Genome-wide sexually antagonistic variants reveal long-standing constraints on sexual dimorphism in fruit flies

Welcome!

This github page contains the code underlying analyses and figures presented in 'Genome-wide sexually antagonistic variants reveal long-standing constraints on sexual dimorphism in fruit flies' (Ruzicka et al. 2019, PLOS Biol).

The code is organised into text files 1-9, which approximately follow the order of the analyses presented in the manuscript. The text files are usually a mixture of R and UNIX scripts. The contents of each file are described in bullet-point form below. We have also annotated the code for clarity, but if it's insufficiently clear please e-mail me at filip.ruzicka [at] monash.edu.

The 9th text file contains more detailed descriptions of the datafiles shared on zenodo (https://zenodo.org/record/2623225). These zenodo datafiles contain the raw data used to produce the figures in the manuscript, and will be the most useful objects for future users.

1. Phenotype and quant. gen analysis

-Male and female fitness data QC

-Male and female heritability and rmf estimation using MCMCglmm

-Define rotated matrix to map antagonism/concordant fitness effects of each line

-Male and female fitness plot + antagonism index overlay

2. Genotype quality control

-Depth and GQ filtering

-MAF and call rate filtering

-PCA including outgroup population (DGRP) to identify outliers

3. LHm pop gen

-Pairwise relatedness between individuals

-LD in LHm

4. Association mapping

-SNP heritability of the antagonism index

-LDAK mixed model association analysis

-Manhattan plot

-QQ-plot

-FDR estimation

-Permutation-based analysis

-LD clumping to identify number of independent hits

-Comparison of antagonistic and concordant P-values and Q-values

-Degree of clustering of antagonistic SNPs

5. Functional analyses

-Partitioning heritability into autosome vs. X

-Partitioning heritability into variant effect categories

-Antagonistic vs. non-antagonistic candidates on autosome vs. X

-Antagonistic vs. non-antagonistic candidates among variant effect categories

-Antagonistic genes: sex-biased expression

-Antagonistic genes: pleiotropy

6. SNP-based bal sel analyses (Dmel)

-Convert Genome Nexus fasta files to vcf format and r6 coordinates

-Estimate MAFs in three populations (DGRP, ZI, SA)

-Incoporate linked selection / recombination rate estimates

-Plot MAF spectrum at LD-pruned antagonistic vs. non-antagonistic SNPs

-Comparison of antagonistic and non-antagonistic MAF while correcting for MAF ascertainemnt bias and linked selection ('Analysis A')

-Relationship between GWAS effect size and probability that SNP is polymorphic, correcting for MAF ascertainemnt bias and linked selection ('Analysis B')

-Relationship between GWAS effect size and MAF, correcting for MAF ascertainemnt bias and linked selection ('Analysis C')

7. Window- and LD-based bal sel analyses

-Conduct window-based association test

-Calculate Tajima's D values in sliding windows

-Calculate Fst values in sliding windows

-Model Tajima's D and Fst as a function of antagonistic/non-antagonistic status + include LS as covariate

-Estimate LD between pairs of sites in ZI

-Compare pairwise LD between antagonistic and non-antagonistic sites in ZI

8. SNP-based bal sel analyses (Dsim, Dyak)

-Import allele frequencies for two D. simulans datasets and one D. yakuba dataset

-Relationship between GWAS effect size and probability that SNP is polymorphic, correcting for MAF ascertainemnt bias and linked selection ('Analysis B')

-Comparison of antagonistic and non-antagonistic trans-specific status while correcting for MAF ascertainemnt bias and linked selection ('Analysis A')

9. Data files

-Description of summary data files

-Code used to produce summary data files. These data files should be sufficient to reproduce the figures presented in the manuscript