The genetic basis of fitness in Drosophila; A genome-wide association study

Last updated: 2021-11-10

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Knit directory: fitnessGWAS/

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This website relates to the forthcoming paper ‘A genome-wide scan for pleiotropic fitness effects on sexes and age classes in Drosophila’, by Wong and Holman.

Click the headings below to see the code, results, plots, tables and figures associated with this study.

1. Setting up a database to hold variant/gene annotations and GWAS results

This script creates a SQLite3 database holding two tables: one with annotations for each variant (provided by the Mackay lab), and one with annotations for each gene (from annotation hub). In step 3, we also add the GWAS results to this database, allowing memory-efficient handling of the results. Using this database also avoids having to load Bioconductor packages that conflict with dplyr::select(), etc.

2. Estimating line mean fitness using Bayesian models

This script uses Bayesian mixed models implemented in brms to estimate the line means for our four fitness traits.

3. Calculating quantitative genetic parameters

We first present a table showing the proportion of variance in fitness explained by ‘DGRP line’, which was calculated using the models in Section 2. We then use gcta64 software to estimate the genetics (co)variances for the four fitness metrics from the genomic relatedness matrix.

4. Running the GWAS

This script first performs quality control and imputation on the dataset of SNPs and indels for the DGRP. Second, it runs mixed model association tests on our four phenotypes using the software GEMMA. Third, it uses PLINK to identify groups of loci that are in complete linkage disequilibrium (‘SNP clumps’).

5. Applying adaptive shrinkage to the GWAS results

Here, we use the R package mashr to perform multivariate adaptive shrinkage on the results of the GWAS, for an LD-pruned subset of loci. This produces corrected estimates of each SNP’s effect size, and allows estimation of the frequencies of different types of loci (e.g. sexually- or age-antagonistic loci).

6. Running the TWAS and applying mashr to the TWAS results

This script uses the transcriptomic data on the DGRP from Huang et al. 2015 PNAS. In this script, we

Calculate the average expression and sex bias in expression for each transcript in Huang et al.
Perform ‘TWAS’, i.e. searching for transcripts whose expression correlates with our fitness measures across lines
Adjust the results of the TWAS linear models using mashr in canonical and data-driven modes.

Plots, tables, and analyses of the results

8. Plots showing line mean fitness

This script plots the estimated line means for each of the four fitness metrics, i.e. Figure 1 in the paper.

9. Tables of GWAS results

This script presents a searchable HTML table showing list of significant loci from the GWAS using GEMMA, with annotations, effect sizes, and $p$ -values for each one.

10. Tables of TWAS results

Text here

11. Plot and model the variant effect sizes

Here, we plot the correlations in the estimated effects of each variant (or group of variants in perfect linkage disequilibrium with one another). For the GWAS results, we run linear models to test whether variants differ in their effect on fitness according to chromosome, site class, and minor allele frequency. For the TWAS results, we run linear models to test whether transcripts differ in their effect on fitness according to chromosome, average expression level, and the sex bias in expression.