Input and output sumstats

Example

import gwaslab as gl

Example

gl.show_version()

stdout:

2025/12/25 21:52:10 GWASLab v4.0.0 https://cloufield.github.io/gwaslab/
2025/12/25 21:52:10 (C) 2022-2025, Yunye He, Kamatani Lab, GPL-3.0 license, gwaslab@gmail.com
2025/12/25 21:52:10 Python version: 3.12.0 | packaged by conda-forge | (main, Oct  3 2023, 08:43:22) [GCC 12.3.0]

Input

Loading data

Example

mysumstats = gl.Sumstats("../0_sample_data/t2d_bbj.txt.gz",
             snpid="SNP",
             chrom="CHR",
             pos="POS",
             ea="ALT",
             nea="REF",
             neaf="Frq",
             beta="BETA",
             se="SE",
             p="P",
             direction="Dir",
             build="19",
             n="N", verbose=False)

# select just 1000 variants for example
mysumstats.random_variants(n=1000, inplace=True, random_state=123,verbose=False)

# basic_check
mysumstats.basic_check(verbose=False)

SNPID	CHR	POS	EA	NEA	STATUS	EAF	BETA	SE	P	DIRECTION	N
1:2005486_C_T	1	2005486	C	T	1960099	0.9863	-0.0969	0.0471	0.039820	+---	191764
1:2247939_AAGG_A	1	2247939	AAGG	A	1960399	0.9966	0.0330	0.1249	0.791900	++--	191764
1:3741853_G_A	1	3741853	G	A	1960099	0.8849	-0.0375	0.0142	0.008282	----	191764
1:5017526_G_A	1	5017526	G	A	1960099	0.9822	0.0126	0.0373	0.736200	+-++	191764
1:5843475_C_T	1	5843475	C	T	1960099	0.9857	-0.0011	0.0433	0.980100	--++	191764
...	...	...	...	...	...	...	...	...	...	...	...
X:139318378_A_G	23	139318378	G	A	1960099	0.5252	-0.0132	0.0071	0.061670	+---	191764
X:144540038_G_T	23	144540038	G	T	1960099	0.9866	0.0379	0.0363	0.295800	++++	191764
X:145299627_C_T	23	145299627	C	T	1960099	0.9984	-0.0663	0.1671	0.691400	---+	191764
X:146441317_G_A	23	146441317	G	A	1960099	0.7345	0.0037	0.0078	0.635100	++-+	191764
X:152025052_A_G	23	152025052	G	A	1960099	0.2417	0.0041	0.0082	0.622200	-++-	191764

Example

mysumstats.data

SNPID	CHR	POS	EA	NEA	STATUS	EAF	BETA	SE	P	DIRECTION	N
1:2005486_C_T	1	2005486	C	T	1960099	0.9863	-0.0969	0.0471	0.039820	+---	191764
1:2247939_AAGG_A	1	2247939	AAGG	A	1960399	0.9966	0.0330	0.1249	0.791900	++--	191764
1:3741853_G_A	1	3741853	G	A	1960099	0.8849	-0.0375	0.0142	0.008282	----	191764
1:5017526_G_A	1	5017526	G	A	1960099	0.9822	0.0126	0.0373	0.736200	+-++	191764
1:5843475_C_T	1	5843475	C	T	1960099	0.9857	-0.0011	0.0433	0.980100	--++	191764
...	...	...	...	...	...	...	...	...	...	...	...
X:139318378_A_G	23	139318378	G	A	1960099	0.5252	-0.0132	0.0071	0.061670	+---	191764
X:144540038_G_T	23	144540038	G	T	1960099	0.9866	0.0379	0.0363	0.295800	++++	191764
X:145299627_C_T	23	145299627	C	T	1960099	0.9984	-0.0663	0.1671	0.691400	---+	191764
X:146441317_G_A	23	146441317	G	A	1960099	0.7345	0.0037	0.0078	0.635100	++-+	191764
X:152025052_A_G	23	152025052	G	A	1960099	0.2417	0.0041	0.0082	0.622200	-++-	191764

[1000 rows x 12 columns]

Load and filtering by pattern

Load and filtering by snp pattern

Example

mysumstats_snp2 = gl.Sumstats("../0_sample_data/t2d_bbj.txt.gz",fmt="auto",
             snpid_pat="^2:123",
             build="19",
             n="N", verbose=True)
mysumstats_snp2.data

stdout:

2025/12/25 21:52:37 GWASLab v4.0.0 https://cloufield.github.io/gwaslab/
2025/12/25 21:52:37 (C) 2022-2025, Yunye He, Kamatani Lab, GPL-3.0 license, gwaslab@gmail.com
2025/12/25 21:52:37 Python version: 3.12.0 | packaged by conda-forge | (main, Oct  3 2023, 08:43:22) [GCC 12.3.0]
2025/12/25 21:52:37 Start to load format from formatbook....
2025/12/25 21:52:37  -auto format meta info:
2025/12/25 21:52:37   - format_name  : auto
2025/12/25 21:52:37   - format_separator  : \t
2025/12/25 21:52:37   - format_na  : #NA
2025/12/25 21:52:37   - format_assumption  : Note: auto-detection assumes A1=EA; Alt=EA and Frq=EAF
2025/12/25 21:52:37   - format_version  :  20230328
2025/12/25 21:52:37   - Auto-detection mode. Note: auto-detection assumes A1=EA; Alt=EA and Frq=EAF...
2025/12/25 21:52:37   - Header conversion source: https://github.com/Cloufield/formatbook/blob/main/formats/auto.json
2025/12/25 21:52:37 Start to initialize gl.Sumstats from file :../0_sample_data/t2d_bbj.txt.gz
2025/12/25 21:52:37  -Columns used to filter variants: SNP
2025/12/25 21:52:37  -Loading only variants with pattern :  ^2:123 ...
2025/12/25 21:52:53  -Loaded 5722 variants with pattern : ^2:123 ...
2025/12/25 21:52:53  -Reading columns          : POS,ALT,N,CHR,BETA,SNP,REF,Frq,P,SE
2025/12/25 21:52:53  -Renaming columns to      : POS,EA,N,CHR,BETA,SNPID,NEA,EAF,P,SE
2025/12/25 21:52:53  -Current Dataframe shape : 5722  x  10
2025/12/25 21:52:53  -Initiating a status column: STATUS ...
2025/12/25 21:52:53  -Genomic coordinates are based on GRCh37/hg19...
2025/12/25 21:52:53 Start to reorder the columns ...(v4.0.0)
2025/12/25 21:52:53  -Reordering columns to    : SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,N
2025/12/25 21:52:53 Finished reordering the columns.
2025/12/25 21:52:53  -Trying to convert datatype for CHR: string -> Int64...Success
2025/12/25 21:52:53  -Column  : SNPID  CHR   POS   EA       NEA      STATUS EAF     BETA    SE      P       N    
2025/12/25 21:52:53  -DType   : object Int64 int64 category category int64  float64 float64 float64 float64 int64
2025/12/25 21:52:53  -Verified: T      T     T     T        T        T      T       T       T       T       T    
2025/12/25 21:52:53  -Current Dataframe memory usage: 0.46 MB
2025/12/25 21:52:53 Finished loading data successfully!

SNPID	CHR	POS	EA	NEA	STATUS	EAF	BETA	SE	P	N
2:12320_AAT_A	2	12320	AAT	A	1999999	0.0572	-0.0158	0.0294	0.5907	191764
2:12371_G_C	2	12371	G	C	1999999	0.0350	-0.0190	0.0294	0.5179	191764
2:123233_G_A	2	123233	G	A	1999999	0.8736	0.0191	0.0135	0.1561	191764
2:123332_C_G	2	123332	G	C	1999999	0.1264	-0.0191	0.0135	0.1561	191764
2:123554_C_A	2	123554	C	A	1999999	0.4748	0.0115	0.0088	0.1929	191764
...	...	...	...	...	...	...	...	...	...	...
2:123998245_G_A	2	123998245	G	A	1999999	0.1047	-0.0101	0.0144	0.4834	191764
2:123998250_T_C	2	123998250	C	T	1999999	0.8355	0.0099	0.0119	0.4062	191764
2:123998340_A_G	2	123998340	G	A	1999999	0.9566	-0.0082	0.0222	0.7132	191764
2:123999824_A_G	2	123999824	G	A	1999999	0.8953	0.0103	0.0144	0.4760	191764
2:123999965_G_A	2	123999965	G	A	1999999	0.0021	-0.1152	0.1654	0.4861	191764

[5722 rows x 11 columns]

Load and filtering by chr pattern

Example

mysumstats_chr22 = gl.Sumstats("../0_sample_data/t2d_bbj.txt.gz",fmt="auto",
             chrom_pat="^22",
             build="19",
             n="N", verbose=True)
mysumstats_chr22.data

stdout:

2025/12/25 21:52:53 GWASLab v4.0.0 https://cloufield.github.io/gwaslab/
2025/12/25 21:52:53 (C) 2022-2025, Yunye He, Kamatani Lab, GPL-3.0 license, gwaslab@gmail.com
2025/12/25 21:52:53 Python version: 3.12.0 | packaged by conda-forge | (main, Oct  3 2023, 08:43:22) [GCC 12.3.0]
2025/12/25 21:52:53 Start to load format from formatbook....
2025/12/25 21:52:53  -auto format meta info:
2025/12/25 21:52:53   - format_name  : auto
2025/12/25 21:52:53   - format_separator  : \t
2025/12/25 21:52:53   - format_na  : #NA
2025/12/25 21:52:53   - format_assumption  : Note: auto-detection assumes A1=EA; Alt=EA and Frq=EAF
2025/12/25 21:52:53   - format_version  :  20230328
2025/12/25 21:52:53   - Auto-detection mode. Note: auto-detection assumes A1=EA; Alt=EA and Frq=EAF...
2025/12/25 21:52:53   - Header conversion source: https://github.com/Cloufield/formatbook/blob/main/formats/auto.json
2025/12/25 21:52:53 Start to initialize gl.Sumstats from file :../0_sample_data/t2d_bbj.txt.gz
2025/12/25 21:52:53  -Columns used to filter variants: CHR
2025/12/25 21:52:53  -Loading only variants on chromosome with pattern : ^22 ...
2025/12/25 21:53:08  -Loaded 157050 variants on chromosome with pattern :^22 ...
2025/12/25 21:53:08  -Reading columns          : POS,ALT,N,CHR,BETA,SNP,REF,Frq,P,SE
2025/12/25 21:53:08  -Renaming columns to      : POS,EA,N,CHR,BETA,SNPID,NEA,EAF,P,SE
2025/12/25 21:53:08  -Current Dataframe shape : 157050  x  10
2025/12/25 21:53:08  -Initiating a status column: STATUS ...
2025/12/25 21:53:08  -Genomic coordinates are based on GRCh37/hg19...
2025/12/25 21:53:08 Start to reorder the columns ...(v4.0.0)
2025/12/25 21:53:08  -Reordering columns to    : SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,N
2025/12/25 21:53:08 Finished reordering the columns.
2025/12/25 21:53:08  -Trying to convert datatype for CHR: string -> Int64...Success
2025/12/25 21:53:09  -Column  : SNPID  CHR   POS   EA       NEA      STATUS EAF     BETA    SE      P       N    
2025/12/25 21:53:09  -DType   : object Int64 int64 category category int64  float64 float64 float64 float64 int64
2025/12/25 21:53:09  -Verified: T      T     T     T        T        T      T       T       T       T       T    
2025/12/25 21:53:09  -Current Dataframe memory usage: 12.93 MB
2025/12/25 21:53:09 Finished loading data successfully!

stderr:

/home/yunye/anaconda3/envs/py312/lib/python3.12/site-packages/gwaslab/io/io_preformat_input.py:860: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
  sumstats_filtered = pd.concat([chunk[chunk[chunk_chrom].str.match(chrom_pat, case=False,na=False) ] for chunk in sumstats_iter])
/home/yunye/anaconda3/envs/py312/lib/python3.12/site-packages/gwaslab/io/io_preformat_input.py:860: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
  sumstats_filtered = pd.concat([chunk[chunk[chunk_chrom].str.match(chrom_pat, case=False,na=False) ] for chunk in sumstats_iter])

SNPID	CHR	POS	EA	NEA	STATUS	EAF	BETA	SE	P	N
22:16847963_A_G	22	16847963	G	A	1999999	0.5903	0.0115	0.0163	0.4819	166718
22:16848015_C_G	22	16848015	G	C	1999999	0.6942	0.0110	0.0155	0.4795	166718
22:16848470_A_G	22	16848470	G	A	1999999	0.5918	0.0118	0.0156	0.4487	166718
22:16848520_A_T	22	16848520	T	A	1999999	0.5918	0.0116	0.0155	0.4537	166718
22:16849105_A_G	22	16849105	G	A	1999999	0.5919	0.0109	0.0154	0.4810	166718
...	...	...	...	...	...	...	...	...	...	...
22:51230048_T_C	22	51230048	C	T	1999999	0.9988	-0.0645	0.2269	0.7762	166718
22:51230086_A_T	22	51230086	T	A	1999999	0.9906	-0.0543	0.0766	0.4780	166718
22:51237069_T_C	22	51237069	C	T	1999999	0.9883	-0.0356	0.0720	0.6212	191764
22:51239678_G_T	22	51239678	G	T	1999999	0.0592	-0.0038	0.0331	0.9077	166718
22:51239752_G_A	22	51239752	G	A	1999999	0.0013	0.2937	0.2318	0.2051	166718

[157050 rows x 11 columns]

Output

general output

Example

mysumstats.to_format("./mysumstats",fmt="gwaslab",xymt_number=True)

stdout:

2025/12/25 21:53:09 Start to convert the output sumstats in:  gwaslab  format
2025/12/25 21:53:09  -Formatting statistics ...
2025/12/25 21:53:09  -Float statistics formats:
2025/12/25 21:53:09   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:09   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:09  -Start outputting sumstats in gwaslab format...
2025/12/25 21:53:09  -gwaslab format will be loaded...
2025/12/25 21:53:09  -gwaslab format meta info:
2025/12/25 21:53:09   - format_name  : gwaslab
2025/12/25 21:53:09   - format_source  : https://cloufield.github.io/gwaslab/
2025/12/25 21:53:09   - format_version  : 20231220_v4
2025/12/25 21:53:09  -Output path: ./mysumstats.gwaslab.tsv.gz
2025/12/25 21:53:09  -Output columns: SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,DIRECTION,N
2025/12/25 21:53:09  -Writing sumstats to: ./mysumstats.gwaslab.tsv.gz...
2025/12/25 21:53:09  -Saving log file to: ./mysumstats.gwaslab.log
2025/12/25 21:53:09 Finished outputting successfully!

Example

!zcat mysumstats.gwaslab.tsv.gz | head

stdout:

SNPID   CHR POS EA  NEA STATUS  EAF BETA    SE  P   DIRECTION   N
1:2005486_C_T   1   2005486 C   T   1960099 0.9863  -0.0969 0.0471  3.9820e-02  +---    191764
1:2247939_AAGG_A    1   2247939 AAGG    A   1960399 0.9966  0.0330  0.1249  7.9190e-01  ++--    191764
1:3741853_G_A   1   3741853 G   A   1960099 0.8849  -0.0375 0.0142  8.2820e-03  ----    191764
1:5017526_G_A   1   5017526 G   A   1960099 0.9822  0.0126  0.0373  7.3620e-01  +-++    191764
1:5843475_C_T   1   5843475 C   T   1960099 0.9857  -0.0011 0.0433  9.8010e-01  --++    191764
1:9405103_T_C   1   9405103 C   T   1960099 0.0021  -0.0729 0.1516  6.3050e-01  +---    191764
1:9443411_G_A   1   9443411 G   A   1960099 0.9916  0.0362  0.0532  4.9690e-01  +-++    191764
1:12866348_G_C  1   12866348    G   C   1960099 0.9728  -0.0352 0.0431  4.1450e-01  ---+    191764
1:14466316_A_G  1   14466316    G   A   1960099 0.6942  -0.0042 0.0096  6.6360e-01  --+-    191764

gzip: stdout: Broken pipe

Example

!zcat mysumstats.gwaslab.tsv.gz | tail

stdout:

X:121023171_A_ACTT  23  121023171   ACTT    A   1960399 0.5117  0.0096  0.0071  1.7390e-01  ++++    191764
X:134838698_C_CTA   23  134838698   C   CTA 1960399 0.9355  0.0060  0.0145  6.8080e-01  ++-+    191764
X:135939006_G_T 23  135939006   G   T   1960099 0.2068  -0.0037 0.0085  6.6300e-01  -+-+    191764
X:136020644_C_T 23  136020644   C   T   1960099 0.8756  0.0103  0.0106  3.3370e-01  +++-    191764
X:138148816_C_T 23  138148816   C   T   1960099 0.7842  0.0089  0.0088  3.1330e-01  ++++    191764
X:139318378_A_G 23  139318378   G   A   1960099 0.5252  -0.0132 0.0071  6.1670e-02  +---    191764
X:144540038_G_T 23  144540038   G   T   1960099 0.9866  0.0379  0.0363  2.9580e-01  ++++    191764
X:145299627_C_T 23  145299627   C   T   1960099 0.9984  -0.0663 0.1671  6.9140e-01  ---+    191764
X:146441317_G_A 23  146441317   G   A   1960099 0.7345  0.0037  0.0078  6.3510e-01  ++-+    191764
X:152025052_A_G 23  152025052   G   A   1960099 0.2417  0.0041  0.0082  6.2220e-01  -++-    191764

output each chromosome to a single file

Example

mysumstats.to_format("./mysumstats.@",fmt="gwaslab",xymt_number=True)

stdout:

2025/12/25 21:53:09 Start to convert the output sumstats in:  gwaslab  format
2025/12/25 21:53:09  -Formatting statistics ...
2025/12/25 21:53:09  -Float statistics formats:
2025/12/25 21:53:09   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:09   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:09  -Start outputting sumstats in gwaslab format...
2025/12/25 21:53:09  -gwaslab format will be loaded...
2025/12/25 21:53:09  -gwaslab format meta info:
2025/12/25 21:53:09   - format_name  : gwaslab
2025/12/25 21:53:09   - format_source  : https://cloufield.github.io/gwaslab/
2025/12/25 21:53:09   - format_version  : 20231220_v4
2025/12/25 21:53:09  -Output path: ./mysumstats.@.gwaslab.tsv.gz
2025/12/25 21:53:09  -Output columns: SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,DIRECTION,N
2025/12/25 21:53:09  -Writing sumstats to: ./mysumstats.@.gwaslab.tsv.gz...
2025/12/25 21:53:09   -@ detected: writing each chromosome to a single file...
2025/12/25 21:53:09   -Chromosomes:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]...
2025/12/25 21:53:09  -Saving log file to: ./mysumstats.@.gwaslab.log
2025/12/25 21:53:09 Finished outputting successfully!

load sumstats for each chromosome

Example

mysumstats = gl.Sumstats("./mysumstats.@.gwaslab.tsv.gz",
             fmt="gwaslab", verbose=True)

stdout:

2025/12/25 21:53:09 GWASLab v4.0.0 https://cloufield.github.io/gwaslab/
2025/12/25 21:53:09 (C) 2022-2025, Yunye He, Kamatani Lab, GPL-3.0 license, gwaslab@gmail.com
2025/12/25 21:53:09 Python version: 3.12.0 | packaged by conda-forge | (main, Oct  3 2023, 08:43:22) [GCC 12.3.0]
2025/12/25 21:53:09 Start to load format from formatbook....
2025/12/25 21:53:09  -gwaslab format meta info:
2025/12/25 21:53:09   - format_name  : gwaslab
2025/12/25 21:53:09   - format_source  : https://cloufield.github.io/gwaslab/
2025/12/25 21:53:09   - format_version  : 20231220_v4
2025/12/25 21:53:09  -Detected @ in path: load sumstats by each chromosome...
2025/12/25 21:53:09  -Chromosomes detected: 1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,23,3,4,5,6,7,8,9
2025/12/25 21:53:09 Start to initialize gl.Sumstats from files with pattern :./mysumstats.@.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.1.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.10.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.11.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.12.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.13.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.14.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.15.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.16.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.17.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.18.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.19.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.2.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.20.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.21.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.22.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.23.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.3.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.4.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.5.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.6.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.7.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.8.gwaslab.tsv.gz
2025/12/25 21:53:09  -Loading:./mysumstats.9.gwaslab.tsv.gz
2025/12/25 21:53:09  -Merging sumstats for chromosomes: 1,10,11,12,13,14,15,16,17,18,19,2,20,21,22,23,3,4,5,6,7,8,9
2025/12/25 21:53:09  -Reading columns          : POS,N,CHR,DIRECTION,BETA,EA,P,EAF,SNPID,STATUS,NEA,SE
2025/12/25 21:53:09  -Renaming columns to      : POS,N,CHR,DIRECTION,BETA,EA,P,EAF,SNPID,STATUS,NEA,SE
2025/12/25 21:53:09  -Current Dataframe shape : 1000  x  12
2025/12/25 21:53:09  -Initiating a status column: STATUS ...
2025/12/25 21:53:09 #WARNING! Version of genomic coordinates is unknown...
2025/12/25 21:53:09 Start to reorder the columns ...(v4.0.0)
2025/12/25 21:53:09  -Reordering columns to    : SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,DIRECTION,N
2025/12/25 21:53:09 Finished reordering the columns.
2025/12/25 21:53:09  -Trying to convert datatype for CHR: string -> Int64...Success
2025/12/25 21:53:09  -Column  : SNPID  CHR   POS   EA       NEA      STATUS EAF     BETA    SE      P       DIRECTION N    
2025/12/25 21:53:09  -DType   : object Int64 int64 category category int64  float64 float64 float64 float64 object    int64
2025/12/25 21:53:09  -Verified: T      T     T     T        T        T      T       T       T       T       T         T    
2025/12/25 21:53:09  -Current Dataframe memory usage: 0.08 MB
2025/12/25 21:53:09 Finished loading data successfully!

Check available formats

List the formats that GWASLab supports

Example

formats = gl.list_formats()

stdout:

2025/12/25 21:53:09 Available formats: auto,auto_0,auto_1,auto_2,auto_neaf,auto_ref,bolt_lmm,ccgwas,cojo,fastgwa,genomicsem,gwascatalog,gwascatalog_hm,gwaslab,ldsc,mesusie,metal,mrmega,mtag,pgscatalog,pgscatalog_hm,pheweb,plink,plink2,plink2_firth,plink2_linear,plink2_logistic,plink_assoc,plink_bim,plink_dosage,plink_fam,plink_fisher,plink_linear,plink_logistic,plink_psam,plink_pvar,popcorn,regenie,regenie_gene,saige,ssf,template,vcf

Check the contents of the specified format

Example

ssf_format_dict = gl.check_format("ssf")

stdout:

2025/12/25 21:53:09 Available formats:2025/12/25 21:53:09 meta_data2025/12/25 21:53:09 format_dict2025/12/25 21:53:09 
2025/12/25 21:53:09 {'format_name': 'ssf', 'format_source': 'https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1.full', 'format_cite_name': 'GWAS-SSF v0.1', 'format_separator': '\t', 'format_na': '#NA', 'format_comment': None, 'format_col_order': ['chromosome', 'base_pair_location', 'effect_allele', 'other_allele', 'beta', 'odds_ratio', 'hazard_ratio', 'standard_error', 'effect_allele_frequency', 'p_value', 'neg_log_10_p_value', 'ci_upper', 'ci_lower', 'rsid', 'variant_id', 'info', 'ref_allele', 'n'], 'format_version': 20230328}2025/12/25 21:53:09 {'variant_id': 'SNPID', 'rsid': 'rsID', 'chromosome': 'CHR', 'base_pair_location': 'POS', 'other_allele': 'NEA', 'effect_allele': 'EA', 'effect_allele_frequency': 'EAF', 'n': 'N', 'beta': 'BETA', 'standard_error': 'SE', 'p_value': 'P', 'neg_log_10_p_value': 'MLOG10P', 'info': 'INFO', 'odds_ratio': 'OR', 'hazard_ratio': 'HR', 'ci_lower': 'OR_95L', 'ci_upper': 'OR_95U'}

Formatting and saving

get ready for submission to gwas catalog (GWAS-ssf format)

fmt: specify the output format
ssfmeta: if True, output the meta file
md5sum: if True, create a file with the md5sum of the output sumstats

Example

mysumstats.to_format("./mysumstats", fmt="ssf", ssfmeta=True, md5sum=True)

stdout:

2025/12/25 21:53:09 Start to convert the output sumstats in:  ssf  format
2025/12/25 21:53:09  -Formatting statistics ...
2025/12/25 21:53:09  -Float statistics formats:
2025/12/25 21:53:09   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:09   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:09  -Replacing SNPID separator from ":" to "_"...
2025/12/25 21:53:09  -Start outputting sumstats in ssf format...
2025/12/25 21:53:09  -ssf format will be loaded...
2025/12/25 21:53:09  -ssf format meta info:
2025/12/25 21:53:09   - format_name  : ssf
2025/12/25 21:53:09   - format_source  : https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1.full
2025/12/25 21:53:09   - format_cite_name  : GWAS-SSF v0.1
2025/12/25 21:53:09   - format_separator  : \t
2025/12/25 21:53:09   - format_na  : #NA
2025/12/25 21:53:09   - format_col_order  : chromosome,base_pair_location,effect_allele,other_allele,beta,odds_ratio,hazard_ratio,standard_error,effect_allele_frequency,p_value,neg_log_10_p_value,ci_upper,ci_lower,rsid,variant_id,info,ref_allele,n
2025/12/25 21:53:09   - format_version  :  20230328
2025/12/25 21:53:09  -gwaslab to ssf format dictionary:
2025/12/25 21:53:09   - gwaslab keys: SNPID,rsID,CHR,POS,NEA,EA,EAF,N,BETA,SE,P,MLOG10P,INFO,OR,HR,OR_95L,OR_95U
2025/12/25 21:53:09   - ssf values: variant_id,rsid,chromosome,base_pair_location,other_allele,effect_allele,effect_allele_frequency,n,beta,standard_error,p_value,neg_log_10_p_value,info,odds_ratio,hazard_ratio,ci_lower,ci_upper
2025/12/25 21:53:09  -Output path: ./mysumstats.ssf.tsv.gz
2025/12/25 21:53:09  -Output columns: chromosome,base_pair_location,effect_allele,other_allele,beta,standard_error,effect_allele_frequency,p_value,variant_id,n
2025/12/25 21:53:09  -Writing sumstats to: ./mysumstats.ssf.tsv.gz...
2025/12/25 21:53:09  -md5sum hashing for the file: ./mysumstats.ssf.tsv.gz
2025/12/25 21:53:09  -md5sum path: ./mysumstats.ssf.tsv.gz.md5sum
2025/12/25 21:53:09  -md5sum: e8e9b4cf01d7b0166d0cd9a208a2808f
2025/12/25 21:53:09  -Exporting SSF-style meta data to ./mysumstats.ssf.tsv.gz.ssf.tsv-meta.yaml
2025/12/25 21:53:09  -Saving log file to: ./mysumstats.ssf.log
2025/12/25 21:53:09 Finished outputting successfully!
2025/12/25 21:53:09 Validating SSF format output...
2025/12/25 21:53:09 Using built-in SSF validator (gwas-ssf CLI not available)...
2025/12/25 21:53:09 Validating file extension...
2025/12/25 21:53:09 ✓ File extension OK
2025/12/25 21:53:09 Validating column order...
2025/12/25 21:53:09 ✓ Column order OK
2025/12/25 21:53:09 Validating chromosomes...
2025/12/25 21:53:09 ✓ Chromosomes OK (All autosomes exist. Optional chromosomes ['24', '25'] do not exist.)
2025/12/25 21:53:09 Validating minimum row count...
2025/12/25 21:53:09 #WARNING! ✗ SSF validation failed: The file has fewer than the minimum rows required: 1000 < 100000
2025/12/25 21:53:09 #WARNING!   - The file has fewer than the minimum rows required: 1000 < 100000

Example

!zcat mysumstats.ssf.tsv.gz | head

stdout:

chromosome  base_pair_location  effect_allele   other_allele    beta    standard_error  effect_allele_frequency p_value variant_id  n
1   2005486 C   T   -0.0969 0.0471  0.9863  3.9820e-02  1_2005486_C_T   191764
1   2247939 AAGG    A   0.0330  0.1249  0.9966  7.9190e-01  1_2247939_AAGG_A    191764
1   3741853 G   A   -0.0375 0.0142  0.8849  8.2820e-03  1_3741853_G_A   191764
1   5017526 G   A   0.0126  0.0373  0.9822  7.3620e-01  1_5017526_G_A   191764
1   5843475 C   T   -0.0011 0.0433  0.9857  9.8010e-01  1_5843475_C_T   191764
1   9405103 C   T   -0.0729 0.1516  0.0021  6.3050e-01  1_9405103_T_C   191764
1   9443411 G   A   0.0362  0.0532  0.9916  4.9690e-01  1_9443411_G_A   191764
1   12866348    G   C   -0.0352 0.0431  0.9728  4.1450e-01  1_12866348_G_C  191764
1   14466316    G   A   -0.0042 0.0096  0.6942  6.6360e-01  1_14466316_A_G  191764

Example

!head mysumstats.ssf.tsv.gz.md5sum

stdout:

e8e9b4cf01d7b0166d0cd9a208a2808f

Example

!head ./mysumstats.ssf.tsv.gz.ssf.tsv-meta.yaml

stdout:

coordinate_system: 1-based
data_file_md5sum: e8e9b4cf01d7b0166d0cd9a208a2808f
data_file_name: ./mysumstats.ssf.tsv.gz
date_last_modified: 2025-12-25-21:53:09
file_type: GWAS-SSF v0.1
genome_assembly: Unknown
genotyping_technology: Unknown
gwas_id: Unknown
gwaslab:
  basic_check:

ldsc default format

hapmap3: if True, only output hapmap3 SNPs
exclude_hla: if True, exclude variants in HLA region from output

Example

mysumstats.to_format("./mysumstats",fmt="ldsc",hapmap3=True,exclude_hla=True,build="19")

stdout:

2025/12/25 21:53:10 Start to convert the output sumstats in:  ldsc  format
2025/12/25 21:53:10 Start to exclude variants in HLA regions ...(v4.0.0)
2025/12/25 21:53:10  -Current Dataframe shape : 1000 x 12 ; Memory usage: 0.08 MB
2025/12/25 21:53:10  -Excluded 3 variants in HLA region (chr6: 25000000-34000000 )...
2025/12/25 21:53:10  -Filtered out variants: 3
2025/12/25 21:53:10  -Current Dataframe shape : 997 x 12 ; Memory usage: 0.09 MB
2025/12/25 21:53:10 Finished filtering variants.
2025/12/25 21:53:10  -Genomic coordinates are based on GRCh37/hg19...
2025/12/25 21:53:10 Start to extract HapMap3 SNPs ...(v4.0.0)
2025/12/25 21:53:10  -Current Dataframe shape : 997 x 12 ; Memory usage: 0.09 MB
2025/12/25 21:53:10  -Loading Hapmap3 variants from built-in datasets...
2025/12/25 21:53:11  -Since rsID not in sumstats, CHR:POS( build 19) will be used for matching...
2025/12/25 21:53:12  -Checking if alleles are same...
2025/12/25 21:53:12  -Variants with matched alleles: 81
2025/12/25 21:53:12  -Raw input contains 81 Hapmap3 variants based on CHR:POS...
2025/12/25 21:53:12  -Current Dataframe shape : 997 x 13 ; Memory usage: 0.10 MB
2025/12/25 21:53:12 Finished extracting HapMap3 SNPs.
2025/12/25 21:53:12  -Extract 81 variants in Hapmap3 datasets for build 19.
2025/12/25 21:53:12  -Formatting statistics ...
2025/12/25 21:53:12  -Float statistics formats:
2025/12/25 21:53:12   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:12   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:12  -Start outputting sumstats in ldsc format...
2025/12/25 21:53:12  -ldsc format will be loaded...
2025/12/25 21:53:12  -ldsc format meta info:
2025/12/25 21:53:12   - format_name  : ldsc
2025/12/25 21:53:12   - format_source  : https://github.com/bulik/ldsc/wiki/Summary-Statistics-File-Format
2025/12/25 21:53:12   - format_source2  : https://github.com/bulik/ldsc/blob/master/munge_sumstats.py
2025/12/25 21:53:12   - format_version  :  20150306
2025/12/25 21:53:12  -gwaslab to ldsc format dictionary:
2025/12/25 21:53:12   - gwaslab keys: rsID,NEA,EA,EAF,N,BETA,P,Z,INFO,OR,CHR,POS
2025/12/25 21:53:12   - ldsc values: SNP,A2,A1,Frq,N,Beta,P,Z,INFO,OR,CHR,POS
2025/12/25 21:53:12  -Output path: ./mysumstats.hapmap3.noMHC.ldsc.tsv.gz
2025/12/25 21:53:12  -Output columns: CHR,POS,A1,A2,Frq,Beta,P,N,SNP
2025/12/25 21:53:12  -Writing sumstats to: ./mysumstats.hapmap3.noMHC.ldsc.tsv.gz...
2025/12/25 21:53:12  -Saving log file to: ./mysumstats.hapmap3.noMHC.ldsc.log
2025/12/25 21:53:12 Finished outputting successfully!

Example

!zcat ./mysumstats.hapmap3.noMHC.ldsc.tsv.gz | head

stdout:

CHR POS A1  A2  Frq Beta    P   N   SNP
1   14900419    G   A   0.3952  0.0144  1.3750e-01  191764  rs6703840
1   19593199    C   T   0.1323  -0.0127 3.2570e-01  191764  rs7527253
1   35282297    G   A   0.5434  0.0041  6.4190e-01  191764  rs1407135
1   66001402    C   T   0.2103  -0.0148 1.7720e-01  191764  rs1171261
1   83510491    G   A   0.0025  0.0378  6.9800e-01  191764  rs2022427
1   166110693   C   T   0.8627  0.0286  2.5250e-02  191764  rs4656480
1   175886511   G   A   0.1828  -0.0141 2.2480e-01  191764  rs6656281
1   181612041   C   T   0.9603  0.0135  5.5050e-01  191764  rs199955
1   196329362   C   T   0.0301  0.0300  2.5060e-01  191764  rs11801881

vcf

bgzip : if True, bgzip the output vcf/bed
tabix : if True, index the bgzipped file with tabix

Example

mysumstats.to_format("./mysumstats",fmt="vcf",bgzip=True,tabix=True,build="19")

stdout:

2025/12/25 21:53:12 Start to convert the output sumstats in:  vcf  format
2025/12/25 21:53:12  -Formatting statistics ...
2025/12/25 21:53:12  -Float statistics formats:
2025/12/25 21:53:12   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:12   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:12  -Start outputting sumstats in vcf format...
2025/12/25 21:53:12  -vcf format will be loaded...
2025/12/25 21:53:12  -vcf format meta info:
2025/12/25 21:53:12   - format_name  : vcf
2025/12/25 21:53:12   - format_source  : https://github.com/MRCIEU/gwas-vcf-specification/tree/1.0.0
2025/12/25 21:53:12   - format_version  :  20220923
2025/12/25 21:53:12   - format_citation  : Lyon, M.S., Andrews, S.J., Elsworth, B. et al. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol 22, 32 (2021). https://doi.org/10.1186/s13059-020-02248-0
2025/12/25 21:53:12   - format_fixed  : #CHROM,POS,ID,REF,ALT,QUAL,FILTER,INFO,FORMAT
2025/12/25 21:53:12   - format_format  : ID,SS,ES,SE,LP,SI,EZ
2025/12/25 21:53:12  -gwaslab to vcf format dictionary:
2025/12/25 21:53:12   - gwaslab keys: rsID,CHR,POS,NEA,EA,N,EAF,BETA,SE,MLOG10P,INFO,Z
2025/12/25 21:53:12   - vcf values: ID,#CHROM,POS,REF,ALT,SS,AF,ES,SE,LP,SI,EZ
2025/12/25 21:53:12  -Creating VCF file header...
2025/12/25 21:53:12   -VCF header contig build:19
2025/12/25 21:53:12   -ID:Study_1
2025/12/25 21:53:12   -StudyType:Unknown
2025/12/25 21:53:12   -TotalVariants:1000
2025/12/25 21:53:12   -HarmonisedVariants:0
2025/12/25 21:53:12   -VariantsNotHarmonised:1000
2025/12/25 21:53:12   -SwitchedAlleles:0
2025/12/25 21:53:12  -Writing sumstats to: ./mysumstats.vcf...
2025/12/25 21:53:12  -bgzip compressing : ./mysumstats.vcf.gz...
2025/12/25 21:53:12  -tabix indexing : : ./mysumstats.vcf.gz.tbi...
2025/12/25 21:53:12  -Saving log file to: ./mysumstats.vcf.log
2025/12/25 21:53:12 Finished outputting successfully!

parquet

Example

!mkdir -p mysumstats

Example

mysumstats.to_format("./mysumstats",
                     fmt="gwaslab",
                     tab_fmt="parquet",
                     to_tabular_kwargs={"partition_cols":["CHR"]})

stdout:

2025/12/25 21:53:12 Start to convert the output sumstats in:  gwaslab  format
2025/12/25 21:53:12  -Start outputting sumstats in gwaslab format...
2025/12/25 21:53:12  -gwaslab format will be loaded...
2025/12/25 21:53:12  -gwaslab format meta info:
2025/12/25 21:53:12   - format_name  : gwaslab
2025/12/25 21:53:12   - format_source  : https://cloufield.github.io/gwaslab/
2025/12/25 21:53:12   - format_version  : 20231220_v4
2025/12/25 21:53:12  -Output path: ./mysumstats.gwaslab.parquet
2025/12/25 21:53:12  -Output columns: SNPID,CHR,POS,EA,NEA,STATUS,EAF,BETA,SE,P,DIRECTION,N
2025/12/25 21:53:12  -Writing sumstats to: ./mysumstats.gwaslab.parquet...
2025/12/25 21:53:12   -Partitioned parquet will be written to directory: ./mysumstats.gwaslab
2025/12/25 21:53:13  -Saving log file to: ./mysumstats.gwaslab.log
2025/12/25 21:53:13 Finished outputting successfully!

For annotation

convert to bed format

Example

mysumstats.to_format("./mysumstats",fmt="bed")

stdout:

2025/12/25 21:53:13 Start to convert the output sumstats in:  bed  format
2025/12/25 21:53:13  -Formatting statistics ...
2025/12/25 21:53:13  -Float statistics formats:
2025/12/25 21:53:13   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:13   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:13  -Start outputting sumstats in bed format...
2025/12/25 21:53:13  -Number of SNPs : 920
2025/12/25 21:53:13  -Number of Insertions : 52
2025/12/25 21:53:13  -Number of Deletions : 28
2025/12/25 21:53:13  -formatting to 0-based bed-like file...
2025/12/25 21:53:13  -format description: https://genome.ucsc.edu/FAQ/FAQformat.html#format1
2025/12/25 21:53:13  -Adjusting positions in format-specific manner..
2025/12/25 21:53:13  -Output columns: CHR,START,END,NEA/EA,STRAND,SNPID
2025/12/25 21:53:13  -Writing sumstats to: ./mysumstats.bed...
2025/12/25 21:53:13  -Saving log file to: ./mysumstats.bed.log
2025/12/25 21:53:13 Finished outputting successfully!

Example

!cat mysumstats.bed | head

stdout:

1   2005485 2005486 T/C +   1:2005486_C_T
1   2247939 2247939 -/AGG   +   1:2247939_AAGG_A
1   3741852 3741853 A/G +   1:3741853_G_A
1   5017525 5017526 A/G +   1:5017526_G_A
1   5843474 5843475 T/C +   1:5843475_C_T
1   9405102 9405103 T/C +   1:9405103_T_C
1   9443410 9443411 A/G +   1:9443411_G_A
1   12866347    12866348    C/G +   1:12866348_G_C
1   14466315    14466316    A/G +   1:14466316_A_G
1   14900418    14900419    A/G +   1:14900419_A_G

convert to vep default format

Example

mysumstats.to_format("./mysumstats",fmt="vep")

stdout:

2025/12/25 21:53:13 Start to convert the output sumstats in:  vep  format
2025/12/25 21:53:13  -Formatting statistics ...
2025/12/25 21:53:13  -Float statistics formats:
2025/12/25 21:53:13   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:13   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:13  -Start outputting sumstats in vep format...
2025/12/25 21:53:13  -Number of SNPs : 920
2025/12/25 21:53:13  -Number of Insertions : 52
2025/12/25 21:53:13  -Number of Deletions : 28
2025/12/25 21:53:13  -formatting to 1-based bed-like file (for vep)...
2025/12/25 21:53:13  -format description: http://asia.ensembl.org/info/docs/tools/vep/vep_formats.html
2025/12/25 21:53:13  -Adjusting positions in format-specific manner..
2025/12/25 21:53:13  -Output columns: CHR,START,END,NEA/EA,STRAND,SNPID
2025/12/25 21:53:13  -Writing sumstats to: ./mysumstats.vep...
2025/12/25 21:53:13  -Saving log file to: ./mysumstats.vep.log
2025/12/25 21:53:13 Finished outputting successfully!

Example

!cat mysumstats.vep | head

stdout:

1   2005486 2005486 T/C +   1:2005486_C_T
1   2247940 2247939 -/AGG   +   1:2247939_AAGG_A
1   3741853 3741853 A/G +   1:3741853_G_A
1   5017526 5017526 A/G +   1:5017526_G_A
1   5843475 5843475 T/C +   1:5843475_C_T
1   9405103 9405103 T/C +   1:9405103_T_C
1   9443411 9443411 A/G +   1:9443411_G_A
1   12866348    12866348    C/G +   1:12866348_G_C
1   14466316    14466316    A/G +   1:14466316_A_G
1   14900419    14900419    A/G +   1:14900419_A_G

convert to annovar default input format

Example

mysumstats.to_format("./mysumstats",fmt="annovar")

stdout:

2025/12/25 21:53:13 Start to convert the output sumstats in:  annovar  format
2025/12/25 21:53:13  -Formatting statistics ...
2025/12/25 21:53:13  -Float statistics formats:
2025/12/25 21:53:13   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:13   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:13  -Start outputting sumstats in annovar format...
2025/12/25 21:53:13  -Number of SNPs : 920
2025/12/25 21:53:13  -Number of Insertions : 52
2025/12/25 21:53:13  -Number of Deletions : 28
2025/12/25 21:53:13  -formatting to 1-based bed-like file...
2025/12/25 21:53:13  -format description: https://annovar.openbioinformatics.org/en/latest/user-guide/input/
2025/12/25 21:53:13  -Adjusting positions in format-specific manner..
2025/12/25 21:53:13  -Output columns: CHR,START,END,NEA_out,EA_out,SNPID
2025/12/25 21:53:13  -Writing sumstats to: ./mysumstats.annovar...
2025/12/25 21:53:13  -Saving log file to: ./mysumstats.annovar.log
2025/12/25 21:53:13 Finished outputting successfully!

Example

!cat mysumstats.annovar | head

stdout:

1   2005486 2005486 T   C   1:2005486_C_T
1   2247939 2247939 -   AGG 1:2247939_AAGG_A
1   3741853 3741853 A   G   1:3741853_G_A
1   5017526 5017526 A   G   1:5017526_G_A
1   5843475 5843475 T   C   1:5843475_C_T
1   9405103 9405103 T   C   1:9405103_T_C
1   9443411 9443411 A   G   1:9443411_G_A
1   12866348    12866348    C   G   1:12866348_G_C
1   14466316    14466316    A   G   1:14466316_A_G
1   14900419    14900419    A   G   1:14900419_A_G

Filter and then output

Example

mysumstats.filter_value("EAF >0.05 and EAF < 0.95").to_format("./mysumstats_maf005", fmt="ssf", ssfmeta=True, md5sum=True)

stdout:

2025/12/25 21:53:13 Start to filter variants by condition... ...(v4.0.0)
2025/12/25 21:53:13  -Current Dataframe shape : 1000 x 12 ; Memory usage: 0.08 MB
2025/12/25 21:53:13  -Expression: EAF >0.05 and EAF < 0.95
2025/12/25 21:53:13  -Filtered out variants: 483
2025/12/25 21:53:13  -Current Dataframe shape : 517 x 12 ; Memory usage: 0.05 MB
2025/12/25 21:53:13 Finished filtering variants.
2025/12/25 21:53:13 Start to convert the output sumstats in:  ssf  format
2025/12/25 21:53:13  -Formatting statistics ...
2025/12/25 21:53:13  -Float statistics formats:
2025/12/25 21:53:13   - Columns       : ['EAF', 'BETA', 'SE', 'P']
2025/12/25 21:53:13   - Output formats: ['{:.4g}', '{:.4f}', '{:.4f}', '{:.4e}']
2025/12/25 21:53:13  -Replacing SNPID separator from ":" to "_"...
2025/12/25 21:53:13  -Start outputting sumstats in ssf format...
2025/12/25 21:53:13  -ssf format will be loaded...
2025/12/25 21:53:13  -ssf format meta info:
2025/12/25 21:53:13   - format_name  : ssf
2025/12/25 21:53:13   - format_source  : https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1.full
2025/12/25 21:53:13   - format_cite_name  : GWAS-SSF v0.1
2025/12/25 21:53:13   - format_separator  : \t
2025/12/25 21:53:13   - format_na  : #NA
2025/12/25 21:53:13   - format_col_order  : chromosome,base_pair_location,effect_allele,other_allele,beta,odds_ratio,hazard_ratio,standard_error,effect_allele_frequency,p_value,neg_log_10_p_value,ci_upper,ci_lower,rsid,variant_id,info,ref_allele,n
2025/12/25 21:53:13   - format_version  :  20230328
2025/12/25 21:53:13  -gwaslab to ssf format dictionary:
2025/12/25 21:53:13   - gwaslab keys: SNPID,rsID,CHR,POS,NEA,EA,EAF,N,BETA,SE,P,MLOG10P,INFO,OR,HR,OR_95L,OR_95U
2025/12/25 21:53:13   - ssf values: variant_id,rsid,chromosome,base_pair_location,other_allele,effect_allele,effect_allele_frequency,n,beta,standard_error,p_value,neg_log_10_p_value,info,odds_ratio,hazard_ratio,ci_lower,ci_upper
2025/12/25 21:53:13  -Output path: ./mysumstats_maf005.ssf.tsv.gz
2025/12/25 21:53:13  -Output columns: chromosome,base_pair_location,effect_allele,other_allele,beta,standard_error,effect_allele_frequency,p_value,variant_id,n
2025/12/25 21:53:13  -Writing sumstats to: ./mysumstats_maf005.ssf.tsv.gz...
2025/12/25 21:53:13  -md5sum hashing for the file: ./mysumstats_maf005.ssf.tsv.gz
2025/12/25 21:53:13  -md5sum path: ./mysumstats_maf005.ssf.tsv.gz.md5sum
2025/12/25 21:53:13  -md5sum: 0370f9f0dcb5360b8d1f13460fba43c0
2025/12/25 21:53:13  -Exporting SSF-style meta data to ./mysumstats_maf005.ssf.tsv.gz.ssf.tsv-meta.yaml
2025/12/25 21:53:13  -Saving log file to: ./mysumstats_maf005.ssf.log
2025/12/25 21:53:13 Finished outputting successfully!
2025/12/25 21:53:13 Validating SSF format output...
2025/12/25 21:53:13 Using built-in SSF validator (gwas-ssf CLI not available)...
2025/12/25 21:53:13 Validating file extension...
2025/12/25 21:53:13 ✓ File extension OK
2025/12/25 21:53:13 Validating column order...
2025/12/25 21:53:13 ✓ Column order OK
2025/12/25 21:53:13 Validating chromosomes...
2025/12/25 21:53:13 ✓ Chromosomes OK (All autosomes exist. Optional chromosomes ['24', '25'] do not exist.)
2025/12/25 21:53:13 Validating minimum row count...
2025/12/25 21:53:13 #WARNING! ✗ SSF validation failed: The file has fewer than the minimum rows required: 517 < 100000
2025/12/25 21:53:13 #WARNING!   - The file has fewer than the minimum rows required: 517 < 100000