

experienced an even number of mutations in total between

them. In such cases, the match probabilities become





The zero-order modifed

Type I Bessel Function






Under the SMM model, the prior hyperparameter

can now become important.

This is the case when the number n of markers is

small and/or k/n is not very close to one

Why? Under the prior, TMRCA is forced by a

geometric with 1/Ne. Under the IAM model for

most values this is still much more time than

the likelihood function predicts from the marker data

Under the SMM model, the likelihood alone predicts

a much longer value so that the forcing effect of the

initial geometric can come into play


n =5, k = 3, u = 0.02

IAM, both flat prior and Ne = 5000

SSMO, Ne = 5000

SSMO, flat prior

Pr(TMRCA < t)

Prior with Ne =5000

Time, t

an exact treatment smme



The jth-order modifed

Type I Bessel Function

An Exact Treatment: SMME

With a little work we can show that the probability

two sites differ by j steps is just

The resulting likelihood thus becomes

Where nj is the number of sites that differ

by k (observed) steps





Number of exact matches

Number of k steps differences

With this likelihood, the resulting prior becomes

This rearranges to give the general posterior

under the Exact SMM model (SMME) as


Consider comparing haplotypes 1 and 3 from Thomas

et al.’s (2000) study on the Lemba and Cohen Y

chromosome modal haplotypes. Here six markers used,

four exactly match, one differs by one repeat, the other

by two repeats

Hence, n = 6, k = 4 for IAM and SMM0 models

n0 = 4, n1 = 1, n2 = 1, n = 6 under SMME model

Assume Hammer’s value of Ne=5000 for the prior




P(t | markers)


Time to MRCA, t

TMRCA for Lemba and Cohen Y




Pr(TMRCA < t)


Time to MRCA, t

technology transfer
Technology Transfer

The expressions developed above have direct

commercial applications

Family Tree DNA (ftDNA) -- provides Y

chromosome marker kits for genealogical studies

So far, ftDNA has processed over 10,000 such


This amounts to a rough gross of around 3 million


forensic applications of the y
Forensic applications of the Y
  • A not uncommon situation is the only DNA is from fingernail scrappings.
  • The result is a mixture wherein the victim's DNA often overwhelms the DNA of the perpetrator.
  • Result: only modest match probability as many autosomal markers cannot be detected
  • One solution: use Y chromosome markers. Easily amplified over (female) background
problem how do we combine y match with autosomal match
Problem: How do we combine Y match with autosomal match?

NRC 1996 recommendations (autosomal loci)

Product rule across markers

Population substructure correction within markers

Prob(Y match)*Prob(autosomal match)

Problem: Y markers may provide information

about population substructure membership

For example, a particular haplotype may be restricted

to a certain subpopulation, e.g., Native Americans

correcting for y substructure
Correcting for Y substructure

Let y denote the observed Y haplotype

A the multilocus autosomal marker genotype

P(y,A) = P(A | y)*P(y)

Simple approach: knowledge of y indicates membership

in a particular subpopulation, P(A) computed using

allele frequencies for that subpopulation.

Suggestion: Multiply freq(y)* max Freq(A over subgroups)

a more precise accounting
A more precise accounting

Suppose two individuals share the same y haplotype.

What is there average coancestry, q?

Balding and Nichols give expressions for autosomal

single-locus genotype frequencies given that the

population shows structure with coancestry q.

Second approach: Compute q from haplotype matching.

Using this q value in Balding - Nichols expressions

to compute (single-locus) autosomal frequencies.





Posterior Distribution for a match at all

n markers with a prior of l = 1/Ne

With a MRCA of t generations, q = (1/2)2t+1



Typical situation is where we can exclude

father-son and paternal half-sibs, k > 2

Typical values, n = 11, m = 1/500

• For l = 1/5000, E [ q ] = 0.00186

• For l = 1/500, E [ q ] = 0.00194

• For l = 1/50, E [ q ] = 0.00272

For these values, unless pi < 0.01, Balding-Nichols

expression are essentially HW.

formal procedure
Formal procedure

Estimate P(y) from a database (counting methods,

Bayesian estimators)

Compute mutlilocus autosomal frequencies by

each major ethnic group using the product of

the single-locus genotypes computed using

group-specific allele frequencies and q = 0.002


Conservative P(y,A) = P(y)*max P(A)

thanks to assistance from
Thanks to Assistance from

Jay Taylor

Mike Hammer

Matt Kaplan