Analysis of Contemporary Selection- Comparison with Neutral Markers

Analysis of Contemporary Selection- Comparison with Neutral Markers

APPENDIX

Analysis of contemporary selection- comparison with neutral markers

The genetic structure of neutral genes (microsatellites) was compared with that of DRB loci. Because we could not unambiguously delineate Mimo alleles to specific DRB loci, we used the Average Percent Difference (APD) of MHC and microsatellite loci between pairs of individuals within populations (Miller and Lambert 2004; Miller et al. 2010), calculated in excel after the method of (Yuhki and O'Brien (1990)). This metric of differentiation is the percentage of variable alleles between two individuals, controlling for the number of pairwise comparisons within the population and the number of loci used. Owing to limitations in sample size, our power to detect significant correlations and differences between neutral and adaptive markers is low, so we focus on highlighting trends. We tested the correlation between APD and allelic richness in MHC and microsatellites using Spearman rank correlations in SPSS v.20. A positive correlation would support genetic drift as the main driver of diversification for both types of loci.

Strength of genetic drift

Signals of genetic drift based on positive correlations between microsatellite and MHC APD were stronger for 2008 (R2 = 0.79; P < 0.001) than 2009 (R2 = 0.24; P = 0.67; Table S7). This indicates that genetic drift affected both MHC and microsatellite markers more during the lower density year (2008). We could not perform more formal tests using outlier analysis because we could not assign MHC alleles to specific loci and generate exact genotypes. Year was not a significant predictor of differences in microsatellite heterozygosity and d2 (heterozygosity: T = 0.01, df =118, P = 0.99; d2: T = -0.39, df =118, P = 0.70), implying that these measures of neutral diversity were similar during high and low density years.

REFERENCES

Miller, H. C., F. Allendorf, and C. H. Daugherty. 2010. Genetic diversity and differentiation at MHC genes in island populations of tuatara (Sphenodon spp.). Molecular Ecology 19:3894-3908.

Miller, H. C. and D. M. Lambert. 2004. Genetic drift outweighs balancing selection in shaping post-bottleneck major histocompatibility complex variation in New Zealand robins (Petroicidae). Molecular Ecology 13:3709-3721.

Yuhki, N. and S. J. O'Brien. 1990. DNA variation of the mammalian major histocompatibility complex reflects genomic diversity and population history. Proceedings of the National Academy of Sciences 87:836-840.

TABLES

Table S1. Properties of the six microsatellitelociused to gentotypeM. montanus. Panel refers to the multiplexing group, dyeindicates the fluorescent dyeattached to the 5' end of the F primer, Ho isobservedheterozygosity, HE isunbiasedexpectedheterozygosity.

Table S2. Quantification of genotypingerror rate across the 6 microsatelliteloci. Number of samplesre-genotypedrangedfrom 10-18 acrossloci.

Locus / Correct / Number of duplicates / Number of mistyped duplicates / Number of mistyped alleles / Error rate per reaction / Error rate per allele
Mar076 / 18 / 18 / 0 / 0 / 0.000 / 0.000
Ma68 / 17 / 17 / 0 / 0 / 0.000 / 0.000
Ma88 / 10 / 10 / 0 / 0 / 0.000 / 0.000
AV13 / 17 / 17 / 0 / 0 / 0.000 / 0.000
Ma54 / 17 / 18 / 1 / 1 / 0.056 / 0.028
Msmoe02 / 16 / 16 / 0 / 0 / 0.000 / 0.000
Overall / 95 / 96 / 1 / 1 / 0.001 / 0.000

Table S3. Analysis of molecular variance (AMOVA) within and amongyears 2008 and 2009 by sites and acrossindividuals.

Source of variation / d.f. / SS / VC / FST / % Var / P-value
Microsatellites (6 loci)
Among sites / 2 / 7.43 / 0.01 / 0.00 / 0.46 / 0.19
Among years within sites / 3 / 7.03 / 0.01 / 0.01 / 0.59 / 0.18
Among individuals within sites by year / 132 / 268.21 / 0.00 / 0.00 / -0.01 / 0.73
Within individuals / 138 / 285.00 / 2.07 / 0.00 / 99.75 / 0.54
MHC loci
Among sites / 2 / 1.02 / 0.01 / 0.01 / 1.41 / 0.20
Among years within sites / 3 / 0.93 / -0.01 / -0.02 / -1.49 / 0.82
Within years by site / 255 / 108.80 / 0.43 / 0.00 / 100.08 / 0.63
SS, sums of squares; VC, variance component; FST, fixation indice; % Var, percentage of genetic variation; P-value, level of significance based on 10100 permutations.

Table S4. Gstestimates for all 6 microsatellite lociaccounting for smallsample size (Nei 1983) and standardizedaccording to the method of Hedrick (2005). Variance wasestimatedusingbootstrappingmethodswith 1000 randomizations.

Locus / GST_est ± 95% CI / G'ST_est ± 95% CI
Mar076 / 0.009 ± (0.002-0.018) / 0.084 ± (0.017-0.175)
Ma68 / 0.007 ± (0.001-0.016) / 0.094 ± (0.010-0.191)
Ma88 / 0.023 ± (0.012-0.036) / 0.196 ± (0.109-0.297)
AV13 / 0.015 ± (0.008-0.024) / 0.230 ± (0.127-0.346)
Ma54 / 0.016 ± (0.005-0.031) / 0.155 ± (0.055-0.270)
Msmoe02 / 0.017 ± (0.007-0.028) / 0.178 ± (0.083-0.281)
GST_est = nearly unbiased estimator of relative differentiation (Nei 1983); G'ST_est = standardized measure of genetic differentiation (Hedrick 2005). Calculated in SMOGD: Software for the Measurement of Genetic Diversity (vsn. 1.2.5)

Table S5. Allelefrequencies of MHC class II DRB alleles for M. montanus in the total population, in subpopulations, and years.

Table S6. Estimates of microsatellite and MHC Average Percent Difference (APD) by site per year. N, sample size; A, averagenumber of allelesobserved; SE, standard error.

Sample year / Sample site / N / A / APD ± SE
Microsatellite loci
2008
KP1 / 6 / 6.17 / 30.56 ± 2.05
KP2 / 6 / 6.33 / 38.8 ± 5.02
Rmed / 11 / 8.67 / 45.35 ± 2.51
Total / 23 / 7.06 / 41.58 ± 4.28
2009
KP1 / 32 / 11.00 / 37.78 ± 0.54
KP2 / 38 / 11.67 / 35.74 ± 0.61
Rmed / 45 / 13.17 / 40.88 ± 0.54
Total / 115 / 11.94 / 38.53 ± 1.49
MHC
2008
KP1 / 2 / 2.00 / 33.3 ± 0
KP2 / 4 / 6.00 / 34.17 ± 4.76
Rmed / 11 / 8.00 / 40.55 ± 2.83
Total / 17 / 9.00 / 39.81 ± 2.28
2009
KP1 / 30 / 14.00 / 32.6 ± 2.42
KP2 / 38 / 15.00 / 34.41 ± 1.65
Rmed / 38 / 15.00 / 35.56 ± 1.59
Total / 106 / 21.00 / 35.52 ± 0.82

Table S7.Model-averaged estimates of parameters in the subset of models with strong support (Δ AICc<4), as well as the unconditional standard variance, 95% confidence intervals, and importance (based on the sum of the parameter weights in the subset of models in which the parameter is present). Parameter estimates with 95% CIs that did not cross zero are highlighted in bold.