Liftover¶

In [1]:

import gwaslab as gl

import gwaslab as gl

Load sample data¶

In [2]:

mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
             snpid="SNP",
             chrom="CHR",
             pos="POS",
             ea="ALT",
             nea="REF",
             neaf="Frq",
             beta="BETA",
             se="SE",
             p="P",
             nrows=500000,
             verbose=False)
mysumstats.basic_check(verbose=False)

mysumstats = gl.Sumstats("t2d_bbj.txt.gz", snpid="SNP", chrom="CHR", pos="POS", ea="ALT", nea="REF", neaf="Frq", beta="BETA", se="SE", p="P", nrows=500000, verbose=False) mysumstats.basic_check(verbose=False)

In [3]:

mysumstats.data

mysumstats.data

Out[3]:

	SNPID	CHR	POS	EA	NEA	EAF	BETA	SE	P	STATUS
0	1:725932_G_A	1	725932	G	A	0.9960	-0.0737	0.1394	0.597000	9960099
1	1:725933_A_G	1	725933	G	A	0.0040	0.0737	0.1394	0.597300	9960099
2	1:737801_T_C	1	737801	C	T	0.0051	0.0490	0.1231	0.690800	9960099
3	1:749963_T_TAA	1	749963	TAA	T	0.8374	0.0213	0.0199	0.284600	9960399
4	1:751343_T_A	1	751343	T	A	0.8593	0.0172	0.0156	0.270500	9960099
...	...	...	...	...	...	...	...	...	...	...
499995	1:116768304_A_G	1	116768304	G	A	0.0020	0.0881	0.1312	0.502100	9960099
499996	1:116768310_T_C	1	116768310	C	T	0.0059	0.0575	0.0584	0.324800	9960099
499997	1:116768460_G_A	1	116768460	G	A	0.9896	0.1191	0.0442	0.006983	9960099
499998	1:116768479_T_A	1	116768479	T	A	0.9003	0.0067	0.0149	0.652700	9960099
499999	1:116768615_TA_T	1	116768615	TA	T	0.6184	-0.0121	0.0094	0.198500	9960399

500000 rows × 10 columns

Liftover¶

In [4]:

mysumstats.liftover(n_cores=3, from_build="19", to_build="38")

mysumstats.liftover(n_cores=3, from_build="19", to_build="38")

Sat Feb  3 00:39:42 2024 Start to perform liftover...v3.4.38
Sat Feb  3 00:39:42 2024  -Current Dataframe shape : 500000 x 10 ; Memory usage: 49.83 MB
Sat Feb  3 00:39:42 2024  -Number of threads/cores to use: 3
Sat Feb  3 00:39:42 2024  -Creating converter : hg19 to hg38
Sat Feb  3 00:39:42 2024  -Converting variants with status code xxx0xxx :500000...
Sat Feb  3 00:40:18 2024  -Removed unmapped variants: 114
Sat Feb  3 00:40:18 2024 Start to fix chromosome notation (CHR)...v3.4.38
Sat Feb  3 00:40:18 2024  -Current Dataframe shape : 499886 x 10 ; Memory usage: 53.64 MB
Sat Feb  3 00:40:18 2024  -Checking CHR data type...
Sat Feb  3 00:40:20 2024  -Variants with standardized chromosome notation: 499886
Sat Feb  3 00:40:20 2024  -All CHR are already fixed...
Sat Feb  3 00:40:22 2024 Finished fixing chromosome notation (CHR).
Sat Feb  3 00:40:22 2024 Start to fix basepair positions (POS)...v3.4.38
Sat Feb  3 00:40:22 2024  -Current Dataframe shape : 499886 x 10 ; Memory usage: 53.64 MB
Sat Feb  3 00:40:22 2024  -Converting to Int64 data type ...
Sat Feb  3 00:40:23 2024  -Position bound:(0 , 250,000,000)
Sat Feb  3 00:40:24 2024  -Removed outliers: 0
Sat Feb  3 00:40:24 2024  -Removed 0 variants with bad positions.
Sat Feb  3 00:40:24 2024 Finished fixing basepair positions (POS).
Sat Feb  3 00:40:24 2024 Finished liftover.

In [5]:

mysumstats.data

mysumstats.data

Out[5]:

	SNPID	CHR	POS	EA	NEA	EAF	BETA	SE	P	STATUS
0	1:725932_G_A	1	790552	G	A	0.9960	-0.0737	0.1394	0.597000	3860099
1	1:725933_A_G	1	790553	G	A	0.0040	0.0737	0.1394	0.597300	3860099
2	1:737801_T_C	1	802421	C	T	0.0051	0.0490	0.1231	0.690800	3860099
3	1:749963_T_TAA	1	814583	TAA	T	0.8374	0.0213	0.0199	0.284600	3860399
4	1:751343_T_A	1	815963	T	A	0.8593	0.0172	0.0156	0.270500	3860099
...	...	...	...	...	...	...	...	...	...	...
499995	1:116768304_A_G	1	116225682	G	A	0.0020	0.0881	0.1312	0.502100	3860099
499996	1:116768310_T_C	1	116225688	C	T	0.0059	0.0575	0.0584	0.324800	3860099
499997	1:116768460_G_A	1	116225838	G	A	0.9896	0.1191	0.0442	0.006983	3860099
499998	1:116768479_T_A	1	116225857	T	A	0.9003	0.0067	0.0149	0.652700	3860099
499999	1:116768615_TA_T	1	116225993	TA	T	0.6184	-0.0121	0.0094	0.198500	3860399

499886 rows × 10 columns