QCGWAS (version 1.0-9)

QQ_plot: QQ plot(s) of expected vs. reported p-values

Description

QQ_plot generates a simple QQ plot of the expected and reported p-value distribution. It includes the option to filter the data with the high-quality filter. QQ_series generates a series of such QQ plots for multiple filter settings.

Usage

QQ_plot(dataset, save_name = "dataset", save_dir = getwd(),
        filter_FRQ = NULL, filter_cal = NULL,
        filter_HWE = NULL, filter_imp = NULL,
        filter_NA = TRUE,
        filter_NA_FRQ = filter_NA, filter_NA_cal = filter_NA,
        filter_NA_HWE = filter_NA, filter_NA_imp = filter_NA,
        p_cutoff = 0.05, plot_QQ_bands = FALSE,
        header_translations,
        check_impstatus = FALSE, ignore_impstatus = FALSE,
        T_strings = c("1", "TRUE", "yes", "YES", "y", "Y"),
        F_strings = c("0", "FALSE", "no", "NO", "n", "N"),
        NA_strings = c(NA, "NA", ".", "-"), ...)
QQ_series(dataset, save_name = "dataset", save_dir = getwd(),
          filter_FRQ = NULL, filter_cal = NULL,
          filter_HWE = NULL, filter_imp = NULL,
          filter_NA = TRUE,
          filter_NA_FRQ = filter_NA, filter_NA_cal = filter_NA,
          filter_NA_HWE = filter_NA, filter_NA_imp = filter_NA,
          p_cutoff = 0.05, plot_QQ_bands = FALSE,
          header_translations,
          check_impstatus = FALSE, ignore_impstatus = FALSE,
          T_strings = c("1", "TRUE", "yes", "YES", "y", "Y"),
          F_strings = c("0", "FALSE", "no", "NO", "n", "N"),
          NA_strings = c(NA, "NA", ".", "-"), ...)

Arguments

dataset

a data frame containing the p-value column and (depending on the settings) columns for chromosome number, position, the quality parameters, sample size and imputation status.

save_name

for QQ_plot, a character string; for QQ_series, a vector of character strings; specifying the filename(s) of the graph, without extension.

save_dir

character string; the directory where the output files are saved. Note that R uses forward slash (/) where Windows uses the backslash (\).

filter_FRQ, filter_cal, filter_HWE, filter_imp

Filter threshold-values for allele-frequency, callrate, HWE p-value and imputation quality, respectively. Passed to HQ_filter. QQ_plot takes only single values, but QQ_series accepts vectors as well (see 'details').

filter_NA

logical; if TRUE, then missing filter variables will be excluded; if FALSE, they will be ignored. QQ_plot takes only single values, but QQ_series accepts vectors as well (see 'Details'). filter_NA is the default setting for all variables; variable-specific settings can be specified with the following arguments.

filter_NA_FRQ, filter_NA_cal, filter_NA_HWE, filter_NA_imp

logical; variable-specific settings for filter_NA. These arguments are passed to HQ_filter.

p_cutoff

numeric; the threshold of p-values to be shown in the QQ plot(s). Higher (less significant) p-values are excluded from the plot. The default setting is 0.05, which excludes 95% of data-points. It's not recommended to increase the value above 0.05, as this may dramatically increase running time and memory usage.

plot_QQ_bands

logical; should probability bands be added to the QQ plot?

header_translations

translation table for column names. See translate_header for more information. If the argument is left empty, dataset is assumed to use the standard column-names of QC_GWAS.

check_impstatus

logical; should the imputation-status column be passed to convert_impstatus?

ignore_impstatus

logical; if FALSE, HWE p-value and callrate filters are applied only to genotyped SNPs, and imputation quality filters only to imputed SNPs. If TRUE, the filters are applied to all SNPs regardless of the imputation status.

T_strings, F_strings, NA_strings

arguments passed to convert_impstatus.

…

arguments passed to plot.

Value

Both functions return an invisible value NULL.

Details

QQ_series accepts multiple filter-values, and passes these one by one to QQ_plot to generate a series of plots. For example, specifying:

filter_FRQ = c(0.05, 0.10), filter_cal = c(0.90, 0.95)

will generate two plots. The first excludes SNPs with allele frequency < 0.05 or callrate < 0.90; the second allele frequency < 0.10 or callrate < 0.95. The same principle applies to the NA_filter settings. If the vectors submitted to the filter arguments are of unequal length, the shorter vector will be recycled until it equals the length of the longer (if possible). To filter missing values only, set the filter to NA and the corresponding NA-filter argument to TRUE. Setting the filter argument to NULL will disable the filter entirely, regardless of the NA-filter setting.

See Also

QC_plots for generating more complex QQ plots as well as Manhattan plots.

QC_histogram for creating histograms.

check_P for comparing the reported p-values to the p expected from the effect size and standard error.

Examples

# NOT RUN {
  
# }
# NOT RUN {
    data("gwa_sample")
  
    QQ_plot(dataset = gwa_sample,
            save_name = "sample_QQ",
            filter_FRQ = 0.01, filter_cal = 0.95,
            filter_NA = FALSE)
  
    QQ_series(dataset = gwa_sample,
              save_name = "sample_QQ",
              filter_FRQ = c(NA, 0.01, 0.01),
              filter_cal = c(NA, 0.95, 0.95),
              filter_NA = c(FALSE, FALSE, TRUE))
  
# }