Running the meta-analysis
Last updated: 2026-03-03
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Knit directory: exp_evol_metaanalysis/
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Load packages, the meta-analysis data, and a helper function.
library(tidyverse)
library(metafor)
library(DT)
library(ggh4x)
library(kableExtra)
# function to make the searchable, exportable HTML table
my_data_table <- function(df){
num_cols <- names(df)[sapply(df, is.numeric)]
num_cols <- num_cols[num_cols != "Generations"]
datatable(
df, rownames = FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
autoWidth = TRUE,
dom = 'Bfrtip',
deferRender = TRUE,
scrollX = TRUE,
scrollY = 1000,
scrollCollapse = TRUE,
buttons = list(
'pageLength',
'colvis',
list(
extend = 'csv',
filename = 'holman_meta_analysis'
)
),
pageLength = 100
)
) %>%
formatRound(
columns = num_cols,
digits = 3
)
}
# Text formatters
pretty2 <- function(x) {
formatC(x, format = "f", digits = 2)
}
pretty3 <- function(x) {
formatC(x, format = "f", digits = 3)
}
# Load up the meta-analysis data:
meta_analysis_data <- list.files("effect_sizes/", full.names = T) %>%
lapply(read_csv, show_col_types = FALSE) %>%
bind_rows() %>%
left_join(read_csv("input_data/study_metadata.csv"), by = join_by(Study)) %>%
mutate(year = str_extract(Short_name, "[:digit:]+")) %>%
arrange(year) %>%
mutate(
first_word = map_chr(str_split(Trait, " "), ~.x[[1]]),
Sex = NA,
Sex = replace(Sex, first_word %in% c("Male", "Sperm", "Mating"), "Male"),
Sex = replace(Sex, first_word %in% c("Viability", "Egg-to-adult"), "Both"),
Sex = replace(Sex, first_word == "Female", "Female")) %>%
select(-first_word)Data collected for the meta-analysis
This table is scrollable, searchable, and can be exported as a
.csv file. You can also sort by column, or hide columns.
The table lists 66 effect sizes (and their associated variance and 95%
CIs) from 10 independent experimental evolution projects, which were
published across 18 journal articles. The Trait column
gives the name of the trait being measured, Generations
gives the number of generations of selection before measuring this
trait, Interpretation gives a plain English description of
what the effect size means, Origin_of_lines column gives
the reference that first created the lines, the two
Experiment_type columns are two ways of categorising the
type of experiment, and Sex gives the sex of the
individuals being measured
meta_analysis_data %>%
select(-year, -Short_name) %>%
my_data_table()Meta-analysis without moderators
This meta-analysis can be used to calculate the grand mean effect
size. It’s a mixed effects meta-analysis with the random effects
structure ~ 1 | Origin_of_lines/Study. This means we fit a
random intercept for Origin_of_lines, a variable which
gives the name of the study that first created the experimental
evolution lines being measured. We also fit a random intercept for
Study nested within Origin_of_lines, where
Study is a variable giving the name of the study doing the
measuring (which is often the same study, but not always, since some
studies re-measured lines that were created in an earlier study).
meta_analysis <- rma.mv(
yi = Estimate,
V = Var,
random = ~ 1 | Origin_of_lines/Study,
data = meta_analysis_data,
method = "REML"
)
summary(meta_analysis)
Multivariate Meta-Analysis Model (k = 66; method: REML)
logLik Deviance AIC BIC AICc
-468.6145 937.2290 943.2290 949.7521 943.6224
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0000 0.0001 10 no Origin_of_lines
sigma^2.2 0.1342 0.3663 18 no Origin_of_lines/Study
Test for Heterogeneity:
Q(df = 65) = 1335.9187, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
0.3268 0.0898 3.6383 0.0003 0.1508 0.5029 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The above results table shows that the grand mean effect size is 0.327, with a standard error of 0.090 and 95% CIs of 0.151 to 0.503.
Testing for effects of moderators
Here, I use AICc model selection to compare 7 competing meta-analysis
models, which fit Generations, Sex,
Experiment_type, a combination of 2 or all of these, or the
intercept-only null model. Generations gives the number of
generations of selection at the time the focal trait was measured,
Sex gives the sex of the individuals expressing the trait
(male, female, or both sexes), and Experiment_type is
either “Sex-specific autosome(s)”, “Sex-specific X”, or “Sex-specific
relaxed selection” (as described by the point colours in Figure 1).
The top model includes the moderator Generations, and it
is ahead of second-best model (which also includes Sex) by
much more than 2 AICc, suggesting that Generations explains
substantial variation in the data but the other moderators do not.
fit_ma <- function(formula){
# there are only 2 effect sizes relating to both sexes, so they crash the model. Just remove from this moderator analysis. Also scale the generations variable to mean 0, variance 1.
scaled_meta_analysis_data <-
meta_analysis_data %>%
mutate(Generations = as.numeric(scale(Generations))) %>%
filter(Sex != "Both")
if(formula == "Intercept only"){
return(meta_analysis)
}
rma.mv(
yi = Estimate,
V = Var,
mods = as.formula(formula),
random = ~ 1 | Origin_of_lines/Short_name,
data = scaled_meta_analysis_data,
method = "REML"
)
}
fit_all <- function(formulae){
meta <- lapply(formulae, fit_ma)
get_p <- function(ma) as.numeric(ma$p)
get_logLik <- function(ma) as.numeric(logLik(ma))
get_AICc <- function(ma) as.numeric(AIC(ma, correct = T))
data.frame(
Model = formulae,
k = sapply(meta, get_p),
logLik = sapply(meta, get_logLik),
AICc = sapply(meta, get_AICc)) %>%
arrange(AICc) %>%
mutate(Model = str_remove_all(Model, "~ "),
delta_AICc = round(AICc - AICc[1], 2),
delta_AICc = c(" ", delta_AICc[2:length(delta_AICc)]))
}
fit_all(c("~ Generations + Sex + Experiment_type1",
"~ Generations + Sex",
"~ Sex + Experiment_type1",
"~ Generations",
"~ Sex",
"~ Experiment_type1",
"Intercept only")) %>%
kable() %>%
kable_styling(full_width = FALSE)| Model | k | logLik | AICc | delta_AICc |
|---|---|---|---|---|
| Generations | 2 | -460.9838 | 930.6694 | |
| Generations + Sex | 3 | -461.0740 | 933.2388 | 2.57 |
| Generations + Sex + Experiment_type1 | 5 | -459.6127 | 935.4215 | 4.75 |
| Intercept only | 1 | -468.6145 | 943.6224 | 12.95 |
| Experiment_type1 | 3 | -467.1326 | 945.3562 | 14.69 |
| Sex | 2 | -470.8512 | 950.4042 | 19.73 |
| Sex + Experiment_type1 | 4 | -469.1194 | 951.8236 | 21.15 |
Below are the results of the top model (ranked by AICc) with one or
more moderators, showing the significant negative effect of the
moderator Generations on effect size (note:
Generations was scaled to have mean 0, SD 1, prior to
analysis). This is a counter-intuitive result since we might expect
greater effects after more generations of selection, but it is not very
informative since Generations is confounded with other
factors (e.g. species, type of experimental design, type of trait being
measured, and whether I used the raw data or summary statistics to
calculate effect size). The grand mean effect size (predicted here for
the average value of Generations, which is 52.8) is 0.327
(0.170-0.483), which is much the same as the previous meta-analysis that
did not adjust for the effect of Generations.
fit_ma("~ Generations")
Multivariate Meta-Analysis Model (k = 64; method: REML)
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0000 0.0001 10 no Origin_of_lines
sigma^2.2 0.1034 0.3215 18 no Origin_of_lines/Short_name
Test for Residual Heterogeneity:
QE(df = 62) = 1303.7468, p-val < .0001
Test of Moderators (coefficient 2):
QM(df = 1) = 17.3452, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.3266 0.0797 4.0979 <.0001 0.1704 0.4829 ***
Generations -0.1197 0.0288 -4.1648 <.0001 -0.1761 -0.0634 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Forest plot of effect sizes (Figure 1)
exp2_levs <- c("Male-limited haplotypes",
"Male-limited autosome",
"Female-limited markers",
"Reciprocal sex-limited markers",
"Male-limited X chromosomes",
"Female-limited X chromosomes",
"Relaxed selection in females",
"Relaxed selection in both sexes")
MA_result <- paste0(pretty2(as.numeric(meta_analysis$b)), " (",
pretty2(as.numeric(meta_analysis$ci.lb)),
"-",
pretty2(as.numeric(meta_analysis$ci.ub)),
")", collapse = "")
plot_data <- meta_analysis_data %>%
select(Origin_of_lines, Short_name,
Experiment_type1, Experiment_type2, Trait, Estimate,
Lower_95_CI,
Upper_95_CI) %>%
mutate(Experiment_type2 = factor(Experiment_type2, exp2_levs))
mean_text <- paste0("**Mean effect size:** ", MA_result, collapse="")
mean_effect <- tibble(
Origin_of_lines = " ", Short_name = mean_text,
Experiment_type1 = " ", Trait = " ",
Estimate = as.numeric(meta_analysis$b),
Lower_95_CI = as.numeric(meta_analysis$ci.lb),
Upper_95_CI = as.numeric(meta_analysis$ci.ub)
)
plot_data <- plot_data %>%
group_by(Origin_of_lines) %>%
mutate(Short_name = paste0(unique(Short_name), collapse = ";\n")) %>%
bind_rows(mean_effect) %>% ungroup() %>%
arrange((Experiment_type2))
plot_data$Short_name[plot_data$Short_name == "Rice (1996);\nRice (1998)"] <- "**Male-limited haplotypes**<br>Rice (1996, 1998)"
plot_data$Short_name[plot_data$Short_name == "Prasad et al. (2007);\nBedhomme et al. (2008);\nAbbott et al. (2010);\nBedhomme et al. (2011);\nJiang et al. (2011)"] <- "**Male-limited haplotypes**<br>Prasad et al. (2007)<br>Bedhomme et al. (2008, 2011)<br>Jiang et al. (2011)"
plot_data$Short_name[plot_data$Short_name == "Thyagarajan et al. (2025)"] <- "**Male-limited haplotypes**<br>Thyagarajan et al. (2025)"
plot_data$Short_name[plot_data$Short_name == "Rice (1992)"] <- "**Female-limited region** (Rice 1992)"
plot_data$Short_name[plot_data$Short_name == "Grieshop et al. (2025);\nMelo-Gavin et al. (2025)"] <- "**Male-limited autosome**<br>Grieshop et al. (2025)<br>Melo-Gavin et al. (2025)"
plot_data$Short_name[plot_data$Short_name == "Nordén et al. (2020)"] <- "**Reciprocal sex-limited markers**<br>Nordén et al. (2020)"
plot_data$Short_name[plot_data$Short_name == "Abbott et al. (2013);\nAbbott et al. (2020)"] <- "**Male-limited X**<br>Abbott et al. (2013, 2020)"
plot_data$Short_name[plot_data$Short_name == "Lund-Hansen et al. (2020);\nManat et al. (2021)"] <- "**Female-limited X**<br>Lund-Hansen et al. (2020)<br>Manat et al. (2021)"
plot_data$Short_name[plot_data$Short_name == "Radwan et al. (2004)"] <- "**Relaxed selection on females**<br>Radwan et al. (2004)"
plot_data$Short_name[plot_data$Short_name == "Morrow et al. (2008)"] <- "**Relaxed selection on both sexes**<br>Morrow et al. (2004)"
plot_data$Short_name <- str_remove_all(plot_data$Short_name, " et al[.]")
levs <- plot_data$Short_name %>% unique()
plot_data <- plot_data %>%
mutate(Short_name = factor(Short_name, levs))
bands <- plot_data %>%
arrange(Estimate) %>%
group_by(Short_name) %>%
mutate(y = row_number()) %>%
summarise(
ymin = min(y) - 0.5,
ymax = max(y) + 0.5,
stripe = cur_group_id() %% 2,
.groups = "drop"
)
# order the traits within studies by effect size
plot_data <- plot_data %>%
mutate(Short_name = factor(Short_name, levs)) %>%
arrange(Estimate) %>%
mutate(Trait_study = factor(paste(Trait, Short_name, sep = "~"),
unique(paste(Trait, Short_name, sep = "~"))))
mean_effect <- plot_data %>%
filter(Short_name == mean_text)
plot_data %>%
mutate(Experiment_type1 = factor(
Experiment_type1, levels = c("Sex-specific autosome(s)",
"Sex-specific X",
"Sex-specific relaxed selection", " "))) %>%
ggplot(aes(y = Trait_study, x = Estimate, colour = Experiment_type1)) +
# Horizontal grey bars
geom_rect(
data = bands,
aes(
xmin = -Inf,
xmax = Inf,
ymin = -Inf,
ymax = Inf,
fill = factor(stripe)),
alpha = 0.3,
inherit.aes = FALSE) +
# vertical line at zero
geom_vline(xintercept = 0, linetype = 2, colour = "grey10") +
# Points and error bars for primary effects
geom_errorbar(aes(xmin = Lower_95_CI,
xmax = Upper_95_CI)) +
geom_point() +
# Point and bars for the overall mean effect
geom_errorbar(data = mean_effect, aes(xmin = Lower_95_CI,
xmax = Upper_95_CI), colour = "black") +
geom_point(data = mean_effect, colour = "black") +
# Scales, theme and parsed text labels
scale_y_discrete(labels = function(x) sub("~.*$", "", x)) +
facet_grid2(rows = vars(Short_name),
scales = "free_y",
space = "free_y") +
theme_minimal() +
theme(legend.position = "none",
strip.text.y = ggtext::element_markdown(angle = 0, hjust = 0, size = 7),
axis.text.y = element_text(size = 7),
strip.background = element_blank(),
panel.grid.major.y = element_blank(),
axis.ticks.x = element_line(colour = "grey20", linewidth = 0.2),
panel.border = element_rect(colour = "grey20", linewidth = 0.2),
panel.spacing.y=unit(0, "pt"))+
labs(x = "Effect size (Cohen's d) \u00B1 95% CIs",
y = NULL,
colour = NULL) +
coord_cartesian(clip = "off") +
scale_colour_manual(values = c("steelblue", "tomato", "forestgreen", NA)) +
scale_fill_manual(values = c("0" = "grey80",
"1" = "grey50")) +
guides(fill = "none")
Figure 1: Forest plot showing the estimated effect sizes (Cohen’s d \(\pm\) 95% CIs). The y-axis indicates the trait being measured while horizontal grey bands show the 10 independent experimental evolution projects (projects are measured in one or more publications, named on the right). Positive effect sizes indicate evolutionary change in the direction predicted under IaSC, while negative effects indicate evolution in the opposite direction. Point colour highlights experiments using sex-limited inheritance of autosomal (blue) or X (red) genomic regions, or sex-specific relaxed selection (green). The mean effect size from meta-analysis is shown in black.
Plotting effect of moderator Generations
On the right of x-axis (high generation number), this result is driven by a couple of studies, Lund-Hansen et al. (2020) and Manat et al. (2021), which both relate to the same set of experimental evolution lines, and which both focus on female-limited evolution of X chromosomes (uniquely in the full dataset). I also calculated the effect sizes from the raw data for these studies, which avoids falsely inflated the effect sizes (as for some studies where only the overall means were available, incurring pseudoreplication). To the left of the x-axis (short generation number), the trend is driven by a couple of older studies I calculated the effect sizes from overall means, possibly inflating them.
Therefore, I don’t feel there is good evidence for a true negative
effect of Generations, since this result could easily
result from confounding.
library(ggrepel)
meta_analysis_data %>%
ggplot(aes(Generations, Estimate)) +
geom_point() +
geom_text_repel(aes(label = Short_name), size = 2, max.overlaps = 1000)
| Version | Author | Date |
|---|---|---|
| 8915f97 | luekholman | 2026-02-27 |
| 65e7263 | luekholman | 2026-02-27 |
| c6491af | luekholman | 2026-02-27 |
| c3874eb | luekholman | 2026-02-27 |
| 4a05564 | luekholman | 2026-02-27 |
| 8d9eb2b | luekholman | 2026-02-26 |
| f80a6dd | luekholman | 2026-02-26 |
| 0560e72 | luekholman | 2026-02-26 |
| cbdc84d | luekholman | 2026-02-25 |
| 4719bf8 | luekholman | 2026-02-20 |
| 3f80499 | luekholman | 2026-02-10 |
| f546a4c | luekholman | 2026-02-10 |
| 67d1c7a | luekholman | 2026-02-09 |
| b0251e4 | luekholman | 2026-02-09 |
| f4fb206 | luekholman | 2026-02-09 |
| 6029ddf | luekholman | 2026-02-09 |
| 7057ba1 | luekholman | 2026-02-09 |
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.2
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/London
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggrepel_0.9.6 kableExtra_1.4.0 ggh4x_0.3.1
[4] DT_0.34.0 metafor_4.8-0 numDeriv_2016.8-1.1
[7] metadat_1.4-0 Matrix_1.7-4 lubridate_1.9.4
[10] forcats_1.0.1 stringr_1.6.0 dplyr_1.2.0
[13] purrr_1.2.1 readr_2.1.6 tidyr_1.3.2
[16] tibble_3.3.1 ggplot2_4.0.2 tidyverse_2.0.0
[19] workflowr_1.7.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2 S7_0.2.1
[5] fastmap_1.2.0 mathjaxr_2.0-0 promises_1.5.0 digest_0.6.39
[9] timechange_0.4.0 lifecycle_1.0.5 processx_3.8.6 magrittr_2.0.4
[13] compiler_4.5.2 rlang_1.1.7 sass_0.4.10 tools_4.5.2
[17] yaml_2.3.12 knitr_1.51 labeling_0.4.3 htmlwidgets_1.6.4
[21] bit_4.6.0 xml2_1.5.2 RColorBrewer_1.1-3 withr_3.0.2
[25] grid_4.5.2 git2r_0.36.2 scales_1.4.0 cli_3.6.5
[29] rmarkdown_2.30 crayon_1.5.3 generics_0.1.4 otel_0.2.0
[33] rstudioapi_0.18.0 httr_1.4.7 tzdb_0.5.0 commonmark_2.0.0
[37] cachem_1.1.0 parallel_4.5.2 vctrs_0.7.1 jsonlite_2.0.0
[41] litedown_0.9 callr_3.7.6 hms_1.1.4 bit64_4.6.0-1
[45] systemfonts_1.3.1 crosstalk_1.2.2 jquerylib_0.1.4 glue_1.8.0
[49] ps_1.9.1 ggtext_0.1.2 stringi_1.8.7 gtable_0.3.6
[53] later_1.4.5 pillar_1.11.1 htmltools_0.5.9 R6_2.6.1
[57] textshaping_1.0.4 rprojroot_2.1.1 vroom_1.7.0 evaluate_1.0.5
[61] lattice_0.22-7 markdown_2.0 gridtext_0.1.6 httpuv_1.6.16
[65] bslib_0.10.0 Rcpp_1.1.1 svglite_2.2.2 nlme_3.1-168
[69] whisker_0.4.1 xfun_0.56 fs_1.6.6 getPass_0.2-4
[73] pkgconfig_2.0.3