Extract lead variants

GWASLab can extract the lead variants based on MLOG10P values or P values (By default, GWASLab will use MLOG10P first since v3.4.37) from identified significant loci using a sliding window, and return the result as a pandas.DataFrame or gl.Sumstats Object.

.get_lead()

mysumstats.get_lead(
           scaled=False,
           use_p=False,
           windowsizekb=500,
           sig_level=5e-8,
           anno=False,
           build="19",
           source="ensembl",
           verbose=True,
           gls=False)

Options

`.get_lead()` options	DataType	Description	Default
`scaled`	`boolean`	(deprecated since v3.4.37) If True, use MLOG10P for extraction instead of P values	`False`
`use_p`	`boolean`	(available since v3.4.37)If True, use P for extraction instead of MLOG10P values	`False`
`windowsizekb`	`int`	Specify the sliding window size in kb	`500`
`sig_level`	`float`	Specify the P value threshold	`5e-8`
`anno`	`boolean`	If True, annotate the lead variants with nearest gene names.	`False`
`source`	`ensembl` or `refseq`	When `anno=True`, annotate variants using gtf files from `ensembl` or `refseq`	`ensembl`
`build`	`"19"` or `"38"`	genome build version "19" or "38".	`"19"`
`verbose`	`boolean`	If True, print logs	`True`
`gls`	`boolean`	If True, return a new gl.Sumstats Object instead of pandas DataFrame	`False`

Note

.get_lead() simply extracts the lead variants of each significant loci. It is different from clumping.

Note

Please try running .basic_check() to standardize the sumstats if there are any errors.

Quote

GWASLab adopted the definition for novel loci from Global Biobank Meta-analysis Initiative flagship paper.

"We defined genome-wide significant loci by iteratively spanning the ±500 kb region around the most significant variant and merging overlapping regions until no genome-wide significant variants were detected within ±1 Mb."

(Details are described in Zhou, W., Kanai, M., Wu, K. H. H., Rasheed, H., Tsuo, K., Hirbo, J. B., ... & Study, C. O. H. (2022). Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. Cell Genomics, 2(10), 100192. )

GWASlab currently iteratively extends ± windowsizekb kb region around the most significant variant and merges overlapping regions until no genome-wide significant variants were detected within ± windowsizekb. (slightly different from the GBMI paper. When windowsizekb=1000, it is equivalent to GBMI's definition.)

Example

See Lead and novel variants