Supplementary Material
Supplementary Table 1
SNPs genotyped in this analysis.
Locus
Target Fragment Base position of SNPs genotyped2
exon
length1
TLR1LA
TLR1LB
TLR2A
TLR2B
TLR3
TLR4
TLR5
TLR7
TLR15
TLR21
1
2
2
1
2
2
4
3
1
2
1
1
1,166
971
522
649
1,229
1,279
618
172 C/T
185 C/T
(not genotyped: too similar to TLR2B)
324 C/T
(not genotyped: monomorphic in founders)
94 A/C/T ; 142 C/T ; 220 A/T ; 520 G/A
371 G/A ; 817 G/T
(not genotyped: TLR7 is duplicated in robins)
997 G/A
118 G/A ; 169 C/T ; 300 G/A
Length of sequence over which the combinations of listed SNPs inform allelic status.
Positions given refer to the sites in original sequence, see Table 1 of Grueber et al. (2012) for more details.
Supplementary Table 2
Top model set (top 2AICC) of models for the effect of inbreeding (F) and composite
heterozygosity at seven TLR loci (H) on juvenile robin survival. All models also included the
random factor cohort.
Model1
Deviance AICC ΔAICC wi
β0 + H
664.4
670.4
0.36
β0
666.6
670.6
0.2
0.32
β0 + F + H
663.7
671.8 1.37 0.18
β0 + F
666.4
672.4 1.95 0.14
model predictors: β0 = model intercept; H = standardised observed multilocus heterozygosity; F = individual
inbreeding coefficient.
1
1
Supplementary Table 3
Observed (and expected) number of robins that survived to 1 year or not, based on TLR4
genotype (birds from 2001 excluded, for consistency with the main survival analysis).
Survived
Died
Total
TLR4BE
54 (47.1)
6 (12.9)
60
All others
452 (458.9) 133 (126.1)
585
Total
506
139
645
Supplementary Table 4
Model-averaged standardised predictors of first year overwinter survival.
Variable1
β
SEβ
95% CI
RI
β0
1.386 0.161 1.070; 1.703
TLR4BE
1.003 0.448 0.124; 1.881 1.00
F
-0.177 0.196 -0.560; 0.207 0.28
RBM
0.038 0.202 -0.358; 0.435 0.19
β0 = model intercept; TLR4BE = presence/absence of TLR4BE genotype; F = individual inbreeding coefficient;
RBM = relatedness to blue-metal based on the pedigree.
1
2
Supplementary Table 5
Effect of TLR4 genotypes on first year survival probability of Ulva Island robins, data
restricted to the period 2006 onwards.
Model1
βij (± SEβ)2
Deviance
AICC
ΔAICC
wi
Nij3 Survivalij4
Base + BE
1.358 (0.745)
422.5
430.6
0.00 0.302 32
94%
Base + BD
-0.815 (0.436)
424.1
432.2
1.61 0.135 26
65%
Base + BC
0.916 (0.622)
424.6
432.7
2.10 0.105 33
91%
5
Base
427.3
433.4
2.78 0.075 434
80%5
Base + AD
-0.552 (0.416)
425.7
433.8
3.18 0.061 32
72%
Base + AC
0.305 (0.323)
425.8
433.9
3.30 0.058 50
74%
Base + DD
-1.482 (1.438)
426.3
434.4
3.84 0.044
2
50%
Base + AB
-0.443 (0.35)
426.4
434.5
3.89 0.043 88
84%
Base + BB
-0.343 (0.352)
426.4
434.5
3.91 0.043 52
75%
Base + AA
0.234 (0.335)
426.8
434.9
4.32 0.035 75
83%
Base + AE
0.43 (0.639)
426.8
434.9
4.32 0.035 22
86%
Base + CE
-0.829 (1.24)
426.9
435.0
4.42 0.033
3
67%
Base + CD
-0.366 (0.687)
427.0
435.1
4.55 0.031 12
75%
Base + DE
6
100%
Base + CC
1
0%
Base + EE
0
1
Generalised linear mixed-effects model with a binomial response (survived or not), where the base model
contains individual inbreeding coefficient F and cohort as a random factor. Genotype models include base
parameters plus a 1/0 binary predictor for presence/absence of the specified genotype.
2
Parameter estimates for the specified genotype (ij, as indicated in the “Model” column), namely effect size
coefficient (β) and its standard error (SEβ).
3
Number of animals containing the specified genotype (ij) – note that these vary widely, as a result of uneven
allele frequencies in the population (see Supplementary Figure 2).
4
Survival rate of animals containing the specified genotype.
5
Overall sample size and survival rate (all genotypes combined).
Models shown in bold are the most parsimonious models supported by the data for each locus (where the “best”
models are ≥2 AICC units from the next-best model); models could only be fitted for genotypes with mixed
survival.
3
Supplementary Table 6
Characteristics of the variable amino acids of each TLR4 haplotype.
Translation and amino acid characterisitics1
Allele
Site2 32
Site 48
Site 74
Site 174
A
Phenylalanine Leucine
Threonine
Aspartic Acid
Aromatic
Aliphatic
Small
Small
Hydrophobic Hydrophobic Hydrophobic Negatively charged
Polar
Polar
B
Isoleucine
Leucine
Serine
Aspartic Acid
Aliphatic
Aliphatic
Small
Small
Hydrophobic Hydrophobic Polar
Negatively charged
Polar
C
Leucine
Phenylalanine Threonine
Aspartic Acid
Aliphatic
Aromatic
Small
Small
Hydrophobic Hydrophobic Hydrophobic Negatively charged
Polar
Polar
D
Isoleucine
Leucine
Serine
Asparagine
Aliphatic
Aliphatic
Small
Small
Hydrophobic Hydrophobic Polar
Polar
E
Phenylalanine Phenylalanine Threonine
Aspartic Acid
Aromatic
Aromatic
Small
Small
Hydrophobic Hydrophobic Hydrophobic Negatively charged
Polar
Polar
1
2
Graur and Li (2000).
Position in translated sequence (Grueber et al. 2012).
4
Supplementary Figure 1
Forest plot of standardised coefficients for effects of TLR heterozygosity on over-winter
survival of Ulva robins; positive values indicate that heterozygotes are more likely to survive
than homozygotes; also shown is the multilocus effect of H for comparison. Sample sizes are
provided on the right-hand side; only those years with mixed survival among genotyped birds
were included (2002 onwards). All models also included individual inbreeding coefficient
(F) and the random factor cohort.
5
Supplementary Figure 2
Allele frequencies for the seven genotyped loci in birds that survived (“Alive”, N = 507) or not (“Died”, N = 139) their first winter (for
consistency with the main analysis, these data exclude years where all offspring survived, and birds with fewer than 3 loci genotyped). Different
levels of shading indicate the different alleles at each locus (range 2 – 5 alleles per locus).
6
Supplementary Figure 3
Change in observed frequencies of each of TLR4 allele in the adult population over time. Founders are shown for reference; surviving founders
are excluded from subsequent population estimates.
7
Supplementary Figure 4
Frequencies of each TLR4 genotype over time among the non-founder adult population.
Filled circles indicate observed proportion of adults with each genotype (error bars are
Agresti-Coull 95% confidence interval for proportions [Agresti & Coull 1998], evaluated
using R package binom [Sundar 2009]); open circles indicate expected frequency of TLR4BE
genotypes under Hardy-Weinberg (error bars are 95% confidence intervals derived using
logit transformation for frequencies, Sutton et al. 2011).
8
Supplementary Figure 5
Number of surviving juveniles produced by each pair in each breeding season, including all known Ulva robin breeding pairs (i.e. including
ungenotyped animals); N = 475 pair-years. Data are spread out along the x-axis to reduce overlay of multiple points. The fitted trend shows a
statistically significant decline over time, based on a generalised linear model with a Poisson link function: Njuveniles ~ -0.142 [± 0.020 SE] × Year
+ 284.2 [± 39.4].
9
Supplementary Figure 6
Correlation between relatedness of genotyped animals to blue-metal and to his first partner
white-metal (N = 624 individuals with non-zero relatedness to either); points shown may
represent overlay of multiple individuals with equal relatedness. Points on the diagonal (solid
line) represent individuals with equal relatedness to blue-metal and white-metal (N = 594).
The dashed lines indicate the relatedness of first-degree relatives. A small number of animals
(N = 30; 5.1%) show higher relatedness to blue-metal because he continued breeding after
white-metal died (these were inbred matings, so the offspring are partially related to whitemetal).
10
Supplementary Methods
Generalised linear mixed-effects modelling
For all survival models, the binomial response variable was whether or not birds banded at
the juvenile stage (age approximately 2 weeks) survived to the following breeding season
(age 1 year; lived = 1, died = 0), also termed “over-winter survival”. Models were fitted
using lme4 (Bates & Maechler 2009) in R (R Core Development Team 2011), and inference
was based on the top 2 AICC sub-models of the global model (using the package MuMIn
[Bartoń 2009], following Grueber et al. 2011). To permit direct comparison of effect sizes
among predictors on different scales, all predictors were standardised to 0.5 SD (following
Gelman 2008) using the package arm (Gelman et al. 2009). We report the effect sizes of our
final models, with standard errors or confidence intervals as an estimate of the precision of
effects (Nakagawa & Cuthill 2007). Where appropriate, effects are back-transformed onto
their native scale to facilitate interpretation of biological relevance.
Prediction of Mendelian frequencies
In the dataset, 637 genotyped juveniles had two genotyped parents; 144 (22.6%) of these had
parents with genotype combinations that could potentially give rise to TLR4BE offspring (i.e.
TLR4BX × TLR4EX). For each offspring, we calculated the Mendelian probability that it would
have TLR4BE genotype given its parent’s genotypes (i.e. values for each individual were 0,
0.25 or 0.5 [there were no TLR4BB × TLR4EE pairs in the dataset to give a 1.0 probability]).
We used Monte-Carlo simulations written in Visual Basic to estimate the overall mean and
95% confidence interval for the proportion of TLR4BE offspring each year. In each iteration
of the simulation, offspring were assigned genotypes (TLR4BE or not) by comparing the
Mendelian probability of TLR4BE to a random number between 0 and 1 from a uniform
distribution. If the random number was less than the expected probability of being TLR4BE
for that individual, it was assigned TLR4BE; if higher, it was assigned “other”. The number of
simulated TLR4BE individuals in each year was then divided by the total number of offspring
produced in that year to give a proportion. We used 5,000 iterations to generate a distribution
of expected proportions, and the mean and 95% quantiles of this distribution were taken as
the overall expected proportion of TLR4BE individuals for each year.
Simulation of random mating
We used a randomisation simulation to estimate the number of TLR4BE offspring that would
be produced each year under random mating. Random mating was approximated by
11
randomising the pool of breeding adults within each year. Members of established pairs
(pairs that bred together for >1 year) were only available for randomisation in the first year
that they bred, but were “fixed” in subsequent breeding years. Established breeders that split
up from former partners and found new ones were treated the same as first-time breeders in
that year. After identifying available breeders within each year, we randomised the list of
breeding females relative to the males (all known breeders were included, even those that
were ungenotyped, to reduce potential bias that would result from repeatedly excluding the
same, ungenotyped pairs). This procedure held constant the total number of breeding pairs
within each year, as well as the number of partners per bird. The number of offspring
produced by each pair was sampled (with replacement) from the distribution of observed
family sizes among the pool of “established” or “new” breeders in that year (excluding
ungenotyped offspring, as these cannot be compared to observed data). These family sizes
were bootstrapped separately because new breeding pairs are represented mainly by first-time
breeders, which tend to have reduced annual reproductive fitness. Where both members of a
pair were genotyped, each offspring was assigned either a TLR4BE or “other” genotype, based
on Mendelian probabilities, as outlined in the Supplementary Methods section “Prediction of
Mendelian frequencies”. The annual proportion of 1-year-old TLR4BE offspring produced
under this random mating scheme was calculated by dividing the number produced by the
total number of offspring produced in that year, and 5,000 iterations provided a mean and
95% quantiles for the overall expected annual proportion of TLR4BE offspring produced under
random mating.
Genedropping
In each iteration of the simulation, the known genotypes of the founders were randomly
passed down the pedigree following Mendelian inheritance, to derive simulated descendant
genotypes. Of the 722 descendants in the pedigree, 14 (1.9%) had missing parental data: 13
individuals were banded as adults and so nest of origin could not be assigned reliably (wholly
unknown pedigree), and one had an unbanded mother (half unknown pedigree). These gaps
in the pedigree were handled by assigning missing genotypes (“NA”) to unknown birds and
“dropping” these unknown alleles alongside the known alleles. In this way, our estimates of
genetic diversity in the descendant population are unbiased by any assumptions about the
genotypes of unidentified individuals. Taking this missing data into account, genotype
information could be simulated for 95.6% of all banded descendants.
12
Relatedness to blue-metal
We calculated the pedigree-based relatedness of every individual (survived and died) to bluemetal (denoted RBM, using the pedigree analysis software PMx [Lacy et al. 2011]) and used
this value as a predictor in a GLMM with survival as the binomial response variable.
Interpretation of RBM is complicated by an inability to distinguish whether a “good gene”
came specifically from blue-metal, or alternatively from his first partner, “white-metal”,
because 94.9% of blue-metal’s descendants in the population are equally related to whitemetal (Supplementary Figure 6). His remaining offspring were also partially related to whitemetal, as they resulted from inbred matings. We therefore interpret RBM as a measure of
lineage membership. We also fitted the interaction between RBM and TLR4BE genotype
(present/absent), to determine whether the degree of relatedness to blue-metal affected the
influence of TLR4BE genotype on survival. Models also contained F as a further fixed factor
and cohort as a random factor. Model selection and inference were based on AICC, as for the
main analysis (see Supplementary Methods section “Generalised linear mixed-effects
modelling”.
13
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