www.sciencemag.org/cgi/content/full/334/6052/86/DC1

Supporting Online Material for

A Map of Local Adaptation in Arabidopsis thaliana

A. Fournier-Level, A. Korte, M. D. Cooper, M. Nordborg, J. Schmitt,* A. M. Wilczek

*To whom correspondence should be addressed. E-mail: johanna_schmitt@brown.edu

Published 7 October 2011, Science 334, 86 (2011) DOI: 10.1126/science.1209271

This PDF file includes: Materials and Methods

Figs. S1 to S7

Tables S1 to S6

References (26–32)

2

Materials and Methods Plant material and phenotypic measurements

A set of 157 A. thaliana accessions was planted to coincide with germination of natural populations in natural field environments in fall 2006 in Halle, Germany (51.5°N, 12°E), Norwich, United Kingdom (52.6°N, 1.2°E) and Valencia, Spain (39.6°N, 0.4°W) in 15 randomized blocks as described in (13). We also planted a core-collection of 68 accessions in fall 2007 in Oulu, Finland (65°N, 25.3°E). Seeds in Finland were sown on the week of September 12th 2007 and transplanted to the field two weeks later to coincide with germination of Finnish populations. Survival was measured as the proportion of plants that produced seed among the ones that survived transplanting. Silique number was counted for each plant in a subset of five blocks. Accession means for silique number reflected these manual counts from plants that survived but also accounted for individuals that did not survive: individuals that died, producing no silique, were assigned a fruit count of zero. Genotypes and geo-referenced origins of 971 A. thaliana accessions were downloaded from publicly available resources (http://walnut.usc.edu/2010/data/250k-data-version-3.06) and used for geographic, geo-climatic and genomic selection analyses. 867 accessions with geo-referenced origin within 2000km of at least one of the field sites were included in geographic and climatic analysis. Genome-wide association (GWA) mapping

The SNP marker set as well as the statistical methods employed for association genetics tests were described in a previous study (14). We relied on the confounding effect-corrected mixed-model implemented in the EMMAX software (26). All the comparative analysis of the traits variation and of the GWA tests (Phenotype clustering, genetic effect distribution and correlation) was performed on the core-collection of 68 accessions. The set of associated SNPs for geographic, geo-climatic and genomic selection analysis was defined by taking the 0.05% SNPs explaining the most variance (R-squared) from the GWA test performed on 157 genotypes in Halle, Norwich and Valencia and on 68 genotypes in Oulu. Geographic mapping

For each allele of the 213,248 SNPs, allelic centroids were computed in three steps: (1) for the location of each accession containing the allele of interest, transforming polar coordinates

(expressed in radian) in Cartesian referential as

(2) for all accessions containing the allele, calculating the gravity center of the population as and (3) returning to the polar coordinates of the gravity center as

.

Distances from centroids to the field sites were calculated using the haversine formula. Visualization of the data was obtained on DIVA-GIS (version 7.1.7.2, http://www.diva-gis.org). Geo-climatic mapping

A climate-envelope distribution was estimated for each allele of the 213,248 SNPs through maximum entropy density models as implemented in the MaxEnt software (v3, 27), using 11 bioclimatic variables with default settings: isothermality (BIO3), temperature seasonality (BIO4), temperature during the wettest, the driest, the warmest and the coolest quarter of the year (BIO8-BIO11), precipitation seasonality (BIO15) and precipitation during the wettest, the driest, the warmest and the coolest quarter of the year (BIO16-BIO19). Climate variables

3

layers corresponded to five minute resolution grids available at WorldClim Global Climate (http://www.worldclim.org, 28). We chose these variables to maximally capture climate variability with respect to temperature and precipitation features. Climate envelopes were built using the MaxEnt default regularization parameters (27). For each allele distribution model, MaxEnt estimates independent normalized loadings upon each climate variable in a logistic framework, thus limiting the effect of correlation between variables. The presence of an allele is counted only once per location in a five minute resolution grid. The goodness-of-fit of the MaxEnt output was checked and SNPs with Receiver-Operator Area Under the Curve (AUC) under 0.70 were discarded. Difference in climate contribution to the distribution of the beneficial and the deleterious alleles of each SNP was calculated for each climate variable in each GWAS as the difference between the normalized loading (actual contribution) of the fittest allele minus the normalized loading of the deleterious allele. Among the ~213,000 SNPs, we considered as significant the two-tailed 0.5% SNPs with most climatically distant alleles for each of the 11 variables. Genomic selection mapping

A genome scan for positive differential selection was performed using the Extended Haplotype Homozygosity framework (18). For each SNP, EHH statistics were calculated separately for the two alleles using the 10 flanking SNP sites, except when the next flanking site was further than 30Kbp (three times the expected linkage disequilibrium range in A. thaliana, 29) or when EHH decreased below 0.10 (30). The area between the EHH curve of each allele was then integrated (iEH) and used as a measure of allelic differential selection. Global iEH values were calculated using all 971 genotypes. Local iEH values were calculated using the 100 ecotypes with the closest origin from each planting site. We considered as significant the genome-wide 5% SNPs showing the highest iEH values. Deviation from global genome pattern

For each set of associated SNPs, the deviation of geographic, climatic, iEH and fittest allele frequency distribution of the associated SNPs from what is expected from the genome-wide pattern was tested through ANOVA (P-val<0.05) against 1000 random samplings of equal number of SNPs as the number of associated SNPs. The random sampling was performed as in (31) by 1- randomly rotating the starting position of each chromosome, 2- permuting the chromosomes order and 3- selecting the same SNPs on the permuted genome as the one associated in the original genome. This permutation method is more conservative than simple genome-wide random draws as it preserves the initial linkage disequilibrium structure of the data. The permutation p-value was calculated as the 95th percentile of the p-values from these 1000 tests. Analysis of the allelic effects

The allelic effects were calculated on the mixed-model residual variance after fitting the pairwise kinship effect as the difference in the genotypic additive value between the reference Col-0 genotype and the alternative genotype. For each GWAS, the allelic effects were normalized between -1 and 1 to allow cross-GWAS comparison. All the associated polymorphisms with genomic location, geographic distribution of each allele and summary statistics used in the analysis are available for download in Data S1. Gene Ontologies analysis

GO annotations were downloaded from the TAIR database (http://www.arabidopsis.org) on 03/08/11. The annotated genes were merged with their genomic positions according to TAIR v.9 annotations. For candidate SNPs a window of 10 kb centered on the respective SNP was created. For each set of candidate SNPs tested, the respective GO Slim Term of genes (32) within

4

the SNP window was recorded. To specifically address the molecular function of candidate genes, the GO category ‘Molecular Function’ which consist of 15 distinct GO Slim Terms, was analyzed. The ‘Molecular Function’ GO annotations for 24,963 genes, corresponding to a total of 35,963 distinct gene-GO Slim associations, were compared to the ‘Molecular Function’ GO annotations of the candidate gene. Differences in the proportions of the GO Slim Terms were noted for each set of candidate genes. The significance of the observed differences was statistically tested by comparing the result to 1000 permutations of the SNPs in the respective candidate list (for details on the permutation method see 14). Candidate gene selection The final set of candidate loci was selected by empirically combining the different independent lines of evidence developed in our analysis. Firstly, we selected the 0.05% SNPs explaining the most variation in each of the GWAS, defining a set of ~100 SNPs per GWAS. The second criteria shown to have an influence on fitness-associated SNPs, was the climatic differentiation. A less stringent cutoff (10 times) was applied for the climatic differentiation than for the fitness association (0.5%), defining a set of ~1000 SNPs. Third, the Global iEH showed a moderate influence on fitness associated SNPs in two GWAS. An even less stringent cutoff (100 times) was applied for the iEH index than for the fitness association (5%), defining a set of ~10000 SNPs. Finally, we removed the SNPs with a minor allele frequency lower than 0.08. This ensured that at least 5 different ecotypes were bearing the allele, even for the limited set of ecotypes planted in Finland. The set of candidate SNPs presented are the ones consistently showing all three lines of evidence of being involved in local adaptation. Supplemental References 26. H. M. Kang et al., Nature Genet. 42, 348 (Apr, 2009). 27. S. J. Phillips, M. Dudik, Ecography 31, 161 (2008). 28. R. J. Hijmans, C. H. Graham, Global Change Biology 12, 2272 (2006). 29. S. Kim et al., Nature Genet. 39, 1151 (2007). 30. K. Tang, K. R. Thornton, M. Stoneking, PLoS. Biol. 5, 1587 (2007). 31. M. Nordborg, PLoS Biol. 3, e196 (2005) 32. Gene Ontology Consortium, Nucleic Acids Res 38, 331 (2010).

5

Fig. S1. Location of the field sites and origin of the ecotypes. Red dots represent the origin of the 867 accessions included in the geo-climatic analysis, purple circles for the 157 accessions planted in Halle (GER, green square), Norwich (UK, blue square) and Valencia (SP, red square), black circles from the 68 accession planted in Oulu (FIN, light blue square), all sample being nested in one-another.

6

Fig. S2 Positions and Scores (-logP-val) of the ~100 SNPs the most associated with Survival in Germany (diamonds), England (triangles), Finland (squares) and Spain (circles).

7

Fig. S3 Positions and Scores (-logP-val) of the ~100 SNPs the most associated with Silique number in Germany (diamonds), England (triangles), Finland (squares) and Spain (circles).

8

Fig. S4 a, Variation in isothermality over the year. b, Variation in temperature seasonality over the year, following a Continental to Oceanic gradient. c, Variation in temperature during the coolest quarter of the year d, Variation in precipitation during the wettest quarter of the year. d, e, f, g, h, i, j, k and l, Box plots of the difference in climatic contribution between pairs of SNP alleles significantly associated with fitness in Germany, England, Finland and Spain, expressed in %. Negative values indicate when the deleterious alleles are more correlated with the climate variable, positive values indicate when the beneficial allele are more correlated with the climate variable, light and dark shaded boxes in the background represent the 50% and 25% quantile statistics of genome-wide neutral expectation obtained through permutation (see Methods). All test statistics are reported in Supplementary Table 2.

9

Fig. S5 Box plots of the distribution frequency of the fittest allele for SNPs associated with Silique number and survival in Germany, England, Finland and Spain.

10

Fig. S6 a. Hierarchical clustering for silique number and survival in Finland, England, Germany and Spain among the common 68 genotypes. b. diagonal, Histograms of the distribution of genetic effect for each GWAS and upper triangle, correlation plots between the genetic effect across different GWAS.

11

Fig. S7

Slim-Gene Ontology of the associated SNPs and the total slim-GO annotation present in the genome. Gene ontologies from other enzyme activity to other molecular activity were classified as structural or enzymatic activities (right half of the chart), gene ontologies from transcription factor activity to other binding were classified as regulatory activities (left half of the chart).

12

Table S1. Summary information for Survival and Silique # traits (Phenotypes) in the four plantings for a common set of 68 genotypes and summary information for SNP markers in GWA models (Association tests) for the 157 genotypes in Germany, England and Spain and 68 genotypes in Finland. R2: individual SNP R-squared in association model, 2a/σp: individual SNP additive effect standardized on the phenotype standard deviation.

                             

      Survival           Silique #          

      Germany  England  Finland  Valencia  Germany  England  Finland  Valencia 

Phenotype                         

  mean (sd) 

0.98  (0.04) 

0.64  (0.19) 

0.68 (0.33) 

0.83  (0.09) 

2097 (913) 

457  (444) 

151  (87) 

729  (442) 

  min ‐ max  0.75 ‐ 1  0.14 ‐ 1  0 ‐ 1  0.61 ‐ 1  430 ‐ 5470  0 ‐ 1902  0 ‐ 291  6 ‐ 1785 

Association tests                         

R2 mean (sd) 

0.13  (0.03) 

0.09  (0.01) 

0.24 (0.05) 

0.11 (0.02) 

0.11 (0.01) 

0.12 (0.02) 

0.21 (0.03) 

0.10  (0.01) 

  min ‐ max  0.03 ‐ 0.21  0.01 ‐ 0.16  0.05 ‐ 0.43  0.02 ‐ 0.16  0.01 ‐ 0.18  0.02 ‐ 0.23  0.03 ‐ 0.29  0.01 ‐ 0.14 

2a/σp mean (sd) 

1.57  (0.78) 

0.82  (0.19) 

1.53 (0.28) 

1.02 (0.21) 

1.08  (0.52) 

1.08  (0.35) 

1.09 (0.23) 

0.98  (0.3) 

  min ‐ max  0.78 ‐ 4.73  0.19 ‐ 1.38  0.28 ‐ 2.12  0.21 ‐ 2.18  0.52 ‐ 3.93  0.35 ‐ 2.68  0.23 ‐ 1.66  0.3 ‐ 1.9 

P‐val mean (sd) 

1.91E‐04 (4.09E‐04) 

1.32E‐03 (1.77E‐03) 

1.89E‐04 (2.31E‐04) 

1.45E‐04 (1.00E‐04) 

2.29E‐04 (2.31E‐04) 

7.39E‐05 (7.05E‐05) 

8.80E‐04 (6.97E‐04) 

5.05E‐04 (6.56E‐04) 

  min ‐ max 

1.47E‐07 ‐ 2.41E‐03 

2.35E‐06 ‐ 7.47E‐03 

2.06E‐06 ‐ 9.01E‐04 

1.44E‐06 ‐ 5.32E‐04 

3.48E‐08 ‐ 1.14E‐03 

3.49E‐08 ‐ 4.23E‐04 

2.08E‐05 ‐ 3.00E‐03 

1.82E‐05 ‐ 5.36E‐03 

13

Table S2. Test for the closer proximity of the fittest allele geographic centroids to the planting site where they were detected compared to random draws for various allelic frequency cutoffs. Associated SNPs were tested through ANOVA against 1000 permutations as genomic control. Upper 95% permutation p-values are reported.

                          

Survival Silique number

Germany England Finland Spain Germany England Finland Spain

All SNPs 0.101977 0.003172 0.9388837 0.0762944 0.0040004 2.38E-08 0.5336125 0.0005829

Freq >5% 0.2632758 0.0028095 0.9305216 0.0354658 0.0021026 1.89E-13 0.4926168 0.0014572

Freq >10% 0.3414133 0.0052541 0.8736719 0.0641946 0.0322359 1.15E-12 0.4672645 0.03972

Freq >20% 0.5392119 0.0354023 0.0457413 0.5847007 0.0147039 4.92E-14 0.092435 0.0043135                           

14

Table S3. Geographic co-distribution between alleles of the most associated SNPs and climate (All SNPs). Associated SNPs were tested against 1000 permutation as genomic control. Upper 95% permutation p-values and mean regression coefficients (italic) are reported. The Bonferroni threshold for 11 tests per associated SNP set for a nominal risk of 5% is 0.004. Light and dark shaded cells correspond to P-values inferior than 0.004 and <10-4, respectively. Positive coefficients indicate that the fittest alleles are more strongly associated with a climatic factor, and negative coefficients indicate that the deleterious alleles are more strongly associated.

Survival Silique Number

Germany England Finland Spain Germany England Finland Spain

p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef.

Temperature

Isothermality 6.64E-05 ‐4.13  0.4658 ‐1.18 6.69E-06 ‐4.86 2.95E-06 ‐4.03 0.9628 0.23 0.0000 5.42 0.1189 ‐2.03 0.0574 2.40 

Seasonality 0.2551 3.79  6.39E-05 7.70 7.64E-10 ‐12.30 2.40E-09 10.64 0.0054 6.39 0.4203 3.21 0.0175 ‐5.27 0.6496 2.69 

Coolest 0.0008 4.90  0.0915 2.76 1.37E-07 5.68 2.10E-07 5.92 0.0979 2.90 0.1090 ‐2.65 1.52E-05 5.20 0.8273 ‐1.20 

Warmest 0.0020 5.00  0.8680 ‐1.28 9.56E-21 11.56 0.9334 ‐0.98 1.10E-05 ‐6.22 5.01E-10 ‐8.09 6.90E-07 7.14 3.86E-08 ‐7.20 

Wettest 0.0004 ‐2.43  0.9171 ‐0.35 6.17E-07 ‐3.30 0.9499 ‐0.13 0.8478 0.55 0.9151 ‐0.23 0.1807 ‐1.14 0.4164 0.82 

Driest 0.1798 ‐1.88  0.0029 ‐3.41 0.0806 1.78 0.9146 ‐0.52 0.2728 ‐1.59 0.9148 ‐0.41 0.0180 2.30 0.9024 0.47 

Precipitation                        

Seasonality 0.9616 0.08  0.0035 ‐3.95 0.7770 1.23 0.0217 ‐3.16 0.0002 ‐4.84 0.8676 ‐0.98 0.9556 0.12 0.0277 ‐3.08 

Coolest 0.0013 ‐3.39  0.0146 ‐2.38 7.32E-10 ‐7.79 2.99E-11 ‐6.43 0.8704 ‐0.73 2.93E-05 3.79 8.00E-09 ‐6.21 0.7992 0.94 

Warmest 0.2219 ‐2.84  0.2368 ‐2.67 0.2946 ‐2.22 0.2953 ‐2.67 0.9611 ‐0.24 0.7806 1.46 0.9496 0.13 0.7950 1.39 

Wettest 3.17E-11 4.65  0.9323 0.43 8.34E-07 3.92 0.9466 ‐0.28 0.2469 ‐1.87 5.38E-12 ‐4.80 0.9418 0.34 5.53E-08 ‐3.85 

Driest 0.4352 ‐3.76  0.2010 4.32 0.0210 6.29 0.9194 1.65 0.0950 5.40 0.5297 3.27 0.9507 ‐0.58 0.0035 6.62 

15

Table S4 Geographic co-distribution between alleles of the most associated SNPs and climate (SNPs with frequency > 5%). Associated SNPs were tested against 1000 permutations as genomic control. Upper 95% permutation p-values and mean regression coefficients (italic) are reported. The Bonferroni threshold for 11 tests per associated SNP set for a nominal risk of 5% is 0.004. Light and dark shaded cells correspond to P-values inferior than 0.004 and <10-4, respectively. Positive regression coefficients (green) indicate that the fittest alleles are more strongly associated with a climatic factor, and negative regression coefficients (red) indicate that the deleterious alleles are more strongly associated.

   Survival Silique Number

  

   Germany England Finland Spain Germany England Finland Spain

   p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef. p-value coef.

Temperature Isothermality 2.23E-05 -4.00 0.3761 -1.26 3.80E-06 -4.96 1.82E-06 -4.18 0.9598 0.46 8.40E-07 5.36 0.1095 -2.08 0.0478 2.48 Seasonality 0.0339 5.18 4.20E-05 7.49 2.55E-06 -8.90 2.69E-10 11.13 0.0031 6.53 0.5595 2.80 0.0246 -4.89 0.9023 1.47 Coolest 2.55E-10 6.59 0.1149 2.76 2.48E-08 5.77 1.01E-07 5.91 0.0767 2.90 0.0468 -2.87 1.44E-05 5.20 0.8779 -1.18 Warmest 0.1003 3.37 0.7379 -1.76 2.90E-21 11.59 0.9102 -1.22 0.0002 -5.57 1.56E-09 -7.79 1.60E-06 6.98 2.29E-07 -7.05 Wettest 0.0001 -2.32 0.8454 -0.44 5.31E-06 -2.94 0.9528 -0.20 0.9338 -0.20 0.928 -0.28 0.1419 -1.16 0.3112 0.84 Driest 0.4846 -0.87 0.0001 -2.83 0.0002 1.90 0.8985 -0.48 0.0364 -1.86 0.8702 -0.50 0.0002 2.40 0.9206 0.38 Precipitation Seasonality 0.9678 0.04 0.0049 -3.36 0.3157 1.79 0.0315 -2.99 9.58E-06 -5.31 0.7989 -1.18 0.959256 -0.05 0.0264 -3.07 Coolest 9.71E-05 -4.10 0.0589 -2.01 2.40E-10 -7.84 6.83E-12 -6.59 0.746 -0.93 3.07E-05 3.82 1.45E-08 -6.14 0.8346 0.86 Warmest 0.0431 -3.58 0.1391 -3.00 0.4233 -1.95 0.2469 -2.81 0.9657 -0.44 0.8247 1.41 0.96103 0.08 0.6931 1.51 Wettest 5.05E-08 4.02 0.9419 0.19 0.0002 3.05 0.938 -0.30 0.0016 -2.76 6.97E-12 -4.78 0.95133 0.32 1.28E-07 -3.79 Driest 0.208 -4.34 0.2301 4.21 0.7936 2.48 0.9114 1.73 0.0027 7.18 0.2771 4.01 0.944554 -0.66 0.0003 7.55

16

Table S5. Test of the Fittest Allele Frequency (FAF), Global iEH and Local iEH for all and for the associated SNPs. Upper 95% permutation p-values and mean regression coefficient for the ANOVA tests between associated alleles and 10000 random draw as genomic control are reported. Positive values correspond to higher values for the observed associated alleles than for the genomic control.

        

FAF Global iEH   

Local iEH   

        

p-value coef p-value coef    p-value coef

        

Survival Germany 2.54E-22 0.38 2.52E-04 1228.39    0.73 44.68

England 0.00115 0.17 0.66 270.28    0.93 20.75

Finland 1.27E-27 0.43 0.08 520.29    0.90 176.19

Spain 6.11E-06 0.22 0.39 357.40    0.86 1161.95

  

Silique # Germany 0.39037 -0.08 0.16 465.76    0.77 45.18

England 3.01E-20 -0.36 0.72 247.23    0.82 42.58

Finland 2.08E-06 0.21 0.92 136.71    0.50 564.96

Spain 6.51E-07 -0.23 0.92 125.63    0.92 -42.93

        

17

Table S6. Enrichment in molecular function for the gene linked to the associated SNPs. Ratio of gene enrichment was calculated between the list of non-redundant genes found in windows of 10Kbp around associated SNP and 1000 random permutations. Color code corresponds to the level of enrichment for particular activities of the genes linked to associated SNPs with respect to TAIR v.9 reference gene ontology, red corresponding to underrepresented activities among associated genes and blue overrepresented activities.

Survival Silique #

Molecular Function Germany England Finland Spain Germany England Finland Spain

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

Enrichment Ratio p-val

other enzyme activity 0.794 0.18 1.028 0.44 1.251 0.21 0.919 0.20 1.476 0.09 1.013 0.49 1.350 0.13 1.197 0.25

transferase activity 0.815 0.23 0.971 0.48 1.830 0.02 0.950 0.28 0.853 0.22 1.264 0.26 1.080 0.39 0.992 0.48

hydrolase activity 0.894 0.35 0.925 0.38 0.801 0.18 0.624 0.05 1.008 0.40 0.964 0.43 0.805 0.26 1.092 0.40

kinase activity 0.694 0.20 0.868 0.37 0.469 0.04 0.650 0.14 0.630 0.13 0.700 0.21 0.593 0.16 0.862 0.39 structural molecule activity 1.016 0.36 1.161 0.30 0.235 0.09 2.935 0.04 0.790 0.47 2.928 0.03 0.406 0.21 1.217 0.27

transporter activity 0.932 0.46 1.181 0.30 1.008 0.47 1.117 0.47 2.031 0.04 1.191 0.36 1.274 0.28 1.795 0.08 other molecular functions 1.190 0.29 0.963 0.48 0.827 0.36 0.637 0.15 1.173 0.35 0.972 0.49 1.478 0.14 1.109 0.35 unknown molecular functions 1.124 0.06 0.905 0.45 1.026 0.36 1.204 0.32 0.832 0.18 0.966 0.47 0.753 0.16 0.867 0.32 transcription factor activity 0.739 0.27 1.062 0.32 0.674 0.19 1.245 0.35 1.308 0.20 0.373 0.03 0.904 0.49 0.430 0.05 DNA or RNA binding 0.986 0.36 1.352 0.07 0.914 0.46 0.823 0.24 1.135 0.30 0.795 0.29 1.496 0.07 0.630 0.12

protein binding 1.809 0.02 0.452 0.03 0.825 0.28 1.270 0.43 0.677 0.14 0.969 0.49 0.948 0.47 1.528 0.15

nucleotide binding 0.564 0.03 0.916 0.34 1.086 0.50 0.950 0.24 0.614 0.06 1.386 0.22 0.821 0.27 0.821 0.24

nucleic acid binding 1.460 0.06 1.022 0.31 0.789 0.42 0.875 0.39 0.848 0.42 0.932 0.45 0.997 0.35 0.816 0.47 receptor binding or activity 1.002 0.40 1.002 0.41 0.542 0.42 0.375 0.27 0.364 0.25 1.685 0.20 0.311 0.25 0.934 0.37

other binding 1.029 0.30 1.167 0.15 1.002 0.46 0.887 0.16 1.028 0.49 0.917 0.40 1.135 0.25 1.263 0.16

18

Supplemental Data Supplemental Database S1 for the phenotype data and Supplemental Database S2 are available online at http://dx.doi.org/10.5061/dryad.37f9t.