Haplotype Inferences with PHASE v.2
The three-marker haplotypes frequency distributions for LG2 and LG12
showed a large number of unique haplotypes and very similar frequencies of
shared haplotypes (Table S4-1). Two-marker haplotypes, used to estimate
haplotypic LD, were obtained by collapsing the three-marker haplotypes
from either side (LG2 and LG12) or by direct inference (LG10). Shared
haplotype frequencies increased in a similar way for Qp46-Qr87, Qr8-Qr112
and Qr112-Qr30, while Qp119-Qp46 departed from the previous pattern
with almost twice the number of unique haplotypes. The haplotypes
frequency distribution in LG10 (Qr11-Qr96) showed the lowest number of
unique haplotypes, but resembled rather closely those from both LG12 and
from the second LG2 segments.
All segments showed rather high haplotype probabilities (Figure S4-1),
which are a function of the data homozygosity and of the coalescent based
approach implemented in PHASE (Stephens et al., 2001). Shared haplotypes
had slightly higher probabilities than unique haplotypes (Figure S4-1 left
panel), as expected because of the use of the coalescent approach. On the
other hand, the small probability differences shown by the two species
(Figure S4-1 right panel) could be explained by the single marker
heterozygosities. Anyway, median haplotype probabilities were very high in
all three LGs (LG2: 0.78; LG10: 0.96; and LG12: 0.76). Note that
collapsing the three-marker into two-marker haplotypes logically increases
the corresponding probabilities.
1
Haplotype Inferences with the ELB Algorithm
(using ARLEQUIN v.3.5.1)
The ELB algorithm (Excoffier et al., 2003) is a Bayesian method for
reconstructing the (unknown) gametic phase of multilocus genotypic data. A
detailed description of the method is available within ARLEQUIN's manual
(http://cmpg.unibe.ch/software/arlequin3).
The comparison with the haplotype reconstructions made with PHASE (Table
S4-2) showed that the ELB algorithm identified a slightly larger number of
unique haplotypes than PHASE, which might be a consequence of the
coalescent-based approach. However, both methods identified the same
frequent haplotypes. The inferred haplotypes probabilities were lower with
the ELB algorithm than with PHASE (Figures S4-1,2), again reflecting
differences in the coalescent-based approach. Finally, the two methods
similarly identified few haplotypes shared between the two oak species
(Figure S4-3 and Figure 3). In both instances, LG 10 was an exception to
this rule, as its most frequent haplotype was common to both species.
In spite of the differences between the two haplotype reconstruction
methods, the final outcomes regarding haplotypic LD were very much alike
(Table 3 and Table S5-3).
Methods S4
Methods for haplotype reconstructions using PHASE have been described in
the main text and in Supplemental File 3.
Haplotype reconstructions from multilocus genotype data, using the ELB
algorithm, were carried out with Arlequin v.3.5.1 (Excoffier et al., 2010).
2
The Dirichlet prior (alfa value), the weights given to haplotypes differing by
a single mutation from present haplotypes (epsilon value) and the parameter
preventing adaptive windows to grow too much (gamma value) were those
recommended for SSRs (0.01, 0.1 and 0 respectively). The heterozygote site
influence zone was allowed to include all markers from each linkage group
(3 markers for LG 2, LG9 and LG 12 and 2 markers for LG 10). The burn-in
steps in the Gibbs sampler were set up to the maximum allowed value
(9999999). We then obtained 20.000 gametic phases which were sampled
every 1.000 iterations. The gametic phases with highest posterior
probabilities from each LG were selected to represent the true haplotypes.
Figures S4-2 and S4-3 from this supplemental file were prepared with the
library “Rcomander” (Fox et al., 2010) under the R environment (R
Development Core Team, 2010).
References S4
Excoffier L, Laval G, Balding D (2003) Gametic phase estimation over large
genomic regions using and adaptive window approach. Human
Genomics, 1 (1), 7-19
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of
programs to perform population genetic analyses under Linux and
Windows. Molecular Ecology Resources (in press).
Fox J, with contributions from Liviu Andronic, Michael Ash, Theophilius
Boye, Stefano Calza, Andy Chang, Philippe Grosjean, Richard
Heiberger, G. Jay, Kerns, Renaud Lancelot, Matthieu Lesnoff, Uwe
Ligges, Samir Messad, Martin Maechler, Robert Muenchen, Duncan
Murdoch, Erich Neuwirth, Dan Putler, Brian Ripley, Miroslav Ristic
and Peter Wolf. (2010). Rcmdr: R Commander. R package version
1.5-5. http://CRAN.R-project.org/package=Rcmdr
R Development Core Team (2010). R: A language and environment for
statistical computing. R Foundation for Statistical Computing, Vienna,
Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
3
Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for
haplotype reconstruction from population data. American Journal of
Human Genetics, 68, 978-989.
4
Table S4-1 Frequency distributions of the haplotypes inferred by PHASE.
Two-marker haplotypes (Bi-H1 and Bi-H2) were obtained after collapsing
the respective three-marker haplotypes (Tri-H) from both sides. K is the
number of different haplotypes and N is the total number of haplotypes.
Haplotype
Linkage Group 2
Frequencies Tri-H Bi-H1 Bi-H2
1
2
3
4
5
6
7
8
9
10
11
13
14
16
17
18
19
20
21
24
27
30
55
204
37
15
3
0
1
1
0
1
0
K
N
262
356
129
35
24
6
3
1
1
1
1
0
60
33
14
10
1
2
1
2
0
2
Linkage Group 9
LG10
Linkage Group 12
Tri-H Bi-H1 Bi-H2
Bi-H
Tri-H Bi-H1 Bi-H2
114
20
1
3
0
0
0
0
0
0
51
25
8
3
3
0
1
0
1
0
87
24
5
3
0
1
0
0
0
0
41
21
13
8
4
3
2
4
0
1
1
2
200
38
12
4
0
0
1
0
0
0
1
59
28
12
6
3
3
2
2
2
0
1
70
24
6
3
6
3
3
1
0
1
1
2
1
1
1
1
1
2
1
2
1
1
1
1
1
202
356
136
356
138
168
5
92
168
120
168
104
368
256
346
122
346
119
346
Table S4-2: Comparison between the haplotype frequencies estimated by
PHASE and the ELB algorithm. LG2, LG9 and LG12 show the three-marker
haplotypes frequencies, while LG 10 shows the two-marker haplotypes
frequencies.
LG 2
Haplotype
Frequencies Phase
1
2
3
4
5
6
7
8
9
10
11
13
14
16
17
18
19
20
21
24
27
30
55
204
37
15
3
0
1
1
0
1
0
K
N
262
356
LG 9
LG 10
ELB
Phase
ELB
Phase
218
37
13
2
0
1
0
0
1
0
114
20
1
3
0
0
0
0
0
0
124
15
2
2
0
0
0
0
0
0
41
21
13
8
4
3
2
4
0
1
1
2
LG 12
ELB
46
21
10
10
3
2
1
5
0
0
1
1
2
Phase
ELB
200
38
12
4
0
0
1
0
0
0
1
212
34
6
3
5
0
0
0
0
0
1
256
346
261
346
1
1
1
1
2
1
1
272
356
138
168
143
168
6
104
368
106
368
Figure S4-1: Probabilities assigned by PHASE to the inferred haplotypes.
The distributions compare the probabilities for All vs. Shared haplotypes
(left panel) and for the haplotypes that belong to each of the two species
(right panel).
LG2
LG9
LG10
LG12
All
Shared
Q. robur
Q. petraea
7
Figure S4-2: Haplotype probabilities frequency distributions for the best
reconstructions with the ELB algorithm.
8
Figure S4-3: Within-species frequency distributions for the best haplotype
reconstructions with the ELB algorithm.
9