1
Supplementary methods
2
3
Microsatellite genotyping methods
4
Kiwi and kokako were genotyped at 13 and 8 microsatellite loci respectively (Supplementary
5
Table S2). Microsatellites were amplified in 5 μl reactions containing 1 μl purified DNA, 2.5
6
μl Type-it Master Mix (QIAGEN) and primer mix containing forward (to a final
7
concentration of 0.04 μM, modified with M13 tail sequence, following Schuelke 2000) and
8
reverse (final concentration 0.16 μM) locus-specific primers, and an M13-tagged fluorescent
9
dye (6-FAM, VIC, NED or PET; final concentration 0.16 μM) (Schuelke 2000).
10
Thermocycling conditions consisted of an initial activation of 95°C for 5 min, then 30 cycles
11
of 95°C for 30 s, 60°C for 90 s and 72°C for 30 s, followed by 8 cycles of 94°C for 15s, 53°C
12
for 20 s and 72°C for 35 s, with a final extension of 72°C for 15 min. Fragment sizes were
13
resolved on an ABI 3730 Genetic Analyser (service provided by Genetic Analysis Services at
14
Otago) with GeneScan 500 (LIZ) size standard. Genotyping chromatograms were examined
15
and scored visually using Genemapper v4.1 (Life Technologies).
16
We tested these loci for deviation from Hardy-Weinberg equilibrium using Arlequin.
17
Of the 35 polymorphic loci genotyped herein, only one appeared to deviate from Hardy-
18
Weinberg equilibrium (kokako Ase18, exact p = 0.041; Supplementary Table S1), but we
19
retained this locus in the analysis as the deviation was not significant after correction for
20
multiple testing using the sequential Bonferroni approach (Holm 1979). We used Micro-
21
Checker v2.2.3 (van Oosterhout et al 2004) to test for allelic dropout, null alleles, mis-scoring
22
of stutters and possible data entry errors; none of the microsatellite data obtained herein
23
showed evidence of these potential issues. We used Genepop (Raymond and Rousset 1995)
24
to test for linkage disequilibrium between pairs of microsatellite loci genotyped here; no
25
significant linkage disequilibrium was observed for either kiwi or kokako, after correction for
26
multiple testing (data not shown)
27
28
TLR sequencing editing and haplotype reconstruction
29
Sequences were edited (i.e. generation of consensus sequences for individuals and removal of
30
primer sequences) using Sequencher v5.0 (Gene Codes Corporation); all ambiguities were
31
checked by eye and IUPAC ambiguity codes introduced where double-peaks were observed.
32
Separate alignments for each species/gene combination were generated using Sequencher and
33
exported as FASTA files for downstream analyses; sequence data within the alignments used
34
for analyses were >99% complete. Previously published sequences obtained from the same
35
primers and study populations (Grueber and Jamieson 2013) were also included in the
36
alignments. None of the regions we sequenced contained stop-codons or frame-shift
37
variation.
38
For each locus in each population, we used PHASE v2.1.1 (Stephens et al 2001;
39
Stephens and Donnelly 2003) implemented in DNAsp v5.1 (Librado and Rozas 2009) to infer
40
haplotypes within each population. PHASE was run using the following settings: number of
41
iterations = 1,000; thinning interval = 10; burn-in iterations = 1,000; recombination model =
42
MS (no recombination) due to the relatively small sample sizes. Haplotypes were assigned 1-
43
letter codes and used as genotype data for calculating heterozygosity.
44
45
MCMC model specification and diagnostics
46
All MCMC models were run for 7×106 iterations, with a burnin period of 2.5×106 iterations
47
and a thinning interval of 4,500 iterations, thus providing us with 1,000 samples of the
48
posterior distribution of each parameter estimated by the model. Statistical significance is
49
inferred when the 95% credible interval (CI; as produced by MCMCglmm) of these
50
distributions does not cross zero. Models were observed to have converged by running three
51
independent MCMC chains and assessing the results thereof using Gelman-Rubin
52
diagnostics; all analyses produced estimates of the potential scale reduction factor < 1.1
53
(Gelman and Rubin 1992). Within-chain mixing was assessed using the autocor function in
54
MCMCglmm; all results come from chains with an autocorrelation < 0.1 between subsequent
55
lags; we report parameter estimates from the chain with the lowest deviance information
56
criterion (DIC). All presented results come from models fitting an inverse gamma prior
57
(specified in MCMCglmm as V = diag(2), nu = 0.002; where V is an estimate of variance
58
associated with model variance components, and nu is the degree-of-belief parameter for that
59
variance) for random factors and the default prior for fixed effects. The sensitivity of the
60
models results to prior specification was checked by repeating the analysis with a parameter
61
expanded prior for all random effects (as described in Hadfield 2010). Models with both
62
priors produced qualitatively identical results (data not shown).
63
Supplementary Table S1 Microsatellite loci genotyped in this study, with number of
64
individuals (N), number of alleles (A), observed and expected heterozygosity (HO and HE,
65
respectively) and P-value from Hardy-Weinberg exact test.
Species
Locus
N
A HO
HEa
P-value
Reference
Kiwi
Apt29
22
5
0.818
0.733
0.156
(Shepherd and Lambert 2006)
Apt37
22
3
0.136
0.132
1.000
(Shepherd and Lambert 2006)
Apt59
22
6
0.864
0.796
0.243
(Shepherd and Lambert 2006)
Apt68
22
3
0.591
0.534
0.670
(Shepherd and Lambert 2006)
Aptowe3
22
3
0.364
0.551
0.156
(Ramstad et al 2010)
Aptowe8
19
8
0.789
0.782
0.611
(Ramstad et al 2010)
Aptowe24
22
3
0.591
0.606
1.000
(Ramstad et al 2010)
Aptowe28
22
4
0.682
0.557
0.713
(Ramstad et al 2010)
KMS1
22
2
0.136
0.130
1.000
(Jensen et al 2008)
KMS7R
22
3
0.136
0.132
1.000
(Jensen et al 2008)
KMS14B
21
3
0.529
0.542
1.000
(Jensen et al 2008)
KMS30
22
4
0.273
0.360
0.141
(Jensen et al 2008)
KMS74B
22
2
0.500
0.460
1.000
(Jensen et al 2008)
Ase18
21
6
0.429
0.481
0.048 *
(Richardson et al 2000)
HrU6
21
7
0.857
0.823
0.771
(Primmer et al 1995)
K3/K4
21
9
0.714
0.775
0.125
(Hudson et al 2000)
K11/K12
21
3
0.571
0.668
0.034 *
(Hudson et al 2000)
Pca01
21
2
0.333
0.285
1.000
(Lambert et al 2005)
Pca07
21
9
0.857
0.819
0.541
(Lambert et al 2005)
Pca08
21
3
0.190
0.180
1.000
(Lambert et al 2005)
Pca13
20
3
0.450
0.574
0.335
(Lambert et al 2005)
Kokako
66
a
adjusted for sample size using Levene’s correction (Levene 1949)
67
*
statistically significant at α = 0.05
68
69
Supplementary Table S2 Observed and expected heterozygosity (HO and HE, respectively)
70
of polymorphic TLR loci in population samples of 10 threatened New Zealand birds. Also
71
shown is the P-value for deviation from Hardy-Weinberg equilibrium (exact test).
Locus
Species
TLR1LA Kiwi
TLR3
TLR4
TLR5
HO
HE
P-value
18 0.111 0.108
1.000
Kakapo
17 0.176 0.166
1.000
Kakariki
18 0.778 0.787
0.859
Rock wren
21 0.952 0.902
0.858
Mohua
21 0.476 0.587
0.243
Robin
22 0.500 0.613
0.205
Hihi
19 0.947 0.740
0.000 *
Kokako
21 0.333 0.575
0.050
Saddleback 20 0.400 0.581
0.242
TLR1LB Kakariki
TLR2B
N
20 0.800 0.765
0.997
Rock wren
22 0.682 0.885
0.069
Mohua
21 0.524 0.682
0.257
Hihi
20 0.250 0.273
0.228
Robin
19 0.737 0.797
0.122
Kokako
23 0.087 0.162
0.132
Kiwi
20 1.000 0.755
0.007 *
Robin
24 0.542 0.530
0.253
Kiwi
20 0.200 0.188
1.000
Rock wren
21 0.524 0.556
0.430
Mohua
23 0.522 0.394
0.269
Robin
20 0.300 0.354
0.326
Kokako
19 0.053 0.053
1.000
Takahe
19 0.158 0.149
1.000
Kakariki
20 0.750 0.801
0.266
Rock wren
21 0.714 0.774
0.317
Mohua
24 0.667 0.664
0.304
Robin
23 0.652 0.756
0.437
Kiwi
19 0.158 0.235
0.259
Kakariki
20 0.750 0.678
0.804
TLR7
TLR15
TLR21
Mohua
20 0.800 0.751
1.000
Robin
19 0.579 0.579
0.569
Kokako
24 0.833 0.867
0.078
Kiwi
20 0.450 0.450
1.000
Kakariki
18 0.556 0.457
0.598
Takahe
20 0.750 0.481
0.015 *
Mohua
21 0.762 0.743
0.135
Robin
19 0.737 0.683
0.092
Hihi
18 0.611 0.475
0.320
Kokako
21 0.905 0.833
0.824
Hihi
19 0.421 0.408
0.551
Robin
22 0.500 0.702
0.025 *
72
* indicates statistically significant deviation from Hardy-Weinberg equilibrium at α = 0.05; none remained
73
significant at α = 0.05 after accounting for multiple comparisons.
74
75
Supplementary Table S3 Haplotype-frequency based tests of neutrality for species with ≥ 2
76
toll-like receptor genes with ≥ 5 haplotypes (h). P-values (deviation from neutrality) are
77
based on 1,000 permutations of the data (using Arlequin).
Species
Locus
N
h
Kakariki
TLR1LA
18
TLR1LB
Rock wren
Robin
Kokako
78
Tajima’s D
Ewens-Watterson test
statistic
P-value
statistic
P-value
8
0.817
0.823
0.235
0.462
20
6
1.176
0.902
0.254
0.143
TLR4
20
7
0.776
0.824
0.219
0.167
TLR5
20
8
0.771
0.810
0.339
0.850
TLR1LA
21
13
0.538
0.755
0.119
0.170
TLR1LB
22
16
-0.235
0.465
0.135
0.808
TLR3
21
6
-1.005
0.185
0.457
0.829
TLR4
21
8
0.028
0.570
0.245
0.443
TLR1LA
22
7
-0.646
0.340
0.401
0.824
TLR1LB
19
7
1.043
0.854
0.224
0.208
TLR2B
24
6
0.003
0.581
0.481
0.759
TLR4
23
8
0.340
0.696
0.261
0.528
TLR15
19
8
-0.630
0.292
0.335
0.854
TLR21
22
5
1.285
0.900
0.314
0.195
TLR5
24
8
0.571
0.747
0.151
0.004 *
TLR15
21
14
-1.255
0.089
0.187
0.919
* indicates statistically significant deviation from neutral expectation at α = 0.05.
79
Supplementary Table S4 Effect of TLR ligand type (viral or non-viral) on levels of diversity in wild birds. In all models “non-viral” was the
80
reference category – negative effect sizes for “Viral” indicate that viral TLRs had lower diversity. “Species” was entered in each model as a
81
random factor.
Response variable
GLMM error
structure
Intercept (SE)
Viral (β, SE)
ΔAIC versus
null model
h – number of haplotypes
Poisson
1.608 (0.158)
-0.582 (0.198) *
-7.8
k – mean nucleotide differences among haplotypes
Gaussiana
-0.089 (0.276)
-0.939 (0.340) *
-4.5
π – nucleotide diversity
Gaussianb
-6.976 (0.300)
-0.725 (0.348) *
-1.8
Number of SNPs – total
Poisson
1.500 (0.235)
-0.902 (0.222) *
-18.8
Number of SNPs – non-synonymous
Poisson
0.965 (0.168)
-0.571 (0.281) *
-2.7
Number of SNPs – synonymous
Poisson
0.785 (0.290)
-1.323 (0.369) *
-16.8
82
a
the response variable is count-based, so it was normalised by log transformation
83
b
the response variable is proportion-based, so it was normalised by logit transformation
84
* indicates the 95% confidence interval for the effect size excludes zero (significantly lower diversity in the “viral” group at α = 0.05)
85
86
Supplementary Fig. S1 Genetic diversity of TLRs proposed to bind viral ligands (“Viral
87
TLRs”: TLR3, TLR7, TLR21) versus those proposed to bind proteinaceous ligands (“Nonviral
88
TLRs”: TLR1LA, TLR1LB, TLR2A, TLR2B, TLR4, TLR5, TLR15) (Keestra et al 2013). Four
89
measures of diversity are presented: (a) total number of SNPs, (b) number of haplotypes h,
90
(c) nucleotide diversity π and (d) haplotype diversity k. Two data-points are joined by a line
91
if they are derived from the same species; where species were sequenced at > 1 TLR locus in
92
either category, these data were averaged for the figure (all loci were treated separately in
93
statistical analysis, see Methods).
94
95
96
Supplementary Fig. S2 Relationship between number of microsatellite loci used (a), or their
97
diversity in terms of either mean gene diversity (b) or mean number of alleles (c), and the
98
magnitude of error surrounding the slope estimate for microsatellite MLH and TLR
99
heterozygosity (width of the 95% CI) in each of the 10 species (i.e. N = 10 in each panel).
100
See also Table 2 (microsatellite summary data) and Figure 2 (model slopes summary data).
101
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