Coalescent tree imbalance and a simple test for selective sweeps based on microsatellite variation - PubMed (original) (raw)
Coalescent tree imbalance and a simple test for selective sweeps based on microsatellite variation
Haipeng Li et al. PLoS Comput Biol. 2013.
Abstract
Selective sweeps are at the core of adaptive evolution. We study how the shape of coalescent trees is affected by recent selective sweeps. To do so we define a coarse-grained measure of tree topology. This measure has appealing analytical properties, its distribution is derived from a uniform, and it is easy to estimate from experimental data. We show how it can be cast into a test for recent selective sweeps using microsatellite markers and present an application to an experimental data set from Plasmodium falciparum.
Conflict of interest statement
The authors have declared that no competing interests exist.
Figures
Figure 1. Coalescent trees under recombination and selection.
A: Sketch of a neutral coalescent tree with tree size 
. B and C: A selective sweep in locus C leads to a tree of low height (
small). The selective sweep was initiated by a beneficial mutation at time 
. At some distance from C, a single lineage (circled branch in C) has “recombined away” leading to the unbalanced tree shown at locus B. Note that tree height between trees B and C changes drastically and that 
at locus C and 
at locus B. Multiple recombination events (indicated by the crosses at the bottom line) between loci A and B lead to essentially uncorrelated trees at A and B.
Figure 2. Mean and standard deviation of and for coalescent trees of size .
Shown are the values for 
independent realizations. 
-axis: values of 
(black circles) and 
(red squares) are determined for the subtrees originating at node 
, 
. The solid gray line shows the theoretical expectation according to eq (3).
Figure 3. Correlation across distance.
Correlation based on simulations (
replicates) of the statistic 
of the true tree. Pearson's correlation coefficient is measured between 
and 
for pairs of trees at position 
and position 
. Three scenarios are compared: standard neutral model with constant population size (green), population bottleneck (blue) and selective sweep (red). Sample size 
, 
, 
and a recombinaton rate of 
is assumed. The bottleneck parameters are: 
, 
. The selective sweep has a strength of 
. The selected site is at position 
. Under standard neutrality, 
correlation is reached at position 
cM, corresponding to about 
bp.
Figure 4. Estimation of .
A: estimation of 
by 
. B: estimation of 
by 
. First row: standard neutral model. Second row: Selective sweep; estimation of 
at distance 
from selected site. Third row: Selective sweep; distance 
from selected site. Parameters: 
; 
(top and bottom row); 
(middle row); 
; 
; 
.
Figure 5. Profile of and along a recombining chromosome.
Plots in column A show the distribution of 
, i.e. when the tree topology is known. Plots in column B show the distribution of the estimate 
when the tree topology is unknown, but estimated from microsatellite polymorphism data. Each boxplot corresponds to one of 
marker loci located at the positions indicated on the 
axis. The regions spans 
kb in total. Symmetric step-wise mutation model with 
. Other parameters: 
, 
and recombination rate per bp 
(corresponding to 1 cM/Mb). First row: standard neutral model with constant 
. Second row: bottleneck model with severity 
and onset 
. Third row: Selective sweep at locus 
with 
which was completed 
time units ago. For comparison with the theoretical expectation, the leftmost boxplot in each panel shows the standard normal distribution (labeled ‘N’).
Figure 6. Power to detect loci under recent selection by the three tests defined in eqs (14) to (16)
Parameters: level 
(solid) and 
(dotted); selection coefficient 
; time since fixation 
; sample size 
; mutation rate 
; recombination rate 
. The 
axis shows positions to the left (negative values) and right (positive values) of the locus under selection at position 
. Scale is in cM x
, corresponding here to kb.
Figure 7. Traces of selection around a drug resistance locus in Plasmodium.
Results of tests 
(stars), 
(circles) and 
(triangles) applied to a 
kb region sorrounding the pfmdr1 locus in P.falciparum. Shown are significant results on the 5% (open symbols) and 1% (filled symbols) levels. Positions of the examined microsatellite markers are indicated by arrows. Data from .
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References
- Kingman JFC (1982) The coalescent. Stochastic Processes and their Applications 13: 235–248.
- Hudson RR (1990) Gene genealogies and the coalescent process. In: Oxford Surveys in Evolutionary Biology, volume 7. Oxford University Press. pp. 1–44.
- Wakeley J (2009) Coalescent theory – an introduction. Greenwood Village, Colorado: Roberts&Company.
- Ewens W (1972) The sampling theory of selectively neutral alleles. Theor Popul Biol 3: 87–112. - PubMed
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HL was supported by the National Key Basic Research Program of China (2012CB316505), the NSFC grants (31172073 and 91131010) and the Bairen Program, and through a grant to TW by the German Research Foundation (DFG-SFB680, www.dfg.de). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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