gwaslab 1.0.4

A collection of handy tools for GWAS

gwaslab

A collection of handy python scripts for GWAS.

Just want to make lif eaiser and save myself from repetitive work.

What you can do with gwaslab:

1. Side-by-side Manhattan and QQ plot
2. Manhattan plot
3. QQ plot
4. Calculate lamda GC
5. [Select top SNPs based on a given window size.]
6. Convert beta/se <-> OR/95%L_U/95%L_L
7. Select hapmap3 SNPs from sumstats and convert to ldsc format
8. Convert Observed scale heritability to liability scale heritability
9. read ldsc log and extract numeric results directly into a pandas dataframe.
10. compare the effect size of select variants / or automatically detected lead variants from two sumstats.

Requirements:

1. Python>3 2. "scipy" 3. "numpy" 4. "pandas" 5. "matplotlib" 6. "seaborn" 7."adjustText"

Install:

pip install gwaslab

Current version: 1.0.2

Usage:

Input: pandas dataframe

Create Manhattan plot and QQ plot with just one line

import gwaslab as gl

## load gwaslab Sumstats object
AF = gl.Sumstats("./AF_bbj.txt.gz",
                   snpid="SNP",
                   eaf="FREQ1",
                   chrom="CHR",
                   pos="POS",
                   ea="A1",
                   nea="A2",
                   n=12121,
                   p="PVALUE",
                   beta="EFFECT1",
                   se="STDERR",
                   other=["WALDCHISQ"])

## creat qqplot and manhattan plot with just one line
myplot = AF.plot_mqq(
        snpid="MARKERNAME",
        mode="mqq",
        stratified=True,
        eaf="EAF",
        anno=True,
        cut=20,
        highlight=["rs7434417","rs12044963"], #the lead SNPs to highlight
        highlight_color="#33FFA0", 
        maf_bin_colors = ["#f0ad4e","#5cb85c", "#7878BA","#000042"])

Or you can plot it separately.

Calculate genomic inflation factor

AF.get_gc()

Extract top snps given a sliding window size

AF.get_lead()

Ref: Zhou, Wei, and Global Biobank Meta-analysis Initiative. "Global Biobank Meta-analysis Initiative: Powering genetic discovery across human diseases." medRxiv (2021).

Converting observed scale heritability to liability scale heritability

gl.h2_obs_to_liab(h2_obs, P, K)

gl.h2_obs_to_liab(h2_obs, P, K, se_obs=None)

Ref: Equation 23 Lee, Sang Hong, et al. "Estimating missing heritability for disease from genome-wide association studies." The American Journal of Human Genetics 88.3 (2011): 294-305.

Read ldsc results in to pandas DataFrame

Directly read ldsc -h2 or -rg into pandas dataframe...

pathlist=["./test.results.log","./test2.results.log"]

ldsc_h2 = gl.read_ldsc(pathlist, mode="h2")
ldsc_rg = gl.read_ldsc(pathlist, mode="rg")

ldsc_h2
Filename	h2_obs	h2_se	Lambda_gc	Mean_chi2	Intercept	Intercept_se	Ratio	Ratio_se
test.results.log	42.9954	8.657	1.2899	1.3226	0.0098	0.0098	0.6538	0.0304
test2.results.log	NA	NA	1.2899	1.3226	0.0098	0.0098	Ratio < 0	NA

ldsc_rg
p1	p2	rg	se	z	p	h2_obs	h2_obs_se	h2_int	h2_int_se	gcov_int	gcov_int_se
./test.results.log	./test.results.log	0.2317	0.0897	2.5824	0.0098	0.3305	0.0571	0.9612	0.009	-0.0001	0.0062
./test.results.log	./test2.results.log	0.2317	0.0897	2.5824	0.0098	0.3305	0.0571	0.9612	0.009	-0.0001	0.0062

Compare effect sizes of selected variants from two sumstats

gl.compare_effect()

preformat your sumstats for a qc workflow

---

Log

• 1.0.0 implemented Sumstats object

• 0.0.5 - 0.0.6

• added compare_effect, read_ldsc

• 0.0.4

  • added mqqplot feature
  • fixed gtesig algorithm
  • recreated mplot and qqplot

For more information: https://gwaslab.com/