# Empirical comparison of P-value combination methods in mvMAPIT

    library(mvMAPIT)
    library(GGally)
    library(tidyr)
    library(dplyr)

In Stamp et al. (2023)1 we discuss the meta analysis of the pariwise comparison \(P\)-values that mvMAPIT gives. We provide three methods to compute a per-variant combined \(P\)-value:

1. Fisher’s Method2
2. The harmonic mean \(P\)3
3. The Cauchy combination test4

The Fisher’s method requires independent \(P\)-values. The other two tests handle arbitrary covariances between the \(P\)-values. Here we show how these three methods compare empirically when applied to the same \(P\)-values.

## Generate data

Draw random uniform \(P\)-values from [0, 0.05].

    n_variants <- 10000
    n_combine <- 3
    pvalues <- tidyr::tibble(
       id = rep(as.character(c(1:n_variants)), each = n_combine),
       trait = rep(as.character(c(1:n_combine)), n_variants),
       p =  runif(n_variants * n_combine, min = 0, max = 1)
    )

## Combine \(P\)-Values with all methods

Use the provided methods to compute the meta analysis \(P\)-values.

    cauchy <- cauchy_combined(pvalues) %>%
      rename(p_cauchy = p) %>%
      select(-trait)
    fisher <- fishers_combined(pvalues) %>%
      rename(p_fisher = p) %>%
      select(-trait)
    harmonic <- harmonic_combined(pvalues) %>%
      rename(p_harmonic = p) %>%
      select(-trait)
    min_max <- pvalues %>%
      group_by(id) %>%
      summarise(p_min = min(p), p_max = max(p))

    combined_wide <- fisher %>%
      left_join(harmonic) %>%
      left_join(cauchy) %>%
      left_join(min_max) %>%
      select(-id)
    # Joining with `by = join_by(id)`
    # Joining with `by = join_by(id)`
    # Joining with `by = join_by(id)`

# Plot the result

The figure shows the data distribution on the diagonal and paired 2D historgam plots for all combinations of \(P\)-values. The brighter yellows correspond to higher counts, the green is in the middle of the scale, and the darker blues correspond to the low values of the histogram.

    my_bin <- function(data, mapping) {
      ggplot(data = data, mapping = mapping) +
      geom_bin2d() +
      scale_fill_continuous(type = "viridis")
    }
    ggpairs(combined_wide, columns = 1:5,
            lower = list(continuous = my_bin)) +
      theme_bw() +
      theme(axis.text.x = element_text(angle=90, hjust=1))

# References

1. J. Stamp, A. DenAdel, D. Weinreich, L. Crawford (2023). Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies. G3 Genes|Genomes|Genetics, 13(8), jkad118; doi: https://doi.org/10.1093/g3journal/jkad118↩︎

2. Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). ISBN 0-05-002170-2.↩︎

3. Wilson, D.J., 2019. The harmonic mean p-value for combining dependent tests. Proceedings of the National Academy of Sciences, 116(4), pp.1195-1200. https://doi.org/10.1073/pnas.1814092116↩︎

4. Liu, Y. and Xie, J., 2020. Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. Journal of the American Statistical Association, 115(529), pp.393-402. https://doi.org/10.1080/01621459.2018.1554485↩︎