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Statistics conversion

GWASLab can convert equvalent statistics, including:

Target stats Original stats Implementation
MLOG10P P sumstats["MLOG10P"] = -np.log10(sumstats["P"])
P MLOG10P sumstats["P"] = np.power(10,-sumstats["MLOG10P"])
P Z sumstats["P"] = ss.norm.sf(np.abs(sumstats["Z"])) * 2
P CHISQ sumstats["P"] = ss.chi2.sf(sumstats["CHISQ"], 1)
OR
OR_95L
OR_95U		 BETA
SE		 sumstats["OR"] = np.exp(sumstats["BETA"]),
sumstats["OR_95L"] = np.exp(sumstats["BETA"]-ss.norm.ppf(0.975)*sumstats["SE"]),
sumstats["OR_95U"] = np.exp(sumstats["BETA"]+ss.norm.ppf(0.975)*sumstats["SE"])
BETA
SE		 OR
OR_95L
OR_95U		 sumstats["BETA"] = np.log(sumstats["OR"]) ,
sumstats["SE"]=(np.log(sumstats["OR"]) - np.log(sumstats["OR_95L"]))/ss.norm.ppf(0.975),
sumstats["SE"]=(np.log(sumstats["OR_95U"]) - np.log(sumstats["OR"]))/ss.norm.ppf(0.975)
Z BETA/SE sumstats["Z"] = sumstats["BETA"]/sumstats["SE"]
CHISQ P sumstats["CHISQ"] = ss.chi2.isf(sumstats["P"], 1)
CHISQ Z sumstats["CHISQ"] = (sumstats["Z"])**2
MAF EAF sumstats["MAF"] = sumstats["EAF"].apply(lambda x: min(x,1-x) if pd.notnull(x) else np.nan)

!!! info "Extreme P values" For extreme P, extreme=True can be added to overcome the limitation of extreme P values (P<1e-308). MLOG10P will be calculated using the methods described here:

```mysumstats.fill_data(to_fill=["MLOG10P"], extreme=True)```

<img width="446" alt="image" src="https://github.com/Cloufield/gwaslab/assets/40289485/7c1c96e3-f6e0-4232-a13f-af7fc551cc24">

Z socres (or BETA and SE) will be used to calculate MLOG10P, two additional columns `P_MANTISSA` and `P_EXPONENT` will be added to present p values.

!!! note The conversion is implemented using scipy and numpy.

- ss : `import scipy.stats as ss`
- np : `import numpy as np`

See examples here.

fill_data()

mysumstats.fill_data( 
    to_fill=[],
    df=None,
    overwrite=False,
    only_sig=False
    )

Options

  • to_fill: the columns to fill. ["OR","OR_95L","OR_95U","BETA","SE","P","MLOG10P","Z","CHISQ"]
  • df : columns name for degree of freedom
  • overwrite: if overwrite when the specified column existed
  • only_sig : fill the data only for significant variants

Priority

  • For P : using MLOG10P, Z, CHISQ
  • For MLOG10P : using P, MLOG10P, Z, CHISQ
  • For BETA/SE : using OR/OR_95L/OR_95U
  • For OR/OR_95L/OR_95U : using BETA/SE
  • For Z : using BETA/SE
  • For CHISQ : using Z, P

Example

!!! example ``` # raw data #SNPID CHR POS EA NEA EAF BETA SE P STATUS #1:725932_G_A 1 725932 G A 0.9960 -0.0737 0.1394 0.5970 9999999 #1:725933_A_G 1 725933 G A 0.0040 0.0737 0.1394 0.5973 9999999 #1:737801_T_C 1 737801 C T 0.0051 0.0490 0.1231 0.6908 9999999

# let's fill "MLOG10P","Z","OR","OR_95L","OR_95U"
# gwaslab will automatically search for equivalent statistics

mysumstats.fill_data(to_fill=["MLOG10P","Z","OR","OR_95L","OR_95U"])

Wed Oct 19 10:13:30 2022 Start filling data using existing columns...
Wed Oct 19 10:13:30 2022  -Raw input columns:  ['SNPID', 'CHR', 'POS', 'EA', 'NEA', 'EAF', 'BETA', 'SE', 'P', 'STATUS']
Wed Oct 19 10:13:30 2022  -Overwrite mode:  False
Wed Oct 19 10:13:30 2022   - Skipping columns:  []
Wed Oct 19 10:13:30 2022 Filling columns:  ['MLOG10P', 'OR', 'OR_95L', 'OR_95U']
Wed Oct 19 10:13:30 2022   - Filling OR using BETA column...
Wed Oct 19 10:13:31 2022   - Filling OR_95L/OR_95U using BETA/SE columns...
Wed Oct 19 10:13:32 2022   - Filling MLOG10P using P column...
Wed Oct 19 10:13:38 2022 Finished filling data using existing columns.
```