1. Commandline options

This is the simplest way to plot manhattan and QQ plots if you already have PLINK2.x association output. You can directly type qmplot --help and will find all the options below:

usage: qmplot [-h] -I INPUT -O OUTPREFIX [-T TITLE] [-P SIGN_PVALUE] [-M M_ID]
          [--open-gui]

qmplot: Creates high-quality manhattan and QQ plots from PLINK association
output (or any dataframe with chromosome, position, and p-value).

optional arguments:
  -h, --help            show this help message and exit
  -I INPUT, --input INPUT
                        Input file
  -O OUTPREFIX, --outprefix OUTPREFIX
                        The prefix of output file
  -T TITLE, --title TITLE
                        Title of figure
  -P SIGN_PVALUE, --sign-mark-pvalue SIGN_PVALUE
                        Genome wide significant p-value sites. [1e-6]
  -M M_ID, --top-sign-signal-mark-id M_ID
                        A string denoting the column name for which you want
                        to annotate the Top Significant SNPs. Default:
                        PLINK2.x's "ID"
  --open-gui            Set to be GUI backend, which can show the figure.

The following command will give you the two png plots with 300 dpi resolution:

$ qmplot -I data/gwas_plink_result.tsv -T Test -M ID --dpi 300 -O test

The manhattan plot looks like:

The Q-Q plot looks like:

Note: You can only modify the plots throught qmplot commandline options which is a big limitation when you want to make more change.

2. Python package

This is the most flexible way. You can use qmplot as a package in you Python code and create the plots by your mind.

Manhattan plot with default parameters:

The manhattanplot() function in qmplot package takes a data frame with columns containing the chromosomal name/id, chromosomal position, P-value and optionally the SNP name(e.g. rsID in dbSNP).

By default, manhattanplot() looks fro column names corresponding to those outout by the plink2 association results, namely, “#CHROM”, “POS”, “P”, and “ID”, although different column names can be specificed by user. Calling manhattanplot() function with a data frame of GWAS results as the single argument draws a basic manhattan plot, defaulting to a darkblue and lightblue color scheme.

import pandas as pd
from qmplot import manhattanplot

if __name__ == "__main__":

    df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
    df = df.dropna(how="any", axis=0)  # clean data
    ax = manhattanplot(data=df, figname="output_manhattan_plot.png")

Rotate the x-axis tick label by setting xticklabel_kws to avoid label overlap:

ax = manhattanplot(data=df,
                   xticklabel_kws={"rotation": "vertical"},  # set vertical(or other angle) label.
                   is_show=False,  # do not display the plot
                   figname="output_manhattan_plot.png")

The parameter of manhattanplot() defined the name of output figure file and the format of the figure file is depended on the file suffix, which could be “.png”, “.jpg”, or “.pdf”.

When run with default parameters, the manhattanplot() function draws horizontal lines drawn at $-log_{10}{(1e-5)}$ for “suggestive” associations and $-log_{10}{(5e-8)}$ for the “genome-wide significant” threshold. These can be move to different locations or turned off completely with the arguments suggestiveline and genomewideline, respectively.

ax = manhattanplot(data=df,
                   suggestiveline=None,  # Turn off suggestiveline
                   genomewideline=None,  # Turn off genomewideline
                   xticklabel_kws={"rotation": "vertical"},
                   is_show=False,  # do not display the plot
                   figname="output_manhattan_plot.png")

The behavior of the manhattanplot function changes slightly when results from only a single chromosome are used. Here, instead of plotting alternating colors and chromosome ID on the x-axis, the SNP’s position on the chromosome is plotted on the x-axis:

# plot only results on chromosome 8.
manhattanplot(data=df, CHR="8", xlabel="Chromosome 8", is_show=False,
              figname="output_chr8_manhattan_plot.png")

manhattanplot() funcion has the ability to highlight SNPs with significant GWAS signal and annotate the Top SNP:

ax = manhattanplot(data=df,
                   xtick_label_set=xtick,
                   sign_marker_p=1e-6,  # highline the significant SNP with ``sign_marker_color`` color.
                   is_annotate_topsnp=True,  # annotate the top SNP
                   xticklabel_kws={"rotation": "vertical"},
                   is_show=False,
                   figname="output_manhattan_anno_plot.png")

Additionally, highlighting SNPs of interest can be combined with limiting to a single chromosome to enable “zooming” into a particular region containing SNPs of interest.

A better Manhattan plot

Futher graphical parameters can be passed to the manhattanplot() function to control thing like plot title, point character, size, colors, etc. Here is the example:

import pandas as pd
from qmplot import manhattanplot

if __name__ == "__main__":

    df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
    df = df.dropna(how="any", axis=0)  # clean data

    # Create a manhattan plot
    f, ax = plt.subplots(figsize=(12, 4), facecolor='w', edgecolor='k')
    xtick = set(list(map(str, range(1, 15))) + ['16', '18', '20', '22', 'X'])
    manhattanplot(data=data,
                  marker=".",
                  sign_marker_p=1e-6,  # Genome wide significant p-value
                  sign_marker_color="r",
                  snp="ID",

                  title="Test",
                  xtick_label_set=xtick,
                  xlabel="Chromosome",
                  ylabel=r"$-log_{10}{(P)}$",

                  sign_line_cols=["#D62728", "#2CA02C"],
                  hline_kws={"linestyle": "--", "lw": 1.3},

                  is_annotate_topsnp=True,
                  ld_block_size=50000,  # 50000 bp
                  annotext_kws={"size": 12,  # The fontsize of annotate text
                                "xycoords": "data",
                                "xytext": (15, +15),
                                "textcoords": "offset points",
                                "bbox": dict(boxstyle="round", alpha=0.2),
                                "arrowprops": dict(arrowstyle="->",  # "-|>"
                                                   connectionstyle="angle,angleA=0,angleB=80,rad=10",
                                                   alpha=0.6, relpos=(0, 0))},

                  dpi=300,
                  figname="output_manhattan_plot.png",
                  ax=ax)

Find more detail about the parameters can be found by typing manhattanplot? in IPython console.

QQ plot with defualt parameters.

The qqplot() function can be used to generate a Q-Q plot to visualize the distribution of association “P-value”. The qqplot() function takes a vector of P-values as its the only required argument.

import pandas as pd
from qmplot import qqplot

if __name__ == "__main__":

    df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
    df = df.dropna(how="any", axis=0)  # clean data
    ax = qqplot(data=list(df["P"]), figname="output_QQ_plot.png")

A better QQ plot

Futher graphical parameters can be passed to qqplot() to control the plot title, axis labels, point characters, colors, points sizes, etc. Here is the example:

import pandas as pd
from qmplot import qqplot

if __name__ == "__main__":

    df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
    df = df.dropna(how="any", axis=0)  # clean data
    # Create a Q-Q plot
    f, ax = plt.subplots(figsize=(6, 6), facecolor="w", edgecolor="k")
    qqplot(data=list(data["P"]),
           marker="o",
           title="Test",
           xlabel=r"Expected $-log_{10}{(P)}$",
           ylabel=r"Observed $-log_{10}{(P)}$",
           dpi=300,
           figname="output_QQ_plot.png",
           ax=ax)

Find more detail about the parameters by typing qqplot? in IPython console.