QC_plots: QQ and Manhattan plots
In QCGWAS: Quality Control of Genome Wide Association Study Results

QC_plots

R Documentation

QQ and Manhattan plots

Description

This function creates the most important graphs of the QC: the QQ plots and the Manhattan plot. It also calculates lambda, and determines the effect of the filters.

Usage

QC_plots(dataset,
         plot_QQ = TRUE, plot_Man = TRUE,
         FRQfilter_values = NULL, FRQfilter_NA = filter_NA,
         HWEfilter_values = NULL, HWEfilter_NA = filter_NA,
         calfilter_values = NULL, calfilter_NA = filter_NA,
         impfilter_values = NULL, impfilter_NA = filter_NA,
         impfilter_min = min(dataset$IMP_QUALITY, na.rm = TRUE),
         manfilter_FRQ = NULL, manfilter_HWE = NULL,
         manfilter_cal = NULL, manfilter_imp = NULL,
         filter_NA = TRUE,
         plot_cutoff_p = 0.05, plot_names = FALSE,
         QQ_colors = c("red", "blue", "orange", "green3", "yellow"),
         plot_QQ_bands = FALSE,
         save_name = "dataset", save_dir = getwd(),
         header_translations, use_log = FALSE,
         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`	vector of p-values or 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.
`plot_QQ, plot_Man`	logical; should QQ and Manhattan plots be saved?
`FRQfilter_values, HWEfilter_values, calfilter_values, impfilter_values`	numeric vectors; the threshold values for the QQ plot filters. The filters are for allele-frequency, HWE p-values, callrate and imputation-quality parameters, respectively. A maximum of five values can be specified per parameter. Set to `NULL` to disable the QQ filter for that parameter. To filter missing values only, set the filter value to `NA` and the corresponding `filter_NA` argument to `TRUE`. The allele-frequency filter is two-sided: for a filter-value of `x`, it will exclude entries with `freq < x` and `freq > 1 - x`. Values >= 1 will be divided by the SNP's sample size. This allows sample-size dependent filtering of allele frequencies. Note that this uses the sample size reported in the sample-size column for that specific SNP. SNPs without sample size will be excluded if the corresponding `filter_NA` argument is `TRUE` and ignored if it is `FALSE`.
`FRQfilter_NA, HWEfilter_NA, calfilter_NA, impfilter_NA, filter_NA`	logical; should the filters exclude (`TRUE`) or ignore (`FALSE`) missing values? `filter_NA` is the default setting, the others allow variable-specific settings.
`impfilter_min`	numeric; the lowest possible value for imputation-quality. This argument is currently redundant, as it is calculated automatically.
`manfilter_FRQ, manfilter_HWE, manfilter_cal, manfilter_imp`	single, numeric values; the filter-settings for allele-frequency, HWE p-values, callrate and imputation quality respectively, for the Manhattan plot. The arguments are passed to `HQ_filter`. To filter missing values only, set to `NA` and the corresponding `filter_NA` argument to `TRUE`. To disable filtering entirely, set to `NULL`.
`plot_cutoff_p`	numeric; the threshold of p-values to be shown in the QQ & Manhattan plots. 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_names`	argument currently redundant.
`QQ_colors`	vector of R color-values; the color of the QQ filter-plots. The unfiltered data is black by default. This argument sets the colors of the least (first value) to most (last value) stringent filters. (For this setting, filter values `>= 1` (i.e. sample-size based filtering) are considered less stringent than values `< 1`.)
`plot_QQ_bands`	logical; should probability bands be added to the QQ plot?
`save_name`	character string; the filename, without extension, for the graphs.
`save_dir`	character string; the directory where the graphs are saved. Note that R uses forward slash (/) where Windows uses the backslash (\).
`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`.
`use_log`	argument used by `QC_GWAS`; redundant when `QC_plots` is used separately.
`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`.

Details

The function QC_plots grew out of phase 4 of QC_GWAS. It carries out three functions, hence the vague name: it calculates lambda, it applies the QQ filters, and it creates the QQ and Manhattan plots (a separate function is available for creating regional-association plots: see below). The function schematic is as follows:

Preparing the dataset: this step involves translating the dataset header to the standard column-names (by identify_column) and converting imputation status (by convert_impstatus). Both steps are optional, and are disabled by default. If the function cannot identify the imputation status column, it will generate a warning message and disable the imputation-status dependent filters.
Calculating the QC stats: here it generates the filters an calculates how many SNPs are removed. Lambda is also calculated at this point.
Creating a QQ graph of every variable for which filters have been specified. Every graph contains an unfiltered plot, plus plots for every effective filter. ("Effective" means "excludes more SNPs than the previous, less-stringent filter".)
Creating the Manhattan plot. The default Manhattan plot covers chromosomes 1 to 23 (X). Fields for XY, Y and M are added when such SNPs are present.

Value

An object of class 'list' with the following components:

`lambda`	vector of the lambda values of all SNPs, genotyped SNPs and imputed SNPs, respectively.
`ignore_impstatus`	logical value indicating whether imputation status was used when applying the filters.
`FRQfilter_names, HWEfilter_names, calfilter_names, impfilter_names`	character vectors naming the specified QQ filters.
`FRQfilter_N, HWEfilter_N, calfilter_N, impfilter_N`	numeric vectors; the number of SNPs removed by the specified filters. Note that the filters are sorted before being applied, so the order may not match that of the input. Check the `filter_names` output to see the order that was used inside `QC_plots`.
`Manfilter_N`	numeric; the number of SNPs removed by the Manhattan filter. This does not include those SNPs removed because they lacked p or chromosome/position-values, or failed the p-cutoff threshold.

Note

By default, QC_plots expects dataset to use the standard column-names used by QC_GWAS. A translation table can be specified in header_translations to allow non-standard names. See translate_header for more information.

The function accepts both integer and character chromosome values. Character values of "X", "Y", "XY" and "M" are automatically converted to integers. By default, the Manhattan plot shows all autosomal chromosomes and chromosome X. Fields for Y, XY and M are added only when such SNPs are present.

There must be more than 10 p-values at or below the plot_cutoff_p threshold for the QQ and Manhattan plots to be created.

Examples

  ## Not run: 
  data("gwa_sample")

  QC_plots(dataset = gwa_sample,
    plot_QQ = TRUE, plot_QQ_bands = TRUE, plot_Man = TRUE,
    FRQfilter_values = c(NA, 0.01, 0.05, 3),
    calfilter_values = c(NA, 0.95, 0.99),
    manfilter_FRQ = 0.05, manfilter_cal = 0.95,
    filter_NA = TRUE, save_name = "sample_plots")
    
## End(Not run)

QCGWAS documentation built on May 30, 2022, 5:05 p.m.