Illustrating multivariate MAPIT with Simulated Data

Julian Stamp

2024-10-21

Source: vignettes/mvMAPIT.Rmd

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If you use multivariate MAPIT in published research, please cite:

Getting Started

Load necessary libraries. For the sake of getting started, mvMAPIT comes with a small set of simulated data. This data contains random genotype-like data and two simulated quantitative traits with epistatic interactions. To make use of this data, call the genotype data as simulated_data$genotype, and the simulated trait data as simulated_data$trait. The vignette traces the analysis of simulated data. The simulations are described in vignette("simulations").

For a working installation of mvMAPIT please look at theREADME.md or vignette("docker-mvmapit")

Running mvMAPIT

The R routine mvmapit can be run in multiple modes. By default it runs in a hybrid mode, performing tests both wtih a normal Z-test as well as the Davies method. The resulting p-values can be combined using functions provided by mvMAPIT, e.g. fishers_combined(), that work on the pvalues tibble that mvmapit returns.

NOTE: mvMAPIT takes the X matrix as p×np \times n; not as n×pn \times p.

mvmapit_hybrid <- mvmapit(
        t(data$genotype),
        t(data$trait),
        test = "hybrid"
)
fisher <- fishers_combined(mvmapit_hybrid$pvalues)

To visualize the genome wide p-values, we use a Manhattan plot. The p-values are plotted after combining the results from the multivariate analysis using Fisher’s method.

manhplot <- ggplot(fisher, aes(x = 1:nrow(fisher), y = -log10(p))) +
  geom_hline(yintercept = -log10(thresh), color = "grey40", linetype = "dashed") +
  geom_point(alpha = 0.75, color = "grey50") +
  geom_text_repel(aes(label=ifelse(p < thresh, as.character(id), '')))
plot(manhplot)

To control the type I error despite multiple testing, we recommend the conservative Bonferroni correction. The significant SNPs returned by the mvMAPIT analysis are shown in the output below. There are in total 6 significant SNPs after multiple test correction. Of the significant SNPs, 4 are true positives.

thresh <- 0.05 / nrow(fisher)
significant_snps <-  fisher %>%
    filter(p < thresh) # Call only marginally significant SNPs

truth <- simulated_data$epistatic %>%
  ungroup() %>%
  mutate(discovered = (name %in% significant_snps$id)) %>%
  select(name, discovered) %>%
  unique()

significant_snps %>%
  mutate_if(is.numeric, ~(as.character(signif(., 3)))) %>%
  mutate(true_pos = id %in% truth$name) %>%
  kable(., linesep = "") %>%
  kable_material(c("striped"))
id trait p true_pos
snp_00068 fisher 2.65e-10 TRUE
snp_00460 fisher 7.86e-07 FALSE
snp_00469 fisher 1.47e-09 TRUE
snp_00665 fisher 1.17e-09 TRUE
snp_00917 fisher 2.83e-07 TRUE

True Epistatic SNPs

To compare this list to the full list of causal epistatic SNPs of the simulations, refer to the following list. There are 5 causal SNPs. Of these 5 causal SNPs, 4 were succesfully discovered by mvMAPIT.

truth %>%
  kable(., linesep = "") %>%
  kable_material(c("striped"))
name discovered
snp_00068 TRUE
snp_00156 FALSE
snp_00469 TRUE
snp_00665 TRUE
snp_00917 TRUE

Now we may take only the significant SNPs according to their marginal epistatic effects and run a simple exhaustive search between them.

The search itself is a simple regression on the interaction terms between all significant interactions.

# exhaustive search for p-values
pairs <- NULL
if (nrow(significant_snps) > 1) {
  pairnames <- comboGeneral(significant_snps$id, 2)
  # Generate unique pairs of SNP names;
  # for length(names) = n, the result is a (n * (n-1)) x 2 matrix with one row corresponding to a pair
  for (k in seq_len(nrow(pairnames))) {
    fit <- lm(y ~ X[, pairnames[k, 1]]:X[, pairnames[k, 2]])
    p_value1 <- coefficients(summary(fit))[[1]][2, 4]
    p_value2 <- coefficients(summary(fit))[[2]][2, 4]
    tib <- dplyr::tibble(
            x = p_value1,
            y = p_value2,
            u = pairnames[k, 1],
            v = pairnames[k, 2]
    )
    pairs <- bind_rows(pairs, tib)
  }
}

colnames(pairs) <- c(colnames(y), "var1", "var2")

Visualize Exhaustive Search Results

We plot the −log10(p)-\mathrm{log}_{10}(p) of the p-values for the regression coefficients as tile plot to highlight the identified interaction structure.

plotable <- pairs %>%
  pivot_longer(
    cols = starts_with("p_"),
    names_to = "trait",
    names_prefix = "trait ",
    values_to = "p",
    values_drop_na = TRUE
  ) %>%
  mutate(trait = case_when(
    trait == "p_01" ~ "Trait 1",
    trait == "p_02"  ~ "Trait 2"))
tiles <- ggplot(data = plotable, aes(x=var1, y=var2, fill=-log10(p)))+
  geom_tile() +
  facet_wrap(~trait) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_fill_viridis_c()
plot(tiles)

The only significant interactions after multiple testing correction are the interaction between snp00068 and snp_00665 as well as snp_00469 and snp_00917.

pairs %>%
  filter(p_01 < 0.05/nrow(pairs) | p_02 < 0.05/nrow(pairs)) %>%
  kable(., linesep = "", digits = 14) %>%
  kable_material(c("striped"))
p_01 p_02 var1 var2
1.000000e-14 1.645102e-07 snp_00068 snp_00665
2.640252e-07 1.559000e-11 snp_00469 snp_00917

True epistataic structure

Compare the results of the exhaustive search to the true interaction structure. Notice that the only significant interactions in the exhaustive search are the two with the largest true effects.

true_interactions <- simulated_data$interactions %>%
  mutate(var1 = sprintf(group1, fmt = "snp_%05d")) %>%
  mutate(var2 = sprintf(group2, fmt = "snp_%05d")) %>%
  mutate(trait = case_when(
    trait == 1 ~ "Trait 1",
    trait == 2  ~ "Trait 2")) %>%
  select(-c("group1", "group2"))
X <- true_interactions[, c("var1", "var2")]
X  <- t(apply(X, 1, sort))
true_interactions[,c("var1", "var2")]  <- X

epistatic_pairnames <- comboGeneral(simulated_data$epistatic$name %>% unique(), 2)
true_pairs <- NULL
for (k in seq_len(nrow(epistatic_pairnames))) {
  tib <- dplyr::tibble(var1 = epistatic_pairnames[k, 1],
                        var2 = epistatic_pairnames[k, 2])
  true_pairs <- bind_rows(true_pairs, tib)
}
anti <- anti_join(true_pairs, true_interactions) %>%
  mutate(effect_size = 0)
# Joining with `by = join_by(var1, var2)`

true_int_plot <- true_interactions %>%
  bind_rows(anti %>% mutate(trait = "Trait 1")) %>%
  bind_rows(anti %>% mutate(trait = "Trait 2"))

true_tiles <- ggplot(data = true_int_plot, aes(x=var1, y=var2, fill=effect_size)) +
  geom_tile() +
  facet_wrap(~trait) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white")
plot(true_tiles)

While mvMAPIT does not identify the explicit partner, it still implicates more correct SNPs in this example. All true epistatic SNPs are listed in the following table.

true_interactions %>%
  kable(., linesep = "") %>%
  kable_material(c("striped"))
effect_size trait var1 var2
-0.1446703 Trait 1 snp_00156 snp_00469
-0.1463573 Trait 1 snp_00068 snp_00156
0.0876213 Trait 1 snp_00469 snp_00665
-0.4940839 Trait 1 snp_00068 snp_00665
0.4244533 Trait 1 snp_00469 snp_00917
0.1504647 Trait 1 snp_00068 snp_00917
-0.4336821 Trait 2 snp_00156 snp_00469
-0.3663968 Trait 2 snp_00068 snp_00156
0.2189245 Trait 2 snp_00469 snp_00665
-0.7666908 Trait 2 snp_00068 snp_00665
0.9583180 Trait 2 snp_00469 snp_00917
-0.2884704 Trait 2 snp_00068 snp_00917