Efficient Maximum-Likelihood Inference For The Isolation-With-Initial-Migration Model With Potentially Asymmetric Gene Flow

0.63 MB·

by  Rui J. Costa

Efficient Maximum-Likelihood Inference For The
Isolation-With-Initial-Migration Model With
Potentially Asymmetric Gene Flow
Rui J. Costa and Hilde Wilkinson-Herbots
6 Department of Statistical Science, University College London
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Gower Street, London WC1E 6BT, UK
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2 email: [email protected]

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Abstract
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The isolation-with-migration (IM) model is a common tool to make inferences

] about the presence of gene flow during speciation, using polymorphism data. How-
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ever, Becquet and Przeworski (2009) report that the parameter estimates obtained
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. by fitting the IM model are very sensitive to the model’s assumptions – including
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i the assumption of constant gene flow until the present. This paper is concerned
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- with the isolation-with-initial-migration (IIM) model of Wilkinson-Herbots (2012),
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which drops precisely this assumption. In the IIM model, one ancestral population
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dividesintotwodescendantsubpopulations,betweenwhichthereisaninitialperiod
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v ofgeneflowandasubsequentperiodofisolation. Wederiveafastmethodoffitting
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an extended version of the IIM model, which allows for asymmetric gene flow and
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6 unequal subpopulation sizes. This is a maximum-likelihood method, applicable to
3 observations on the number of segregating sites between pairs of DNA sequences
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from a large number of independent loci. In addition to obtaining parameter es-
.
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timates, our method can also be used to distinguish between alternative models
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6 representing different evolutionary scenarios, by means of likelihood ratio tests. We
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illustratetheprocedureonpairsofDrosophilasequencesfromapproximately30,000
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loci. The computing time needed to fit the most complex version of the model to
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this data set is only a couple of minutes. The R code to fit the IIM model can be
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found in the supplementary files of this paper.
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Keywords: speciation, coalescent, maximum-likelihood, gene flow,
isolation
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1 Introduction
The2-demeisolation-with-migration(IM)modelisapopulationgeneticmodelinwhich,
at some point in the past, an ancestral population divided into two subpopulations.
After the division, these subpopulations exchanged migrants at a constant rate until
the present. The IM model has become one of the most popular probabilistic models
in use to study genetic diversity under gene flow and population structure. Although
applicable to populations within species, many researchers are using it to detect gene
flowbetweendivergingpopulationsandtoinvestigatetheroleofgeneflowintheprocess
of speciation. A meta-analysis of published research articles that used the IM model in
the context of speciation can be found in Pinho and Hey (2010).
Several authors have developed computational methods to fit IM models to real
DNA data. Some of the most used programs are aimed at data sets consisting of a large
number of sequences from a small number of loci. This is the case of MDIV (Nielsen
and Wakeley, 2001), IM (Hey and Nielsen, 2004; Hey, 2005), IMa (Hey and Nielsen,
2007) and IMa2 (Hey, 2010), which rely on Bayesian MCMC methods to estimate the
model parameters and are computationally very intensive.
In the past decade, the availability of whole genome sequences has increased signif-
icantly. Very large data sets consisting of thousands of loci, usually from just a few
individuals, became available for analysis, and such multilocus data sets are more infor-
mative than their single locus counterparts. In fact, as the sample size for a single locus
increases, the probability that an extra sequence adds a deep (i.e. informative) branch
to the coalescent tree quickly becomes negligible (see, e.g., Hein et al., 2005, p. 28-29).
This new type of data set is also much more suitable for likelihood inference: if at each
locus the observation consists only of a pair or a triplet of sequences, the coalescent
process of these sequences is relatively simple and can more easily be used to derive
the likelihood for the locus concerned. Furthermore, if the loci studied are distant from
each other, observations at different loci can be considered independent and thus the
likelihood of a whole set will consist of the product of the likelihoods for the individual
loci.
For these reasons, some of the most recent theoretical papers and computer imple-
mentations of the IM model are aimed at data sets consisting of a small number of
sequences at a large number of loci. Wang and Hey (2010) and Zhu and Yang (2012),
for example, developed maximum-likelihood methods based on numerical integration.
Computationally less intensive methods of fitting the IM model were studied by Lohse
et al. (2011), who used moment generating functions, and Andersen et al. (2014), who
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resorted to matrix exponentiation and decomposition.
Despite all the progress, several authors have recently pointed out some limitations
of the IM model. Becquet and Przeworski (2009) report that the parameter estimates
obtained by fitting the IM model are highly sensitive to the model’s assumptions – in-
cluding the assumption of constant gene flow until the present. Strasburg and Rieseberg
(2011) and Sousa et al. (2011) note that the gene flow timing estimates reported in the
literature, basedontheIMmodelandobtainedwiththeIMA2 program, haveextremely
wide confidence intervals, as the time of gene flow is non-identifiable in the IM model.
As a step to overcome these limitations, Wilkinson-Herbots (2012) studied an exten-
sion of the IM model, the isolation-with-initial-migration (IIM) model, which is more
realisticthantheIMmodelinthecontextofspeciation. Broadlyspeaking,theIIMmodel
is an IM model in which gene flow ceased at some point in the past. Explicit formulae
for the distribution of the coalescence time of a pair of sequences, and the distribution
of the number of nucleotide differences between them, are derived in Wilkinson-Herbots
(2012). These analytic results enable a very fast computation of the likelihood of a data
set consisting of observations on pairs of sequences at a large number of independent
loci. However, for mathematical reasons this work was limited to the case of symmetric
migration and equal subpopulation sizes during the migration period.
In this paper, we study a more general IIM model which allows for asymmetric
gene flow during the migration period. It also allows for unequal subpopulation sizes
during gene flow, as well as during the isolation stage. Both this model and other
simpler models studied in this paper assume haploid DNA sequences, which accumulate
mutations according to the infinite sites assumption (Watterson, 1975). An extension to
the Jukes-Cantor model of mutation is feasible but beyond the scope of this paper.
We first describe, for different versions of the IIM model, an efficient method to
compute the likelihood of a set of observations on the number of different nucleotides
between pairs of sequences (also termed the number of pairwise differences). Each pair
of sequences comes from a different locus and we assume free recombination between
loci and no recombination within loci. Secondly, we illustrate how to use this method
to fit the IIM model to real data. The data set of Drosophila sequences from Wang
and Hey (2010), containing over 30,000 observations (i.e. loci), is used for this purpose.
Finally, we demonstrate, using this data set, how different models, representing different
evolutionary scenarios, can be compared using likelihood ratio tests.
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Figure 1: TheIIMmodel. Theleft-handsidesubpopulationissubpopulation1; theright-hand
side subpopulation is subpopulation 2.
2 Theory and methods
For the purposes of the present paper, and from a forward-in-time perspective, the
isolation-with-migration (IM) model makes the following assumptions: a) until time τ
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ago(τ > 0),apopulationofDNAsequencesfromasinglelocusfollowedaWright-Fisher
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haploid model (Fisher, 1930; Wright, 1931); b) at time τ ago, this ancestral population
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split into two Wright-Fisher subpopulations with constant gene flow between them. If
we take an IM model and add the assumption that, at time τ ago (0 < τ < τ ), gene
1 1 0
flow ceased, we get an isolation-with-initial-migration (IIM) model. Figure 1 illustrates
the fullest IIM model dealt with in this paper.
IntheIIMmodelofFigure1, thepopulationsizesaregiveninsidetheboxes, inunits
of DNA sequences. All population sizes are assumed constant and strictly positive. The
parameters a, b, c and c indicate the relative size of each population with respect
1 2
to subpopulation 1 during the migration stage. For example, if 2N is the number
anc
of sequences in the ancestral population, then a = 2N /2N. Between times τ and
anc 0
τ ago (two time parameters in units of 2N generations) there is gene flow between the
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subpopulations: ineachgeneration,afractionm ofsubpopulationiareimmigrantsfrom
i
subpopulation j (i,j ∈ {1,2} with i (cid:54)= j), i.e. m is the migration rate per generation
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from subpopulation i to subpopulation j backward in time. Within each subpopulation,
reproduction follows the neutral Wright-Fisher model and, in each generation, restores
the subpopulations to their original sizes, i.e. reproduction undoes any decrease or
increase in size caused by gene flow (this assumption of constant population size is
common and usually reflects restrictions on food and habitat size).
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Under the IIM model, the genealogy of a sample of two DNA sequences from the
presentsubpopulationscanbedescribedbysuccessiveMarkovchains,workingbackward
in time. We will define these in the simplest possible way, using the smallest state space
necessary for the derivation of the coalescence time distribution. Hence, during the
isolation stage (until time τ into the past) and the migration stage (between τ and
1 1
τ ), the process can only be in state 1 – both lineages in subpopulation 1 –, state 2 –
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both lineages in subpopulation 2 –, state 3 – one lineage in each subpopulation –, or
state 4 – in which lineages have coalesced. After τ , the lineages have either coalesced
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already – state 4 –, or have not – state 0. Only states 1, 2 and 3 can be initial states,
according to whether we sample two sequences from subpopulation 1, two sequences
from subpopulation 2, or one sequence from each subpopulation. When the genealogical
process starts in state i (with i ∈ {1,2,3}), the time until the most recent common
ancestor of the two sampled sequences is denoted T(i), whereas S(i) denotes the number
of nucleotide differences between them.
Iftimeismeasuredinunitsof2N generationsandN islarge,thegenealogicalprocess
is well approximated by a succession of three continuous-time Markov chains, one for
each stage of the IIM model (Kingman, 1982a,b; Notohara, 1990). We refer to this
stochastic process in continuous time as the coalescent under the IIM model. During
the isolation stage, the approximation is by a Markov chain defined by the generator
matrix
(i) (4)
(cid:34) (cid:35)
Q(i) = (i) −c1i c1i , (1)
iso (4) 0 0
with i ∈ {1,2} being the initial state (Kingman, 1982a,b). If 3 is the initial state, the
lineages cannot coalesce before τ . During the ancestral stage, the genealogical process
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is approximated by a Markov chain with generator matrix
(0) (4)
 
(0) −1 1
Q =  a a . (2)
anc  
(4) 0 0
(Kingman, 1982a,b). In between, during the migration stage, the approximation is by a
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Markov chain with generator matrix
(1) (3) (2) (4)
 
(1) −(1+M1) M1 0 1
 
 
Qmig = (3)  M22 −(cid:0)M1+2M2(cid:1) M21 0  (3)
 
 
(2)  0 M2 −(1/b+M2) 1/b 
 
(4) 0 0 0 0
(Notohara, 1990). In this matrix, M /2 = 2Nm represents the rate of migration (in
i i
continuous time) of a single sequence when in subpopulation i. The rates of coalescence
for two lineages in subpopulation 1 or 2 are 1 and 1/b respectively. Note that state 3
corresponds to the second row and column, and state 2 to the third row and column.
This swap was dictated by mathematical convenience: the matrix Q should be as
mig
symmetric as possible because this facilitates a proof in the next section.
2.1 Distribution of the time until coalescence under bidirectional gene
flow (M > 0, M > 0)
1 2
To find f(i), the density of the coalescence time T(i) of two lineages under the IIM
T
model, given that the process starts in state i and there is gene flow in both directions,
(i)
weconsiderseparatelythethreeMarkovchainsmentionedabove. WeletT (i ∈ {1,2}),
iso
(i) (0)
T (i ∈ {1,2,3}) and T denote the times until absorption of the time-homogeneous
mig anc
(i)
Markov chains defined by the generator matrices Q , Q and Q respectively.
iso mig anc
(i) (i) (0) (i)
And we let the corresponding pdf’s (or cdf’s) be denoted by f , f and f (or F ,
iso mig anc iso
(i) (0) (i)
F and F ). Then f can be expressed in terms of the distribution functions just
mig anc T
mentioned:

(i)
 fiso(t) for 0 ≤ t ≤ τ1,
(i)  (cid:104)1−Fi(sio)(τ1)(cid:105)fm(i)ig(t−τ1) for τ1 < t ≤ τ0,
f (t) = (cid:104) (cid:105)(cid:104) (cid:105) (4)
T (i) (i) (0)
 1−Fiso(τ1) 1−Fmig(τ0−τ1) fanc(t−τ0) for τ0 < t < ∞,


 0 otherwise.
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for i ∈ {1,2}. If 3 is the initial state,

(3)
 fmig(t−τ1) for τ1 < t ≤ τ0,
 (cid:104) (cid:105)
(3) (3) (0)
f (t) = 1−F (τ −τ ) f (t−τ ) for τ < t < ∞, (5)
T mig 0 1 anc 0 0


 0 otherwise.
The important conclusion to draw from these considerations is that to find the distribu-
tion of the coalescence time under the IIM model, we only need to find the distributions
of the absorption times under the simpler processes just defined.
A Markov process defined by the matrix Q , and starting in state 0, is simply
anc
Kingman’s coalescent (Kingman, 1982a,b). For such a process, the distribution of the
coalescence time is exponential, with rate equal to the inverse of the relative population
size:
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fa(0n)c(t) = ae−a1t, 0 ≤ t < ∞.
(i)
A Markov process defined by Q , i ∈ {1,2}, is again Kingman’s coalescent, so
iso
(i) 1 −1t
fiso(t) = c e ci , 0 ≤ t < ∞.
i
Finally, with respect to the ‘structured’ coalescent process defined by the matrix Q ,
mig
we prove below that, for i ∈ {1,2,3},
3
f(i) (t) = −(cid:88)V−1V λ e−λjt, (6)
mig ij j4 j
j=1
where V is the (i,j) entry of the (non-singular) matrix V, whose rows are the left
ij
eigenvectors of Q . The (i,j) entry of the matrix V−1 is denoted by V−1. The λ
mig ij j
(j ∈ {1,2,3}) are the absolute values of those eigenvalues of Q which are strictly
mig
negative (the remaining one is zero). Since the λ are real and strictly positive, the
j
(i)
density function of T is a linear combination of exponential densities.
mig
Proof of (6):
This proof has three parts. Part (i) proves the above result under two assumptions:
a) Q has three strictly negative eigenvalues and one zero eigenvalue, all of them
mig
real; and b) Q is diagonalizable. Part (ii) proves assumption a). Part (iii) proves
mig
assumption b). To simplify the notation, we denote Q by Q throughout the proof.
mig
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(i)
Consider the continuous-time Markov chain defined by the matrix Q. Let P (t), the
ij
(i,j) entry of the matrix P(t), be the probability that the process is in state j at time t
into the past, given that the process starts in state i. P(t) can be calculated by solving
the following initial value problem:
P(cid:48)(t) = P(t)Q ;
P(0) = I ,
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where I is the four by four identity matrix. Under the assumptions that Q is diagonal-
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izable and that its eigenvalues are real, the solution to this initial value problem is given
by:
P(t) = P(0)eQt
= V−1eBtV ,
where B denotes the diagonal matrix containing the real eigenvalues β , j ∈ {1,2,3,4},
j
of Q, and V is the matrix of left eigenvectors of Q. Note that P (t) is the probability
i4
thattheprocesshasreachedcoalescencebytimet, ifitstartedinstatei. Inotherwords,
(i)
it is the cdf of T :
mig
P (t) = F(i) (t) = v−1eBtv ,
i4 mig i. .4
where v−1 is the ith row vector of V−1, and v the 4th column vector of V. Differenti-
i. .4
ating, we get the pdf:
f(i) (t) = v−1BeBtv
mig i. .4
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(cid:88)
= V−1V β eβjt .
ij j4 j
j=1
Ifwedenotetheeigenvalueequaltozerobyβ ,andtheremainingeigenvaluesarestrictly
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negative, this pdf can be written as a linear combination of exponential densities:
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f(i) (t) = −(cid:88)V−1V λ e−λjt , (7)
mig ij j4 j
j=1
where λ = |β | for j ∈ {1,2,3}.
j j
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(ii)
As Q is given by equation (3), its characteristic polynomial, P (β), is of the form
Q
β ×P (β), where Q(r) is the three by three upper-left submatrix of Q, that is:
Q(r)
 
−(1+M ) M 0
 1 1 
 
Q(r) =  M2/2 −(M1+M2)/2 M1/2 .
 
 
0 M −(1/b+M )
2 2
Thus the eigenvalues of Q are the solutions to β ×P (β) = 0. Consequently, one of
Q(r)
them is zero (β , say) and the remaining three eigenvalues are also eigenvalues of Q(r).
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Now consider the similarity transformation
 
(cid:113)
−(1+M1) M12M2 0 
 
 
 
S = DQ(r)D−1 = (cid:113)M12M2 −(M1+M2)/2 (cid:113)M12M2 ,
 
 
 (cid:113) 
 0 M12M2 −(1/b+M2)
(cid:113) 
M2 0 0
2M1
 
where D = 0 1 0  .
 (cid:113) 
0 0 M1
2M2
Because S is a symmetric matrix, its eigenvalues are real. Therefore, all the eigenvalues
of Q(r) are real (a similarity transformation does not change the eigenvalues). S is
also a negative definite matrix, since its first, second and third upper-left determinants
are respectively negative, positive, and negative. Hence its eigenvalues are all strictly
negative, and so are the eigenvalues of Q(r). Hence Q has one zero eigenvalue (β ) and
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three real, strictly negative eigenvalues (β ,β and β ).
1 2 3
(iii)
Being a symmetric matrix, S has three independent eigenvectors. A similarity transfor-
mationpreservesthenumberofindependenteigenvectors, soQ(r) hasthreeindependent
eigenvectors as well. We denote by V(r) the matrix whose rows are the left eigenvectors
of Q(r).
By definition, any left eigenvector v of Q satisfies the system of equations x(Q−
j.
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Iβ ) = 0, where x = [x x x x ]. The first three linear equations of this system are
j 1 2 3 4
identical to x(r)(Q(r)−Iβ ) = 0, for j ∈ {1,2,3} and x(r) = [x x x ], which is solved
j 1 2 3
byx(r) = v(r). Sothisimpliesthat, forβ ∈ {β ,β ,β }, anyrowvectorxinR4 thathas
j. j 1 2 3
(r)
v as its first three elements will solve the first three equations of the system, whatever
j.
the value of x . If x = (V(r) + 1V(r))/β , that vector will be an eigenvector of Q,
4 4 j1 b j3 j
because it also solves the fourth equation of the system:
 
−(1+M1)−βj M1 0 1 
 
 
 
(cid:104) ——–vj(.r)——– Vj(1r)+βj1bVj(3r) (cid:105)  M22 −(M1+2M2) −βj M21 0 
 0 M −(1 +M )−β 1 
 2 b 2 j b 
 
 
 
0 0 0 −β
j
(cid:104) (cid:105)
= 0 0 0 0 ,
for β ∈ {β ,β ,β }. For the case of β = β = 0, a row eigenvector is [0 0 0 1].
j 1 2 3 j 4
Collecting these row eigenvectors in a single matrix, we get V. So,
 
(r) (V(r)+1V(r))
—— v —— 11 b 13
 1. β1 
 
 (r) (V(r)+1V(r))
—— v —— 21 b 23 
V =  2. β2 .
 (r) (V(r)+1V(r))
—— v3. —— 31 β3b 33 
 
0 0 0 1
If the matrix V can be shown to be invertible, then Q is diagonalizable. This will be
the case if the system xV = 0 can only be solved by x = [0 0 0 0]. Now since the three
by three upper-left submatrix of V, V(r), is full-ranked, x = x = x = 0 is a necessary
1 2 3
condition for xV = 0. But then x = 0, from the last equation of the system. Thus we
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have shown that Q is diagonalizable. (cid:3)
Wearenowinpositiontoupdateequations(4)and(5)withtheresultsjustobtained.
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