1.5.2_format_harmonized_GWAS_sumstats_for_SPrediXcan

Author

Saideep Gona

Published

December 14, 2023

Code

import pandas as pd
import numpy as np

Code

gwas_file = "/beagle3/haky/users/saideep/projects/aracena_modeling/SPrediXcan/sumstats_formatted/T2D_harmonized_FORMATTED_hg19.txt"

gwas = pd.read_csv(gwas_file, sep="\t")
gwas

	chromosome	panel_variant_id	non_effect_allele	effect_allele	effect_size	pvalue	position	frequency	zscore	standard_error	sample_size
0	chr1	chr1_758351_A_G_b38	A	G	-0.0060	0.5806	693731.0	0.112621	-0.552509	0.010860	328697
1	chr1	chr1_770988_A_G_b38	A	G	-0.0103	0.2501	706368.0	0.516505	-1.150106	0.008956	328697
2	chr1	chr1_785910_G_C_b38	G	C	0.0021	0.9179	721290.0	0.045631	0.103079	0.020373	328697
3	chr1	chr1_794299_C_G_b38	C	G	-0.0040	0.6551	729679.0	0.840777	-0.446688	0.008955	328697
4	chr1	chr1_794707_T_C_b38	T	C	-0.0016	0.9288	730087.0	0.050485	-0.089355	0.017906	328697
...	...	...	...	...	...	...	...	...	...	...	...
9980951	NaN	chr19_39907860_C_T_b38	C	T	-0.0134	0.7430	NaN	0.001942	-0.327883	0.040868	328697
9980952	NaN	chr19_54303139_T_G_b38	T	G	-0.0011	0.8442	NaN	0.698058	-0.196524	0.005597	328697
9980953	NaN	chr21_28872555_G_T_b38	G	T	-0.0050	0.3521	NaN	0.323301	-0.930524	0.005373	328697
9980954	NaN	chr22_22052260_C_T_b38	C	T	-0.0012	0.8196	NaN	0.598058	-0.228059	0.005262	328697
9980955	NaN	chr22_22244312_T_G_b38	T	G	-0.0072	0.2162	NaN	0.597087	-1.236696	0.005822	328697

9980956 rows × 11 columns

Code

# Count NA rows

gwas.isna().sum()

# Filter NA rows

gwas = gwas.dropna()

# Count NaN
print(gwas.isnull().sum())

chromosome           0
panel_variant_id     0
non_effect_allele    0
effect_allele        0
effect_size          0
pvalue               0
position             0
frequency            0
zscore               0
standard_error       0
sample_size          0
dtype: int64

Code

gwas["panel_variant_id"] = gwas["chromosome"].astype(str) + "_" + gwas["position"].astype(int).astype(str) + "_" + gwas["non_effect_allele"].astype(str) + "_" + gwas["effect_allele"].astype(str) + "_b37"
gwas["position"] = gwas["position"].astype(int)

Code

gwas["panel_variant_id"]

0             chr1_693731_A_G_b37
1             chr1_706368_A_G_b37
2             chr1_721290_G_C_b37
3             chr1_729679_C_G_b37
4             chr1_730087_T_C_b37
                    ...          
9980880    chr22_51219387_T_C_b37
9980881    chr22_51219704_G_A_b37
9980882    chr22_51221731_T_C_b37
9980883    chr22_51222100_G_T_b37
9980884    chr22_51229805_T_C_b37
Name: panel_variant_id, Length: 9945081, dtype: object

Code

# gwas.to_csv(gwas_file+".chromb38", sep="\t", index=False, header=True)
gwas.to_csv(gwas_file+".chromb37", sep="\t", index=False, header=True)

Code

gwas_file_raw = "/beagle3/haky/users/saideep/projects/aracena_modeling/SPrediXcan/sumstats_raw/Asthma_eczema_farreira_GRCh38_preharmonized.txt.head"
gwas_raw = pd.read_csv(gwas_file_raw, sep="\t")
gwas_raw

	hm_variant_id	hm_rsid	hm_chrom	hm_pos	hm_other_allele	hm_effect_allele	hm_beta	hm_odds_ratio	hm_ci_lower	hm_ci_upper	...	effect_allele	other_allele	beta	standard_error	p_value	variant_id	effect_allele_frequency	odds_ratio	ci_lower	ci_upper
0	1_99534456_G_T	rs10875231	1	99534456	G	T	-0.0072	NaN	NaN	NaN	...	t	g	-0.0072	0.0067	0.2826	rs10875231	0.24490	NaN	NaN	NaN
1	1_99535271_C_T	rs6678176	1	99535271	C	T	-0.0070	NaN	NaN	NaN	...	t	c	-0.0070	0.0063	0.2656	rs6678176	0.31970	NaN	NaN	NaN
2	1_99535287_T_C	rs78286437	1	99535287	T	C	-0.0002	NaN	NaN	NaN	...	t	c	0.0002	0.0131	0.9865	rs78286437	0.07483	NaN	NaN	NaN
3	1_99535433_A_ATC	rs146963890	1	99535433	A	ATC	-0.0078	NaN	NaN	NaN	...	a	atc	0.0078	0.0138	0.5728	rs146963890	0.06803	NaN	NaN	NaN
4	1_99535582_A_G	rs144406489	1	99535582	A	G	0.0049	NaN	NaN	NaN	...	a	g	-0.0049	0.0214	0.8196	rs144406489	0.02041	NaN	NaN	NaN
5	1_99535645_G_T	rs76909621	1	99535645	G	T	-0.0131	NaN	NaN	NaN	...	t	g	-0.0131	0.0096	0.1728	rs76909621	0.08163	NaN	NaN	NaN
6	1_99536599_GTTAGT_G	rs147727421	1	99536599	GTTAGT	G	-0.0141	NaN	NaN	NaN	...	g	gttagt	-0.0141	0.0100	0.1584	rs147727421	0.08163	NaN	NaN	NaN
7	1_99536934_C_T	rs78642210	1	99536934	C	T	0.0004	NaN	NaN	NaN	...	t	c	0.0004	0.0131	0.9776	rs78642210	0.07653	NaN	NaN	NaN
8	1_99537157_C_T	rs77140576	1	99537157	C	T	-0.0111	NaN	NaN	NaN	...	t	c	-0.0111	0.0094	0.2386	rs77140576	0.08333	NaN	NaN	NaN

9 rows × 24 columns