Skip to main content
Dryad logo

Data from: Ecology shapes epistasis in a genotype-phenotype-fitness map for stick insect colour


Gompert, Zachariah et al. (2020), Data from: Ecology shapes epistasis in a genotype-phenotype-fitness map for stick insect colour, Dryad, Dataset,


Genetic interactions such as epistasis are widespread in nature and can shape evolutionary dynamics. Epistasis occurs due to non-linearity in biological systems, which can arise via cellular processes that convert genotype to phenotype and via selective processes that connect phenotype to fitness. Few studies in nature have connected genotype to phenotype to fitness for multiple potentially interacting genetic variants. Thus, the causes of epistasis in the wild remain poorly understood. Here, we show that epistasis for fitness is an emergent and predictable property of non-linear selective processes. We do so by measuring the genetic basis of cryptic colouration and survival in a field experiment with stick insects. We find that colouration exhibits a largely additive genetic basis, but with some effects of epistasis that enhance differentiation between colour morphs. In terms of fitness, different combinations of loci affecting colouration confer high survival in one host-plant treatment. Specifically, non-linear correlational selection for specific combinations of colour traits in this treatment drives the emergence of pairwise and higher-order epistasis for fitness at loci underlying colour. In turn, this results in a rugged fitness landscape for genotypes. In contrast, fitness epistasis was dampened in another treatment, where selection was weaker. Patterns of epistasis that are shaped by ecologically based selection could be common, and central to understanding fitness landscapes, the dynamics of evolution, and potentially other complex systems.


The experimental T. chumash were collected from Cercocarpus sp. (Mountain Mahogany, MM) in the vicinity of the locality Horse Flats 5 (HF5, N 34 15.584, W 118 6.254). Over 700 individuals were collected between June 11th and June 13th, 2019. These were kept alive in plastic containers and moved to laboratory space where 465 healthy adults were chosen for use in the transplant experiment, including photography for phenotyping and molecular genotyping.

All individuals from the transplant experiment were photographed with a digital Nikon 5600 camera equipped with a macro lens (Nikon AF-S VR Micro-Nikkor 105mm f/2.8G IF-ED) and two external flashes (Yongnuo YN560-II speedlights). The images were taken with the camera set on manual, an aperture of f/14, a shutter speed of 1/200 s, a sensitivity of 100 ISO, and flashes adjusted to 1/4+0.5 env power in S1 mode in an output angle corresponding to 24-mm focal length on full frame (~84° diagonal).DNA sequence data were generated by  on an Illumina HiSeq 200 (100 bp, single-end reads) by the Genome Sequencing and Analysis Facility at the University of Texas (Austin, TX).

Usage Notes

The following files are included in this data archive:

2019_Tchumash_transplant_table.csv = csv text file with data from the release-recapture selection experiment. The files includes one row per individual with data on treatment, block, survival (binary) and color.

selectionGradBayes.R = R script with code for fitting selection gradients with Bayesan methods.

bayes_glm and bayes_hglm = JAGS models required by selectionGradBayes.R. = zip compressed directory with the raw color spectra data.

2020Tchumash_quantum_catch_epistasis.R = R script to make graphs summarizing the spectra data.

Tchumash.spectra.epistasis.txt and Tchumash.list.epistasis.txt = Text file tables required to run 2020Tchumash_quantum_catch_epistasis.R.
filtered2x_tchum_mel_gbs_2019.vcf.gz = gzip compressed vcf file. This has the variant (SNP) data used in this study. This version of the file has been filtered as described in the manuscript.
mod_g_epi_tchum.txt, mod_g_tchum_AC.txtmod_g_tchum_MM.txtmod_g_tchum.txt = geno format text files for gemma. These files contain the genotype data (posterior mean estimates of genotypes) used for fitting BSLMMs with gemma. The AC and MM files contain only individuals in the AC or MM treatment, respectively. mod_g_tchum.txt contains all individuals. mod_g_epi_tchum.txt contains all individuals and includes the epsitatic effects (genotype products) for SNPs showing evidence of marginal epistasis based on the MAPIT analysis.
pheno_MM, pheno_AC, pheno_color =  pheno format text files for gemma. pheno_MM and pheno_AC contains survival and sex as phenotypes (binary), whereas pheno_color has the color data. These are input for the BSLMM fit with gemma.
pheno_rg_cv.txt and pheno_gb_cv.txt = pheno format text files for cross-validation with gemma. Here, each color phenotype (RG or GB) has been repeated across multiple columns with missing data for subsets of individuals. 
models.R = main R script for analyzing expected fitness as a function of color-associated SNPs. Various versions of this analysis are included.
tchumSnpTable.txt = text file with one row per SNP with columns for LG, scaffold and position. This file is required by models.R
cvScript*R = set of R scripts for cross-validation analyses under different conditions, that is based on survival or expected fitness as the response and with additive effects only, or including dominance or epistasis.
cvSummary.R = R script to summarize cross-validation analyses.
ld.R = R script used to summarize patterns of LD among color-associated SNPs.
runMapitColor.R and runMapitSurv.R = R scripts used to run the MAPIT analyses (tests for marginal epistasis) with color and survival as responses.
calcBV.R = R script used to calculate genomic-estimated breeding values for RG and GB color traits with and without allowing for epistasis.
pleoitropy.R = R script used to run the core pleiotropy analyses.
nullSims.R and nullSimsPl.R = R script used to perform null simulations fitting the Bayes model selection/model averaging models to sets of random SNPs with or without allowing for pleiotropy.