Manhattan plot and QQ plot

GWASLab provides a customizable plotting function for Manhattan and Q-Q plots.

.plot_mqq()

.plot_mqq()

A simple example

Quick Manhattan and Q-Q plot without any options

mysumstats.plot_mqq()

See other examples here.

Options

Using P or MLOG10P
Adjusting x axis
Adjusting y axis
Changing layout
Annotation
Adding lines
Highlight loci and Pinpoint variants
Colors and fonts
MAF-stratified QQ plot.
Changing titles
Saving figures

By setting the options, you can create highly customized Manhattan plots and Q-Q plots.

A customized Manhattan and QQ plot

mysumstats.plot_mqq(
                  mode="qqm",
                  cut=14,
                  skip=3, 
                  anno_set=["rs12509595","rs7989823"] ,
                  pinpoint=["rs7989823"], 
                  highlight=["rs12509595","19:15040733:T:C"],
                  highlight_windowkb =1000,
                  stratified=True,
                  marker_size=(5,10),
                  figargs={"figsize":(15,5),"dpi":300})

Plot layout

Option	DataType	Description	Default
`mode`	`mqq`,`qqm`,`qq`,`m`	Determine the layout of manhattan plot and qq plot. `mqq`: left manhatan, right QQ plot `qqm`: left QQ plot, right Manhattan plot `m`: only Manhattan plot `"qq"`: only qq plot	`mqq`
`mqqratio`	`float`	width ratio of Manhattan plot and QQ plot	`3`

Layout

Use MLOG10P for extreme P values

Option	DataType	Description	Default
`scaled`	`boolean`	By default, GWASLab uses P values for mqq plot. But you can set `scaled=Ture` to use MLOG10P to plot.	`False`

Variant with extreme P values

To plot the variant with extreme P values (P < 1e-300), you can use scaled=False to create the plot with MLOG10P instead of raw P values. To calculate MLOG10P for extreme P values from BETA/SE or Z scores, you can use mysumstats.fill_data(to_fill=["MLOG10P"], extreme=True). For details, please refer to the "Extreme P values" section in https://cloufield.github.io/gwaslab/Conversion/.

X axis: Physical position or rank

Option	DataType	Description	Default
`use_rank`	`boolean`	If True, GWASLab will use position rank instead of the physical base-pair positions for x aixs.	`False`

Note

If using rank, there will be no gap in the plot. If using base-pair positions, certain regions of the chromosome might be reflected in the plot like the heterochromatin.

Y axis: Skip "low" and shrink "high"

Option	DataType	Description	Default
`skip`	`float`	Sometimes it is not necessary to plot all variants, we can skip the variants with low -log10(P) values for plotting. For example, we can omit varints with -log10(P) lower than 3 from the plot by specifying `skip=3`. Calculation of lambda GC won't be affected by this	`None`
`cut`	`float`	loci with extremly large -log10(P) value are very likely to dwarf other significant loci, so we want to scale down the -log10(P) for variants above a certain threshold.	`None`
`cutfactor`	`float`	shrinkage factor	`10`
`cut_line_color`	`float`	the color of the line above which y axis is rescaled.	`500`
`sig_level`	`float`	genome-wide significance threshold	`5e-8`

Auxiliary lines

Note

lambda GC calculation for QQ plot will not be affected by skip and cut. The calculation is conducted using all variants in the original dataset.

Annotation

Option	DataType	Description	Default
`anno`	`boolean` or `string` or `"GENENAME"`	If `anno = True`, variants will anotated with chr:pos; or `string`, the column name used for annotation; or `"GENENAME"`, automatically annotate nrearest gene names, using pyensembl. (remember to specify `build`, default is `build="19"`)	`False`
`anno_set`	`list`	If you want to annotate only a few specific variants, you can simply provide a list of SNPIDs or rsIDs for annotation. If None, the variants to annotate will be selected automatically using a sliding window with `windowsize=500`kb.	`None`
`repel_force`	`float`	when the annotation overlaps with other, try increasing the repel_force to increase the padding between annotations.	`0.01`
`anno_alias`	`dict`	snpid:text dictionary for customized annotation	`None`

Repel force

Skip variants with -log10P<3 and annotate the lead variants with chr:pos

mysumstats.plot_mqq(skip=3,anno=True)

Skip variants with -log10P<3 and annotate the lead variants with GENENAME

mysumstats.plot_mqq(skip=3,anno="GENENAME",build="19")

Skip variants with -log10P<3 and annotate the variants in anno_set

mysumstats.plot_mqq(skip=3, anno_set=["rs12509595","19:15040733:T:C"])

Skip variants with -log10P<3 and annotate the variants in anno_set with alias in anno_alias

mysumstats.plot_mqq(skip=3, anno_set=["rs12509595","19:15040733:T:C"], anno_alias={"rs12509595":"anything you want here"})

Annotation style

GWASLab now support 3 types of annotation styles:

expand
right
tight

anno_style="expand"

anno_style="right"

anno_style="tight"

Adjust arm positions

Option	DataType	Description	Default
`anno_d`	`dict`	key is the number of arm starting form 0, value is the direction you want the arm to shift towards . For example, `anno_d = {4:"r"}` means shift the 4th arm to the right	`None`
`arm_offset`	`float`	distance in points	`500`
`arm_scale`	`float`	factors to adjust the height for all arms	`1.0`
`arm_scale_d`	`dict`	factors to adjust the height for specific arms. key is the number of arm startinf form 0, value is the factor which will be multiplied to arm height.	`None`

Adjust the direction the first to left and the thrd to right

mysumstats.plot_mqq(skip=2,anno=True)

mysumstats.plot_mqq(skip=2,anno=True,          
                    anno_d={1:"l",3:"r"},
                    arm_offset=50)

Adjust the length of arm

mysumstats.plot_mqq(skip=2,anno=True,arm_scale=1.5)

Adjust the length of arm for each variant

mysumstats.plot_mqq(skip=2,anno=True,arm_scale_d={1:1.5,2:1.2,3:1.1})

Highlight loci

Highlight specified loci (color all variants in a region by specifying variants and the length of flanking regions).

Highlighting Option	DataType	Description	Default
`highlight`	`list`	a list of SNPID or rsID; these loci (all variants in the specified variants positions +/- `highlight_windowkb`) will be highlighted in `pinpoint_color`	`True`
`highlight_windowkb`	`int`	Specify the span of highlighted region in kbp	`500`
`highlight_color`	`list`	Color for highlighting loci	`"#CB132D"`

Pinpoint variants

Pinpoint certain variants in the Manhattan plot.

Pinpointing Option	DataType	Description	Default
`pinpoint`	`list`	a list of SNPID or rsID; these variants will be highlighted in `pinpoint_color`	`True`
`pinpoint_color`	`list`	color for pinpointing variants	`"red"`

Highlight loci and pinpoint variants

mysumstats.plot_mqq(skip=3,anno="GENENAME",build="19",
                   highlight=["rs12509595","rs7989823"],
                   pinpoint=["rs671","19:15040733:T:C"])

Lines

Line Option	DataType	Description	Default
`sig_line`	`boolean`	If True, plot the significant threshold line	`True`
`sig_level`	`float`	The significance threshold	`5e-8`
`sig_level_lead`	`float`	The significance threshold for extracting lead variants to annotate	`5e-8`
`sig_line_color`	`string`	If True, plot the significant threshold line	`True`
`suggestive_sig_line`	`boolean`	If True, plot the suggestive threshold line	`True`
`suggestive_sig_level`	`float`	The suggestive threshold	`5e-6`
`suggestive_sig_line_color`	`string`	Suggestive level line color	`"grey"`
`additional_line`	`list`	list of P values used to plot additional lines	`None`
`additional_line_color`	`list`	list of colors for the additional lines	`None`
`cut_line_color`	`string`	If True, plot the significant threshold line	`"#ebebeb"`

Plot lines

mysumstats.plot_mqq(skip=3,
                build="19",
                anno="GENENAME",
                windowsizekb=1000000,
                cut=20,
                cut_line_color="purple",
                sig_level=5e-8,  
                sig_level_lead=1e-6, 
                sig_line_color="grey",
                suggestive_sig_line = True,
                suggestive_sig_level = 1e-6,
                suggestive_sig_line_color="blue",
                additional_line=[1e-40,1e-60],
                additional_line_color=["yellow","green"])

MAF-stratified QQ plot

QQ plot Option	DataType	Description	Default
`stratified`	`boolean`	if True, plot MAF straitified QQ plot. Require EAF in sumstats.	`False`
`maf_bins`	`list`	MAF bins for straitification.	`[(0, 0.01), (0.01, 0.05), (0.05, 0.25),(0.25,0.5)]`
`maf_bin_colors`	`list`	colors used for each MAF bin.	`["#f0ad4e","#5cb85c", "#5bc0de","#000042"]`

MAF-stratified Q-Q plot

Colors and Fontsizes

mysumstats.plot_mqq(
          colors=["#597FBD","#74BAD3"],
          cut_line_color="#ebebeb",
          sig_line_color="grey",
          highlight_color="#CB132D",
          pinpoint_color ="red",
          maf_bin_colors = ["#f0ad4e","#5cb85c", "#5bc0de","#000042"],
          fontsize = 10,
          anno_fontsize = 10,
          title_fontsize = 13,
          marker_size=(5,25)
)

Color-related options

Color Option	DataType	Description	Default
`colors`	`list`	a list of colors for chromsomes in the Manhattan plot; it will be used repetitively.	`["#597FBD","#74BAD3"]`
`cut_line_color`	`string`	color for the cut line.	`"#EBEBEB"`
`sig_line_color`	`string`	color for significance threshold line.	`"grey"`
`highlight_color`	`string`	color for highlighting loci	`"#CB132D"`
`pinpoint_color`	`string`	color for pinpointing variants	`"red"`
`maf_bin_colors`	`list`	a list of colors for maf-stratified Q-Q plot.	`["#f0ad4e","#5cb85c", "#5bc0de","#000042"]`

Font-related options

Font Option	DataType	Description	Default
`fontsize`	`list`	fontsize for ticklabels.	`9`
`title_fontsize`	`13`	fontsize for title.	`13`
`anno_fontsize`	`10`	fontsize for annotation.	`9`
`font_family`	`string`	font family	`"Arial"`

Example

mysumstats.plot_mqq(skip=2,
                    cut=20,
                    colors=sns.color_palette("Set3"),
                    sig_line_color="red",
                    fontsize = 8)

Titles

mysumstats.plot_mqq(
          title =None,
          mtitle=None,
          qtitle=None,
          title_pad=1.08
        )

Title Option	DataType	Description	Default
`title`	`string`	title for the figure.	``
`mtitle`	`string`	title for the Manhattan plot	``
`qtitle`	`string`	title for the Q-Q plot	``
`title_pad`	`float`	padding for title	`1.08`

Figure settings

figargs= {"figsize":(15,5),"dpi":100}

Figure Option	DataType	Description	Default
`figargs`	`dict`	key-values pairs that are passed to matplotlib `plt.subplots()`	`{"figsize":(15,5),"dpi":200}`

Commonly used ones:

figsize : figure size
dpi : dots per inch. For pulications, dpi>=300 is on of the common criteria.

Saving plots

mysumstats.plot_mqq(save="mymqqplots.png",save_args={"dpi":400,"facecolor":"white"})

Two options for saving plots in .plot_mqq

Saving Option	DataType	Description	Default
`save`	`string` or `boolean`	If `string`, the plot will be saved to the specified path; If `True`, it will be saved to default path	`True`
`save_args`	`dict`	other parameters passed to matplotlib `savefig` function.	`{"dpi":300,"facecolor":"white"}`

Example

save as png: mysumstats.plot_mqq(save="mymqqplots.png",save_args={"dpi":300})
save as PDF: mysumstats.plot_mqq(save="mymqqplots.pdf",save_args={"dpi":300})